Computational tools to unmask transposable elements.

A substantial proportion of the genome of many species is derived from transposable elements (TEs). Moreover, through various self-copying mechanisms, TEs continue to proliferate in the genomes of most species. TEs have contributed numerous regulatory, transcript and protein innovations and have also been linked to disease.

Optimizing ChIP-seq peak detectors using visual labels and supervised machine learning.

Motivation: Many peak detection algorithms have been proposed for ChIP-seq data analysis, but it is not obvious which algorithm and what parameters are optimal for any given dataset. In contrast, regions with and without obvious peaks can be easily labeled by visual inspection of aligned read counts in a genome browser. We propose a supervised machine learning approach for ChIP-seq data analysis, using labels that encode qualitative judgments about which genomic regions contain or do not contain peaks.