CLIP-seq analysis of multi-mapped reads discovers novel functional RNA regulatory sites in the human transcriptome

• Published in: Nucleic acids research Vol. 45; no. 16; pp. 9260 - 9271
• Main Authors: Zhang, Zijun, Xing, Yi
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 19.09.2017
• Subjects: Adenosine - analogs & derivatives; Adenosine - metabolism; Algorithms; Alternative Splicing; Argonaute Proteins - metabolism; Cell Line; Computational Biology; Gene Expression Regulation; Heterogeneous-Nuclear Ribonucleoprotein Group C - metabolism; High-Throughput Nucleotide Sequencing - methods; Humans; Immunoprecipitation; MicroRNAs - metabolism; Models, Statistical; Regulatory Sequences, Ribonucleic Acid; RNA Splicing Factors - metabolism; RNA Stability; RNA, Messenger - metabolism; Sequence Analysis, RNA - methods; Software; Transcriptome
• ISSN: 0305-1048; 1362-4962; 1362-4962
• DOI: 10.1093/nar/gkx646

Crosslinking or RNA immunoprecipitation followed by sequencing (CLIP-seq or RIP-seq) allows transcriptome-wide discovery of RNA regulatory sites. As CLIP-seq/RIP-seq reads are short, existing computational tools focus on uniquely mapped reads, while reads mapped to multiple loci are discarded. We present CLAM (CLIP-seq Analysis of Multi-mapped reads). CLAM uses an expectation-maximization algorithm to assign multi-mapped reads and calls peaks combining uniquely and multi-mapped reads. To demonstrate the utility of CLAM, we applied it to a wide range of public CLIP-seq/RIP-seq datasets involving numerous splicing factors, microRNAs and m6A RNA methylation. CLAM recovered a large number of novel RNA regulatory sites inaccessible by uniquely mapped reads. The functional significance of these sites was demonstrated by consensus motif patterns and association with alternative splicing (splicing factors), transcript abundance (AGO2) and mRNA half-life (m6A). CLAM provides a useful tool to discover novel protein-RNA interactions and RNA modification sites from CLIP-seq and RIP-seq data, and reveals the significant contribution of repetitive elements to the RNA regulatory landscape of the human transcriptome.