Mengchi Wang

The paper predicts somatic mutation rates in 13 cancers using DNA motifs and identifies impactful motifs and cancer-specific regions for classification.

CRISPRpath enables parallel characterization of cis-regulatory elements for multiple genes in a pathway, identifying functional enhancers and distinguishing their impact on gene expression.

The paper introduces a method to convert position weight matrices into compact consensus sequences with minimal information loss, showing improved performance in identifying transcription factor binding sites.

The paper systematically maps chromatin state landscapes during mouse development by profiling histone modifications across 72 tissue-stages, identifying dynamic enhancers and transcriptional regulators.

The paper identifies DNA motifs associated with methylation and unmethylation and shows their functional relevance in DNA methylation regulation and cancer.

The paper identifies critical transcription factors and their regulatory networks during mouse embryonic development using epigenomic and transcriptomic data.

The paper develops an extended motif finding pipeline to identify de novo methylated DNA motifs in Arabidopsis and human cell lines, revealing novel motifs recognized by transcription factors or their co-factors.

The paper expands DNA methylation data from TCGA using EAGLING, identifies triple-evidenced genes across 13 cancers, and reveals enriched pathways with potential therapeutic value.