PT - JOURNAL ARTICLE AU - Sang Y. Chun AU - Caitlin M. Rodriguez AU - Peter K. Todd AU - Ryan E. Mills TI - SPECtre: a spectral coherence-based classifier of actively translated transcripts from ribosome profiling sequence data AID - 10.1101/034777 DP - 2015 Jan 01 TA - bioRxiv PG - 034777 4099 - http://biorxiv.org/content/early/2015/12/17/034777.short 4100 - http://biorxiv.org/content/early/2015/12/17/034777.full AB - Summary: Active protein translation can be assessed and measured using ribosome profiling sequencing strategies. Existing analytical approaches applied to this technology make use of sequence fragment length or frame occupancy to differentiate between active translation and background noise, however they do not consider additional characteristics inherent to the technology which limits their overall accuracy. Here, we present an analytical tool that models the overall tri-nucleotide periodicity of ribosomal occupancy using a classifier based on spectral coherence. Our software, SPECtre, examines the relationship of normalized ribosome profiling read coverage over a rolling series of windows along a transcript against an idealized reference signal. A comparison of SPECtre against current methods on existing and new data shows a marked improvement in accuracy for detecting active translation and exhibits overall high sensitivity at a low false discovery rate.Availability and Implementation: SPECtre source code is available for download at https://github.com/mills-lab/spectre.Contact: remills{at}med.umich.edu