Leslie Research Group

Lab’s website

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.

Current Research Areas

Modeling cell-type specific transcriptional programs

We have introduced discriminative models trained on ChIP-seq data for capturing the subtle DNA sequence preferences of transcription factors (TFs), and we have shown that some TFs recognize cell-type specific sequence signals, due for example to differences in the composition of the TF binding complex.  We are also using DNase-seq data in human and mouse primary cells to model the establishment, maintenance, and loss of regulatory regions (enhancers and promoters) during lineage development.

Dissecting co- and post-transcriptional regulation

Through computational studies of microRNA-mediated gene regulation, we introduced the mirSVR target prediction method, computationally identified key targets of oncogenic and tumor suppressor microRNAs, and documented the system-level effects of competition of microRNAs for RISC and of targets for microRNAs.  We are now exploiting recent next-generation RNA sequencing technologies to map co- and post-transcriptional regulatory events, including AGO CLIP-seq to analyze the determinants of microRNA binding and 3′-seq to understand the contribution of alternative 3′UTR isoforms to tissue- and pathway-specific regulation.

Cancer systems biology

Cancer genomics projects are generating rich multi-modal tumor profiling data sets, but these data are still underused.  We have developed an integrative strategy to combine mRNA, copy number, and miRNA profiles together with regulatory elements in a mechanistically informed way in order to decipher transcriptional and miRNA-mediated regulatory programs in cancer.  We are also developing computational methods to dissect the complex interplay between cancer cells and cells of the host microenvironment and model how these diverse interactions contribute to cancer progression and invasion.