https://www.jstacs.de/api.php?action=feedcontributions&feedformat=atom&user=Seifert 2026-04-04T18:28:25Z User contributions MediaWiki 1.38.2 https://www.jstacs.de/index.php?title=SHMM&diff=1124 2021-03-24T08:21:44Z

<p>Seifert: </p> <hr /> <div>__NOTOC__<br /> by Michael Seifert, Jens Keilwagen, Marc Strickert, and Ivo Grosse<br /> <br /> == Description ==<br /> === Motivation ===<br /> Array-based analysis of chromatin immunoprecipitation (ChIP-chip) data is a powerful technique for identifying DNA target regions of individual transcription factors. The identification of these target regions from comprehensive promoter array ChIP-chip data is challenging. Here, three approaches for the identification of transcription factor target genes from promoter array ChIP-chip data are presented. We compare (i) a standard log-fold-change analysis (LFC); (ii) a basic method based on a Hidden Markov Model (HMM); and (iii) a new extension of the HMM approach to an HMM with scaled transition matrices (SHMM) that incorporates information about the relative orientation of adjacent gene pairs on DNA. <br /> <br /> === Results ===<br /> All three methods are applied to different promoter array ChIP-chip datasets of the yeast Saccharomyces cerevisiae and the important model plant Arabidopsis thaliana to compare the prediction of transcription factor target genes. In the context of the yeast cell cycle, common target genes bound by the transcription factors ACE2 and SWI5, and ACE2 and FKH2 are identified and evaluated using the Saccharomyces Genome Database. Regarding A.thaliana, target genes of the seed-specific transcription factor ABI3 are predicted and evaluate based on publicly available gene expression profiles and transient assays performed in the wet laboratory experiments. The application of the novel SHMM to these two different promoter array ChIP-chip datasets leads to an improved identification of transcription factor target genes in comparison to the two standard approaches LFC and HMM.<br /> == Paper ==<br /> The paper [http://bioinformatics.oxfordjournals.org/content/25/16/2118 '''''Utilizing gene pair orientations for HMM-based analysis of promoter array ChIP-chip data'''''] has been published in [http://bioinformatics.oxfordjournals.org/ Bioinformatics].<br /> <br /> == Download ==<br /> * [https://imbcloud.medizin.tu-dresden.de/sharing/7JRFhqEPA Supplementary data]: Link to the implementations of LFC, HMM and SHMM.<br /> <br /> == Related Projects ==<br /> * [[ARHMM]]: integrating local chromosomal dependencies into the analysis of tumor expression profiles<br /> * [[PHHMM]]: improved analysis of Array-CGH data<br /> * [[DSHMM]]: exploiting prior knowledge and gene distances in the analysis of tumor expression profiles<br /> * [[MeDIP-HMM]]: HMM-based analysis of DNA methylation profiles<br /> * [https://sites.google.com/site/mseifertweb/hmm-book HMM Book]: Hidden Markov Models with Applications in Computational Biology<br /> <br /> == Follow Me ==<br /> * [https://sites.google.com/site/mseifertweb/home Personal Homepage]</div>

Seifert https://www.jstacs.de/index.php?title=PHHMM&diff=1123 2021-03-24T08:13:45Z

<p>Seifert: </p> <hr /> <div>__NOTOC__<br /> by Michael Seifert, André Gohr, Marc Strickert, and Ivo Grosse<br /> <br /> == Description ==<br /> Array-based comparative genomic hybridization (Array-CGH) is an important technology in molecular biology for the detection of DNA copy number polymorphisms between closely related genomes. Hidden Markov Models (HMMs) are popular tools for the analysis of Array-CGH data, but current methods are only based on first-order HMMs having constrained abilities to model spatial dependencies between measurements of closely adjacent chromosomal regions. Here, we develop parsimonious higher-order HMMs enabling the interpolation between a mixture model ignoring spatial dependencies and a higher-order HMM exhaustively modeling spatial dependencies. We apply parsimonious higher-order HMMs to the analysis of Array-CGH data of the accessions C24 and Col-0 of the model plant Arabidopsis thaliana. We compare these models against first-order HMMs and other existing methods using a reference of known deletions and sequence deviations. We find that parsimonious higher-order HMMs clearly improve the identification of these polymorphisms. Moreover, we perform a functional analysis of identified polymorphisms revealing novel details of genomic differences between C24 and Col-0. Additional model evaluations are also done on widely considered Array-CGH data of human cell lines indicating that parsimonious HMMs are also well-suited for the analysis of non-plant specific data. All these results indicate that parsimonious higher-order HMMs are useful for Array-CGH analyses.<br /> <br /> == Paper ==<br /> The paper '''''Parsimonious higher-order Hidden Markov Models for improved Array-CGH analysis with applications to Arabidopsis thaliana''''' has been published in [http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002286 PloS Comp Biol].<br /> <br /> == Download ==<br /> * [https://imbcloud.medizin.tu-dresden.de/sharing/JT7S0ACNX Parsimonious HMMs]: A ZIP file including a JAR file for analyzing data sets by parsimonious higher-order HMMs and examples for the Arabidopsis and the human cell lines data<br /> <br /> == Related Projects ==<br /> * [[ARHMM]]: integrating local chromosomal dependencies into the analysis of tumor expression profiles<br /> * [[DSHMM]]: exploiting prior knowledge and gene distances in the analysis of tumor expression profiles<br /> * [[SHMM]]: utilizing gene-pair orientations for improved analysis of ChIP-chip promoter array data<br /> * [[MeDIP-HMM]]: HMM-based analysis of DNA methylation profiles<br /> * [https://sites.google.com/site/mseifertweb/hmm-book HMM Book]: Hidden Markov Models with Applications in Computational Biology<br /> <br /> == Follow Me ==<br /> * [https://sites.google.com/site/mseifertweb/home Personal Homepage]</div>

Seifert https://www.jstacs.de/index.php?title=MeDIP-HMM&diff=1122 2021-03-24T08:11:06Z

<p>Seifert: </p> <hr /> <div>__NOTOC__<br /> by Michael Seifert, Sandra Cortijo, Maria Colome-Tatche, Frank Johannes, Francois Roudier, and Vincent Colot<br /> <br /> == Description ==<br /> === Motivation ===<br /> Methylation of cytosines in DNA is an important epigenetic mechanism involved in transcriptional regulation and preservation of genome integrity in a wide range of eukaryotes. Immunoprecipitation of methylated DNA followed by hybridization to genomic tiling arrays (MeDIP-chip) is a cost-effective and sensitive method for methylome analyses. However, existing bioinformatics methods only enable a binary classification into unmethylated and methylated genomic regions, which limits biological interpretations. Indeed, DNA methylation levels can vary substantially within a given DNA fragment depending on the number and degree of methylated cytosines. Therefore, a method for the identification of more than two methylation states is highly desirable.<br /> <br /> === Results ===<br /> Here, we present a three-state Hidden Markov Model (MeDIP-HMM) for analyzing MeDIP-chip data. MeDIP-HMM utilizes a higher-order state-transition process improving modeling of spatial dependencies between chromosomal regions, allows a simultaneous analysis of replicates, and enables a differentiation between unmethylated, methylated and highly methylated genomic regions. We train MeDIP-HMM using a Bayesian Baum-Welch algorithm integrating prior knowledge on methylation levels. We apply MeDIP-HMM to the analysis of the Arabidopsis root methylome and systematically investigate the benefit of using higher-order HMMs. Moreover, we also perform an in-depth comparison study to existing methods and demonstrate the value of using MeDIP-HMM by comparisons to current knowledge on the Arabidopsis methylome. We find that MeDIP-HMM is a fast and precise method for the analysis of DNA methylation data enabling the identification of distinct DNA methylation levels. Finally, we provide evidence for the general applicability of MeDIP-HMM by analyzing promoter DNA methylation data obtained for chicken.<br /> <br /> == Paper ==<br /> The paper [http://bioinformatics.oxfordjournals.org/content/early/2012/09/17/bioinformatics.bts562.abstract '''''MeDIP-HMM: Genome-wide identification of distinct DNA methylation states from high-density tiling arrays'''''] has been published in [http://bioinformatics.oxfordjournals.org/ Bioinformatics].<br /> <br /> == Download ==<br /> * [https://imbcloud.medizin.tu-dresden.de/sharing/iWr3Uix4A MeDIP-HMM]: A ZIP file containing a JAR file for analyzing methylome data sets by MeDIP-HMMs. This file also contains the Arabidopsis root methylome and the chicken data sets considered in our study.<br /> <br /> == Related Projects ==<br /> * [[ARHMM]]: integrating local chromosomal dependencies into the analysis of tumor expression profiles<br /> * [[DSHMM]]: exploiting prior knowledge and gene distances in the analysis of tumor expression profiles<br /> * [[SHMM]]: utilizing gene-pair orientations for improved analysis of ChIP-chip promoter array data<br /> * [[PHHMM]]: improved analysis of Array-CGH data<br /> * [https://sites.google.com/site/mseifertweb/hmm-book HMM Book]: Hidden Markov Models with Applications in Computational Biology<br /> <br /> == Follow Me ==<br /> * [https://sites.google.com/site/michaelseiferthmm/home Personal Homepage]</div>

Seifert https://www.jstacs.de/index.php?title=ARHMM&diff=1056 2019-07-10T06:16:56Z

<p>Seifert: /* Download */</p> <hr /> <div>__NOTOC__<br /> by Michael Seifert, Khalil Abou-El-Ardat, Betty Friedrich, Barbara Klink, and Andreas Deutsch<br /> <br /> == Description ==<br /> === Motivation ===<br /> Changes in gene expression programs play a central role in the development of cancer. Deletions and duplications of chromosomal regions directly influence the expression levels of affected genes. Such local chromosomal dependencies lead to highly significant positive correlations of gene expression levels of neighboring genes. These dependencies should be utilized to improve the modeling and the analysis of individual tumor expression profiles.<br /> <br /> === Results ===<br /> We develop a novel model class of autoregressive higher-order Hidden Markov Models (HMMs) that carefully exploit local data-dependent chromosomal dependencies to improve the identification of differentially expressed genes in individual tumors. Autoregressive higher-order HMMs overcome generally existing limitations of standard first-order HMMs in the modeling of dependencies between genes in close chromosomal proximity by the usage of higher-order state-transitions in combination with autoregressive emissions as novel model features. We train autoregressive higher-order HMMs by a specifically developed Bayesian Baum-Welch algorithm that enables to integrate prior knowledge on the measurement distribution of genes. We apply autoregressive higher-order HMMs to the analysis of breast cancer and different types of brain tumor gene expression data and perform in-depth model evaluation studies. We find that autoregressive higher-order HMMs clearly improve the identification of overexpressed genes with underlying gene copy number duplications in breast cancer in comparison to mixture models, standard first- and higher-order HMMs, and other existing related methods. This performance benefit is attributed to the simultaneous usage of higher-order state-transitions in combination with autoregressive emissions and could not be reached by using each of these two features independently. We also find that autoregressive higher-order HMMs are better able to identify differentially expressed genes in tumor independent of the underlying gene copy number status in comparison to the majority of related methods. This is further supported by the identification of well-known and of previously unreported hotspots of differential expression in glioblastomas demonstrating the efficacy of autoregressive higher-order HMMs for the analysis of individual tumor expression profiles. Moreover, we reveal interesting novel details of systematic alterations of gene expression levels in known cancer signaling pathways distinguishing oligodendrogliomas, astrocytomas and glioblastomas.<br /> <br /> == Paper ==<br /> The paper '''''Autoregressive Higher-Order Hidden Markov Models: Exploiting Local Chromosomal Dependencies in the Analysis of Tumor Expression Profiles''''' has been published in [http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0100295 PLoS One].<br /> <br /> Preprints: [http://imc.zih.tu-dresden.de/downloads/ARHMM/seifert_plosOne_article_preprint.pdf Article] and [http://imc.zih.tu-dresden.de/downloads/ARHMM/seifert_plosOne_appendix_preprint.pdf Appendix]<br /> <br /> Note: ARHMMs can also be applied to the analysis of log-ratio profiles of tumors analyzed by RNA-seq. I recently applied ARHMMs successfully to compare different glioma grades based on log-ratio profiles generated by Cuffdiff.<br /> <br /> == Download ==<br /> * [https://imbcloud.medizin.tu-dresden.de:5001/sharing/jq1z2zaa2 ARHMM]: A ZIP file containing a JAR file for analyzing tumor gene expression data sets by ARHMMs. This file also contains the considered breast cancer and the glioma gene expression data sets.<br /> <br /> == Related Projects ==<br /> * [[DSHMM]]: exploiting prior knowledge and gene distances in the analysis of tumor expression profiles<br /> * [[SHMM]]: utilizing gene-pair orientations for improved analysis of ChIP-chip promoter array data<br /> * [[PHHMM]]: improved analysis of Array-CGH data<br /> * [[MeDIP-HMM]]: HMM-based analysis of DNA methylation profiles<br /> * [https://sites.google.com/site/mseifertweb/hmm-book HMM Book]: Hidden Markov Models with Applications in Computational Biology<br /> <br /> == Follow Me ==<br /> * [https://sites.google.com/site/mseifertweb/home Personal Homepage]</div>

Seifert https://www.jstacs.de/index.php?title=PHHMM&diff=803 2016-10-16T07:24:49Z

<p>Seifert: /* Related Projects */</p> <hr /> <div>__NOTOC__<br /> by Michael Seifert, André Gohr, Marc Strickert, and Ivo Grosse<br /> <br /> == Description ==<br /> Array-based comparative genomic hybridization (Array-CGH) is an important technology in molecular biology for the detection of DNA copy number polymorphisms between closely related genomes. Hidden Markov Models (HMMs) are popular tools for the analysis of Array-CGH data, but current methods are only based on first-order HMMs having constrained abilities to model spatial dependencies between measurements of closely adjacent chromosomal regions. Here, we develop parsimonious higher-order HMMs enabling the interpolation between a mixture model ignoring spatial dependencies and a higher-order HMM exhaustively modeling spatial dependencies. We apply parsimonious higher-order HMMs to the analysis of Array-CGH data of the accessions C24 and Col-0 of the model plant Arabidopsis thaliana. We compare these models against first-order HMMs and other existing methods using a reference of known deletions and sequence deviations. We find that parsimonious higher-order HMMs clearly improve the identification of these polymorphisms. Moreover, we perform a functional analysis of identified polymorphisms revealing novel details of genomic differences between C24 and Col-0. Additional model evaluations are also done on widely considered Array-CGH data of human cell lines indicating that parsimonious HMMs are also well-suited for the analysis of non-plant specific data. All these results indicate that parsimonious higher-order HMMs are useful for Array-CGH analyses.<br /> <br /> == Paper ==<br /> The paper '''''Parsimonious higher-order Hidden Markov Models for improved Array-CGH analysis with applications to Arabidopsis thaliana''''' has been published in [http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002286 PloS Comp Biol].<br /> <br /> == Download ==<br /> * [http://dig.ipk-gatersleben.de/PHHMM/PHHMM_Trainer.zip Parsimonious HMMs]: A ZIP file including a JAR file for analyzing data sets by parsimonious higher-order HMMs and examples for the Arabidopsis and the human cell lines data<br /> <br /> == Related Projects ==<br /> * [[ARHMM]]: integrating local chromosomal dependencies into the analysis of tumor expression profiles<br /> * [[DSHMM]]: exploiting prior knowledge and gene distances in the analysis of tumor expression profiles<br /> * [[SHMM]]: utilizing gene-pair orientations for improved analysis of ChIP-chip promoter array data<br /> * [[MeDIP-HMM]]: HMM-based analysis of DNA methylation profiles<br /> * [https://sites.google.com/site/mseifertweb/hmm-book HMM Book]: Hidden Markov Models with Applications in Computational Biology<br /> <br /> == Follow Me ==<br /> * [https://sites.google.com/site/mseifertweb/home Personal Homepage]</div>

Seifert https://www.jstacs.de/index.php?title=SHMM&diff=802 2016-10-16T07:24:06Z

<p>Seifert: /* Related Projects */</p> <hr /> <div>__NOTOC__<br /> by Michael Seifert, Jens Keilwagen, Marc Strickert, and Ivo Grosse<br /> <br /> == Description ==<br /> === Motivation ===<br /> Array-based analysis of chromatin immunoprecipitation (ChIP-chip) data is a powerful technique for identifying DNA target regions of individual transcription factors. The identification of these target regions from comprehensive promoter array ChIP-chip data is challenging. Here, three approaches for the identification of transcription factor target genes from promoter array ChIP-chip data are presented. We compare (i) a standard log-fold-change analysis (LFC); (ii) a basic method based on a Hidden Markov Model (HMM); and (iii) a new extension of the HMM approach to an HMM with scaled transition matrices (SHMM) that incorporates information about the relative orientation of adjacent gene pairs on DNA. <br /> <br /> === Results ===<br /> All three methods are applied to different promoter array ChIP-chip datasets of the yeast Saccharomyces cerevisiae and the important model plant Arabidopsis thaliana to compare the prediction of transcription factor target genes. In the context of the yeast cell cycle, common target genes bound by the transcription factors ACE2 and SWI5, and ACE2 and FKH2 are identified and evaluated using the Saccharomyces Genome Database. Regarding A.thaliana, target genes of the seed-specific transcription factor ABI3 are predicted and evaluate based on publicly available gene expression profiles and transient assays performed in the wet laboratory experiments. The application of the novel SHMM to these two different promoter array ChIP-chip datasets leads to an improved identification of transcription factor target genes in comparison to the two standard approaches LFC and HMM.<br /> == Paper ==<br /> The paper [http://bioinformatics.oxfordjournals.org/content/25/16/2118 '''''Utilizing gene pair orientations for HMM-based analysis of promoter array ChIP-chip data'''''] has been published in [http://bioinformatics.oxfordjournals.org/ Bioinformatics].<br /> <br /> == Download ==<br /> * [http://dig.ipk-gatersleben.de/SHMMs/ChIPchip/ChIPchip.html Supplementary data]: Link to the implementations of LFC, HMM and SHMM.<br /> <br /> == Related Projects ==<br /> * [[ARHMM]]: integrating local chromosomal dependencies into the analysis of tumor expression profiles<br /> * [[PHHMM]]: improved analysis of Array-CGH data<br /> * [[DSHMM]]: exploiting prior knowledge and gene distances in the analysis of tumor expression profiles<br /> * [[MeDIP-HMM]]: HMM-based analysis of DNA methylation profiles<br /> * [https://sites.google.com/site/mseifertweb/hmm-book HMM Book]: Hidden Markov Models with Applications in Computational Biology<br /> <br /> == Follow Me ==<br /> * [https://sites.google.com/site/mseifertweb/home Personal Homepage]</div>

