Comprehensive assembly of novel transcripts from unmapped human RNA‐Seq data and their association with cancer

Molecular Systems Biology

Majid Kazemian¹  Min Ren¹  Jian-Xin Lin¹  Wei Liao¹  Rosanne Spolski¹ 

[1] Laboratory of Molecular Immunology and the Immunology Center, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA

关键词: cancer‐associated transcripts;    long noncoding RNAs;    unmapped sequencing reads;   

DOI  :  10.15252/msb.156172

来源: Wiley

【 摘 要 】

Abstract

Crucial parts of the genome including genes encoding microRNAs and noncoding RNAs went unnoticed for years, and even now, despite extensive annotation and assembly of the human genome, RNA-sequencing continues to yield millions of unmappable and thus uncharacterized reads. Here, we examined > 300 billion reads from 536 normal donors and 1,873 patients encompassing 21 cancer types, identified ~300 million such uncharacterized reads, and using a distinctive approach de novo assembled 2,550 novel human transcripts, which mainly represent long noncoding RNAs. Of these, 230 exhibited relatively specific expression or non-expression in certain cancer types, making them potential markers for those cancers, whereas 183 exhibited tissue specificity. Moreover, we used lentiviral-mediated expression of three selected transcripts that had higher expression in normal than in cancer patients and found that each inhibited the growth of HepG2 cells. Our analysis provides a comprehensive and unbiased resource of unmapped human transcripts and reveals their associations with specific cancers, providing potentially important new genes for therapeutic targeting.

Synopsis

Comprehensive analysis of unmapped reads from a large number of cancer and normal RNA-Seq datasets identifies 2,550 previously unassembled human transcripts. Some of the transcripts are cancer type- or tissue-specific, suggesting context-dependent functions.

• RNA-sequencing reads that could not be mapped to the human reference genome/transcriptome are de novo assembled to obtain long and mainly noncoding transcripts.
• Alignment to chimp/gorilla genomes enabled predicting the genic neighborhood of these transcripts.
• Some transcripts associated with specific cancers/tissues are identified, and over-expression of three selected transcripts is shown to affect the growth of HepG2 cells.

【 授权许可】

CC BY   
This article is a U.S. Government work and is in the public domain in the USA Published under the terms of the CC BY 4.0 license

Creative Commons Attribution 4.0 License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.