使用 RCRUNCH 对 (e)CLIP 数据进行改进分析，得到了一套 RNA 结合蛋白结合位点和基序的摘要。

Biozentrum, University of Basel, 4056, Basel, Switzerland.

Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland.

Genome Biol. 2023 Apr 17;24(1):77. doi: 10.1186/s13059-023-02913-0.

We present RCRUNCH, an end-to-end solution to CLIP data analysis for identification of binding sites and sequence specificity of RNA-binding proteins. RCRUNCH can analyze not only reads that map uniquely to the genome but also those that map to multiple genome locations or across splice boundaries and can consider various types of background in the estimation of read enrichment. By applying RCRUNCH to the eCLIP data from the ENCODE project, we have constructed a comprehensive and homogeneous resource of in-vivo-bound RBP sequence motifs. RCRUNCH automates the reproducible analysis of CLIP data, enabling studies of post-transcriptional control of gene expression.

我们提出了 RCRUNCH，这是一个用于 CLIP 数据分析的端到端解决方案，用于识别 RNA 结合蛋白的结合位点和序列特异性。RCRUNCH 不仅可以分析唯一映射到基因组的读取，还可以分析映射到多个基因组位置或跨越剪接边界的读取，并且可以在估计读取富集时考虑各种类型的背景。通过将 RCRUNCH 应用于 ENCODE 项目的 eCLIP 数据，我们构建了一个全面且同质的体内结合 RBP 序列基序资源。RCRUNCH 实现了 CLIP 数据的可重复分析，从而能够研究基因表达的转录后调控。