Seifert https://www.jstacs.de/index.php?title=DSHMM&diff=801 2016-10-16T07:23:12Z

<p>Seifert: /* Related Projects */</p> <hr /> <div>__NOTOC__<br /> by Michael Seifert, Marc Strickert, Alexander Schliep, and Ivo Grosse<br /> <br /> == Description ==<br /> === Motivation ===<br /> Changes in gene expression levels play a central role in tumors. Additional information about the distribution of gene expression levels and distances between adjacent genes on chromosomes should be integrated into the analysis of tumor expression profiles.<br /> <br /> === Results ===<br /> We use a Hidden Markov Model with distance-scaled transition matrices (DSHMM) to incorporate chromosomal distances of adjacent genes on chromosomes into the identification of differentially expressed genes in breast cancer. We train the DSHMM by integrating prior knowledge about potential distributions of expression levels of differentially expressed and unchanged genes in tumor. We find that especially the combination of this data and to a lesser extent the modeling of distances between adjacent genes contribute to a substantial improvement of the identification of differentially expressed genes in comparison to other existing methods. This performance benefit is also supported by the identification of genes well-known to be associated with breast cancer. That suggests applications of DSHMMs for screening of other tumor expression profiles.<br /> <br /> == Paper ==<br /> The paper [http://bioinformatics.oxfordjournals.org/content/27/12/1645 '''''Exploiting prior knowledge and gene distances in the analysis of tumor expression profiles with extended Hidden Markov Models'''''] has been published in [http://bioinformatics.oxfordjournals.org/ Bioinformatics].<br /> <br /> == Download ==<br /> * [http://www.jstacs.de/downloads/supplementary_data.zip Supplementary data]: Containing the breast cancer gene expression data set and the breast cancer gene copy number data set (Pollack et al. (2002)) analyzed in the manuscript, and the predictions and scores of the compared methods.<br /> * [http://www.jstacs.de/downloads/ModelTrainer.zip Model trainer]: JAR file for analyzing a data set by Gaussian mixture models, HMMs, SHMMs, and DSHMMs.<br /> <br /> == Related Projects ==<br /> * [[ARHMM]]: integrating local chromosomal dependencies into the analysis of tumor expression profiles<br /> * [[PHHMM]]: improved analysis of Array-CGH data<br /> * [[SHMM]]: utilizing gene-pair orientations for improved analysis of ChIP-chip promoter array data<br /> * [[MeDIP-HMM]]: HMM-based analysis of DNA methylation profiles<br /> * [https://sites.google.com/site/mseifertweb/hmm-book HMM Book]: Hidden Markov Models with Applications in Computational Biology<br /> <br /> == Follow Me ==<br /> * [https://sites.google.com/site/mseifertweb/home Personal Homepage]</div>

Seifert https://www.jstacs.de/index.php?title=MeDIP-HMM&diff=800 2016-10-16T07:22:04Z

<p>Seifert: /* Related Projects */</p> <hr /> <div>__NOTOC__<br /> by Michael Seifert, Sandra Cortijo, Maria Colome-Tatche, Frank Johannes, Francois Roudier, and Vincent Colot<br /> <br /> == Description ==<br /> === Motivation ===<br /> Methylation of cytosines in DNA is an important epigenetic mechanism involved in transcriptional regulation and preservation of genome integrity in a wide range of eukaryotes. Immunoprecipitation of methylated DNA followed by hybridization to genomic tiling arrays (MeDIP-chip) is a cost-effective and sensitive method for methylome analyses. However, existing bioinformatics methods only enable a binary classification into unmethylated and methylated genomic regions, which limits biological interpretations. Indeed, DNA methylation levels can vary substantially within a given DNA fragment depending on the number and degree of methylated cytosines. Therefore, a method for the identification of more than two methylation states is highly desirable.<br /> <br /> === Results ===<br /> Here, we present a three-state Hidden Markov Model (MeDIP-HMM) for analyzing MeDIP-chip data. MeDIP-HMM utilizes a higher-order state-transition process improving modeling of spatial dependencies between chromosomal regions, allows a simultaneous analysis of replicates, and enables a differentiation between unmethylated, methylated and highly methylated genomic regions. We train MeDIP-HMM using a Bayesian Baum-Welch algorithm integrating prior knowledge on methylation levels. We apply MeDIP-HMM to the analysis of the Arabidopsis root methylome and systematically investigate the benefit of using higher-order HMMs. Moreover, we also perform an in-depth comparison study to existing methods and demonstrate the value of using MeDIP-HMM by comparisons to current knowledge on the Arabidopsis methylome. We find that MeDIP-HMM is a fast and precise method for the analysis of DNA methylation data enabling the identification of distinct DNA methylation levels. Finally, we provide evidence for the general applicability of MeDIP-HMM by analyzing promoter DNA methylation data obtained for chicken.<br /> <br /> == Paper ==<br /> The paper [http://bioinformatics.oxfordjournals.org/content/early/2012/09/17/bioinformatics.bts562.abstract '''''MeDIP-HMM: Genome-wide identification of distinct DNA methylation states from high-density tiling arrays'''''] has been published in [http://bioinformatics.oxfordjournals.org/ Bioinformatics].<br /> <br /> == Download ==<br /> * [http://dig.ipk-gatersleben.de/MeDIP-HMM/MeDIP-HMM.zip MeDIP-HMM]: A ZIP file containing a JAR file for analyzing methylome data sets by MeDIP-HMMs. This file also contains the Arabidopsis root methylome and the chicken data sets considered in our study.<br /> <br /> == Related Projects ==<br /> * [[ARHMM]]: integrating local chromosomal dependencies into the analysis of tumor expression profiles<br /> * [[DSHMM]]: exploiting prior knowledge and gene distances in the analysis of tumor expression profiles<br /> * [[SHMM]]: utilizing gene-pair orientations for improved analysis of ChIP-chip promoter array data<br /> * [[PHHMM]]: improved analysis of Array-CGH data<br /> * [https://sites.google.com/site/mseifertweb/hmm-book HMM Book]: Hidden Markov Models with Applications in Computational Biology<br /> <br /> == Follow Me ==<br /> * [https://sites.google.com/site/michaelseiferthmm/home Personal Homepage]</div>

Seifert https://www.jstacs.de/index.php?title=ARHMM&diff=799 2016-10-16T07:19:52Z

<p>Seifert: /* Related Projects */</p> <hr /> <div>__NOTOC__<br /> by Michael Seifert, Khalil Abou-El-Ardat, Betty Friedrich, Barbara Klink, and Andreas Deutsch<br /> <br /> == Description ==<br /> === Motivation ===<br /> Changes in gene expression programs play a central role in the development of cancer. Deletions and duplications of chromosomal regions directly influence the expression levels of affected genes. Such local chromosomal dependencies lead to highly significant positive correlations of gene expression levels of neighboring genes. These dependencies should be utilized to improve the modeling and the analysis of individual tumor expression profiles.<br /> <br /> === Results ===<br /> We develop a novel model class of autoregressive higher-order Hidden Markov Models (HMMs) that carefully exploit local data-dependent chromosomal dependencies to improve the identification of differentially expressed genes in individual tumors. Autoregressive higher-order HMMs overcome generally existing limitations of standard first-order HMMs in the modeling of dependencies between genes in close chromosomal proximity by the usage of higher-order state-transitions in combination with autoregressive emissions as novel model features. We train autoregressive higher-order HMMs by a specifically developed Bayesian Baum-Welch algorithm that enables to integrate prior knowledge on the measurement distribution of genes. We apply autoregressive higher-order HMMs to the analysis of breast cancer and different types of brain tumor gene expression data and perform in-depth model evaluation studies. We find that autoregressive higher-order HMMs clearly improve the identification of overexpressed genes with underlying gene copy number duplications in breast cancer in comparison to mixture models, standard first- and higher-order HMMs, and other existing related methods. This performance benefit is attributed to the simultaneous usage of higher-order state-transitions in combination with autoregressive emissions and could not be reached by using each of these two features independently. We also find that autoregressive higher-order HMMs are better able to identify differentially expressed genes in tumor independent of the underlying gene copy number status in comparison to the majority of related methods. This is further supported by the identification of well-known and of previously unreported hotspots of differential expression in glioblastomas demonstrating the efficacy of autoregressive higher-order HMMs for the analysis of individual tumor expression profiles. Moreover, we reveal interesting novel details of systematic alterations of gene expression levels in known cancer signaling pathways distinguishing oligodendrogliomas, astrocytomas and glioblastomas.<br /> <br /> == Paper ==<br /> The paper '''''Autoregressive Higher-Order Hidden Markov Models: Exploiting Local Chromosomal Dependencies in the Analysis of Tumor Expression Profiles''''' has been published in [http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0100295 PLoS One].<br /> <br /> Preprints: [http://imc.zih.tu-dresden.de/downloads/ARHMM/seifert_plosOne_article_preprint.pdf Article] and [http://imc.zih.tu-dresden.de/downloads/ARHMM/seifert_plosOne_appendix_preprint.pdf Appendix]<br /> <br /> Note: ARHMMs can also be applied to the analysis of log-ratio profiles of tumors analyzed by RNA-seq. I recently applied ARHMMs successfully to compare different glioma grades based on log-ratio profiles generated by Cuffdiff.<br /> <br /> == Download ==<br /> * [http://imc.zih.tu-dresden.de/downloads/ARHMM/ARHMM_Trainer.zip ARHMM]: A ZIP file containing a JAR file for analyzing tumor gene expression data sets by ARHMMs. This file also contains the considered breast cancer and the glioma gene expression data sets.<br /> <br /> == Related Projects ==<br /> * [[DSHMM]]: exploiting prior knowledge and gene distances in the analysis of tumor expression profiles<br /> * [[SHMM]]: utilizing gene-pair orientations for improved analysis of ChIP-chip promoter array data<br /> * [[PHHMM]]: improved analysis of Array-CGH data<br /> * [[MeDIP-HMM]]: HMM-based analysis of DNA methylation profiles<br /> * [https://sites.google.com/site/mseifertweb/hmm-book HMM Book]: Hidden Markov Models with Applications in Computational Biology<br /> <br /> == Follow Me ==<br /> * [https://sites.google.com/site/mseifertweb/home Personal Homepage]</div>

Seifert https://www.jstacs.de/index.php?title=ARHMM&diff=798 2016-10-16T07:19:02Z

<p>Seifert: /* Related Projects */</p> <hr /> <div>__NOTOC__<br /> by Michael Seifert, Khalil Abou-El-Ardat, Betty Friedrich, Barbara Klink, and Andreas Deutsch<br /> <br /> == Description ==<br /> === Motivation ===<br /> Changes in gene expression programs play a central role in the development of cancer. Deletions and duplications of chromosomal regions directly influence the expression levels of affected genes. Such local chromosomal dependencies lead to highly significant positive correlations of gene expression levels of neighboring genes. These dependencies should be utilized to improve the modeling and the analysis of individual tumor expression profiles.<br /> <br /> === Results ===<br /> We develop a novel model class of autoregressive higher-order Hidden Markov Models (HMMs) that carefully exploit local data-dependent chromosomal dependencies to improve the identification of differentially expressed genes in individual tumors. Autoregressive higher-order HMMs overcome generally existing limitations of standard first-order HMMs in the modeling of dependencies between genes in close chromosomal proximity by the usage of higher-order state-transitions in combination with autoregressive emissions as novel model features. We train autoregressive higher-order HMMs by a specifically developed Bayesian Baum-Welch algorithm that enables to integrate prior knowledge on the measurement distribution of genes. We apply autoregressive higher-order HMMs to the analysis of breast cancer and different types of brain tumor gene expression data and perform in-depth model evaluation studies. We find that autoregressive higher-order HMMs clearly improve the identification of overexpressed genes with underlying gene copy number duplications in breast cancer in comparison to mixture models, standard first- and higher-order HMMs, and other existing related methods. This performance benefit is attributed to the simultaneous usage of higher-order state-transitions in combination with autoregressive emissions and could not be reached by using each of these two features independently. We also find that autoregressive higher-order HMMs are better able to identify differentially expressed genes in tumor independent of the underlying gene copy number status in comparison to the majority of related methods. This is further supported by the identification of well-known and of previously unreported hotspots of differential expression in glioblastomas demonstrating the efficacy of autoregressive higher-order HMMs for the analysis of individual tumor expression profiles. Moreover, we reveal interesting novel details of systematic alterations of gene expression levels in known cancer signaling pathways distinguishing oligodendrogliomas, astrocytomas and glioblastomas.<br /> <br /> == Paper ==<br /> The paper '''''Autoregressive Higher-Order Hidden Markov Models: Exploiting Local Chromosomal Dependencies in the Analysis of Tumor Expression Profiles''''' has been published in [http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0100295 PLoS One].<br /> <br /> Preprints: [http://imc.zih.tu-dresden.de/downloads/ARHMM/seifert_plosOne_article_preprint.pdf Article] and [http://imc.zih.tu-dresden.de/downloads/ARHMM/seifert_plosOne_appendix_preprint.pdf Appendix]<br /> <br /> Note: ARHMMs can also be applied to the analysis of log-ratio profiles of tumors analyzed by RNA-seq. I recently applied ARHMMs successfully to compare different glioma grades based on log-ratio profiles generated by Cuffdiff.<br /> <br /> == Download ==<br /> * [http://imc.zih.tu-dresden.de/downloads/ARHMM/ARHMM_Trainer.zip ARHMM]: A ZIP file containing a JAR file for analyzing tumor gene expression data sets by ARHMMs. This file also contains the considered breast cancer and the glioma gene expression data sets.<br /> <br /> == Related Projects ==<br /> * [[DSHMM]]: exploiting prior knowledge and gene distances in the analysis of tumor expression profiles<br /> * [[SHMM]]: utilizing gene-pair orientations for improved analysis of ChIP-chip promoter array data<br /> * [[PHHMM]]: improved analysis of Array-CGH data<br /> * [[MeDIP-HMM]]: HMM-based analysis of DNA methylation profiles<br /> * [https://sites.google.com/site/mseifertweb/home/hmm-book HMM Book]: Hidden Markov Models with Applications in Computational Biology<br /> <br /> == Follow Me ==<br /> * [https://sites.google.com/site/mseifertweb/home Personal Homepage]</div>

Seifert https://www.jstacs.de/index.php?title=SHMM&diff=797 2016-10-16T07:17:15Z

<p>Seifert: /* Follow Me */</p> <hr /> <div>__NOTOC__<br /> by Michael Seifert, Jens Keilwagen, Marc Strickert, and Ivo Grosse<br /> <br /> == Description ==<br /> === Motivation ===<br /> Array-based analysis of chromatin immunoprecipitation (ChIP-chip) data is a powerful technique for identifying DNA target regions of individual transcription factors. The identification of these target regions from comprehensive promoter array ChIP-chip data is challenging. Here, three approaches for the identification of transcription factor target genes from promoter array ChIP-chip data are presented. We compare (i) a standard log-fold-change analysis (LFC); (ii) a basic method based on a Hidden Markov Model (HMM); and (iii) a new extension of the HMM approach to an HMM with scaled transition matrices (SHMM) that incorporates information about the relative orientation of adjacent gene pairs on DNA. <br /> <br /> === Results ===<br /> All three methods are applied to different promoter array ChIP-chip datasets of the yeast Saccharomyces cerevisiae and the important model plant Arabidopsis thaliana to compare the prediction of transcription factor target genes. In the context of the yeast cell cycle, common target genes bound by the transcription factors ACE2 and SWI5, and ACE2 and FKH2 are identified and evaluated using the Saccharomyces Genome Database. Regarding A.thaliana, target genes of the seed-specific transcription factor ABI3 are predicted and evaluate based on publicly available gene expression profiles and transient assays performed in the wet laboratory experiments. The application of the novel SHMM to these two different promoter array ChIP-chip datasets leads to an improved identification of transcription factor target genes in comparison to the two standard approaches LFC and HMM.<br /> == Paper ==<br /> The paper [http://bioinformatics.oxfordjournals.org/content/25/16/2118 '''''Utilizing gene pair orientations for HMM-based analysis of promoter array ChIP-chip data'''''] has been published in [http://bioinformatics.oxfordjournals.org/ Bioinformatics].<br /> <br /> == Download ==<br /> * [http://dig.ipk-gatersleben.de/SHMMs/ChIPchip/ChIPchip.html Supplementary data]: Link to the implementations of LFC, HMM and SHMM.<br /> <br /> == Related Projects ==<br /> * [[ARHMM]]: integrating local chromosomal dependencies into the analysis of tumor expression profiles<br /> * [[PHHMM]]: improved analysis of Array-CGH data<br /> * [[DSHMM]]: exploiting prior knowledge and gene distances in the analysis of tumor expression profiles<br /> * [[MeDIP-HMM]]: HMM-based analysis of DNA methylation profiles<br /> * [https://sites.google.com/site/michaelseiferthmm/hmm-book HMM Book]: Hidden Markov Models with Applications in Computational Biology<br /> <br /> == Follow Me ==<br /> * [https://sites.google.com/site/mseifertweb/home Personal Homepage]</div>

Seifert https://www.jstacs.de/index.php?title=PHHMM&diff=796 2016-10-16T07:16:08Z

