Literature DB >> 32728687

Exploring transcriptional switches from pairwise, temporal and population RNA-Seq data using deepTS.

Zhixu Qiu1, Siyuan Chen1, Yuhong Qi1, Chunni Liu1, Jingjing Zhai1, Shang Xie1, Chuang Ma2.   

Abstract

Transcriptional switch (TS) is a widely observed phenomenon caused by changes in the relative expression of transcripts from the same gene, in spatial, temporal or other dimensions. TS has been associated with human diseases, plant development and stress responses. Its investigation is often hampered by a lack of suitable tools allowing comprehensive and flexible TS analysis for high-throughput RNA sequencing (RNA-Seq) data. Here, we present deepTS, a user-friendly web-based implementation that enables a fully interactive, multifunctional identification, visualization and analysis of TS events for large-scale RNA-Seq datasets from pairwise, temporal and population experiments. deepTS offers rich functionality to streamline RNA-Seq-based TS analysis for both model and non-model organisms and for those with or without reference transcriptome. The presented case studies highlight the capabilities of deepTS and demonstrate its potential for the transcriptome-wide TS analysis of pairwise, temporal and population RNA-Seq data. We believe deepTS will help research groups, regardless of their informatics expertise, perform accessible, reproducible and collaborative TS analyses of large-scale RNA-Seq data.
© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Entities:  

Keywords:  Galaxy; RNA-Seq; pipeline; transcriptional switch; workflow

Year:  2021        PMID: 32728687     DOI: 10.1093/bib/bbaa137

Source DB:  PubMed          Journal:  Brief Bioinform        ISSN: 1467-5463            Impact factor:   11.622


  1 in total

1.  easyMF: A Web Platform for Matrix Factorization-Based Gene Discovery from Large-scale Transcriptome Data.

Authors:  Wenlong Ma; Siyuan Chen; Yuhong Qi; Minggui Song; Jingjing Zhai; Ting Zhang; Shang Xie; Guifeng Wang; Chuang Ma
Journal:  Interdiscip Sci       Date:  2022-05-18       Impact factor: 3.492

  1 in total

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