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.