<p>Seifert: /* Follow Me */</p> <hr /> <div>__NOTOC__<br /> by Michael Seifert, André Gohr, Marc Strickert, and Ivo Grosse<br /> <br /> == Description ==<br /> Array-based comparative genomic hybridization (Array-CGH) is an important technology in molecular biology for the detection of DNA copy number polymorphisms between closely related genomes. Hidden Markov Models (HMMs) are popular tools for the analysis of Array-CGH data, but current methods are only based on first-order HMMs having constrained abilities to model spatial dependencies between measurements of closely adjacent chromosomal regions. Here, we develop parsimonious higher-order HMMs enabling the interpolation between a mixture model ignoring spatial dependencies and a higher-order HMM exhaustively modeling spatial dependencies. We apply parsimonious higher-order HMMs to the analysis of Array-CGH data of the accessions C24 and Col-0 of the model plant Arabidopsis thaliana. We compare these models against first-order HMMs and other existing methods using a reference of known deletions and sequence deviations. We find that parsimonious higher-order HMMs clearly improve the identification of these polymorphisms. Moreover, we perform a functional analysis of identified polymorphisms revealing novel details of genomic differences between C24 and Col-0. Additional model evaluations are also done on widely considered Array-CGH data of human cell lines indicating that parsimonious HMMs are also well-suited for the analysis of non-plant specific data. All these results indicate that parsimonious higher-order HMMs are useful for Array-CGH analyses.<br /> <br /> == Paper ==<br /> The paper '''''Parsimonious higher-order Hidden Markov Models for improved Array-CGH analysis with applications to Arabidopsis thaliana''''' has been published in [http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002286 PloS Comp Biol].<br /> <br /> == Download ==<br /> * [http://dig.ipk-gatersleben.de/PHHMM/PHHMM_Trainer.zip Parsimonious HMMs]: A ZIP file including a JAR file for analyzing data sets by parsimonious higher-order HMMs and examples for the Arabidopsis and the human cell lines data<br /> <br /> == Related Projects ==<br /> * [[ARHMM]]: integrating local chromosomal dependencies into the analysis of tumor expression profiles<br /> * [[DSHMM]]: exploiting prior knowledge and gene distances in the analysis of tumor expression profiles<br /> * [[SHMM]]: utilizing gene-pair orientations for improved analysis of ChIP-chip promoter array data<br /> * [[MeDIP-HMM]]: HMM-based analysis of DNA methylation profiles<br /> * [https://sites.google.com/site/michaelseiferthmm/hmm-book HMM Book]: Hidden Markov Models with Applications in Computational Biology<br /> <br /> == Follow Me ==<br /> * [https://sites.google.com/site/mseifertweb/home Personal Homepage]</div>

Seifert https://www.jstacs.de/index.php?title=DSHMM&diff=795 2016-10-16T07:15:30Z

<p>Seifert: /* Follow Me */</p> <hr /> <div>__NOTOC__<br /> by Michael Seifert, Marc Strickert, Alexander Schliep, and Ivo Grosse<br /> <br /> == Description ==<br /> === Motivation ===<br /> Changes in gene expression levels play a central role in tumors. Additional information about the distribution of gene expression levels and distances between adjacent genes on chromosomes should be integrated into the analysis of tumor expression profiles.<br /> <br /> === Results ===<br /> We use a Hidden Markov Model with distance-scaled transition matrices (DSHMM) to incorporate chromosomal distances of adjacent genes on chromosomes into the identification of differentially expressed genes in breast cancer. We train the DSHMM by integrating prior knowledge about potential distributions of expression levels of differentially expressed and unchanged genes in tumor. We find that especially the combination of this data and to a lesser extent the modeling of distances between adjacent genes contribute to a substantial improvement of the identification of differentially expressed genes in comparison to other existing methods. This performance benefit is also supported by the identification of genes well-known to be associated with breast cancer. That suggests applications of DSHMMs for screening of other tumor expression profiles.<br /> <br /> == Paper ==<br /> The paper [http://bioinformatics.oxfordjournals.org/content/27/12/1645 '''''Exploiting prior knowledge and gene distances in the analysis of tumor expression profiles with extended Hidden Markov Models'''''] has been published in [http://bioinformatics.oxfordjournals.org/ Bioinformatics].<br /> <br /> == Download ==<br /> * [http://www.jstacs.de/downloads/supplementary_data.zip Supplementary data]: Containing the breast cancer gene expression data set and the breast cancer gene copy number data set (Pollack et al. (2002)) analyzed in the manuscript, and the predictions and scores of the compared methods.<br /> * [http://www.jstacs.de/downloads/ModelTrainer.zip Model trainer]: JAR file for analyzing a data set by Gaussian mixture models, HMMs, SHMMs, and DSHMMs.<br /> <br /> == Related Projects ==<br /> * [[ARHMM]]: integrating local chromosomal dependencies into the analysis of tumor expression profiles<br /> * [[PHHMM]]: improved analysis of Array-CGH data<br /> * [[SHMM]]: utilizing gene-pair orientations for improved analysis of ChIP-chip promoter array data<br /> * [[MeDIP-HMM]]: HMM-based analysis of DNA methylation profiles<br /> * [https://sites.google.com/site/michaelseiferthmm/hmm-book HMM Book]: Hidden Markov Models with Applications in Computational Biology<br /> <br /> == Follow Me ==<br /> * [https://sites.google.com/site/mseifertweb/home Personal Homepage]</div>

Seifert https://www.jstacs.de/index.php?title=ARHMM&diff=794 2016-10-16T07:14:40Z

<p>Seifert: /* Follow Me */</p> <hr /> <div>__NOTOC__<br /> by Michael Seifert, Khalil Abou-El-Ardat, Betty Friedrich, Barbara Klink, and Andreas Deutsch<br /> <br /> == Description ==<br /> === Motivation ===<br /> Changes in gene expression programs play a central role in the development of cancer. Deletions and duplications of chromosomal regions directly influence the expression levels of affected genes. Such local chromosomal dependencies lead to highly significant positive correlations of gene expression levels of neighboring genes. These dependencies should be utilized to improve the modeling and the analysis of individual tumor expression profiles.<br /> <br /> === Results ===<br /> We develop a novel model class of autoregressive higher-order Hidden Markov Models (HMMs) that carefully exploit local data-dependent chromosomal dependencies to improve the identification of differentially expressed genes in individual tumors. Autoregressive higher-order HMMs overcome generally existing limitations of standard first-order HMMs in the modeling of dependencies between genes in close chromosomal proximity by the usage of higher-order state-transitions in combination with autoregressive emissions as novel model features. We train autoregressive higher-order HMMs by a specifically developed Bayesian Baum-Welch algorithm that enables to integrate prior knowledge on the measurement distribution of genes. We apply autoregressive higher-order HMMs to the analysis of breast cancer and different types of brain tumor gene expression data and perform in-depth model evaluation studies. We find that autoregressive higher-order HMMs clearly improve the identification of overexpressed genes with underlying gene copy number duplications in breast cancer in comparison to mixture models, standard first- and higher-order HMMs, and other existing related methods. This performance benefit is attributed to the simultaneous usage of higher-order state-transitions in combination with autoregressive emissions and could not be reached by using each of these two features independently. We also find that autoregressive higher-order HMMs are better able to identify differentially expressed genes in tumor independent of the underlying gene copy number status in comparison to the majority of related methods. This is further supported by the identification of well-known and of previously unreported hotspots of differential expression in glioblastomas demonstrating the efficacy of autoregressive higher-order HMMs for the analysis of individual tumor expression profiles. Moreover, we reveal interesting novel details of systematic alterations of gene expression levels in known cancer signaling pathways distinguishing oligodendrogliomas, astrocytomas and glioblastomas.<br /> <br /> == Paper ==<br /> The paper '''''Autoregressive Higher-Order Hidden Markov Models: Exploiting Local Chromosomal Dependencies in the Analysis of Tumor Expression Profiles''''' has been published in [http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0100295 PLoS One].<br /> <br /> Preprints: [http://imc.zih.tu-dresden.de/downloads/ARHMM/seifert_plosOne_article_preprint.pdf Article] and [http://imc.zih.tu-dresden.de/downloads/ARHMM/seifert_plosOne_appendix_preprint.pdf Appendix]<br /> <br /> Note: ARHMMs can also be applied to the analysis of log-ratio profiles of tumors analyzed by RNA-seq. I recently applied ARHMMs successfully to compare different glioma grades based on log-ratio profiles generated by Cuffdiff.<br /> <br /> == Download ==<br /> * [http://imc.zih.tu-dresden.de/downloads/ARHMM/ARHMM_Trainer.zip ARHMM]: A ZIP file containing a JAR file for analyzing tumor gene expression data sets by ARHMMs. This file also contains the considered breast cancer and the glioma gene expression data sets.<br /> <br /> == Related Projects ==<br /> * [[DSHMM]]: exploiting prior knowledge and gene distances in the analysis of tumor expression profiles<br /> * [[SHMM]]: utilizing gene-pair orientations for improved analysis of ChIP-chip promoter array data<br /> * [[PHHMM]]: improved analysis of Array-CGH data<br /> * [[MeDIP-HMM]]: HMM-based analysis of DNA methylation profiles<br /> * [https://sites.google.com/site/michaelseiferthmm/hmm-book HMM Book]: Hidden Markov Models with Applications in Computational Biology<br /> <br /> == Follow Me ==<br /> * [https://sites.google.com/site/mseifertweb/home Personal Homepage]</div>

Seifert https://www.jstacs.de/index.php?title=ARHMM&diff=689 2014-06-27T13:53:29Z

<p>Seifert: /* Paper */</p> <hr /> <div>__NOTOC__<br /> by Michael Seifert, Khalil Abou-El-Ardat, Betty Friedrich, Barbara Klink, and Andreas Deutsch<br /> <br /> == Description ==<br /> === Motivation ===<br /> Changes in gene expression programs play a central role in the development of cancer. Deletions and duplications of chromosomal regions directly influence the expression levels of affected genes. Such local chromosomal dependencies lead to highly significant positive correlations of gene expression levels of neighboring genes. These dependencies should be utilized to improve the modeling and the analysis of individual tumor expression profiles.<br /> <br /> === Results ===<br /> We develop a novel model class of autoregressive higher-order Hidden Markov Models (HMMs) that carefully exploit local data-dependent chromosomal dependencies to improve the identification of differentially expressed genes in individual tumors. Autoregressive higher-order HMMs overcome generally existing limitations of standard first-order HMMs in the modeling of dependencies between genes in close chromosomal proximity by the usage of higher-order state-transitions in combination with autoregressive emissions as novel model features. We train autoregressive higher-order HMMs by a specifically developed Bayesian Baum-Welch algorithm that enables to integrate prior knowledge on the measurement distribution of genes. We apply autoregressive higher-order HMMs to the analysis of breast cancer and different types of brain tumor gene expression data and perform in-depth model evaluation studies. We find that autoregressive higher-order HMMs clearly improve the identification of overexpressed genes with underlying gene copy number duplications in breast cancer in comparison to mixture models, standard first- and higher-order HMMs, and other existing related methods. This performance benefit is attributed to the simultaneous usage of higher-order state-transitions in combination with autoregressive emissions and could not be reached by using each of these two features independently. We also find that autoregressive higher-order HMMs are better able to identify differentially expressed genes in tumor independent of the underlying gene copy number status in comparison to the majority of related methods. This is further supported by the identification of well-known and of previously unreported hotspots of differential expression in glioblastomas demonstrating the efficacy of autoregressive higher-order HMMs for the analysis of individual tumor expression profiles. Moreover, we reveal interesting novel details of systematic alterations of gene expression levels in known cancer signaling pathways distinguishing oligodendrogliomas, astrocytomas and glioblastomas.<br /> <br /> == Paper ==<br /> The paper '''''Autoregressive Higher-Order Hidden Markov Models: Exploiting Local Chromosomal Dependencies in the Analysis of Tumor Expression Profiles''''' has been published in [http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0100295 PLoS One].<br /> <br /> Preprints: [http://imc.zih.tu-dresden.de/downloads/ARHMM/seifert_plosOne_article_preprint.pdf Article] and [http://imc.zih.tu-dresden.de/downloads/ARHMM/seifert_plosOne_appendix_preprint.pdf Appendix]<br /> <br /> Note: ARHMMs can also be applied to the analysis of log-ratio profiles of tumors analyzed by RNA-seq. I recently applied ARHMMs successfully to compare different glioma grades based on log-ratio profiles generated by Cuffdiff.<br /> <br /> == Download ==<br /> * [http://imc.zih.tu-dresden.de/downloads/ARHMM/ARHMM_Trainer.zip ARHMM]: A ZIP file containing a JAR file for analyzing tumor gene expression data sets by ARHMMs. This file also contains the considered breast cancer and the glioma gene expression data sets.<br /> <br /> == Related Projects ==<br /> * [[DSHMM]]: exploiting prior knowledge and gene distances in the analysis of tumor expression profiles<br /> * [[SHMM]]: utilizing gene-pair orientations for improved analysis of ChIP-chip promoter array data<br /> * [[PHHMM]]: improved analysis of Array-CGH data<br /> * [[MeDIP-HMM]]: HMM-based analysis of DNA methylation profiles<br /> * [https://sites.google.com/site/michaelseiferthmm/hmm-book HMM Book]: Hidden Markov Models with Applications in Computational Biology<br /> <br /> == Follow Me ==<br /> * [https://sites.google.com/site/michaelseiferthmm/home Personal Homepage]</div>

Seifert https://www.jstacs.de/index.php?title=ARHMM&diff=688 2014-06-24T06:46:38Z

<p>Seifert: /* Paper */</p> <hr /> <div>__NOTOC__<br /> by Michael Seifert, Khalil Abou-El-Ardat, Betty Friedrich, Barbara Klink, and Andreas Deutsch<br /> <br /> == Description ==<br /> === Motivation ===<br /> Changes in gene expression programs play a central role in the development of cancer. Deletions and duplications of chromosomal regions directly influence the expression levels of affected genes. Such local chromosomal dependencies lead to highly significant positive correlations of gene expression levels of neighboring genes. These dependencies should be utilized to improve the modeling and the analysis of individual tumor expression profiles.<br /> <br /> === Results ===<br /> We develop a novel model class of autoregressive higher-order Hidden Markov Models (HMMs) that carefully exploit local data-dependent chromosomal dependencies to improve the identification of differentially expressed genes in individual tumors. Autoregressive higher-order HMMs overcome generally existing limitations of standard first-order HMMs in the modeling of dependencies between genes in close chromosomal proximity by the usage of higher-order state-transitions in combination with autoregressive emissions as novel model features. We train autoregressive higher-order HMMs by a specifically developed Bayesian Baum-Welch algorithm that enables to integrate prior knowledge on the measurement distribution of genes. We apply autoregressive higher-order HMMs to the analysis of breast cancer and different types of brain tumor gene expression data and perform in-depth model evaluation studies. We find that autoregressive higher-order HMMs clearly improve the identification of overexpressed genes with underlying gene copy number duplications in breast cancer in comparison to mixture models, standard first- and higher-order HMMs, and other existing related methods. This performance benefit is attributed to the simultaneous usage of higher-order state-transitions in combination with autoregressive emissions and could not be reached by using each of these two features independently. We also find that autoregressive higher-order HMMs are better able to identify differentially expressed genes in tumor independent of the underlying gene copy number status in comparison to the majority of related methods. This is further supported by the identification of well-known and of previously unreported hotspots of differential expression in glioblastomas demonstrating the efficacy of autoregressive higher-order HMMs for the analysis of individual tumor expression profiles. Moreover, we reveal interesting novel details of systematic alterations of gene expression levels in known cancer signaling pathways distinguishing oligodendrogliomas, astrocytomas and glioblastomas.<br /> <br /> == Paper ==<br /> The paper '''''Autoregressive Higher-Order Hidden Markov Models: Exploiting Local Chromosomal Dependencies in the Analysis of Tumor Expression Profiles''''' has been published in [http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0100295 PLoS One].<br /> <br /> == Download ==<br /> * [http://imc.zih.tu-dresden.de/downloads/ARHMM/ARHMM_Trainer.zip ARHMM]: A ZIP file containing a JAR file for analyzing tumor gene expression data sets by ARHMMs. This file also contains the considered breast cancer and the glioma gene expression data sets.<br /> <br /> == Related Projects ==<br /> * [[DSHMM]]: exploiting prior knowledge and gene distances in the analysis of tumor expression profiles<br /> * [[SHMM]]: utilizing gene-pair orientations for improved analysis of ChIP-chip promoter array data<br /> * [[PHHMM]]: improved analysis of Array-CGH data<br /> * [[MeDIP-HMM]]: HMM-based analysis of DNA methylation profiles<br /> * [https://sites.google.com/site/michaelseiferthmm/hmm-book HMM Book]: Hidden Markov Models with Applications in Computational Biology<br /> <br /> == Follow Me ==<br /> * [https://sites.google.com/site/michaelseiferthmm/home Personal Homepage]</div>

Seifert https://www.jstacs.de/index.php?title=ARHMM&diff=687 2014-05-26T19:44:43Z

<p>Seifert: /* Paper */</p> <hr /> <div>__NOTOC__<br /> by Michael Seifert, Khalil Abou-El-Ardat, Betty Friedrich, Barbara Klink, and Andreas Deutsch<br /> <br /> == Description ==<br /> === Motivation ===<br /> Changes in gene expression programs play a central role in the development of cancer. Deletions and duplications of chromosomal regions directly influence the expression levels of affected genes. Such local chromosomal dependencies lead to highly significant positive correlations of gene expression levels of neighboring genes. These dependencies should be utilized to improve the modeling and the analysis of individual tumor expression profiles.<br /> <br /> === Results ===<br /> We develop a novel model class of autoregressive higher-order Hidden Markov Models (HMMs) that carefully exploit local data-dependent chromosomal dependencies to improve the identification of differentially expressed genes in individual tumors. Autoregressive higher-order HMMs overcome generally existing limitations of standard first-order HMMs in the modeling of dependencies between genes in close chromosomal proximity by the usage of higher-order state-transitions in combination with autoregressive emissions as novel model features. We train autoregressive higher-order HMMs by a specifically developed Bayesian Baum-Welch algorithm that enables to integrate prior knowledge on the measurement distribution of genes. We apply autoregressive higher-order HMMs to the analysis of breast cancer and different types of brain tumor gene expression data and perform in-depth model evaluation studies. We find that autoregressive higher-order HMMs clearly improve the identification of overexpressed genes with underlying gene copy number duplications in breast cancer in comparison to mixture models, standard first- and higher-order HMMs, and other existing related methods. This performance benefit is attributed to the simultaneous usage of higher-order state-transitions in combination with autoregressive emissions and could not be reached by using each of these two features independently. We also find that autoregressive higher-order HMMs are better able to identify differentially expressed genes in tumor independent of the underlying gene copy number status in comparison to the majority of related methods. This is further supported by the identification of well-known and of previously unreported hotspots of differential expression in glioblastomas demonstrating the efficacy of autoregressive higher-order HMMs for the analysis of individual tumor expression profiles. Moreover, we reveal interesting novel details of systematic alterations of gene expression levels in known cancer signaling pathways distinguishing oligodendrogliomas, astrocytomas and glioblastomas.<br /> <br /> == Paper ==<br /> The paper '''''Autoregressive Higher-Order Hidden Markov Models: Exploiting Local Chromosomal Dependencies in the Analysis of Tumor Expression Profiles''''' has been accepted by [http://www.plosone.org/ PLoS One].<br /> <br /> == Download ==<br /> * [http://imc.zih.tu-dresden.de/downloads/ARHMM/ARHMM_Trainer.zip ARHMM]: A ZIP file containing a JAR file for analyzing tumor gene expression data sets by ARHMMs. This file also contains the considered breast cancer and the glioma gene expression data sets.<br /> <br /> == Related Projects ==<br /> * [[DSHMM]]: exploiting prior knowledge and gene distances in the analysis of tumor expression profiles<br /> * [[SHMM]]: utilizing gene-pair orientations for improved analysis of ChIP-chip promoter array data<br /> * [[PHHMM]]: improved analysis of Array-CGH data<br /> * [[MeDIP-HMM]]: HMM-based analysis of DNA methylation profiles<br /> * [https://sites.google.com/site/michaelseiferthmm/hmm-book HMM Book]: Hidden Markov Models with Applications in Computational Biology<br /> <br /> == Follow Me ==<br /> * [https://sites.google.com/site/michaelseiferthmm/home Personal Homepage]</div>

