Timothy L. Bailey

Modeling methyl-sensitive transcription factor motifs with an expanded epigenetic alphabet

The paper develops methods to model transcription factor binding in modified DNA by expanding the DNA alphabet and adapting existing tools to handle custom alphabets, identifying modification-sensitive motifs and validating them with experimental data.

XSTREME: Comprehensive motif analysis of biological sequence datasets

XSTREME couples MEME and STREME de novo discovery with SEA known-motif enrichment, ranking motifs by enrichment anywhere in sequences, unlike center-focused MEME-ChIP; illustrated on Gata1 Cut&Run.

SEA: Simple Enrichment Analysis of motifs

Motif enrichment scored with a held-out threshold and a single test per motif yields p-values close to uniform under the null, where AME and CentriMo come out conservative.

STREME: Accurate and versatile sequence motif discovery

STREME improves sequence motif discovery by enhancing accuracy and versatility across DNA, RNA, and protein sequences.

T-Gene: Improved target gene prediction

T-Gene predicts target genes of transcription factors by combining histone modification correlation and genomic distance, achieving high positive predictive value.

MoMo: Discovery of statistically significant post-translational modification motifs

MoMo provides a method to assign statistical confidence to PTM motifs, showing improved accuracy over motif-x and offering a web server and software tool.

MoMo: Discovery of post-translational modification motifs

MoMo identifies post-translational modification motifs from mass spectrometry data using re-implemented algorithms and provides web-accessible results.