4.4 Article

Transcriptome analysis of human tissues and cell lines reveals one dominant transcript per gene

期刊

GENOME BIOLOGY
卷 14, 期 7, 页码 -

出版社

BMC
DOI: 10.1186/gb-2013-14-7-r70

关键词

splicing; transcriptome; gene expression; RNA-seq

资金

  1. European Community [241669, 261123]
  2. Wellcome Trust grant [098051]
  3. National Institutes of Health [5U54HG004555]
  4. NATIONAL HUMAN GENOME RESEARCH INSTITUTE [U41HG007234, U54HG004555] Funding Source: NIH RePORTER

向作者/读者索取更多资源

Background: RNA sequencing has opened new avenues for the study of transcriptome composition. Significant evidence has accumulated showing that the human transcriptome contains in excess of a hundred thousand different transcripts. However, it is still not clear to what extent this diversity prevails when considering the relative abundances of different transcripts from the same gene. Results: Here we show that, in a given condition, most protein coding genes have one major transcript expressed at significantly higher level than others, that in human tissues the major transcripts contribute almost 85 percent to the total mRNA from protein coding loci, and that often the same major transcript is expressed in many tissues. We detect a high degree of overlap between the set of major transcripts and a recently published set of alternatively spliced transcripts that are predicted to be translated utilizing proteomic data. Thus, we hypothesize that although some minor transcripts may play a functional role, the major ones are likely to be the main contributors to the proteome. However, we still detect a non-negligible fraction of protein coding genes for which the major transcript does not code a protein. Conclusions: Overall, our findings suggest that the transcriptome from protein coding loci is dominated by one transcript per gene and that not all the transcripts that contribute to transcriptome diversity are equally likely to contribute to protein diversity. This observation can help to prioritize candidate targets in proteomics research and to predict the functional impact of the detected changes in variation studies.

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