Seifert https://www.jstacs.de/index.php?title=ARHMM&diff=686 2014-05-26T19:43:41Z

<p>Seifert: /* Paper */</p> <hr /> <div>__NOTOC__<br /> by Michael Seifert, Khalil Abou-El-Ardat, Betty Friedrich, Barbara Klink, and Andreas Deutsch<br /> <br /> == Description ==<br /> === Motivation ===<br /> Changes in gene expression programs play a central role in the development of cancer. Deletions and duplications of chromosomal regions directly influence the expression levels of affected genes. Such local chromosomal dependencies lead to highly significant positive correlations of gene expression levels of neighboring genes. These dependencies should be utilized to improve the modeling and the analysis of individual tumor expression profiles.<br /> <br /> === Results ===<br /> We develop a novel model class of autoregressive higher-order Hidden Markov Models (HMMs) that carefully exploit local data-dependent chromosomal dependencies to improve the identification of differentially expressed genes in individual tumors. Autoregressive higher-order HMMs overcome generally existing limitations of standard first-order HMMs in the modeling of dependencies between genes in close chromosomal proximity by the usage of higher-order state-transitions in combination with autoregressive emissions as novel model features. We train autoregressive higher-order HMMs by a specifically developed Bayesian Baum-Welch algorithm that enables to integrate prior knowledge on the measurement distribution of genes. We apply autoregressive higher-order HMMs to the analysis of breast cancer and different types of brain tumor gene expression data and perform in-depth model evaluation studies. We find that autoregressive higher-order HMMs clearly improve the identification of overexpressed genes with underlying gene copy number duplications in breast cancer in comparison to mixture models, standard first- and higher-order HMMs, and other existing related methods. This performance benefit is attributed to the simultaneous usage of higher-order state-transitions in combination with autoregressive emissions and could not be reached by using each of these two features independently. We also find that autoregressive higher-order HMMs are better able to identify differentially expressed genes in tumor independent of the underlying gene copy number status in comparison to the majority of related methods. This is further supported by the identification of well-known and of previously unreported hotspots of differential expression in glioblastomas demonstrating the efficacy of autoregressive higher-order HMMs for the analysis of individual tumor expression profiles. Moreover, we reveal interesting novel details of systematic alterations of gene expression levels in known cancer signaling pathways distinguishing oligodendrogliomas, astrocytomas and glioblastomas.<br /> <br /> == Paper ==<br /> The paper '''''Autoregressive Higher-Order Hidden Markov Models: Exploiting Local Chromosomal Dependencies in the Analysis of Tumor Expression Profiles''''' has been recently accepted by [http://www.plosone.org/ PLoS One].<br /> <br /> == Download ==<br /> * [http://imc.zih.tu-dresden.de/downloads/ARHMM/ARHMM_Trainer.zip ARHMM]: A ZIP file containing a JAR file for analyzing tumor gene expression data sets by ARHMMs. This file also contains the considered breast cancer and the glioma gene expression data sets.<br /> <br /> == Related Projects ==<br /> * [[DSHMM]]: exploiting prior knowledge and gene distances in the analysis of tumor expression profiles<br /> * [[SHMM]]: utilizing gene-pair orientations for improved analysis of ChIP-chip promoter array data<br /> * [[PHHMM]]: improved analysis of Array-CGH data<br /> * [[MeDIP-HMM]]: HMM-based analysis of DNA methylation profiles<br /> * [https://sites.google.com/site/michaelseiferthmm/hmm-book HMM Book]: Hidden Markov Models with Applications in Computational Biology<br /> <br /> == Follow Me ==<br /> * [https://sites.google.com/site/michaelseiferthmm/home Personal Homepage]</div>

Seifert https://www.jstacs.de/index.php?title=ARHMM&diff=685 2014-05-26T19:41:21Z

<p>Seifert: /* Results */</p> <hr /> <div>__NOTOC__<br /> by Michael Seifert, Khalil Abou-El-Ardat, Betty Friedrich, Barbara Klink, and Andreas Deutsch<br /> <br /> == Description ==<br /> === Motivation ===<br /> Changes in gene expression programs play a central role in the development of cancer. Deletions and duplications of chromosomal regions directly influence the expression levels of affected genes. Such local chromosomal dependencies lead to highly significant positive correlations of gene expression levels of neighboring genes. These dependencies should be utilized to improve the modeling and the analysis of individual tumor expression profiles.<br /> <br /> === Results ===<br /> We develop a novel model class of autoregressive higher-order Hidden Markov Models (HMMs) that carefully exploit local data-dependent chromosomal dependencies to improve the identification of differentially expressed genes in individual tumors. Autoregressive higher-order HMMs overcome generally existing limitations of standard first-order HMMs in the modeling of dependencies between genes in close chromosomal proximity by the usage of higher-order state-transitions in combination with autoregressive emissions as novel model features. We train autoregressive higher-order HMMs by a specifically developed Bayesian Baum-Welch algorithm that enables to integrate prior knowledge on the measurement distribution of genes. We apply autoregressive higher-order HMMs to the analysis of breast cancer and different types of brain tumor gene expression data and perform in-depth model evaluation studies. We find that autoregressive higher-order HMMs clearly improve the identification of overexpressed genes with underlying gene copy number duplications in breast cancer in comparison to mixture models, standard first- and higher-order HMMs, and other existing related methods. This performance benefit is attributed to the simultaneous usage of higher-order state-transitions in combination with autoregressive emissions and could not be reached by using each of these two features independently. We also find that autoregressive higher-order HMMs are better able to identify differentially expressed genes in tumor independent of the underlying gene copy number status in comparison to the majority of related methods. This is further supported by the identification of well-known and of previously unreported hotspots of differential expression in glioblastomas demonstrating the efficacy of autoregressive higher-order HMMs for the analysis of individual tumor expression profiles. Moreover, we reveal interesting novel details of systematic alterations of gene expression levels in known cancer signaling pathways distinguishing oligodendrogliomas, astrocytomas and glioblastomas.<br /> <br /> == Paper ==<br /> The paper '''''Autoregressive Higher-Order Hidden Markov Models: Exploiting Local Chromosomal Dependencies in the Analysis of Tumor Expression Profiles''''' has been submitted to [http://www.ploscompbiol.org/ PLoS Computational Biology].<br /> <br /> == Download ==<br /> * [http://imc.zih.tu-dresden.de/downloads/ARHMM/ARHMM_Trainer.zip ARHMM]: A ZIP file containing a JAR file for analyzing tumor gene expression data sets by ARHMMs. This file also contains the considered breast cancer and the glioma gene expression data sets.<br /> <br /> == Related Projects ==<br /> * [[DSHMM]]: exploiting prior knowledge and gene distances in the analysis of tumor expression profiles<br /> * [[SHMM]]: utilizing gene-pair orientations for improved analysis of ChIP-chip promoter array data<br /> * [[PHHMM]]: improved analysis of Array-CGH data<br /> * [[MeDIP-HMM]]: HMM-based analysis of DNA methylation profiles<br /> * [https://sites.google.com/site/michaelseiferthmm/hmm-book HMM Book]: Hidden Markov Models with Applications in Computational Biology<br /> <br /> == Follow Me ==<br /> * [https://sites.google.com/site/michaelseiferthmm/home Personal Homepage]</div>

Seifert https://www.jstacs.de/index.php?title=ARHMM&diff=679 2014-02-21T14:09:18Z

<p>Seifert: </p> <hr /> <div>__NOTOC__<br /> by Michael Seifert, Khalil Abou-El-Ardat, Betty Friedrich, Barbara Klink, and Andreas Deutsch<br /> <br /> == Description ==<br /> === Motivation ===<br /> Changes in gene expression programs play a central role in the development of cancer. Deletions and duplications of chromosomal regions directly influence the expression levels of affected genes. Such local chromosomal dependencies lead to highly significant positive correlations of gene expression levels of neighboring genes. These dependencies should be utilized to improve the modeling and the analysis of individual tumor expression profiles.<br /> <br /> === Results ===<br /> We develop the novel model class of autoregressive higher-order Hidden Markov Models (HMMs) that carefully exploit local data-dependent chromosomal dependencies to improve the identification of differentially expressed genes in individual tumors. Autoregressive higher-order HMMs overcome generally existing limitations of standard first-order HMMs in the modeling of dependencies between genes in close chromosomal proximity by the usage of higher-order state-transitions in combination with autoregressive emissions as novel model features. We train autoregressive higher-order HMMs by a specifically developed Bayesian Baum-Welch algorithm that enables to integrate prior knowledge on the measurement distribution of genes. We apply autoregressive higher-order HMMs to the analysis of breast cancer and different types of brain tumor gene expression data and perform in-depth model evaluation studies. We find that autoregressive higher-order HMMs clearly improve the identification of overexpressed genes with underlying gene copy number duplications in breast cancer in comparison to mixture models, standard first- and higher-order HMMs, and other existing related methods. This performance benefit is attributed to the simultaneous usage of higher-order state-transitions in combination with autoregressive emissions, and could not be reached by using each of these two features independently. We also find that autoregressive higher-order HMMs are better able to identify differentially expressed genes in tumor independent of the underlying gene copy number status in comparison to the majority of related methods. This is further supported by the identification of well-known and of previously unreported hotspots of differential expression in glioblastomas demonstrating the efficacy of autoregressive higher-order HMMs for the analysis of individual tumor expression profiles. Moreover, we reveal interesting novel details of systematic alterations of gene expression levels in known cancer signaling pathways distinguishing oligodendrogliomas, astrocytomas and glioblastomas.<br /> <br /> == Paper ==<br /> The paper '''''Autoregressive Higher-Order Hidden Markov Models: Exploiting Local Chromosomal Dependencies in the Analysis of Tumor Expression Profiles''''' has been submitted to [http://www.ploscompbiol.org/ PLoS Computational Biology].<br /> <br /> == Download ==<br /> * [http://imc.zih.tu-dresden.de/downloads/ARHMM/ARHMM_Trainer.zip ARHMM]: A ZIP file containing a JAR file for analyzing tumor gene expression data sets by ARHMMs. This file also contains the considered breast cancer and the glioma gene expression data sets.<br /> <br /> == Related Projects ==<br /> * [[DSHMM]]: exploiting prior knowledge and gene distances in the analysis of tumor expression profiles<br /> * [[SHMM]]: utilizing gene-pair orientations for improved analysis of ChIP-chip promoter array data<br /> * [[PHHMM]]: improved analysis of Array-CGH data<br /> * [[MeDIP-HMM]]: HMM-based analysis of DNA methylation profiles<br /> * [https://sites.google.com/site/michaelseiferthmm/hmm-book HMM Book]: Hidden Markov Models with Applications in Computational Biology<br /> <br /> == Follow Me ==<br /> * [https://sites.google.com/site/michaelseiferthmm/home Personal Homepage]</div>

Seifert https://www.jstacs.de/index.php?title=ARHMM&diff=678 2014-02-14T13:46:44Z

<p>Seifert: </p> <hr /> <div>__NOTOC__<br /> by Michael Seifert, Khalil Abou-El-Ardat, Betty Friedrich, Barbara Klink, and Andreas Deutsch<br /> <br /> == Description ==<br /> === Motivation ===<br /> Changes in gene expression programs play a central role in the development of cancer. Deletions and duplications of chromosomal regions directly influence the expression levels of affected genes. Such local chromosomal dependencies lead to highly significant positive correlations of gene expression levels of neighboring genes. These dependencies should be utilized to improve the modeling and the analysis of individual tumor expression profiles.<br /> <br /> === Results ===<br /> We develop the novel model class of autoregressive higher-order Hidden Markov Models (HMMs) that carefully exploit local data-dependent chromosomal dependencies to improve the identification of differentially expressed genes in individual tumors. Autoregressive higher-order HMMs overcome generally existing limitations of standard first-order HMMs in the modeling of dependencies between genes in close chromosomal proximity by the usage of higher-order state-transitions in combination with autoregressive emissions as novel model features. We train autoregressive higher-order HMMs by a specifically developed Bayesian Baum-Welch algorithm that enables to integrate prior knowledge on the measurement distribution of genes. We apply autoregressive higher-order HMMs to the analysis of breast cancer and different types of brain tumor gene expression data and perform in-depth model evaluation studies. We find that autoregressive higher-order HMMs clearly improve the identification of overexpressed genes with underlying gene copy number duplications in breast cancer in comparison to mixture models, standard first- and higher-order HMMs, and other existing related methods. This performance benefit is attributed to the simultaneous usage of higher-order state-transitions in combination with autoregressive emissions, and could not be reached by using each of these two features independently. We also find that autoregressive higher-order HMMs are better able to identify differentially expressed genes in tumor independent of the underlying gene copy number status in comparison to the majority of related methods. This is further supported by the identification of well-known and previously unreported hotspots of differential expression in glioblastomas demonstrating the efficacy of autoregressive higher-order HMMs for the analysis of individual tumor expression profiles. Moreover, we reveal interesting novel details of systematic alterations of gene expression levels in known cancer signaling pathways distinguishing oligodendrogliomas, astrocytomas and glioblastomas.<br /> <br /> == Paper ==<br /> The paper '''''Autoregressive Higher-Order Hidden Markov Models: Exploiting Local Chromosomal Dependencies in the Analysis of Tumor Expression Profiles''''' has been submitted to [http://www.ploscompbiol.org/ PLoS Computational Biology].<br /> <br /> == Download ==<br /> * [http://imc.zih.tu-dresden.de/downloads/ARHMM/ARHMM_Trainer.zip ARHMM]: A ZIP file containing a JAR file for analyzing tumor gene expression data sets by ARHMMs. This file also contains the considered breast cancer and the glioblastoma gene expression data sets.<br /> <br /> == Related Projects ==<br /> * [[DSHMM]]: exploiting prior knowledge and gene distances in the analysis of tumor expression profiles<br /> * [[SHMM]]: utilizing gene-pair orientations for improved analysis of ChIP-chip promoter array data<br /> * [[PHHMM]]: improved analysis of Array-CGH data<br /> * [[MeDIP-HMM]]: HMM-based analysis of DNA methylation profiles<br /> * [https://sites.google.com/site/michaelseiferthmm/hmm-book HMM Book]: Hidden Markov Models with Applications in Computational Biology<br /> <br /> == Follow Me ==<br /> * [https://sites.google.com/site/michaelseiferthmm/home Personal Homepage]</div>

Seifert https://www.jstacs.de/index.php?title=ARHMM&diff=677 2014-01-28T07:39:28Z

<p>Seifert: </p> <hr /> <div>__NOTOC__<br /> by Michael Seifert, Khalil Abou-El-Ardat, Barbara Klink, and Andreas Deutsch<br /> <br /> == Description ==<br /> === Motivation ===<br /> Changes in gene expression programs play a central role in the development of cancer. Deletions and duplications of chromosomal regions directly influence the expression levels of affected genes. Such local chromosomal dependencies lead to strong positive correlations of gene expression levels of neighboring genes. These dependencies should be utilized to improve the modeling and the analysis of individual tumor expression profiles.<br /> <br /> === Results ===<br /> We develop autoregressive higher-order Hidden Markov Models (HMMs) that carefully exploit local data-dependent chromosomal dependencies to improve the identification of differentially expressed genes in individual tumors. Autoregressive higher-order HMMs overcome generally existing limitations of standard first-order HMMs in the modeling of dependencies between genes in close chromosomal proximity by the usage of higher-order state-transitions in combination with autoregressive emissions as novel model features. We train autoregressive higher-order HMMs by a specifically developed Bayesian Baum-Welch algorithm that enables to integrate prior knowledge on the measurement distribution of genes. We apply autoregressive higher-order HMMs to the analysis of breast cancer and glioblastoma gene expression data and perform in-depth model evaluation studies. We find that autoregressive higher-order HMMs clearly improve the identification of overexpressed genes with underlying gene copy number duplications in breast cancer in comparison to mixture models, standard first- and higher-order HMMs, and other existing related methods. This performance benefit is attributed to the simultaneous usage of higher-order state-transitions in combination with autoregressive emissions, and could not be reached by using each of these two features independently. We also find that autoregressive higher-order HMMs are better able to identify differentially expressed genes in tumor independent of the underlying gene copy number status in comparison to the majority of related methods. This is further supported by the identification of well-known and previously unreported hotspots of differential expression in glioblastomas demonstrating the efficacy of autoregressive higher-order HMMs for the analysis of individual tumor expression profiles.<br /> <br /> == Paper ==<br /> The paper '''''Autoregressive Higher-Order Hidden Markov Models: Exploiting Local Chromosomal Dependencies in the Analysis of Tumor Expression Profiles''''' has been submitted to [http://www.ploscompbiol.org/ PLoS Computational Biology].<br /> <br /> == Download ==<br /> * [http://imc.zih.tu-dresden.de/downloads/ARHMM/ARHMM_Trainer.zip ARHMM]: A ZIP file containing a JAR file for analyzing tumor gene expression data sets by ARHMMs. This file also contains the considered breast cancer and the glioblastoma gene expression data sets.<br /> <br /> == Related Projects ==<br /> * [[DSHMM]]: exploiting prior knowledge and gene distances in the analysis of tumor expression profiles<br /> * [[SHMM]]: utilizing gene-pair orientations for improved analysis of ChIP-chip promoter array data<br /> * [[PHHMM]]: improved analysis of Array-CGH data<br /> * [[MeDIP-HMM]]: HMM-based analysis of DNA methylation profiles<br /> * [https://sites.google.com/site/michaelseiferthmm/hmm-book HMM Book]: Hidden Markov Models with Applications in Computational Biology<br /> <br /> == Follow Me ==<br /> * [https://sites.google.com/site/michaelseiferthmm/home Personal Homepage]</div>

