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  IsoTV: processing and visualizing functional features of translated transcript isoforms

Annaldasula, S., Gajos, M., & Mayer, A. (2021). IsoTV: processing and visualizing functional features of translated transcript isoforms. Bioinformatics, 2021: btab103. doi:10.1093/bioinformatics/btab103.

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 Creators:
Annaldasula, Siddharth1, Author
Gajos, Martyna2, Author           
Mayer, Andreas2, Author           
Affiliations:
1Department of Mathematics and Computer Science, Freie Universitaet Berlin, Berlin 14195, Germany, ou_persistent22              
2Nascent Transcription and Cell Differentiation (Andreas Mayer), Independent Junior Research Groups (OWL), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_2385699              

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 Abstract: Despite the continuous discovery of new transcript isoforms, fueled by the recent increase in accessibility and accuracy of long-read RNA sequencing data, functional differences between isoforms originating from the same gene often remain obscure. To address this issue and enable researchers to assess potential functional consequences of transcript isoform variation on the proteome, we developed IsoTV. IsoTV is a versatile pipeline to process, predict and visualize the functional features of translated transcript isoforms. Attributes such as gene and isoform expression, transcript composition and functional features are summarized in an easy-to-interpret visualization. IsoTV is able to analyze a variety of data types from all eukaryotic organisms, including short- and long-read RNA-seq data. Using Oxford Nanopore long read data, we demonstrate that IsoTV facilitates the understanding of potential protein isoform function in different cancer cell types.

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Language(s): eng - English
 Dates: 2021-02-112021-02-15
 Publication Status: Published online
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 Identifiers: DOI: 10.1093/bioinformatics/btab103
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Title: Bioinformatics
Source Genre: Journal
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Publ. Info: Oxford : Oxford University Press
Pages: - Volume / Issue: 2021 Sequence Number: btab103 Start / End Page: - Identifier: ISSN: 1367-4803
CoNE: https://pure.mpg.de/cone/journals/resource/954926969991