title: Pushing the annotation of cellular activities to a higher resolution: Predicting functions at the isoform level.
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.
authors.affiliation: Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA.
authors.forename: Wenyuan
authors.initials: W
authors.lastname: Li
authors.affiliation: Institute of Genomics and Bioinformatics, National Chung Hsing University, Taiwan; Institute of Biomedical Sciences, National Chung Hsing University, Taiwan; Agricultural Biotechnology Center, National Chung Hsing University, Taiwan.
authors.forename: Chun-Chi
authors.initials: CC
authors.lastname: Liu
authors.affiliation: Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA.
authors.forename: Shuli
authors.initials: S
authors.lastname: Kang
authors.affiliation: Institute of Genomics and Bioinformatics, National Chung Hsing University, Taiwan.
authors.forename: Jian-Rong
authors.initials: JR
authors.lastname: Li
authors.affiliation: Institute of Genomics and Bioinformatics, National Chung Hsing University, Taiwan.
authors.forename: Yu-Ting
authors.initials: YT
authors.lastname: Tseng
authors.affiliation: Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA. Electronic address: xjzhou@usc.edu.
authors.forename: Xianghong Jasmine
authors.initials: XJ
authors.lastname: Zhou
mesh_terms: D000818:Animals; D002468:Cell Physiological Phenomena / Q000502:physiology*; D030541:Databases, Genetic; D005544:Forecasting; D006801:Humans; D020033:Protein Isoforms / Q000502:physiology*; D012313:RNA / Q000502:physiology
chemical_list: D020033:Protein Isoforms; D012313:RNA
keywords: Isoform function prediction; Multiple instance learning; RNA-seq data