Seifert https://www.jstacs.de/index.php?title=ARHMM&diff=676 2014-01-27T11:42:18Z

<p>Seifert: </p> <hr /> <div>__NOTOC__<br /> by Michael Seifert, Khalil Abou-El-Aradat, Barbara Klink, and Andreas Deutsch<br /> <br /> == Description ==<br /> === Motivation ===<br /> Changes in gene expression programs play a central role in the development of cancer. Deletions and duplications of chromosomal regions directly influence the expression levels of affected genes. Such local chromosomal dependencies lead to strong positive correlations of gene expression levels of neighboring genes. These dependencies should be utilized to improve the modeling and the analysis of individual tumor expression profiles.<br /> <br /> === Results ===<br /> We develop autoregressive higher-order Hidden Markov Models (HMMs) that carefully exploit local data-dependent chromosomal dependencies to improve the identification of differentially expressed genes in individual tumors. Autoregressive higher-order HMMs overcome generally existing limitations of standard first-order HMMs in the modeling of dependencies between genes in close chromosomal proximity by the usage of higher-order state-transitions in combination with autoregressive emissions as novel model features. We train autoregressive higher-order HMMs by a specifically developed Bayesian Baum-Welch algorithm that enables to integrate prior knowledge on the measurement distribution of genes. We apply autoregressive higher-order HMMs to the analysis of breast cancer and glioblastoma gene expression data and perform in-depth model evaluation studies. We find that autoregressive higher-order HMMs clearly improve the identification of overexpressed genes with underlying gene copy number duplications in breast cancer in comparison to mixture models, standard first- and higher-order HMMs, and other existing related methods. This performance benefit is attributed to the simultaneous usage of higher-order state-transitions in combination with autoregressive emissions, and could not be reached by using each of these two features independently. We also find that autoregressive higher-order HMMs are better able to identify differentially expressed genes in tumor independent of the underlying gene copy number status in comparison to the majority of related methods. This is further supported by the identification of well-known and previously unreported hotspots of differential expression in glioblastomas demonstrating the efficacy of autoregressive higher-order HMMs for the analysis of individual tumor expression profiles.<br /> <br /> == Paper ==<br /> The paper '''''Autoregressive Higher-Order Hidden Markov Models: Exploiting Local Chromosomal Dependencies in the Analysis of Tumor Expression Profiles''''' has been submitted to [http://www.ploscompbiol.org/ PLoS Computational Biology].<br /> <br /> == Download ==<br /> * [http://imc.zih.tu-dresden.de/downloads/ARHMM/ARHMM_Trainer.zip ARHMM]: A ZIP file containing a JAR file for analyzing tumor gene expression data sets by ARHMMs. This file also contains the considered breast cancer and the glioblastoma gene expression data sets.<br /> <br /> == Related Projects ==<br /> * [[DSHMM]]: exploiting prior knowledge and gene distances in the analysis of tumor expression profiles<br /> * [[SHMM]]: utilizing gene-pair orientations for improved analysis of ChIP-chip promoter array data<br /> * [[PHHMM]]: improved analysis of Array-CGH data<br /> * [[MeDIP-HMM]]: HMM-based analysis of DNA methylation profiles<br /> * [https://sites.google.com/site/michaelseiferthmm/hmm-book HMM Book]: Hidden Markov Models with Applications in Computational Biology<br /> <br /> == Follow Me ==<br /> * [https://sites.google.com/site/michaelseiferthmm/home Personal Homepage]</div>

Seifert https://www.jstacs.de/index.php?title=MeDIP-HMM&diff=652 2013-08-03T18:41:25Z

<p>Seifert: </p> <hr /> <div>__NOTOC__<br /> by Michael Seifert, Sandra Cortijo, Maria Colome-Tatche, Frank Johannes, Francois Roudier, and Vincent Colot<br /> <br /> == Description ==<br /> === Motivation ===<br /> Methylation of cytosines in DNA is an important epigenetic mechanism involved in transcriptional regulation and preservation of genome integrity in a wide range of eukaryotes. Immunoprecipitation of methylated DNA followed by hybridization to genomic tiling arrays (MeDIP-chip) is a cost-effective and sensitive method for methylome analyses. However, existing bioinformatics methods only enable a binary classification into unmethylated and methylated genomic regions, which limits biological interpretations. Indeed, DNA methylation levels can vary substantially within a given DNA fragment depending on the number and degree of methylated cytosines. Therefore, a method for the identification of more than two methylation states is highly desirable.<br /> <br /> === Results ===<br /> Here, we present a three-state Hidden Markov Model (MeDIP-HMM) for analyzing MeDIP-chip data. MeDIP-HMM utilizes a higher-order state-transition process improving modeling of spatial dependencies between chromosomal regions, allows a simultaneous analysis of replicates, and enables a differentiation between unmethylated, methylated and highly methylated genomic regions. We train MeDIP-HMM using a Bayesian Baum-Welch algorithm integrating prior knowledge on methylation levels. We apply MeDIP-HMM to the analysis of the Arabidopsis root methylome and systematically investigate the benefit of using higher-order HMMs. Moreover, we also perform an in-depth comparison study to existing methods and demonstrate the value of using MeDIP-HMM by comparisons to current knowledge on the Arabidopsis methylome. We find that MeDIP-HMM is a fast and precise method for the analysis of DNA methylation data enabling the identification of distinct DNA methylation levels. Finally, we provide evidence for the general applicability of MeDIP-HMM by analyzing promoter DNA methylation data obtained for chicken.<br /> <br /> == Paper ==<br /> The paper [http://bioinformatics.oxfordjournals.org/content/early/2012/09/17/bioinformatics.bts562.abstract '''''MeDIP-HMM: Genome-wide identification of distinct DNA methylation states from high-density tiling arrays'''''] has been published in [http://bioinformatics.oxfordjournals.org/ Bioinformatics].<br /> <br /> == Download ==<br /> * [http://dig.ipk-gatersleben.de/MeDIP-HMM/MeDIP-HMM.zip MeDIP-HMM]: A ZIP file containing a JAR file for analyzing methylome data sets by MeDIP-HMMs. This file also contains the Arabidopsis root methylome and the chicken data sets considered in our study.<br /> <br /> == Related Projects ==<br /> * [[ARHMM]]: integrating local chromosomal dependencies into the analysis of tumor expression profiles<br /> * [[DSHMM]]: exploiting prior knowledge and gene distances in the analysis of tumor expression profiles<br /> * [[SHMM]]: utilizing gene-pair orientations for improved analysis of ChIP-chip promoter array data<br /> * [[PHHMM]]: improved analysis of Array-CGH data<br /> * [https://sites.google.com/site/michaelseiferthmm/hmm-book HMM Book]: Hidden Markov Models with Applications in Computational Biology<br /> <br /> == Follow Me ==<br /> * [https://sites.google.com/site/michaelseiferthmm/home Personal Homepage]</div>

Seifert https://www.jstacs.de/index.php?title=ARHMM&diff=651 2013-08-03T18:40:48Z

<p>Seifert: </p> <hr /> <div>__NOTOC__<br /> by Michael Seifert, Khalil Abou-El-Aradat, Barbara Klink, and Andreas Deutsch<br /> <br /> == Description ==<br /> === Motivation ===<br /> Gene expression profiles of tumors are locally biased by deletions and duplications of chromosomal regions. Such local chromosomal dependencies affect expression levels of genes in close chromosomal proximity and should be utilized to improve the analysis of individual tumor expression profiles.<br /> <br /> === Results ===<br /> We develop autoregressive higher-order Hidden Markov Models (ARHMMs) that exploit data-dependent local chromosomal dependencies to improve the identification of differentially expressed genes in tumors. ARHMMs combine enhanced local chromosomal modeling capabilities of higher-order state-transitions with direct modeling of chromosomal dependencies between successive gene expression levels using autoregressive emissions. We train ARHMMs using a specifically designed Bayesian Baum-Welch algorithm enabling the integration of prior knowledge on the distribution of differentially expressed genes. We apply ARHMMs to the analysis of breast cancer and brain tumor gene expression data and find that these models substantially improve the identification of differentially expressed genes with underlying copy number changes in comparison to existing methods. This is further supported by the identification of well-known and previously unreported hotspots of differential expression in glioblastoma multiforme highlighting the efficacy of ARHMMs for the analysis of individual tumor expression profiles.<br /> <br /> == Paper ==<br /> The paper '''''Exploiting local chromosomal dependencies in the analysis of tumor expression profiles by autoregressive higher-order Hidden Markov Models''''' has been submitted to [http://bioinformatics.oxfordjournals.org/ Bioinformatics].<br /> <br /> == Download ==<br /> * [http://imc.zih.tu-dresden.de/downloads/ARHMM/ARHMM_Trainer.zip ARHMM]: A ZIP file containing a JAR file for analyzing tumor gene expression data sets by ARHMMs. This file also contains the breast cancer and the glioma gene expression data sets considered in our study.<br /> <br /> == Related Projects ==<br /> * [[DSHMM]]: exploiting prior knowledge and gene distances in the analysis of tumor expression profiles<br /> * [[SHMM]]: utilizing gene-pair orientations for improved analysis of ChIP-chip promoter array data<br /> * [[PHHMM]]: improved analysis of Array-CGH data<br /> * [[MeDIP-HMM]]: HMM-based analysis of DNA methylation profiles<br /> * [https://sites.google.com/site/michaelseiferthmm/hmm-book HMM Book]: Hidden Markov Models with Applications in Computational Biology<br /> <br /> == Follow Me ==<br /> * [https://sites.google.com/site/michaelseiferthmm/home Personal Homepage]</div>

Seifert https://www.jstacs.de/index.php?title=Projects&diff=646 2013-07-15T11:56:04Z

<p>Seifert: </p> <hr /> <div>This site contains projects that use Jstacs.<br /> * [[MotifAdjuster]]: a tool for computational reassessment of transcription factor binding site annotations<br /> * [[Prior]]: apples and oranges: avoiding different priors in Bayesian DNA sequence analysis<br /> * [[GenDisMix]]: unifying generative and discriminative learning principles<br /> * [[Dispom]]: de-novo discovery of differentially abundant transcription factor binding sites including their positional preference<br /> * [[MiMB]]: probabilistic approaches to transcription factor binding site prediction<br /> * [[SHMM]]: utilizing gene-pair orientations for improved analysis of ChIP-chip promoter array data<br /> * [[DSHMM]]: exploiting prior knowledge and gene distances in the analysis of tumor expression profiles<br /> * [[PHHMM]]: improved analysis of Array-CGH data<br /> * [[MeDIP-HMM]]: HMM-based analysis of DNA methylation profiles<br /> * [[ARHMM]]: integrating local chromosomal dependencies into the analysis of tumor expression profiles<br /> * [[FlowCap]]: molecular classification of acute myeloid leukaemia (AML) using flow cytometry data<br /> * [[TALgetter]]: prediction of TAL effector target sites</div>

Seifert https://www.jstacs.de/index.php?title=DSHMM&diff=645 2013-07-15T11:55:36Z

<p>Seifert: </p> <hr /> <div>__NOTOC__<br /> by Michael Seifert, Marc Strickert, Alexander Schliep, and Ivo Grosse<br /> <br /> == Description ==<br /> === Motivation ===<br /> Changes in gene expression levels play a central role in tumors. Additional information about the distribution of gene expression levels and distances between adjacent genes on chromosomes should be integrated into the analysis of tumor expression profiles.<br /> <br /> === Results ===<br /> We use a Hidden Markov Model with distance-scaled transition matrices (DSHMM) to incorporate chromosomal distances of adjacent genes on chromosomes into the identification of differentially expressed genes in breast cancer. We train the DSHMM by integrating prior knowledge about potential distributions of expression levels of differentially expressed and unchanged genes in tumor. We find that especially the combination of this data and to a lesser extent the modeling of distances between adjacent genes contribute to a substantial improvement of the identification of differentially expressed genes in comparison to other existing methods. This performance benefit is also supported by the identification of genes well-known to be associated with breast cancer. That suggests applications of DSHMMs for screening of other tumor expression profiles.<br /> <br /> == Paper ==<br /> The paper [http://bioinformatics.oxfordjournals.org/content/27/12/1645 '''''Exploiting prior knowledge and gene distances in the analysis of tumor expression profiles with extended Hidden Markov Models'''''] has been published in [http://bioinformatics.oxfordjournals.org/ Bioinformatics].<br /> <br /> == Download ==<br /> * [http://www.jstacs.de/downloads/supplementary_data.zip Supplementary data]: Containing the breast cancer gene expression data set and the breast cancer gene copy number data set (Pollack et al. (2002)) analyzed in the manuscript, and the predictions and scores of the compared methods.<br /> * [http://www.jstacs.de/downloads/ModelTrainer.zip Model trainer]: JAR file for analyzing a data set by Gaussian mixture models, HMMs, SHMMs, and DSHMMs.<br /> <br /> == Related Projects ==<br /> * [[ARHMM]]: integrating local chromosomal dependencies into the analysis of tumor expression profiles<br /> * [[PHHMM]]: improved analysis of Array-CGH data<br /> * [[SHMM]]: utilizing gene-pair orientations for improved analysis of ChIP-chip promoter array data<br /> * [[MeDIP-HMM]]: HMM-based analysis of DNA methylation profiles<br /> * [https://sites.google.com/site/michaelseiferthmm/hmm-book HMM Book]: Hidden Markov Models with Applications in Computational Biology<br /> <br /> == Follow Me ==<br /> * [https://sites.google.com/site/michaelseiferthmm/home Personal Homepage]</div>

Seifert https://www.jstacs.de/index.php?title=SHMM&diff=644 2013-07-15T11:55:09Z

<p>Seifert: </p> <hr /> <div>__NOTOC__<br /> by Michael Seifert, Jens Keilwagen, Marc Strickert, and Ivo Grosse<br /> <br /> == Description ==<br /> === Motivation ===<br /> Array-based analysis of chromatin immunoprecipitation (ChIP-chip) data is a powerful technique for identifying DNA target regions of individual transcription factors. The identification of these target regions from comprehensive promoter array ChIP-chip data is challenging. Here, three approaches for the identification of transcription factor target genes from promoter array ChIP-chip data are presented. We compare (i) a standard log-fold-change analysis (LFC); (ii) a basic method based on a Hidden Markov Model (HMM); and (iii) a new extension of the HMM approach to an HMM with scaled transition matrices (SHMM) that incorporates information about the relative orientation of adjacent gene pairs on DNA. <br /> <br /> === Results ===<br /> All three methods are applied to different promoter array ChIP-chip datasets of the yeast Saccharomyces cerevisiae and the important model plant Arabidopsis thaliana to compare the prediction of transcription factor target genes. In the context of the yeast cell cycle, common target genes bound by the transcription factors ACE2 and SWI5, and ACE2 and FKH2 are identified and evaluated using the Saccharomyces Genome Database. Regarding A.thaliana, target genes of the seed-specific transcription factor ABI3 are predicted and evaluate based on publicly available gene expression profiles and transient assays performed in the wet laboratory experiments. The application of the novel SHMM to these two different promoter array ChIP-chip datasets leads to an improved identification of transcription factor target genes in comparison to the two standard approaches LFC and HMM.<br /> == Paper ==<br /> The paper [http://bioinformatics.oxfordjournals.org/content/25/16/2118 '''''Utilizing gene pair orientations for HMM-based analysis of promoter array ChIP-chip data'''''] has been published in [http://bioinformatics.oxfordjournals.org/ Bioinformatics].<br /> <br /> == Download ==<br /> * [http://dig.ipk-gatersleben.de/SHMMs/ChIPchip/ChIPchip.html Supplementary data]: Link to the implementations of LFC, HMM and SHMM.<br /> <br /> == Related Projects ==<br /> * [[ARHMM]]: integrating local chromosomal dependencies into the analysis of tumor expression profiles<br /> * [[PHHMM]]: improved analysis of Array-CGH data<br /> * [[DSHMM]]: exploiting prior knowledge and gene distances in the analysis of tumor expression profiles<br /> * [[MeDIP-HMM]]: HMM-based analysis of DNA methylation profiles<br /> * [https://sites.google.com/site/michaelseiferthmm/hmm-book HMM Book]: Hidden Markov Models with Applications in Computational Biology<br /> <br /> == Follow Me ==<br /> * [https://sites.google.com/site/michaelseiferthmm/home Personal Homepage]</div>

Seifert https://www.jstacs.de/index.php?title=PHHMM&diff=643 2013-07-15T11:54:49Z

