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Pushing the annotation of cellular activities to a higher resolution: Predicting functions at the isoform level

  • Wenyuan Li
  • , Chun Chi Liu
  • , Shuli Kang
  • , Jian Rong Li
  • , Yu Ting Tseng
  • , Xianghong Jasmine Zhou*
  • *Corresponding author for this work
  • University of Southern California
  • National Chung Hsing University

Research output: Contribution to journalReview articlepeer-review

9 Scopus citations

Abstract

In past decades, the experimental determination of protein functions was expensive and time-consuming, so numerous computational methods were developed to speed up and guide the process. However, most of these methods predict protein functions at the gene level and do not consider the fact that protein isoforms (translated from alternatively spliced transcripts), not genes, are the actual function carriers. Now, high-throughput RNA-seq technology is providing unprecedented opportunities to unravel protein functions at the isoform level. In this article, we review recent progress in the high-resolution functional annotations of protein isoforms, focusing on two methods developed by the authors. Both methods can integrate multiple RNA-seq datasets for comprehensively characterizing functions of protein isoforms.

Original languageEnglish
Pages (from-to)110-118
Number of pages9
JournalMethods: A Companion to Methods in Enzymology
Volume93
DOIs
StatePublished - 15 01 2016
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2015.

Keywords

  • Isoform function prediction
  • Multiple instance learning
  • RNA-seq data

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