Projects: Difference between revisions
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* [[PMMdeNovo]]: de novo motif discovery based on inhomogeneous parsimonious Markov models (PMMs) for exploiting intra-motif dependencies | * [[PMMdeNovo]]: de novo motif discovery based on inhomogeneous parsimonious Markov models (PMMs) for exploiting intra-motif dependencies | ||
* [[AnnoTALE]]: identifying and analysing TALEs in ''Xanthomonas'' genomes, for clustering TALEs, for assigning novel TALEs to existing classes, for proposing TALE names using a unified nomenclature, and for predicting TALE targets | * [[AnnoTALE]]: identifying and analysing TALEs in ''Xanthomonas'' genomes, for clustering TALEs, for assigning novel TALEs to existing classes, for proposing TALE names using a unified nomenclature, and for predicting TALE targets | ||
*[[GeMoMa]]: Gene Model Mapper (GeMoMa) is a homology-based gene prediction program that uses the annotation of protein-coding genes in a reference genome to infer annotation of protein-coding genes in a target genome | * [[GeMoMa]]: Gene Model Mapper (GeMoMa) is a homology-based gene prediction program that uses the annotation of protein-coding genes in a reference genome to infer annotation of protein-coding genes in a target genome | ||
* [[InMoDe]]: tools for learning and visualizing intra-motif dependencies of DNA binding sites | * [[InMoDe]]: tools for learning and visualizing intra-motif dependencies of DNA binding sites | ||
* [[Disentangler]]: two tools for analyzing complex features in a set of aligned transcription factor (TFBS) binding sites that can be used individually or within a joint pipeline. | |||
* [[PCTLearn]]: efficient learning of parsimonious context trees from sequence data. | |||
* [[Catchitt]]: collection of tools for predicting cell type-specific binding regions of transcription factors | |||
* [[PrediTALE]]: predict TALE target boxes using a novel model learned from quantitative data based on the RVD sequence of a TALE | |||
Latest revision as of 11:48, 3 May 2019
This site contains projects that use Jstacs.
- MotifAdjuster: a tool for computational reassessment of transcription factor binding site annotations
- Prior: apples and oranges: avoiding different priors in Bayesian DNA sequence analysis
- GenDisMix: unifying generative and discriminative learning principles
- Dispom: de-novo discovery of differentially abundant transcription factor binding sites including their positional preference
- MiMB: probabilistic approaches to transcription factor binding site prediction
- SHMM: utilizing gene-pair orientations for improved analysis of ChIP-chip promoter array data
- DSHMM: exploiting prior knowledge and gene distances in the analysis of tumor expression profiles
- PHHMM: improved analysis of Array-CGH data
- MeDIP-HMM: HMM-based analysis of DNA methylation profiles
- ARHMM: integrating local chromosomal dependencies into the analysis of tumor expression profiles
- FlowCap: molecular classification of acute myeloid leukaemia (AML) using flow cytometry data
- TALgetter: prediction of TAL effector target sites
- TALENoffer: genome-wide TALEN off-target prediction
- Dimont: general approach for discriminative de-novo motif discovery from high-throughput data
- AUC-PR: area under ROC and PR curves for weighted and unweighted data
- Slim: Sparse local inhomogeneous mixture (Slim) models and dependency logos
- PMMdeNovo: de novo motif discovery based on inhomogeneous parsimonious Markov models (PMMs) for exploiting intra-motif dependencies
- AnnoTALE: identifying and analysing TALEs in Xanthomonas genomes, for clustering TALEs, for assigning novel TALEs to existing classes, for proposing TALE names using a unified nomenclature, and for predicting TALE targets
- GeMoMa: Gene Model Mapper (GeMoMa) is a homology-based gene prediction program that uses the annotation of protein-coding genes in a reference genome to infer annotation of protein-coding genes in a target genome
- InMoDe: tools for learning and visualizing intra-motif dependencies of DNA binding sites
- Disentangler: two tools for analyzing complex features in a set of aligned transcription factor (TFBS) binding sites that can be used individually or within a joint pipeline.
- PCTLearn: efficient learning of parsimonious context trees from sequence data.
- Catchitt: collection of tools for predicting cell type-specific binding regions of transcription factors
- PrediTALE: predict TALE target boxes using a novel model learned from quantitative data based on the RVD sequence of a TALE
