You can find software and resources developed by the group on bitbucket at https://bitbucket.com/cbgr.

Please find below the list of software and resources developed by group members:

• BiasAway: Open-access tool for generating different sequence backgrounds of comparable nucleotide composition relative to a set of input sequences. doi:10.1186/1471-2164-15-472
• CAGEd-oPOSSUM: Transcription factor binding profile enrichment analysis at regions surrounding CAGE-defined TSSs. doi: 10.1093/bioinformatics/btw337
• ChIP-eat: Open-access tool/pipeline to predict direct TF-DNA interactions from ChIP-seq data. doi:10.1093/nar/gky1210
• DNAshapedTFBS: Python module allowing for the construction and application of machine learning classifiers combining TFFM/PSSM + DNA shape features for improving the predictions of TFBSs in ChIP-seq data. doi:10.1016/j.cels.2016.07.001
• dysmiR: A tool to identify cis-regulatory somatic mutations associated with protein-coding and miRNA genes with trans-effect in the deregulation of the expression of their regulatory network. doi:10.1101/2020.06.25.170738
• Intervene: A tool for intersection and visualization of multiple gene or genomic region sets. doi:10.1186/s12859-017-1708-7
• JASPAR: The high-quality and open-access transcription factor binding profile database since 2004. doi:10.1093/nar/gkx1126
• JASPAR Biopython module: Python module to retrieve and use transcription factor binding profiles from JASPAR. doi:10.1093/nar/gkt997
• JASPAR RESTful API: Easy-to-use REST web interface to query/retrieve matrix profile data from the JASPAR database. doi:10.1093/nar/gkx1126
• JASPAR Ruby gem: Ruby gem to retrieve and use transcription factor binding profiles from JASPAR. doi:10.1093/nar/gkv1176
• MANTA (MongoDB for the analysis of TFBS alteration): Web-interface to access TFBS predicted in ChIP-seq peaks along with the potential impact of all possible single nucleotide variants within these TFBSs. doi:10.1038/sdata.2018.141
• ReMap: Database storing our integrative analysis of transcriptional regulators ChIP-seq experiments from public datasets. The ReMap atlas consits of 80 million peaks from 485 transcription factors (TFs), transcription coactivators (TCAs) and chromatin-remodeling factors (CRFs). Work in collaboration with Benoît Ballester’s lab. doi:10.1093/nar/gkx1092
• TFFM: Python module implementing the Transcription Factor Flexible Models (TFFMs), which are hidden Markov model representations of TFBS motifs. doi:10.1371/journal.pcbi.1003214
• UniBind: UniBind provides a map of direct transcription factor – DNA interactions in the human genome obtained from processing ~2,000 ChIP-seq experiments. doi:10.1093/nar/gky1210