Projects

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