Our group develops computational methods to study and model transcriptional, co-transcriptional, and post-transcriptional gene regulatory mechanisms and to dissect the dysregulation of gene expression in cancer. The techniques we use generally come from machine learning, a field at the intersection of computer science and statistics. In particular, we develop supervised learning methods -- algorithmic approaches for learning predictive models from data -- that we train on high-throughput experimental data, increasingly from next generation sequencing. As we try to advance the state-of-the-art in statistical and machine learning models of gene regulation, we also seek to drive biological discovery through close collaborations with experimentalists to study specific regulatory mechanisms.
- Ubiquitously transcribed genes use alternative polyadenylation to achieve tissue-specific expression. Lianoglou S, Garg V, Yang JL, Leslie CS, and Mayr C. Genes and Development, 2013.
- Inferring transcriptional and microRNA-mediated regulatory programs in glioblastoma. Setty M, Helmy K, Khan AA, Silber J, Arvey A, Neezen F, Agius P, Huse JT, Holland EC, Leslie CS. Mol Syst Biol. 2012.
- Transcriptome-wide miR-155 binding map reveals widespread noncanonical microRNA targeting. Loeb GB, Khan AA, Canner D, Hiatt JB, Shendure J, Darnell RB, Leslie CS, Rudensky AY. Mol Cell, 2012
- Foxp3 exploits a pre-existent enhancer landscape for regulatory T cell lineage specification. Samstein RM, Arvey A, Josefowicz SZ, Peng X, Reynolds A, Sandstrom R, Neph S, Sabo P, Kim JM, Liao W, Li MO, Leslie C, Stamatoyannopoulos JA, Rudensky AY. Cell, 2012.
- Sequence and chromatin determinants of cell-type-specific transcription factor binding. Arvey A, Agius P, Noble WS, Leslie C. Genome Research, 2012.