We have created a new method to find transcription factor motifs in ChIP-seq data using knockout controls. Available on PyPI and GitHub.
Our new method predicts transcription factor and chromatin factor locations in a cell type using new kinds of data like chromatin factor binding in other cell types and learning the association of gene expression patterns with chromatin factor binding patterns. We've made free software available and a track hub that can load our predictions for 36 chromatin factors in 33 human tissue types into the UCSC Genome Browser.
Many biological experiments use DNA sequencing as a readout. We can often map the sequenced DNA back to a specific region of the genome. Sometimes, however, we can't. Genomic data is less reliable in those regions. My lab has developed software that makes it easy to identify these regions. We also developed a new method that lets us find those regions in the context of bisulfite sequencing, a technique used to determine where DNA is chemically modified. [more inside]
I taught a learn-to-program course this summer for biologists with no previous programming experience. I got lots of requests from the participants for an online version of the course, so here it is! Learn to program in Python with no previous experience required, using lots of biological / bioinformatics examples. [more inside]
BEDOPS is a suite of tools to address common questions raised in genomic studies, mostly with regard to overlap and proximity relationships between data sets. BEDOPS aims to be scalable, flexible and performant, facilitating the efficient and accurate analysis and management of large-scale genomic data.