期刊
BRIEFINGS IN BIOINFORMATICS
卷 16, 期 4, 页码 629-639出版社
OXFORD UNIV PRESS
DOI: 10.1093/bib/bbu032
关键词
RNAseq; data mining; SNP; mutation; exogenous RNA
资金
- NIH [K22HL113039, DK20593]
- AHA [14CSA20660001]
- LRI Novel Grant Award
- CCSG [P30 CA068485]
Recent advances in RNA library preparation methods, platform accessibility and cost efficiency have allowed high-throughput RNA sequencing (RNAseq) to replace conventional hybridization microarray platforms as the method of choice for mRNA profiling and transcriptome analyses. RNAseq is a powerful technique to profile both long and short RNA expression, and the depth of information gained from distinct RNAseq methods is striking and facilitates discovery. In addition to expression analysis, distinct RNAseq approaches also allow investigators the ability to assess transcriptional elongation, DNA variance and exogenous RNA content. Here we review the current state of the art in transcriptome sequencing and address epigenetic regulation, quantification of transcription activation, RNAseq output and a diverse set of applications for RNAseq data. We detail how RNAseq can be used to identify allele-specific expression, single-nucleotide polymorphisms and somatic mutations and discuss the benefits and limitations of using RNAseq to monitor DNA characteristics. Moreover, we highlight the power of combining RNA- and DNAseq methods for genomic analysis. In summary, RNAseq provides the opportunity to gain greater insight into transcriptional regulation and output than simply miRNA and mRNA profiling.
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