Identification of Splice Variants and Isoforms in Transcriptomics and Proteomics.

IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Taojunfeng Su, Michael A R Hollas, Ryan T Fellers, Neil L Kelleher
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引用次数: 0

Abstract

Alternative splicing is pivotal to the regulation of gene expression and protein diversity in eukaryotic cells. The detection of alternative splicing events requires specific omics technologies. Although short-read RNA sequencing has successfully supported a plethora of investigations on alternative splicing, the emerging technologies of long-read RNA sequencing and top-down mass spectrometry open new opportunities to identify alternative splicing and protein isoforms with less ambiguity. Here, we summarize improvements in short-read RNA sequencing for alternative splicing analysis, including percent splicing index estimation and differential analysis. We also review the computational methods used in top-down proteomics analysis regarding proteoform identification, including the construction of databases of protein isoforms and statistical analyses of search results. While many improvements in sequencing and computational methods will result from emerging technologies, there should be future endeavors to increase the effectiveness, integration, and proteome coverage of alternative splicing events.

转录组学和蛋白质组学中剪接变异体和异构体的鉴定。
选择性剪接对真核细胞中基因表达和蛋白质多样性的调节至关重要。选择性剪接事件的检测需要特定的组学技术。尽管短读RNA测序已经成功地支持了对替代剪接的大量研究,但新出现的长读RNA测序和自上而下的质谱技术为识别替代剪接和蛋白质异构体提供了新的机会,而不那么模糊。在这里,我们总结了用于选择性剪接分析的短读RNA测序的改进,包括百分比剪接指数估计和差异分析。我们还回顾了自上而下蛋白质组学分析中使用的蛋白质形态鉴定的计算方法,包括蛋白质异构体数据库的构建和搜索结果的统计分析。虽然测序和计算方法的许多改进将来自新兴技术,但未来应该努力提高替代剪接事件的有效性、整合性和蛋白质组覆盖率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
11.10
自引率
1.70%
发文量
0
期刊介绍: The Annual Review of Biomedical Data Science provides comprehensive expert reviews in biomedical data science, focusing on advanced methods to store, retrieve, analyze, and organize biomedical data and knowledge. The scope of the journal encompasses informatics, computational, artificial intelligence (AI), and statistical approaches to biomedical data, including the sub-fields of bioinformatics, computational biology, biomedical informatics, clinical and clinical research informatics, biostatistics, and imaging informatics. The mission of the journal is to identify both emerging and established areas of biomedical data science, and the leaders in these fields.
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