Table 6.
Motif Enrichment and Transcription Factor Footprinting Tools
| Languages | Notes | Docs | Citation | |
|---|---|---|---|---|
| BiFET | R | Identify overrepresented transcription factor footprints. | ++ |
Youn et al. (2019) Last updated: 2019 |
| BinDNase | R | Transcription factor binding prediction using DNase-seq. | + |
Kähärä & Lähdesmäki (2015) Last updated: 2015 |
| CENTIPEDE | R | Transcription factor footprinting and binding site prediction. | ++ |
Pique-Regi et al. (2011) Last updated: 2010 |
| DeFCoM | Python | Detecting transcription factor footprints and underlying motifs using supervised learning. | +++ |
Quach & Furey (2017) Last updated: 2017 |
| DNase2TF | R | Identify footprint candidates from DNase-seq data on user-specified regions. | + |
Sung et al. (2014) Last updated: 2017 |
| HINT-ATAC | Python | Use open chromatin data to identify transcription factor footprints with modifications specific to ATAC-seq data. | +++ |
Li et al. (2019) Last updated: 2019 |
| HOMER | Perl; C++ | A suite of tools for motif discovery and enrichment. | +++ |
Heinz et al. (2010) Last updated: 2019 |
| MEME Suite | Perl; Python | Suite of tools for motif discovery; enrichment; and GO term analyses. | +++ |
Bailey et al. (2009) Last updated: 2020 |
| PIQ | Bash; R | Models genome-wide DNase profiles to identify transcription factor binding sites. | ++ |
Sherwood et al. (2014) Last updated: 2016 |
| TOBIAS | Python | Identify transcription factor footprints. | ++ |
Bentsen et al. (2019) Last updated: 2020 |
| TRACE | Python | Transcription factor footprinting. | ++ |
Ouyang & Boyle (2019) Last updated: 2020 |
| Wellington | Python | Identify TF footprints using DNase-seq data. | +++ |
Piper et al. (2013) Last updated: 2019 |