<p>Seifert: </p> <hr /> <div>__NOTOC__<br /> by Michael Seifert, André Gohr, Marc Strickert, and Ivo Grosse<br /> <br /> == Description ==<br /> Array-based comparative genomic hybridization (Array-CGH) is an important technology in molecular biology for the detection of DNA copy number polymorphisms between closely related genomes. Hidden Markov Models (HMMs) are popular tools for the analysis of Array-CGH data, but current methods are only based on first-order HMMs having constrained abilities to model spatial dependencies between measurements of closely adjacent chromosomal regions. Here, we develop parsimonious higher-order HMMs enabling the interpolation between a mixture model ignoring spatial dependencies and a higher-order HMM exhaustively modeling spatial dependencies. We apply parsimonious higher-order HMMs to the analysis of Array-CGH data of the accessions C24 and Col-0 of the model plant Arabidopsis thaliana. We compare these models against first-order HMMs and other existing methods using a reference of known deletions and sequence deviations. We find that parsimonious higher-order HMMs clearly improve the identification of these polymorphisms. Moreover, we perform a functional analysis of identified polymorphisms revealing novel details of genomic differences between C24 and Col-0. Additional model evaluations are also done on widely considered Array-CGH data of human cell lines indicating that parsimonious HMMs are also well-suited for the analysis of non-plant specific data. All these results indicate that parsimonious higher-order HMMs are useful for Array-CGH analyses.<br /> <br /> == Paper ==<br /> The paper '''''Parsimonious higher-order Hidden Markov Models for improved Array-CGH analysis with applications to Arabidopsis thaliana''''' has been published in [http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002286 PloS Comp Biol].<br /> <br /> == Download ==<br /> * [http://dig.ipk-gatersleben.de/PHHMM/PHHMM_Trainer.zip Parsimonious HMMs]: A ZIP file including a JAR file for analyzing data sets by parsimonious higher-order HMMs and examples for the Arabidopsis and the human cell lines data<br /> <br /> == Related Projects ==<br /> * [[ARHMM]]: integrating local chromosomal dependencies into the analysis of tumor expression profiles<br /> * [[DSHMM]]: exploiting prior knowledge and gene distances in the analysis of tumor expression profiles<br /> * [[SHMM]]: utilizing gene-pair orientations for improved analysis of ChIP-chip promoter array data<br /> * [[MeDIP-HMM]]: HMM-based analysis of DNA methylation profiles<br /> * [https://sites.google.com/site/michaelseiferthmm/hmm-book HMM Book]: Hidden Markov Models with Applications in Computational Biology<br /> <br /> == Follow Me ==<br /> * [https://sites.google.com/site/michaelseiferthmm/home Personal Homepage]</div>

Seifert https://www.jstacs.de/index.php?title=ARHMM&diff=642 2013-07-15T11:50:41Z

<p>Seifert: New project page</p> <hr /> <div>__NOTOC__<br /> by Michael Seifert, Khalil Abou-El-Aradat, Barbara Klink, and Andreas Deutsch<br /> <br /> == Description ==<br /> === Motivation ===<br /> Gene expression profiles of tumors are locally biased by deletions and duplications of chromosomal regions. Such local chromosomal dependencies affect expression levels of genes in close chromosomal proximity and should be utilized to improve the analysis of individual tumor expression profiles.<br /> <br /> === Results ===<br /> We develop autoregressive higher-order Hidden Markov Models (ARHMMs) that exploit data-dependent local chromosomal dependencies to improve the identification of differentially expressed genes in tumors. ARHMMs combine enhanced local chromosomal modeling capabilities of higher-order state-transitions with direct modeling of chromosomal dependencies between successive gene expression levels using autoregressive emissions. We train ARHMMs using a specifically designed Bayesian Baum-Welch algorithm enabling the integration of prior knowledge on the distribution of differentially expressed genes. We apply ARHMMs to the analysis of breast cancer and brain tumor gene expression data and find that these models substantially improve the identification of differentially expressed genes with underlying copy number changes in comparison to existing methods. This is further supported by the identification of well-known and previously unreported hotspots of differential expression in glioblastoma multiforme highlighting the efficacy of ARHMMs for the analysis of individual tumor expression profiles.<br /> <br /> == Paper ==<br /> The paper '''''Exploiting local chromosomal dependencies in the analysis of tumor expression profiles by autoregressive higher-order Hidden Markov Models''''' has been submitted to [http://bioinformatics.oxfordjournals.org/ Bioinformatics].<br /> <br /> == Download ==<br /> * [http://imc.zih.tu-dresden.de/downloads/ARHMM/ARHMM_Trainer.zip ARHMM]: A ZIP file containing a JAR file for analyzing tumor gene expression data sets by ARHMMs. This file also contains the breast cancer and the glioma gene expression data sets considered in our study.<br /> <br /> == Related Projects ==<br /> * [[DSHMM]]: exploiting prior knowledge and gene distances in the analysis of tumor expression profiles<br /> * [[SHMM]]: utilizing gene-pair orientations for improved analysis of ChIP-chip promoter array data<br /> * [[PHHMM]]: improved analysis of Array-CGH data<br /> * [https://sites.google.com/site/michaelseiferthmm/hmm-book HMM Book]: Hidden Markov Models with Applications in Computational Biology<br /> <br /> == Follow Me ==<br /> * [https://sites.google.com/site/michaelseiferthmm/home Personal Homepage]</div>

Seifert https://www.jstacs.de/index.php?title=SHMM&diff=615 2013-03-03T20:47:43Z

<p>Seifert: </p> <hr /> <div>__NOTOC__<br /> by Michael Seifert, Jens Keilwagen, Marc Strickert, and Ivo Grosse<br /> <br /> == Description ==<br /> === Motivation ===<br /> Array-based analysis of chromatin immunoprecipitation (ChIP-chip) data is a powerful technique for identifying DNA target regions of individual transcription factors. The identification of these target regions from comprehensive promoter array ChIP-chip data is challenging. Here, three approaches for the identification of transcription factor target genes from promoter array ChIP-chip data are presented. We compare (i) a standard log-fold-change analysis (LFC); (ii) a basic method based on a Hidden Markov Model (HMM); and (iii) a new extension of the HMM approach to an HMM with scaled transition matrices (SHMM) that incorporates information about the relative orientation of adjacent gene pairs on DNA. <br /> <br /> === Results ===<br /> All three methods are applied to different promoter array ChIP-chip datasets of the yeast Saccharomyces cerevisiae and the important model plant Arabidopsis thaliana to compare the prediction of transcription factor target genes. In the context of the yeast cell cycle, common target genes bound by the transcription factors ACE2 and SWI5, and ACE2 and FKH2 are identified and evaluated using the Saccharomyces Genome Database. Regarding A.thaliana, target genes of the seed-specific transcription factor ABI3 are predicted and evaluate based on publicly available gene expression profiles and transient assays performed in the wet laboratory experiments. The application of the novel SHMM to these two different promoter array ChIP-chip datasets leads to an improved identification of transcription factor target genes in comparison to the two standard approaches LFC and HMM.<br /> == Paper ==<br /> The paper [http://bioinformatics.oxfordjournals.org/content/25/16/2118 '''''Utilizing gene pair orientations for HMM-based analysis of promoter array ChIP-chip data'''''] has been published in [http://bioinformatics.oxfordjournals.org/ Bioinformatics].<br /> <br /> == Download ==<br /> * [http://dig.ipk-gatersleben.de/SHMMs/ChIPchip/ChIPchip.html Supplementary data]: Link to the implementations of LFC, HMM and SHMM.<br /> <br /> == Related Projects ==<br /> * [[PHHMM]]: improved analysis of Array-CGH data<br /> * [[DSHMM]]: exploiting prior knowledge and gene distances in the analysis of tumor expression profiles<br /> * [[MeDIP-HMM]]: HMM-based analysis of DNA methylation profiles<br /> * [https://sites.google.com/site/michaelseiferthmm/hmm-book HMM Book]: Hidden Markov Models with Applications in Computational Biology<br /> <br /> == Follow Me ==<br /> * [https://sites.google.com/site/michaelseiferthmm/home Personal Homepage]</div>

Seifert https://www.jstacs.de/index.php?title=PHHMM&diff=614 2013-03-03T20:47:11Z

<p>Seifert: </p> <hr /> <div>__NOTOC__<br /> by Michael Seifert, André Gohr, Marc Strickert, and Ivo Grosse<br /> <br /> == Description ==<br /> Array-based comparative genomic hybridization (Array-CGH) is an important technology in molecular biology for the detection of DNA copy number polymorphisms between closely related genomes. Hidden Markov Models (HMMs) are popular tools for the analysis of Array-CGH data, but current methods are only based on first-order HMMs having constrained abilities to model spatial dependencies between measurements of closely adjacent chromosomal regions. Here, we develop parsimonious higher-order HMMs enabling the interpolation between a mixture model ignoring spatial dependencies and a higher-order HMM exhaustively modeling spatial dependencies. We apply parsimonious higher-order HMMs to the analysis of Array-CGH data of the accessions C24 and Col-0 of the model plant Arabidopsis thaliana. We compare these models against first-order HMMs and other existing methods using a reference of known deletions and sequence deviations. We find that parsimonious higher-order HMMs clearly improve the identification of these polymorphisms. Moreover, we perform a functional analysis of identified polymorphisms revealing novel details of genomic differences between C24 and Col-0. Additional model evaluations are also done on widely considered Array-CGH data of human cell lines indicating that parsimonious HMMs are also well-suited for the analysis of non-plant specific data. All these results indicate that parsimonious higher-order HMMs are useful for Array-CGH analyses.<br /> <br /> == Paper ==<br /> The paper '''''Parsimonious higher-order Hidden Markov Models for improved Array-CGH analysis with applications to Arabidopsis thaliana''''' has been published in [http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002286 PloS Comp Biol].<br /> <br /> == Download ==<br /> * [http://dig.ipk-gatersleben.de/PHHMM/PHHMM_Trainer.zip Parsimonious HMMs]: A ZIP file including a JAR file for analyzing data sets by parsimonious higher-order HMMs and examples for the Arabidopsis and the human cell lines data<br /> <br /> == Related Projects ==<br /> * [[DSHMM]]: exploiting prior knowledge and gene distances in the analysis of tumor expression profiles<br /> * [[SHMM]]: utilizing gene-pair orientations for improved analysis of ChIP-chip promoter array data<br /> * [[MeDIP-HMM]]: HMM-based analysis of DNA methylation profiles<br /> * [https://sites.google.com/site/michaelseiferthmm/hmm-book HMM Book]: Hidden Markov Models with Applications in Computational Biology<br /> <br /> == Follow Me ==<br /> * [https://sites.google.com/site/michaelseiferthmm/home Personal Homepage]</div>

Seifert https://www.jstacs.de/index.php?title=DSHMM&diff=613 2013-03-03T20:46:48Z

<p>Seifert: </p> <hr /> <div>__NOTOC__<br /> by Michael Seifert, Marc Strickert, Alexander Schliep, and Ivo Grosse<br /> <br /> == Description ==<br /> === Motivation ===<br /> Changes in gene expression levels play a central role in tumors. Additional information about the distribution of gene expression levels and distances between adjacent genes on chromosomes should be integrated into the analysis of tumor expression profiles.<br /> <br /> === Results ===<br /> We use a Hidden Markov Model with distance-scaled transition matrices (DSHMM) to incorporate chromosomal distances of adjacent genes on chromosomes into the identification of differentially expressed genes in breast cancer. We train the DSHMM by integrating prior knowledge about potential distributions of expression levels of differentially expressed and unchanged genes in tumor. We find that especially the combination of this data and to a lesser extent the modeling of distances between adjacent genes contribute to a substantial improvement of the identification of differentially expressed genes in comparison to other existing methods. This performance benefit is also supported by the identification of genes well-known to be associated with breast cancer. That suggests applications of DSHMMs for screening of other tumor expression profiles.<br /> <br /> == Paper ==<br /> The paper [http://bioinformatics.oxfordjournals.org/content/27/12/1645 '''''Exploiting prior knowledge and gene distances in the analysis of tumor expression profiles with extended Hidden Markov Models'''''] has been published in [http://bioinformatics.oxfordjournals.org/ Bioinformatics].<br /> <br /> == Download ==<br /> * [http://www.jstacs.de/downloads/supplementary_data.zip Supplementary data]: Containing the breast cancer gene expression data set and the breast cancer gene copy number data set (Pollack et al. (2002)) analyzed in the manuscript, and the predictions and scores of the compared methods.<br /> * [http://www.jstacs.de/downloads/ModelTrainer.zip Model trainer]: JAR file for analyzing a data set by Gaussian mixture models, HMMs, SHMMs, and DSHMMs.<br /> <br /> == Related Projects ==<br /> * [[PHHMM]]: improved analysis of Array-CGH data<br /> * [[SHMM]]: utilizing gene-pair orientations for improved analysis of ChIP-chip promoter array data<br /> * [[MeDIP-HMM]]: HMM-based analysis of DNA methylation profiles<br /> * [https://sites.google.com/site/michaelseiferthmm/hmm-book HMM Book]: Hidden Markov Models with Applications in Computational Biology<br /> <br /> == Follow Me ==<br /> * [https://sites.google.com/site/michaelseiferthmm/home Personal Homepage]</div>

Seifert https://www.jstacs.de/index.php?title=MeDIP-HMM&diff=612 2013-03-03T20:44:56Z

<p>Seifert: </p> <hr /> <div>__NOTOC__<br /> by Michael Seifert, Sandra Cortijo, Maria Colome-Tatche, Frank Johannes, Francois Roudier, and Vincent Colot<br /> <br /> == Description ==<br /> === Motivation ===<br /> Methylation of cytosines in DNA is an important epigenetic mechanism involved in transcriptional regulation and preservation of genome integrity in a wide range of eukaryotes. Immunoprecipitation of methylated DNA followed by hybridization to genomic tiling arrays (MeDIP-chip) is a cost-effective and sensitive method for methylome analyses. However, existing bioinformatics methods only enable a binary classification into unmethylated and methylated genomic regions, which limits biological interpretations. Indeed, DNA methylation levels can vary substantially within a given DNA fragment depending on the number and degree of methylated cytosines. Therefore, a method for the identification of more than two methylation states is highly desirable.<br /> <br /> === Results ===<br /> Here, we present a three-state Hidden Markov Model (MeDIP-HMM) for analyzing MeDIP-chip data. MeDIP-HMM utilizes a higher-order state-transition process improving modeling of spatial dependencies between chromosomal regions, allows a simultaneous analysis of replicates, and enables a differentiation between unmethylated, methylated and highly methylated genomic regions. We train MeDIP-HMM using a Bayesian Baum-Welch algorithm integrating prior knowledge on methylation levels. We apply MeDIP-HMM to the analysis of the Arabidopsis root methylome and systematically investigate the benefit of using higher-order HMMs. Moreover, we also perform an in-depth comparison study to existing methods and demonstrate the value of using MeDIP-HMM by comparisons to current knowledge on the Arabidopsis methylome. We find that MeDIP-HMM is a fast and precise method for the analysis of DNA methylation data enabling the identification of distinct DNA methylation levels. Finally, we provide evidence for the general applicability of MeDIP-HMM by analyzing promoter DNA methylation data obtained for chicken.<br /> <br /> == Paper ==<br /> The paper [http://bioinformatics.oxfordjournals.org/content/early/2012/09/17/bioinformatics.bts562.abstract '''''MeDIP-HMM: Genome-wide identification of distinct DNA methylation states from high-density tiling arrays'''''] has been published in [http://bioinformatics.oxfordjournals.org/ Bioinformatics].<br /> <br /> == Download ==<br /> * [http://dig.ipk-gatersleben.de/MeDIP-HMM/MeDIP-HMM.zip MeDIP-HMM]: A ZIP file containing a JAR file for analyzing methylome data sets by MeDIP-HMMs. This file also contains the Arabidopsis root methylome and the chicken data sets considered in our study.<br /> <br /> == Related Projects ==<br /> * [[DSHMM]]: exploiting prior knowledge and gene distances in the analysis of tumor expression profiles<br /> * [[SHMM]]: utilizing gene-pair orientations for improved analysis of ChIP-chip promoter array data<br /> * [[PHHMM]]: improved analysis of Array-CGH data<br /> * [https://sites.google.com/site/michaelseiferthmm/hmm-book HMM Book]: Hidden Markov Models with Applications in Computational Biology<br /> <br /> == Follow Me ==<br /> * [https://sites.google.com/site/michaelseiferthmm/home Personal Homepage]</div>

Seifert https://www.jstacs.de/index.php?title=DSHMM&diff=607 2013-01-05T22:20:04Z

<p>Seifert: </p> <hr /> <div>__NOTOC__<br /> by Michael Seifert, Marc Strickert, Alexander Schliep, and Ivo Grosse<br /> <br /> == Description ==<br /> === Motivation ===<br /> Changes in gene expression levels play a central role in tumors. Additional information about the distribution of gene expression levels and distances between adjacent genes on chromosomes should be integrated into the analysis of tumor expression profiles.<br /> <br /> === Results ===<br /> We use a Hidden Markov Model with distance-scaled transition matrices (DSHMM) to incorporate chromosomal distances of adjacent genes on chromosomes into the identification of differentially expressed genes in breast cancer. We train the DSHMM by integrating prior knowledge about potential distributions of expression levels of differentially expressed and unchanged genes in tumor. We find that especially the combination of this data and to a lesser extent the modeling of distances between adjacent genes contribute to a substantial improvement of the identification of differentially expressed genes in comparison to other existing methods. This performance benefit is also supported by the identification of genes well-known to be associated with breast cancer. That suggests applications of DSHMMs for screening of other tumor expression profiles.<br /> <br /> == Paper ==<br /> The paper [http://bioinformatics.oxfordjournals.org/content/27/12/1645 '''''Exploiting prior knowledge and gene distances in the analysis of tumor expression profiles with extended Hidden Markov Models'''''] has been published in [http://bioinformatics.oxfordjournals.org/ Bioinformatics].<br /> <br /> == Download ==<br /> * [http://www.jstacs.de/downloads/supplementary_data.zip Supplementary data]: Containing the breast cancer gene expression data set and the breast cancer gene copy number data set (Pollack et al. (2002)) analyzed in the manuscript, and the predictions and scores of the compared methods.<br /> * [http://www.jstacs.de/downloads/ModelTrainer.zip Model trainer]: JAR file for analyzing a data set by Gaussian mixture models, HMMs, SHMMs, and DSHMMs.<br /> <br /> == Related Projects ==<br /> * [[PHHMM]]: improved analysis of Array-CGH data<br /> * [[SHMM]]: utilizing gene-pair orientations for improved analysis of ChIP-chip promoter array data<br /> * [[MeDIP-HMM]]: HMM-based analysis of DNA methylation profiles<br /> * [https://sites.google.com/site/michaelseiferthmm/hmm-book HMM Book]: Hidden Markov Models with Applications in Computational Biology</div>

Seifert https://www.jstacs.de/index.php?title=PHHMM&diff=606 2013-01-05T22:19:37Z

