PHHMM: Difference between revisions
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== Download == | == Download == | ||
* [ | * [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 | ||
== Related Projects == | == Related Projects == | ||
* [[ARHMM]]: integrating local chromosomal dependencies into the analysis of tumor expression profiles | |||
* [[DSHMM]]: exploiting prior knowledge and gene distances in the analysis of tumor expression profiles | * [[DSHMM]]: exploiting prior knowledge and gene distances in the analysis of tumor expression profiles | ||
* [[SHMM]]: utilizing gene-pair orientations for improved analysis of ChIP-chip promoter array data | * [[SHMM]]: utilizing gene-pair orientations for improved analysis of ChIP-chip promoter array data | ||
* [[MeDIP-HMM]]: HMM-based analysis of DNA methylation profiles | * [[MeDIP-HMM]]: HMM-based analysis of DNA methylation profiles | ||
* [https://sites.google.com/site/ | * [https://sites.google.com/site/mseifertweb/hmm-book HMM Book]: Hidden Markov Models with Applications in Computational Biology | ||
== Follow Me == | == Follow Me == | ||
* [https://sites.google.com/site/ | * [https://sites.google.com/site/mseifertweb/home Personal Homepage] | ||
Latest revision as of 08:13, 24 March 2021
by Michael Seifert, André Gohr, Marc Strickert, and Ivo Grosse
Description
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.
Paper
The paper Parsimonious higher-order Hidden Markov Models for improved Array-CGH analysis with applications to Arabidopsis thaliana has been published in PloS Comp Biol.
Download
- 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
Related Projects
- ARHMM: integrating local chromosomal dependencies into the analysis of tumor expression profiles
- DSHMM: exploiting prior knowledge and gene distances in the analysis of tumor expression profiles
- SHMM: utilizing gene-pair orientations for improved analysis of ChIP-chip promoter array data
- MeDIP-HMM: HMM-based analysis of DNA methylation profiles
- HMM Book: Hidden Markov Models with Applications in Computational Biology
