Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg im Breisgau, Germany.
Signalling Research Centres BIOSS and CIBSS, University of Freiburg, Freiburg im Breisgau, Germany.
Bioinformatics. 2022 Jan 27;38(4):1139-1140. doi: 10.1093/bioinformatics/btab755.
CLIP-seq is by far the most widely used method to determine transcriptome-wide binding sites of RNA-binding proteins (RBPs). The binding site locations are identified from CLIP-seq read data by tools termed peak callers. Many RBPs bind to a spliced RNA (i.e. transcript) context, but all currently available peak callers only consider and report the genomic context. To accurately model protein binding behavior, a tool is needed for the individual context assignment to CLIP-seq peak regions.
Here we present Peakhood, the first tool that utilizes CLIP-seq peak regions identified by peak callers, in tandem with CLIP-seq read information and genomic annotations, to determine which context applies, individually for each peak region. For sites assigned to transcript context, it further determines the most likely splice variant, and merges results for any number of datasets to obtain a comprehensive collection of transcript context binding sites.
Peakhood is freely available under MIT license at: https://github.com/BackofenLab/Peakhood.
Supplementary data are available at Bioinformatics online.
CLIP-seq 是迄今为止最广泛用于确定 RNA 结合蛋白 (RBP) 转录组范围结合位点的方法。通过称为峰调用程序的工具，从 CLIP-seq 读取数据中识别结合位点位置。许多 RBP 结合到拼接 RNA（即转录本）上下文中，但目前所有可用的峰调用程序仅考虑和报告基因组上下文。为了准确地模拟蛋白质结合行为，需要一种工具将 CLIP-seq 峰区域分配到各个上下文。
在这里，我们提出了 Peakhood，这是第一个利用峰调用程序识别的 CLIP-seq 峰区域，以及 CLIP-seq 读取信息和基因组注释，单独确定每个峰区域适用的上下文的工具。对于分配给转录本上下文的位点，它进一步确定最可能的剪接变体，并合并任意数量数据集的结果，以获得综合的转录本上下文结合位点集合。
Peakhood 可在 MIT 许可证下免费获得：https://github.com/BackofenLab/Peakhood。
补充数据可在 Bioinformatics 在线获得。