<p>Seifert: </p> <hr /> <div>__NOTOC__<br /> by Michael Seifert, André Gohr, Marc Strickert, and Ivo Grosse<br /> <br /> == Description ==<br /> Array-based comparative genomic hybridization (Array-CGH) is an important technology in molecular biology for the detection of DNA copy number polymorphisms between closely related genomes. Hidden Markov Models (HMMs) are popular tools for the analysis of Array-CGH data, but current methods are only based on first-order HMMs having constrained abilities to model spatial dependencies between measurements of closely adjacent chromosomal regions. Here, we develop parsimonious higher-order HMMs enabling the interpolation between a mixture model ignoring spatial dependencies and a higher-order HMM exhaustively modeling spatial dependencies. We apply parsimonious higher-order HMMs to the analysis of Array-CGH data of the accessions C24 and Col-0 of the model plant Arabidopsis thaliana. We compare these models against first-order HMMs and other existing methods using a reference of known deletions and sequence deviations. We find that parsimonious higher-order HMMs clearly improve the identification of these polymorphisms. Moreover, we perform a functional analysis of identified polymorphisms revealing novel details of genomic differences between C24 and Col-0. Additional model evaluations are also done on widely considered Array-CGH data of human cell lines indicating that parsimonious HMMs are also well-suited for the analysis of non-plant specific data. All these results indicate that parsimonious higher-order HMMs are useful for Array-CGH analyses.<br /> <br /> == Paper ==<br /> The paper '''''Parsimonious higher-order Hidden Markov Models for improved Array-CGH analysis with applications to Arabidopsis thaliana''''' has been published in [http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002286 PloS Comp Biol].<br /> <br /> == Download ==<br /> * [http://dig.ipk-gatersleben.de/PHHMM/PHHMM_Trainer.zip Parsimonious HMMs]: A ZIP file including a JAR file for analyzing data sets by parsimonious higher-order HMMs and examples for the Arabidopsis and the human cell lines data<br /> <br /> == Related Projects ==<br /> * [[DSHMM]]: exploiting prior knowledge and gene distances in the analysis of tumor expression profiles<br /> * [[SHMM]]: utilizing gene-pair orientations for improved analysis of ChIP-chip promoter array data<br /> * [[MeDIP-HMM]]: HMM-based analysis of DNA methylation profiles<br /> * [https://sites.google.com/site/michaelseiferthmm/hmm-book HMM Book]: Hidden Markov Models with Applications in Computational Biology</div>

Seifert https://www.jstacs.de/index.php?title=MeDIP-HMM&diff=605 2013-01-05T22:18:47Z

<p>Seifert: </p> <hr /> <div>__NOTOC__<br /> by Michael Seifert, Sandra Cortijo, Maria Colome-Tatche, Frank Johannes, Francois Roudier, and Vincent Colot<br /> <br /> == Description ==<br /> === Motivation ===<br /> Methylation of cytosines in DNA is an important epigenetic mechanism involved in transcriptional regulation and preservation of genome integrity in a wide range of eukaryotes. Immunoprecipitation of methylated DNA followed by hybridization to genomic tiling arrays (MeDIP-chip) is a cost-effective and sensitive method for methylome analyses. However, existing bioinformatics methods only enable a binary classification into unmethylated and methylated genomic regions, which limits biological interpretations. Indeed, DNA methylation levels can vary substantially within a given DNA fragment depending on the number and degree of methylated cytosines. Therefore, a method for the identification of more than two methylation states is highly desirable.<br /> <br /> === Results ===<br /> Here, we present a three-state Hidden Markov Model (MeDIP-HMM) for analyzing MeDIP-chip data. MeDIP-HMM utilizes a higher-order state-transition process improving modeling of spatial dependencies between chromosomal regions, allows a simultaneous analysis of replicates, and enables a differentiation between unmethylated, methylated and highly methylated genomic regions. We train MeDIP-HMM using a Bayesian Baum-Welch algorithm integrating prior knowledge on methylation levels. We apply MeDIP-HMM to the analysis of the Arabidopsis root methylome and systematically investigate the benefit of using higher-order HMMs. Moreover, we also perform an in-depth comparison study to existing methods and demonstrate the value of using MeDIP-HMM by comparisons to current knowledge on the Arabidopsis methylome. We find that MeDIP-HMM is a fast and precise method for the analysis of DNA methylation data enabling the identification of distinct DNA methylation levels. Finally, we provide evidence for the general applicability of MeDIP-HMM by analyzing promoter DNA methylation data obtained for chicken.<br /> <br /> == Paper ==<br /> The paper [http://bioinformatics.oxfordjournals.org/content/early/2012/09/17/bioinformatics.bts562.abstract '''''MeDIP-HMM: Genome-wide identification of distinct DNA methylation states from high-density tiling arrays'''''] has been published in [http://bioinformatics.oxfordjournals.org/ Bioinformatics].<br /> <br /> == Download ==<br /> * [http://dig.ipk-gatersleben.de/MeDIP-HMM/MeDIP-HMM.zip MeDIP-HMM]: A ZIP file containing a JAR file for analyzing methylome data sets by MeDIP-HMMs. This file also contains the Arabidopsis root methylome and the chicken data sets considered in our study.<br /> <br /> == Related Projects ==<br /> * [[DSHMM]]: exploiting prior knowledge and gene distances in the analysis of tumor expression profiles<br /> * [[SHMM]]: utilizing gene-pair orientations for improved analysis of ChIP-chip promoter array data<br /> * [[PHHMM]]: improved analysis of Array-CGH data<br /> * [https://sites.google.com/site/michaelseiferthmm/hmm-book HMM Book]: Hidden Markov Models with Applications in Computational Biology</div>

Seifert https://www.jstacs.de/index.php?title=SHMM&diff=604 2013-01-05T22:17:26Z

<p>Seifert: </p> <hr /> <div>__NOTOC__<br /> by Michael Seifert, Jens Keilwagen, Marc Strickert, and Ivo Grosse<br /> <br /> == Description ==<br /> === Motivation ===<br /> Array-based analysis of chromatin immunoprecipitation (ChIP-chip) data is a powerful technique for identifying DNA target regions of individual transcription factors. The identification of these target regions from comprehensive promoter array ChIP-chip data is challenging. Here, three approaches for the identification of transcription factor target genes from promoter array ChIP-chip data are presented. We compare (i) a standard log-fold-change analysis (LFC); (ii) a basic method based on a Hidden Markov Model (HMM); and (iii) a new extension of the HMM approach to an HMM with scaled transition matrices (SHMM) that incorporates information about the relative orientation of adjacent gene pairs on DNA. <br /> <br /> === Results ===<br /> All three methods are applied to different promoter array ChIP-chip datasets of the yeast Saccharomyces cerevisiae and the important model plant Arabidopsis thaliana to compare the prediction of transcription factor target genes. In the context of the yeast cell cycle, common target genes bound by the transcription factors ACE2 and SWI5, and ACE2 and FKH2 are identified and evaluated using the Saccharomyces Genome Database. Regarding A.thaliana, target genes of the seed-specific transcription factor ABI3 are predicted and evaluate based on publicly available gene expression profiles and transient assays performed in the wet laboratory experiments. The application of the novel SHMM to these two different promoter array ChIP-chip datasets leads to an improved identification of transcription factor target genes in comparison to the two standard approaches LFC and HMM.<br /> == Paper ==<br /> The paper [http://bioinformatics.oxfordjournals.org/content/25/16/2118 '''''Utilizing gene pair orientations for HMM-based analysis of promoter array ChIP-chip data'''''] has been published in [http://bioinformatics.oxfordjournals.org/ Bioinformatics].<br /> <br /> == Download ==<br /> * [http://dig.ipk-gatersleben.de/SHMMs/ChIPchip/ChIPchip.html Supplementary data]: Link to the implementations of LFC, HMM and SHMM.<br /> <br /> == Related Projects ==<br /> * [[PHHMM]]: improved analysis of Array-CGH data<br /> * [[DSHMM]]: exploiting prior knowledge and gene distances in the analysis of tumor expression profiles<br /> * [[MeDIP-HMM]]: HMM-based analysis of DNA methylation profiles<br /> * [https://sites.google.com/site/michaelseiferthmm/hmm-book HMM Book]: Hidden Markov Models with Applications in Computational Biology</div>

Seifert https://www.jstacs.de/index.php?title=MeDIP-HMM&diff=596 2012-11-12T16:37:27Z

<p>Seifert: </p> <hr /> <div>__NOTOC__<br /> by Michael Seifert, Sandra Cortijo, Maria Colome-Tatche, Frank Johannes, Francois Roudier, and Vincent Colot<br /> <br /> == Description ==<br /> === Motivation ===<br /> Methylation of cytosines in DNA is an important epigenetic mechanism involved in transcriptional regulation and preservation of genome integrity in a wide range of eukaryotes. Immunoprecipitation of methylated DNA followed by hybridization to genomic tiling arrays (MeDIP-chip) is a cost-effective and sensitive method for methylome analyses. However, existing bioinformatics methods only enable a binary classification into unmethylated and methylated genomic regions, which limits biological interpretations. Indeed, DNA methylation levels can vary substantially within a given DNA fragment depending on the number and degree of methylated cytosines. Therefore, a method for the identification of more than two methylation states is highly desirable.<br /> <br /> === Results ===<br /> Here, we present a three-state Hidden Markov Model (MeDIP-HMM) for analyzing MeDIP-chip data. MeDIP-HMM utilizes a higher-order state-transition process improving modeling of spatial dependencies between chromosomal regions, allows a simultaneous analysis of replicates, and enables a differentiation between unmethylated, methylated and highly methylated genomic regions. We train MeDIP-HMM using a Bayesian Baum-Welch algorithm integrating prior knowledge on methylation levels. We apply MeDIP-HMM to the analysis of the Arabidopsis root methylome and systematically investigate the benefit of using higher-order HMMs. Moreover, we also perform an in-depth comparison study to existing methods and demonstrate the value of using MeDIP-HMM by comparisons to current knowledge on the Arabidopsis methylome. We find that MeDIP-HMM is a fast and precise method for the analysis of DNA methylation data enabling the identification of distinct DNA methylation levels. Finally, we provide evidence for the general applicability of MeDIP-HMM by analyzing promoter DNA methylation data obtained for chicken.<br /> <br /> == Paper ==<br /> The paper [http://bioinformatics.oxfordjournals.org/content/early/2012/09/17/bioinformatics.bts562.abstract '''''MeDIP-HMM: Genome-wide identification of distinct DNA methylation states from high-density tiling arrays'''''] has been published in [http://bioinformatics.oxfordjournals.org/ Bioinformatics].<br /> <br /> == Download ==<br /> * [http://dig.ipk-gatersleben.de/MeDIP-HMM/MeDIP-HMM.zip MeDIP-HMM]: A ZIP file containing a JAR file for analyzing methylome data sets by MeDIP-HMMs. This file also contains the Arabidopsis root methylome and the chicken data sets considered in our study.<br /> <br /> == Related Projects ==<br /> * [[DSHMM]]: exploiting prior knowledge and gene distances in the analysis of tumor expression profiles<br /> * [[SHMM]]: utilizing gene-pair orientations for improved analysis of ChIP-chip promoter array data<br /> * [[PHHMM]]: improved analysis of Array-CGH data</div>

Seifert https://www.jstacs.de/index.php?title=PHHMM&diff=595 2012-11-12T16:37:03Z

<p>Seifert: </p> <hr /> <div>__NOTOC__<br /> by Michael Seifert, André Gohr, Marc Strickert, and Ivo Grosse<br /> <br /> == Description ==<br /> Array-based comparative genomic hybridization (Array-CGH) is an important technology in molecular biology for the detection of DNA copy number polymorphisms between closely related genomes. Hidden Markov Models (HMMs) are popular tools for the analysis of Array-CGH data, but current methods are only based on first-order HMMs having constrained abilities to model spatial dependencies between measurements of closely adjacent chromosomal regions. Here, we develop parsimonious higher-order HMMs enabling the interpolation between a mixture model ignoring spatial dependencies and a higher-order HMM exhaustively modeling spatial dependencies. We apply parsimonious higher-order HMMs to the analysis of Array-CGH data of the accessions C24 and Col-0 of the model plant Arabidopsis thaliana. We compare these models against first-order HMMs and other existing methods using a reference of known deletions and sequence deviations. We find that parsimonious higher-order HMMs clearly improve the identification of these polymorphisms. Moreover, we perform a functional analysis of identified polymorphisms revealing novel details of genomic differences between C24 and Col-0. Additional model evaluations are also done on widely considered Array-CGH data of human cell lines indicating that parsimonious HMMs are also well-suited for the analysis of non-plant specific data. All these results indicate that parsimonious higher-order HMMs are useful for Array-CGH analyses.<br /> <br /> == Paper ==<br /> The paper '''''Parsimonious higher-order Hidden Markov Models for improved Array-CGH analysis with applications to Arabidopsis thaliana''''' has been published in [http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002286 PloS Comp Biol].<br /> <br /> == Download ==<br /> * [http://dig.ipk-gatersleben.de/PHHMM/PHHMM_Trainer.zip Parsimonious HMMs]: A ZIP file including a JAR file for analyzing data sets by parsimonious higher-order HMMs and examples for the Arabidopsis and the human cell lines data<br /> <br /> == Related Projects ==<br /> * [[DSHMM]]: exploiting prior knowledge and gene distances in the analysis of tumor expression profiles<br /> * [[SHMM]]: utilizing gene-pair orientations for improved analysis of ChIP-chip promoter array data<br /> * [[MeDIP-HMM]]: HMM-based analysis of DNA methylation profiles</div>

Seifert https://www.jstacs.de/index.php?title=DSHMM&diff=594 2012-11-12T16:36:39Z

<p>Seifert: </p> <hr /> <div>__NOTOC__<br /> by Michael Seifert, Marc Strickert, Alexander Schliep, and Ivo Grosse<br /> <br /> == Description ==<br /> === Motivation ===<br /> Changes in gene expression levels play a central role in tumors. Additional information about the distribution of gene expression levels and distances between adjacent genes on chromosomes should be integrated into the analysis of tumor expression profiles.<br /> <br /> === Results ===<br /> We use a Hidden Markov Model with distance-scaled transition matrices (DSHMM) to incorporate chromosomal distances of adjacent genes on chromosomes into the identification of differentially expressed genes in breast cancer. We train the DSHMM by integrating prior knowledge about potential distributions of expression levels of differentially expressed and unchanged genes in tumor. We find that especially the combination of this data and to a lesser extent the modeling of distances between adjacent genes contribute to a substantial improvement of the identification of differentially expressed genes in comparison to other existing methods. This performance benefit is also supported by the identification of genes well-known to be associated with breast cancer. That suggests applications of DSHMMs for screening of other tumor expression profiles.<br /> <br /> == Paper ==<br /> The paper [http://bioinformatics.oxfordjournals.org/content/27/12/1645 '''''Exploiting prior knowledge and gene distances in the analysis of tumor expression profiles with extended Hidden Markov Models'''''] has been published in [http://bioinformatics.oxfordjournals.org/ Bioinformatics].<br /> <br /> == Download ==<br /> * [http://www.jstacs.de/downloads/supplementary_data.zip Supplementary data]: Containing the breast cancer gene expression data set and the breast cancer gene copy number data set (Pollack et al. (2002)) analyzed in the manuscript, and the predictions and scores of the compared methods.<br /> * [http://www.jstacs.de/downloads/ModelTrainer.zip Model trainer]: JAR file for analyzing a data set by Gaussian mixture models, HMMs, SHMMs, and DSHMMs.<br /> <br /> == Related Projects ==<br /> * [[PHHMM]]: improved analysis of Array-CGH data<br /> * [[SHMM]]: utilizing gene-pair orientations for improved analysis of ChIP-chip promoter array data<br /> * [[MeDIP-HMM]]: HMM-based analysis of DNA methylation profiles</div>

Seifert https://www.jstacs.de/index.php?title=SHMM&diff=593 2012-11-12T16:35:11Z

<p>Seifert: SHMM page added</p> <hr /> <div>__NOTOC__<br /> by Michael Seifert, Jens Keilwagen, Marc Strickert, and Ivo Grosse<br /> <br /> == Description ==<br /> === Motivation ===<br /> Array-based analysis of chromatin immunoprecipitation (ChIP-chip) data is a powerful technique for identifying DNA target regions of individual transcription factors. The identification of these target regions from comprehensive promoter array ChIP-chip data is challenging. Here, three approaches for the identification of transcription factor target genes from promoter array ChIP-chip data are presented. We compare (i) a standard log-fold-change analysis (LFC); (ii) a basic method based on a Hidden Markov Model (HMM); and (iii) a new extension of the HMM approach to an HMM with scaled transition matrices (SHMM) that incorporates information about the relative orientation of adjacent gene pairs on DNA. <br /> <br /> === Results ===<br /> All three methods are applied to different promoter array ChIP-chip datasets of the yeast Saccharomyces cerevisiae and the important model plant Arabidopsis thaliana to compare the prediction of transcription factor target genes. In the context of the yeast cell cycle, common target genes bound by the transcription factors ACE2 and SWI5, and ACE2 and FKH2 are identified and evaluated using the Saccharomyces Genome Database. Regarding A.thaliana, target genes of the seed-specific transcription factor ABI3 are predicted and evaluate based on publicly available gene expression profiles and transient assays performed in the wet laboratory experiments. The application of the novel SHMM to these two different promoter array ChIP-chip datasets leads to an improved identification of transcription factor target genes in comparison to the two standard approaches LFC and HMM.<br /> == Paper ==<br /> The paper [http://bioinformatics.oxfordjournals.org/content/25/16/2118 '''''Utilizing gene pair orientations for HMM-based analysis of promoter array ChIP-chip data'''''] has been published in [http://bioinformatics.oxfordjournals.org/ Bioinformatics].<br /> <br /> == Download ==<br /> * [http://dig.ipk-gatersleben.de/SHMMs/ChIPchip/ChIPchip.html Supplementary data]: Link to the implementations of LFC, HMM and SHMM.<br /> <br /> == Related Projects ==<br /> * [[PHHMM]]: improved analysis of Array-CGH data<br /> * [[DSHMM]]: exploiting prior knowledge and gene distances in the analysis of tumor expression profiles<br /> * [[MeDIP-HMM]]: HMM-based analysis of DNA methylation profiles</div>

Seifert https://www.jstacs.de/index.php?title=Projects&diff=592 2012-11-12T16:23:23Z

