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Towards next generation CHO cell biology: Bioinformatics methods for RNA‐Seq‐based expression profiling
Author(s) -
Monger Craig,
Kelly Paul S.,
Gallagher Clair,
Clynes Martin,
Barron Niall,
Clarke Colin
Publication year - 2015
Publication title -
biotechnology journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.144
H-Index - 84
eISSN - 1860-7314
pISSN - 1860-6768
DOI - 10.1002/biot.201500107
Subject(s) - rna seq , biology , computational biology , transcriptome , de novo transcriptome assembly , rna , gene expression profiling , chinese hamster ovary cell , small rna , microrna , genetics , gene , gene expression , cell culture
High throughput, cost effective next generation sequencing (NGS) has enabled the publication of genome sequences for Cricetulus griseus and several Chinese hamster ovary (CHO) cell lines. RNA‐Seq, the utilization of NGS technology to study the transcriptome, is expanding our understanding of the CHO cell biological system in areas ranging from the analysis of transcription start sites to the discovery of small noncoding RNAs. The analysis of RNA‐Seq data, often comprised of several million short reads, presents a considerable challenge. If the CHO cell biology field is to fully exploit the potential of RNA‐Seq, the development of robust data analysis pipelines is critical. In this manuscript, we outline bioinformatics approaches for the stages of a typical RNA‐Seq expression profiling experiment including quality control, pre‐processing, alignment and de novo transcriptome assembly. Algorithms for the analysis of mRNA and microRNA (miRNA) expression as well as methods for the detection of alternative splicing from RNA‐Seq data are also presented. At this relatively early stage of Cricetulus griseus genome assembly and annotation, it is likely that a combination of isoform deconvolution and raw count based methods will provide the most complete picture of transcript expression patterns in CHO cell RNA‐Seq experiments.