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元密度:一种用于在各种转录组位点上总结CLIP信号的基于背景感知的Python管道。

Metadensity: a background-aware python pipeline for summarizing CLIP signals on various transcriptomic sites.

作者信息

Her Hsuan-Lin, Boyle Evan, Yeo Gene W

机构信息

Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA 92093, USA.

Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA 92093, USA.

出版信息

Bioinform Adv. 2022 Nov 10;2(1):vbac083. doi: 10.1093/bioadv/vbac083. eCollection 2022.

Abstract

MOTIVATION

Cross-linking and immunoprecipitation (CLIP) is a technology to map the binding sites of RNA-binding proteins (RBPs). The region where an RBP binds within RNA is often indicative of its molecular function in RNA processing. As an example, the binding sites of splicing factors are found within or proximal to alternatively spliced exons. To better reveal the function of RBPs, we developed a tool to visualize the distribution of CLIP signals around various transcript features.

RESULTS

Here, we present Metadensity (https://github.com/YeoLab/Metadensity), a software that allows users to generate metagene plots. Metadensity allows users to input features such as branchpoints and preserves the near-nucleotide resolution of CLIP technologies by not scaling the features by length. Metadensity normalizes immunoprecipitated libraries with background controls, such as size-matched inputs, then windowing in various user-defined features. Finally, the signals are averaged across a provided set of transcripts.

AVAILABILITY AND IMPLEMENTATION

Metadensity is available at https://github.com/YeoLab/Metadensity, with example notebooks at https://metadensity.readthedocs.io/en/latest/tutorial.html.

SUPPLEMENTARY INFORMATION

Supplementary data are available at online.

摘要

动机

交联免疫沉淀(CLIP)是一种用于绘制RNA结合蛋白(RBP)结合位点的技术。RBP在RNA内结合的区域通常表明其在RNA加工中的分子功能。例如,剪接因子的结合位点位于可变剪接外显子内部或附近。为了更好地揭示RBP的功能,我们开发了一种工具来可视化CLIP信号在各种转录本特征周围的分布。

结果

在此,我们展示了Metadensity(https://github.com/YeoLab/Metadensity),这是一款允许用户生成元基因图的软件。Metadensity允许用户输入诸如分支点等特征,并且通过不按长度缩放特征来保留CLIP技术的近核苷酸分辨率。Metadensity使用背景对照(如大小匹配的输入)对免疫沉淀文库进行归一化,然后在各种用户定义的特征中进行窗口化处理。最后,对提供的一组转录本的信号进行平均。

可用性与实现方式

Metadensity可在https://github.com/YeoLab/Metadensity获取,示例笔记本可在https://metadensity.readthedocs.io/en/latest/tutorial.html获取。

补充信息

补充数据可在网上获取。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2f82/9710576/54ee011f925c/vbac083f1.jpg

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