Automated Isoform Diversity Detector (AIDD): a pipeline for investigating transcriptome diversity of RNA-seq data.

Adenosine deaminases acting on RNA (ADAR) Analysis of RNA-seq Differential expression Editome High-throughput sequencing Isoform RNA editing Sequencing variants Transcriptome

Journal

BMC bioinformatics
ISSN: 1471-2105
Titre abrégé: BMC Bioinformatics
Pays: England
ID NLM: 100965194

Informations de publication

Date de publication:
30 Dec 2020
Historique:
received: 16 11 2020
accepted: 18 11 2020
entrez: 30 12 2020
pubmed: 31 12 2020
medline: 13 1 2021
Statut: epublish

Résumé

As the number of RNA-seq datasets that become available to explore transcriptome diversity increases, so does the need for easy-to-use comprehensive computational workflows. Many available tools facilitate analyses of one of the two major mechanisms of transcriptome diversity, namely, differential expression of isoforms due to alternative splicing, while the second major mechanism-RNA editing due to post-transcriptional changes of individual nucleotides-remains under-appreciated. Both these mechanisms play an essential role in physiological and diseases processes, including cancer and neurological disorders. However, elucidation of RNA editing events at transcriptome-wide level requires increasingly complex computational tools, in turn resulting in a steep entrance barrier for labs who are interested in high-throughput variant calling applications on a large scale but lack the manpower and/or computational expertise. Here we present an easy-to-use, fully automated, computational pipeline (Automated Isoform Diversity Detector, AIDD) that contains open source tools for various tasks needed to map transcriptome diversity, including RNA editing events. To facilitate reproducibility and avoid system dependencies, the pipeline is contained within a pre-configured VirtualBox environment. The analytical tasks and format conversions are accomplished via a set of automated scripts that enable the user to go from a set of raw data, such as fastq files, to publication-ready results and figures in one step. A publicly available dataset of Zika virus-infected neural progenitor cells is used to illustrate AIDD's capabilities. AIDD pipeline offers a user-friendly interface for comprehensive and reproducible RNA-seq analyses. Among unique features of AIDD are its ability to infer RNA editing patterns, including ADAR editing, and inclusion of Guttman scale patterns for time series analysis of such editing landscapes. AIDD-based results show importance of diversity of ADAR isoforms, key RNA editing enzymes linked with the innate immune system and viral infections. These findings offer insights into the potential role of ADAR editing dysregulation in the disease mechanisms, including those of congenital Zika syndrome. Because of its automated all-inclusive features, AIDD pipeline enables even a novice user to easily explore common mechanisms of transcriptome diversity, including RNA editing landscapes.

Sections du résumé

BACKGROUND BACKGROUND
As the number of RNA-seq datasets that become available to explore transcriptome diversity increases, so does the need for easy-to-use comprehensive computational workflows. Many available tools facilitate analyses of one of the two major mechanisms of transcriptome diversity, namely, differential expression of isoforms due to alternative splicing, while the second major mechanism-RNA editing due to post-transcriptional changes of individual nucleotides-remains under-appreciated. Both these mechanisms play an essential role in physiological and diseases processes, including cancer and neurological disorders. However, elucidation of RNA editing events at transcriptome-wide level requires increasingly complex computational tools, in turn resulting in a steep entrance barrier for labs who are interested in high-throughput variant calling applications on a large scale but lack the manpower and/or computational expertise.
RESULTS RESULTS
Here we present an easy-to-use, fully automated, computational pipeline (Automated Isoform Diversity Detector, AIDD) that contains open source tools for various tasks needed to map transcriptome diversity, including RNA editing events. To facilitate reproducibility and avoid system dependencies, the pipeline is contained within a pre-configured VirtualBox environment. The analytical tasks and format conversions are accomplished via a set of automated scripts that enable the user to go from a set of raw data, such as fastq files, to publication-ready results and figures in one step. A publicly available dataset of Zika virus-infected neural progenitor cells is used to illustrate AIDD's capabilities.
CONCLUSIONS CONCLUSIONS
AIDD pipeline offers a user-friendly interface for comprehensive and reproducible RNA-seq analyses. Among unique features of AIDD are its ability to infer RNA editing patterns, including ADAR editing, and inclusion of Guttman scale patterns for time series analysis of such editing landscapes. AIDD-based results show importance of diversity of ADAR isoforms, key RNA editing enzymes linked with the innate immune system and viral infections. These findings offer insights into the potential role of ADAR editing dysregulation in the disease mechanisms, including those of congenital Zika syndrome. Because of its automated all-inclusive features, AIDD pipeline enables even a novice user to easily explore common mechanisms of transcriptome diversity, including RNA editing landscapes.

Identifiants

pubmed: 33375933
doi: 10.1186/s12859-020-03888-6
pii: 10.1186/s12859-020-03888-6
pmc: PMC7772930
doi:

Substances chimiques

Protein Isoforms 0
Adenosine Deaminase EC 3.5.4.4

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

578

Subventions

Organisme : NIMH NIH HHS
ID : F31 MH123131
Pays : United States
Organisme : NIA NIH HHS
ID : R21AG064479-01
Pays : United States

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Auteurs

Noel-Marie Plonski (NM)

Department of Biological Sciences, Kent State University, 256 Cunningham Hall, Kent, OH, 44242, USA.
School of Biomedical Sciences, Kent State University, PO Box 5190, Kent, OH, 44242, USA.

Emily Johnson (E)

Department of Biological Sciences, Kent State University, 256 Cunningham Hall, Kent, OH, 44242, USA.

Madeline Frederick (M)

Department of Biological Sciences, Kent State University, 256 Cunningham Hall, Kent, OH, 44242, USA.

Heather Mercer (H)

Department of Biological Sciences, Kent State University, 256 Cunningham Hall, Kent, OH, 44242, USA.
University of Mount Union, 1972 Clark Ave, Alliance, OH, 44601, USA.

Gail Fraizer (G)

Department of Biological Sciences, Kent State University, 256 Cunningham Hall, Kent, OH, 44242, USA.
School of Biomedical Sciences, Kent State University, PO Box 5190, Kent, OH, 44242, USA.

Richard Meindl (R)

School of Biomedical Sciences, Kent State University, PO Box 5190, Kent, OH, 44242, USA.
Department of Anthropology, Kent State University, Kent, OH, 44242, USA.

Gemma Casadesus (G)

Department of Biological Sciences, Kent State University, 256 Cunningham Hall, Kent, OH, 44242, USA.
School of Biomedical Sciences, Kent State University, PO Box 5190, Kent, OH, 44242, USA.
Brain Health Research Institute, Kent State University, Kent, OH, 44242, USA.
Department of Pharmacology & Therapeutics, College of Medicine, University of Florida, Gainesville, FL, 32610, USA.

Helen Piontkivska (H)

Department of Biological Sciences, Kent State University, 256 Cunningham Hall, Kent, OH, 44242, USA. opiontki@kent.edu.
School of Biomedical Sciences, Kent State University, PO Box 5190, Kent, OH, 44242, USA. opiontki@kent.edu.
Brain Health Research Institute, Kent State University, Kent, OH, 44242, USA. opiontki@kent.edu.

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