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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