Author: Menzel, Michael; Hurka, Sabine; Glasenhardt, Stefan; Gogol-Döring, Andreas
Title: NoPeak: k-mer based motif discovery in ChIP-Seq data without peak calling. Cord-id: nn18qp1k Document date: 2020_9_29
ID: nn18qp1k
Snippet: MOTIVATION The discovery of sequence motifs mediating DNA-protein binding usually implies the determination of binding sites using high-throughput sequencing and peak calling. The determination of peaks, however, depends strongly on data quality and is susceptible to noise. RESULTS Here we present a novel approach to reliably identify transcription factor binding motifs from ChIP-Seq data without peak detection. By evaluating the distributions of sequencing reads around the different k-mers in t
Document: MOTIVATION The discovery of sequence motifs mediating DNA-protein binding usually implies the determination of binding sites using high-throughput sequencing and peak calling. The determination of peaks, however, depends strongly on data quality and is susceptible to noise. RESULTS Here we present a novel approach to reliably identify transcription factor binding motifs from ChIP-Seq data without peak detection. By evaluating the distributions of sequencing reads around the different k-mers in the genome, we are able to identify binding motifs in ChIP-Seq data that yield no results in traditional pipelines. AVAILABILITY NoPeak is published under the GNU General Public License and available as a standalone console based Java application at https://github.com/menzel/nopeak. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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