MNI, Technische Hochschule Mittelhessen, University of Applied Sciences, Giessen 35390, Germany.
Institute for Insect Biotechnology, Justus Liebig University, Giessen 35392, Germany.
Bioinformatics. 2021 May 5;37(5):596-602. doi: 10.1093/bioinformatics/btaa845.
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.
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.
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 data are available at Bioinformatics online.
发现介导 DNA-蛋白质结合的序列基序通常意味着使用高通量测序和峰调用来确定结合位点。然而，峰的确定强烈依赖于数据质量，并且容易受到噪声的影响。
在这里，我们提出了一种新颖的方法，无需峰检测即可从 ChIP-Seq 数据中可靠地识别转录因子结合基序。通过评估基因组中不同 k-mer 周围测序读数的分布，我们能够识别在传统管道中没有结果的 ChIP-Seq 数据中的结合基序。
NoPeak 根据 GNU 通用公共许可证发布，并作为一个独立的基于控制台的 Java 应用程序在 https://github.com/menzel/nopeak 上提供。
补充数据可在 Bioinformatics 在线获得。
