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ExpressAnalyst: A unified platform for RNA-sequencing analysis in non-model species

Author

Listed:
  • Peng Liu

    (McGill University)

  • Jessica Ewald

    (McGill University)

  • Zhiqiang Pang

    (McGill University)

  • Elena Legrand

    (McGill University)

  • Yeon Seon Jeon

    (McGill University)

  • Jonathan Sangiovanni

    (McGill University)

  • Orcun Hacariz

    (McGill University)

  • Guangyan Zhou

    (McGill University)

  • Jessica A. Head

    (McGill University)

  • Niladri Basu

    (McGill University)

  • Jianguo Xia

    (McGill University)

Abstract

The increasing application of RNA sequencing to study non-model species demands easy-to-use and efficient bioinformatics tools to help researchers quickly uncover biological and functional insights. We developed ExpressAnalyst ( www.expressanalyst.ca ), a web-based platform for processing, analyzing, and interpreting RNA-sequencing data from any eukaryotic species. ExpressAnalyst contains a series of modules that cover from processing and annotation of FASTQ files to statistical and functional analysis of count tables or gene lists. All modules are integrated with EcoOmicsDB, an ortholog database that enables comprehensive analysis for species without a reference transcriptome. By coupling ultra-fast read mapping algorithms with high-resolution ortholog databases through a user-friendly web interface, ExpressAnalyst allows researchers to obtain global expression profiles and gene-level insights from raw RNA-sequencing reads within 24 h. Here, we present ExpressAnalyst and demonstrate its utility with a case study of RNA-sequencing data from multiple non-model salamander species, including two that do not have a reference transcriptome.

Suggested Citation

  • Peng Liu & Jessica Ewald & Zhiqiang Pang & Elena Legrand & Yeon Seon Jeon & Jonathan Sangiovanni & Orcun Hacariz & Guangyan Zhou & Jessica A. Head & Niladri Basu & Jianguo Xia, 2023. "ExpressAnalyst: A unified platform for RNA-sequencing analysis in non-model species," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-38785-y
    DOI: 10.1038/s41467-023-38785-y
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    3. Peter Menzel & Kim Lee Ng & Anders Krogh, 2016. "Fast and sensitive taxonomic classification for metagenomics with Kaiju," Nature Communications, Nature, vol. 7(1), pages 1-9, September.
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