Special topics course in computational molecular biology (CS294-108, CMPBIO290)

A graduate seminar class that covers recent computational methods for modeling various mechanisms related to the regulation of gene expression, primarily based on high throughput sequencing data.

The course focuses on computational methods, but it will also cover relevant and interesting biological applications, on which these methods were applied. The class meets twice a week for 1.5 hours. The lectures on the first week will be provided by me. They will include a review of the basic biological concepts, followed by a wide review of the various experimental and computational techniques that have been developed and employed over the past couple of years. This introduction will give a chance to the students to choose a compelling topic to be used for their presentations and their final project.

In subsequent weeks, each student will give up to two presentations (depending on the number of students enrolled) about two topics of interest (see sample syllabus below) and, together with me, lead a discussion about the merit and potential pitfalls and alternatives of the presented work.  The final projects usually include some form of high-throughput data analysis (public, or from the student’s lab), using one of the topics covered in the course, and after consultation with me. Examples from last year include: characterization of systematic biases in chromatin accessibility assays, inference of boolean models from single cell data, and annotation of genetic variants in regulatory regions.


Time& place:

177 Stanley. Tues & Thurs from 5:00-6:30pm


This year’s Syllabus:

Date Topic
1/21/2016 Reliably identify the “true” binding sites of a given TF based on ChIP-Seq
1/26/2016 Determine which binding sites are differentially occupied between conditions
1/28/2016 Annotate the “state” of the chromatin based on ChIP-Seq of multiple histone marks
2/2/2016 Specifically predict enhancer regions using heterogeneous data
2/4/2016 TF binding prediction based on DNA sequence
2/9/2016 Predict TF binding sites from chromatin accessibility patterns
2/11/2016 TF binding prediction based on heterogeneous data
2/16/2016 Reliably estimate RNA transcript abundance from aligned RNA-Seq reads
2/18/2016 Alignment-free estimation of transcript abundance
2/23/2016 Determine which transcripts are differentially expressed between conditions
2/25/2016 Characterizing and contolling for technical confounders in single cell RNA-Seq
3/1/2016 Differential expression with single-cell RNA-Seq
3/3/2016 Inderefece of cellular sub-populations
3/8/2016 Low-dimensional projection of single cell RNA-Seq
3/10/2016 Inference of developmental trajectories from single cell RNA-Seq
3/15/2016 Promoter kinetics from single cell mRNA
3/17/2016 Inference of transcriptional regulatory networks from high-throughput data
3/29/2016 “Executable” models of gene regulatory networks (review of methods)
3/31/2016 Boolean network models
4/5/2016 Network inference from single cell profiles (proteins)
4/7/2016 Review on the grammar of gene regulation
4/12/2016 Prediction of gene expression from DNA sequence
4/14/2016 Prediction of cell-type specific gene expression from multiple genomic features
4/19/2016 Direct investigation of grammatical “cards” through carefully designed MPRA
4/21/2016 Thermodynamic models of transcriptional regulation
4/26/2016 Project presentation
4/28/2016 Project presentation