<p>Seifert: </p> <hr /> <div>This site contains projects that use Jstacs.<br /> * [[MotifAdjuster]]: a tool for computational reassessment of transcription factor binding site annotations<br /> * [[Prior]]: apples and oranges: avoiding different priors in Bayesian DNA sequence analysis<br /> * [[GenDisMix]]: unifying generative and discriminative learning principles<br /> * [[Dispom]]: de-novo discovery of differentially abundant transcription factor binding sites including their positional preference<br /> * [[MiMB]]: probabilistic approaches to transcription factor binding site prediction<br /> * [[SHMM]]: utilizing gene-pair orientations for improved analysis of ChIP-chip promoter array data<br /> * [[DSHMM]]: exploiting prior knowledge and gene distances in the analysis of tumor expression profiles<br /> * [[PHHMM]]: improved analysis of Array-CGH data<br /> * [[MeDIP-HMM]]: HMM-based analysis of DNA methylation profiles<br /> * [[FlowCap]]: molecular classification of acute myeloid leukaemia (AML) using flow cytometry data</div>

Seifert https://www.jstacs.de/index.php?title=PHHMM&diff=591 2012-11-12T16:18:25Z

<p>Seifert: </p> <hr /> <div>__NOTOC__<br /> by Michael Seifert, André Gohr, Marc Strickert, and Ivo Grosse<br /> <br /> == Description ==<br /> Array-based comparative genomic hybridization (Array-CGH) is an important technology in molecular biology for the detection of DNA copy number polymorphisms between closely related genomes. Hidden Markov Models (HMMs) are popular tools for the analysis of Array-CGH data, but current methods are only based on first-order HMMs having constrained abilities to model spatial dependencies between measurements of closely adjacent chromosomal regions. Here, we develop parsimonious higher-order HMMs enabling the interpolation between a mixture model ignoring spatial dependencies and a higher-order HMM exhaustively modeling spatial dependencies. We apply parsimonious higher-order HMMs to the analysis of Array-CGH data of the accessions C24 and Col-0 of the model plant Arabidopsis thaliana. We compare these models against first-order HMMs and other existing methods using a reference of known deletions and sequence deviations. We find that parsimonious higher-order HMMs clearly improve the identification of these polymorphisms. Moreover, we perform a functional analysis of identified polymorphisms revealing novel details of genomic differences between C24 and Col-0. Additional model evaluations are also done on widely considered Array-CGH data of human cell lines indicating that parsimonious HMMs are also well-suited for the analysis of non-plant specific data. All these results indicate that parsimonious higher-order HMMs are useful for Array-CGH analyses.<br /> <br /> == Paper ==<br /> The paper '''''Parsimonious higher-order Hidden Markov Models for improved Array-CGH analysis with applications to Arabidopsis thaliana''''' has been published in [http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002286 PloS Comp Biol].<br /> <br /> == Download ==<br /> * [http://dig.ipk-gatersleben.de/PHHMM/PHHMM_Trainer.zip Parsimonious HMMs]: A ZIP file including a JAR file for analyzing data sets by parsimonious higher-order HMMs and examples for the Arabidopsis and the human cell lines data<br /> <br /> == Related Projects ==<br /> * [[DSHMM]]: exploiting prior knowledge and gene distances in the analysis of tumor expression profiles<br /> * [[MeDIP-HMM]]: HMM-based analysis of DNA methylation profiles</div>

Seifert https://www.jstacs.de/index.php?title=DSHMM&diff=590 2012-11-12T16:16:51Z

<p>Seifert: </p> <hr /> <div>__NOTOC__<br /> by Michael Seifert, Marc Strickert, Alexander Schliep, and Ivo Grosse<br /> <br /> == Description ==<br /> === Motivation ===<br /> Changes in gene expression levels play a central role in tumors. Additional information about the distribution of gene expression levels and distances between adjacent genes on chromosomes should be integrated into the analysis of tumor expression profiles.<br /> <br /> === Results ===<br /> We use a Hidden Markov Model with distance-scaled transition matrices (DSHMM) to incorporate chromosomal distances of adjacent genes on chromosomes into the identification of differentially expressed genes in breast cancer. We train the DSHMM by integrating prior knowledge about potential distributions of expression levels of differentially expressed and unchanged genes in tumor. We find that especially the combination of this data and to a lesser extent the modeling of distances between adjacent genes contribute to a substantial improvement of the identification of differentially expressed genes in comparison to other existing methods. This performance benefit is also supported by the identification of genes well-known to be associated with breast cancer. That suggests applications of DSHMMs for screening of other tumor expression profiles.<br /> <br /> == Paper ==<br /> The paper [http://bioinformatics.oxfordjournals.org/content/27/12/1645 '''''Exploiting prior knowledge and gene distances in the analysis of tumor expression profiles with extended Hidden Markov Models'''''] has been published in [http://bioinformatics.oxfordjournals.org/ Bioinformatics].<br /> <br /> == Download ==<br /> * [http://www.jstacs.de/downloads/supplementary_data.zip Supplementary data]: Containing the breast cancer gene expression data set and the breast cancer gene copy number data set (Pollack et al. (2002)) analyzed in the manuscript, and the predictions and scores of the compared methods.<br /> * [http://www.jstacs.de/downloads/ModelTrainer.zip Model trainer]: JAR file for analyzing a data set by Gaussian mixture models, HMMs, SHMMs, and DSHMMs.<br /> <br /> == Related Projects ==<br /> * [[PHHMM]]: improved analysis of Array-CGH data<br /> * [[MeDIP-HMM]]: HMM-based analysis of DNA methylation profiles</div>

Seifert https://www.jstacs.de/index.php?title=MeDIP-HMM&diff=589 2012-11-12T16:15:14Z

<p>Seifert: </p> <hr /> <div>__NOTOC__<br /> by Michael Seifert, Sandra Cortijo, Maria Colome-Tatche, Frank Johannes, Francois Roudier, and Vincent Colot<br /> <br /> == Description ==<br /> === Motivation ===<br /> Methylation of cytosines in DNA is an important epigenetic mechanism involved in transcriptional regulation and preservation of genome integrity in a wide range of eukaryotes. Immunoprecipitation of methylated DNA followed by hybridization to genomic tiling arrays (MeDIP-chip) is a cost-effective and sensitive method for methylome analyses. However, existing bioinformatics methods only enable a binary classification into unmethylated and methylated genomic regions, which limits biological interpretations. Indeed, DNA methylation levels can vary substantially within a given DNA fragment depending on the number and degree of methylated cytosines. Therefore, a method for the identification of more than two methylation states is highly desirable.<br /> <br /> === Results ===<br /> Here, we present a three-state Hidden Markov Model (MeDIP-HMM) for analyzing MeDIP-chip data. MeDIP-HMM utilizes a higher-order state-transition process improving modeling of spatial dependencies between chromosomal regions, allows a simultaneous analysis of replicates, and enables a differentiation between unmethylated, methylated and highly methylated genomic regions. We train MeDIP-HMM using a Bayesian Baum-Welch algorithm integrating prior knowledge on methylation levels. We apply MeDIP-HMM to the analysis of the Arabidopsis root methylome and systematically investigate the benefit of using higher-order HMMs. Moreover, we also perform an in-depth comparison study to existing methods and demonstrate the value of using MeDIP-HMM by comparisons to current knowledge on the Arabidopsis methylome. We find that MeDIP-HMM is a fast and precise method for the analysis of DNA methylation data enabling the identification of distinct DNA methylation levels. Finally, we provide evidence for the general applicability of MeDIP-HMM by analyzing promoter DNA methylation data obtained for chicken.<br /> <br /> == Paper ==<br /> The paper [http://bioinformatics.oxfordjournals.org/content/early/2012/09/17/bioinformatics.bts562.abstract '''''MeDIP-HMM: Genome-wide identification of distinct DNA methylation states from high-density tiling arrays'''''] has been published in [http://bioinformatics.oxfordjournals.org/ Bioinformatics].<br /> <br /> == Download ==<br /> * [http://dig.ipk-gatersleben.de/MeDIP-HMM/MeDIP-HMM.zip MeDIP-HMM]: A ZIP file containing a JAR file for analyzing methylome data sets by MeDIP-HMMs. This file also contains the Arabidopsis root methylome and the chicken data sets considered in our study.<br /> <br /> == Related Projects ==<br /> * [[DSHMM]]: exploiting prior knowledge and gene distances in the analysis of tumor expression profiles<br /> * [[PHHMM]]: improved analysis of Array-CGH data</div>

Seifert https://www.jstacs.de/index.php?title=MeDIP-HMM&diff=587 2012-09-23T08:44:34Z

<p>Seifert: </p> <hr /> <div>__NOTOC__<br /> by Michael Seifert, Sandra Cortijo, Maria Colome-Tatche, Frank Johannes, Francois Roudier, and Vincent Colot<br /> <br /> == Description ==<br /> === Motivation ===<br /> Methylation of cytosines in DNA is an important epigenetic mechanism involved in transcriptional regulation and preservation of genome integrity in a wide range of eukaryotes. Immunoprecipitation of methylated DNA followed by hybridization to genomic tiling arrays (MeDIP-chip) is a cost-effective and sensitive method for methylome analyses. However, existing bioinformatics methods only enable a binary classification into unmethylated and methylated genomic regions, which limits biological interpretations. Indeed, DNA methylation levels can vary substantially within a given DNA fragment depending on the number and degree of methylated cytosines. Therefore, a method for the identification of more than two methylation states is highly desirable.<br /> <br /> === Results ===<br /> Here, we present a three-state Hidden Markov Model (MeDIP-HMM) for analyzing MeDIP-chip data. MeDIP-HMM utilizes a higher-order state-transition process improving modeling of spatial dependencies between chromosomal regions, allows a simultaneous analysis of replicates, and enables a differentiation between unmethylated, methylated and highly methylated genomic regions. We train MeDIP-HMM using a Bayesian Baum-Welch algorithm integrating prior knowledge on methylation levels. We apply MeDIP-HMM to the analysis of the Arabidopsis root methylome and systematically investigate the benefit of using higher-order HMMs. Moreover, we also perform an in-depth comparison study to existing methods and demonstrate the value of using MeDIP-HMM by comparisons to current knowledge on the Arabidopsis methylome. We find that MeDIP-HMM is a fast and precise method for the analysis of DNA methylation data enabling the identification of distinct DNA methylation levels. Finally, we provide evidence for the general applicability of MeDIP-HMM by analyzing promoter DNA methylation data obtained for chicken.<br /> <br /> == Paper ==<br /> The paper [http://bioinformatics.oxfordjournals.org/content/early/2012/09/17/bioinformatics.bts562.abstract '''''MeDIP-HMM: Genome-wide identification of distinct DNA methylation states from high-density tiling arrays'''''] has been published in [http://bioinformatics.oxfordjournals.org/ Bioinformatics].<br /> <br /> == Download ==<br /> * [http://dig.ipk-gatersleben.de/MeDIP-HMM/MeDIP-HMM.zip MeDIP-HMM]: A ZIP file containing a JAR file for analyzing methylome data sets by MeDIP-HMMs. This file also contains the Arabidopsis root methylome and the chicken data sets considered in our study.</div>

Seifert https://www.jstacs.de/index.php?title=MeDIP-HMM&diff=574 2012-05-26T20:14:39Z

<p>Seifert: </p> <hr /> <div>__NOTOC__<br /> by Michael Seifert, Sandra Cortijo, Maria Colome-Tatche, Frank Johannes, Francois Roudier, and Vincent Colot<br /> <br /> == Description ==<br /> === Motivation ===<br /> Methylation of cytosines in DNA is an important epigenetic mechanism involved in transcriptional regulation and preservation of genome integrity in a wide range of eukaryotes. Immunoprecipitation of methylated DNA followed by hybridization to genomic tiling arrays (MeDIP-chip) is a cost-effective and sensitive method for methylome analyses. However, existing bioinformatics methods only enable a binary classification into unmethylated and methylated genomic regions, which limits biological interpretations. Indeed, DNA methylation levels can vary substantially within a given DNA fragment depending on the number of contained methylated cytosines. Therefore, a method for the identification of more than two methylation states from MeDIP-chip data is highly desirable.<br /> <br /> === Results ===<br /> Here, we present a three-state Hidden Markov Model (MeDIP-HMM) for analyzing MeDIP-chip data. MeDIP-HMM utilizes a higher-order state-transition process improving modeling of spatial dependencies between chromosomal regions, allows a simultaneous analysis of replicates, and enables a differentiation between unmethylated, methylated and highly methylated genomic regions. We train MeDIP-HMM using a Bayesian Baum-Welch algorithm integrating prior knowledge on methylation levels. We apply MeDIP-HMM to the analysis of the Arabidopsis root methylome and systematically investigate the benefit of using higher-order HMMs. Moreover, we also perform an in-depth comparison study to existing methods and demonstrate the value of using MeDIP-HMM by comparisons to current knowledge on the Arabidopsis methylome. We find that MeDIP-HMM is a fast and precise method for the analysis of DNA methylation data enabling the identification of distinct DNA methylation levels. These results suggest that MeDIP-HMM could also be useful for analyses of other methylomes.<br /> <br /> == Paper ==<br /> The paper '''''MeDIP-HMM: Genome-wide identification of distinct DNA methylation states from high-density tiling arrays''''' has been submitted to [http://bioinformatics.oxfordjournals.org/ Bioinformatics].<br /> <br /> == Download ==<br /> * [http://dig.ipk-gatersleben.de/MeDIP-HMM/MeDIP-HMM.zip MeDIP-HMM]: A ZIP file containing a JAR file for analyzing methylome data sets by MeDIP-HMMs and the Arabidopsis root methylome data set considered in our study.</div>

Seifert https://www.jstacs.de/index.php?title=MeDIP-HMM&diff=573 2012-05-24T19:42:33Z

<p>Seifert: </p> <hr /> <div>__NOTOC__<br /> by Michael Seifert, Sandra Cortijo, Maria Colome-Tatche, Frank Johannes, Francois Roudier, and Vincent Colot<br /> <br /> == Description ==<br /> === Motivation ===<br /> Methylation of cytosines in DNA is an important epigenetic mechanism involved in transcriptional regulation and preservation of genome integrity in a wide range of eukaryotes. Immunoprecipitation of methylated DNA followed by hybridization to genomic tiling arrays (MeDIP-chip) is a cost-effective and sensitive method for methylome analyses. However, existing bioinformatic methods only enable a binary classification into unmethylated and methylated genomic regions, which limits biological interpretations. Indeed, DNA methylation levels can vary substantially within a given DNA fragment depending on the number of contained methylated cytosines. Therefore, a method for the identification of more than two methylation states from MeDIP-chip data is highly desirable.<br /> <br /> === Results ===<br /> Here, we present a three-state Hidden Markov Model (MeDIP-HMM) for analyzing MeDIP-chip data. MeDIP-HMM utilizes a higher-order state-transition process improving modeling of spatial dependencies between chromosomal regions, allows a simultaneous analysis of replicates, and enables a differentiation between unmethylated, methylated and highly methylated genomic regions. We train MeDIP-HMM using a Bayesian Baum-Welch algorithm integrating prior knowledge on methylation levels. We apply MeDIP-HMM to the analysis of the Arabidopsis root methylome and systematically investigate the benefit of using higher-order HMMs. Moreover, we also perform an in-depth comparison study to existing methods and demonstrate the value of using MeDIP-HMM by comparisons to current knowledge on the Arabidopsis methylome. We find that MeDIP-HMM is a fast and precise method for the analysis of DNA methylation data enabling the identification of distinct DNA methylation levels. These results suggest that MeDIP-HMM could also be useful for analyses of other methylomes.<br /> <br /> == Paper ==<br /> The paper '''''MeDIP-HMM: Genome-wide identification of distinct DNA methylation states from high-density tiling arrays''''' has been submitted to [http://bioinformatics.oxfordjournals.org/ Bioinformatics].<br /> <br /> == Download ==<br /> * [http://dig.ipk-gatersleben.de/MeDIP-HMM/MeDIP-HMM.zip MeDIP-HMM]: A ZIP file containing a JAR file for analyzing methylome data sets by MeDIP-HMMs and the Arabidopsis root methylome data set considered in our study.</div>

Seifert https://www.jstacs.de/index.php?title=MeDIP-HMM&diff=572 2012-05-23T19:22:02Z

<p>Seifert: </p> <hr /> <div>__NOTOC__<br /> by Michael Seifert, Sandra Cortijo, Maria Colome-Tatche, Frank Johannes, Francois Roudier, and Vincent Colot<br /> <br /> == Description ==<br /> === Motivation ===<br /> Methylation of cytosines in DNA is an important epigenetic mechanism involved in transcriptional regulation and preservation of genome integrity in a wide range of eukaryotes. Immunoprecipitation of methylated DNA followed by hybridization to genomic tiling arrays (MeDIP-chip) is a cost-effective and sensitive method for methylome analyses. However, existing bioinformatic methods only enable a binary classification into unmethylated and methylated genomic regions, which limits biological interpretations. Indeed, DNA methylation levels can vary substantially within a given DNA fragment depending on the number of contained methylated cytosines. Therefore, a method for the identification of more than two methylation states from MeDIP-chip data is highly desirable.<br /> <br /> === Results ===<br /> Here, we present a three-state Hidden Markov Model (MeDIP-HMM) for analyzing MeDIP-chip data. MeDIP-HMM utilizes a higher-order state-transition process improving modeling of spatial dependencies between chromosomal regions, allows a simultaneous analysis of replicates, and enables a differentiation between unmethylated, methylated and highly methylated genomic regions. We train MeDIP-HMM using a Bayesian Baum-Welch algorithm integrating prior knowledge on methylation levels. We apply MeDIP-HMM to the analysis of the Arabidopsis root methylome and systematically investigate the benefit of using higher-order HMMs. Moreover, we also perform an in-depth comparison study to existing methods and demonstrate the value of using MeDIP-HMM by comparisons to current knowledge on the Arabidopsis methylome. We find that MeDIP-HMM is a fast and precise method for the analysis of DNA methylation data enabling the identification of distinct DNA methylation levels. These results suggest that MeDIP-HMM could also be useful for analyses of other methylomes.<br /> <br /> == Paper ==<br /> The paper '''''MeDIP-HMM: Genome-wide identification of distinct DNA methylation states from high-density tiling arrays''''' has been submitted to [http://bioinformatics.oxfordjournals.org/ Bioinformatics].<br /> <br /> == Download ==<br /> * The Arabidopsis root methylome data set and a JAR file containing the MeDIP-HMM will soon be available.</div>

Seifert