Home Page

Papers

Submissions

News

Editorial Board

Announcements

Proceedings

Open Source Software

Search

Login



RSS Feed

PeakSeg: constrained optimal segmentation and supervised penalty learning for peak detection in count data

Toby Hocking, Guillem Rigaill, Guillaume Bourque
Proceedings of The 32nd International Conference on Machine Learning, pp. 324–332, 2015

Abstract

Peak detection is a central problem in genomic data analysis, and current algorithms for this task are unsupervised and mostly effective for a single data type and pattern (e.g. H3K4me3 data with a sharp peak pattern). We propose PeakSeg, a new constrained maximum likelihood segmentation model for peak detection with an efficient inference algorithm: constrained dynamic programming. We investigate unsupervised and supervised learning of penalties for the critical model selection problem. We show that the supervised method has state-of-the-art peak detection across all data sets in a benchmark that includes both sharp H3K4me3 and broad H3K36me3 patterns.

Related Material