Data and Tools
The QBiC-Pred web server for quantitative predictions of transcription factor binding changes due to sequence variants
QBiC-Pred is a web server that uses OLS 6-mer models trained on universal PBM data to predict TF binding changes due to sequence variants, as well as the significance of the changes (which implicitly depends on the quality of the data and models).
Zhao J*, Li D*, Seo J, Allen AS, Gordan R (2017) Quantifying the impact of non-coding variants on transcription factor- DNA binding. Research in Computational Molecular Biology 2017 (RECOMB17). Lecture Notes in Computer Science 10229:336-352 (* co-first authors)
Martin V*, Zhao J*, Afek A, Mielko Z, Gordan R (2019) QBiC-Pred — Quantitative predictions of transcription factor binding changes due to sequence variants. Nucleic Acids Research. pii: gkz363. doi: 10.1093/nar/gkz363. [Epub ahead of print] (* co-first authors)
The iMADS web server for modeling and analysis of TF-DNA binding specificity and differential specificity
iMADS (integrative Modeling and Analysis of Differential Specificity) is a combined computational-experimental strategy to identify and study the differences in DNA-binding specificity between transcription factor (TF) family members, i.e. paralogous TFs.
Shen N, Zhao J, Schipper J, Zhang Y, Bepler T, Leehr D, Bradley J, Horton J, Lapp H, Gordan R (2018) Divergence in DNA specificity among paralogous transcription factors contributes to their differential in vivo binding. Cell Systems 6(4):470-483
Couger: CO-factors associated with Uniquely-bound GEnomic Regions:
COUGER implements a classification-based approach that can be applied to any two sets of genomic regions bound by paralogous TFs (e.g., regions derived from ChIP-seq experiments) to identify putative co-factors that provide specificity to each TF.
Munteanu A, Ohler U, Gordân R (2014) COUGER – CO-factors associated with Uniquely-bound GEnomic Regions. Nucleic Acids Research. 42(W1):W461–W467.
Munteanu A, Gordân R (2013) Distinguishing between genomic regions bound by paralogous transcription factors. Research in Computational Molecular Biology 2013 (RECOMB13). 7821:145.
gcPBM data for 11 human transcription factors: GSE97794. Reference: Shen N, Zhao J, Schipper J, Zhang Y, Bepler T, Leehr D, Bradley J, Horton J, Lapp H, Gordân R (2018) Divergence in DNA specificity among paralogous transcription factors contributes to their differential in vivo binding. Cell Systems 6(4):470-483
gcPBM data for human Myc, Mad, and Max transcription factors: GSE59845. Reference: Zhou T+, Shen N+, Yang L, Abe N, Horton J, Mann RS, Bussemaker HJ, Gordân R*, Rohs R* (2015) Quantitative modeling of transcription factor binding specificities using DNA shape. PNAS 112(15):4654–4659
Universal PBM data for human Myc:Max and Max:Max: GSE58570. Reference: Guo J, Li T, Schipper J, Nilson KA, Fordjour FK, Cooper JJ, Gordân R, Price DH (2014) Sequence specificity incompletely defines the genome-wide occupancy of Myc. Genome Biology 15:482.
gcPBM data for human Myc, Mad, and Max transcription factors: GSE47026. Reference: Mordelet F, Horton J, Hartemink A, Engelhardt B, Gordân R (2013) Stability selection for regression-based models of transcription factor-DNA binding specificity. Bioinformatics 29(13):i117–i125
gcPBM data for yeast transcription factors Cbf1 and Tye7: GSE44604, GSE44436, GSE44437. Reference: Gordân R, Shen N, Dror I, Zhou T, Horton J, Rohs R, Bulyk ML (2013) “Genomic regions flanking E-box binding sites influence DNA binding specificity of bHLH transcription factors through DNA shape.“ Cell Reports 3(4):1093–1104.