ARPEGGIO: Automated Reproducible Polyploid EpiGenetic GuIdance workflOw

BMC Genomics

Jun Sese¹  Tony Kuo²  Samuele Decarli³  Rie Shimizu-Inatsugi⁴  Lucas Mohn⁴  Kentaro K. Shimizu⁵  Stefan Milosavljevic⁶  Mark D. Robinson⁷ 

[1] AIST Artificial Intelligence Research Center, Tokyo, Japan;Humanome Lab Inc., Chuo-ku, Tokyo, Japan;Centre for Biodiversity Genomics, University of Guelph, Guelph, Canada;Department of Computer Science, ETH Zurich, Zurich, Switzerland;Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland;Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland;Kihara Institute for Biological Research, Yokohama City University, Yokohama, Japan;Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland;SIB Swiss Institute of Bioinformatics, University of Zurich, Zurich, Switzerland;SIB Swiss Institute of Bioinformatics, University of Zurich, Zurich, Switzerland;Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland;

关键词: Snakemake;    Epigenetics;    Bisulfite-sequencing;    Polyploidy;    Allopolyploids;    Reproducibility;    Automation;    Workflow;    Dna-methylation;    Whole-genome-bisulfite-sequencing;   

DOI  :  10.1186/s12864-021-07845-2

来源: Springer

【 摘 要 】

BackgroundWhole genome duplication (WGD) events are common in the evolutionary history of many living organisms. For decades, researchers have been trying to understand the genetic and epigenetic impact of WGD and its underlying molecular mechanisms. Particular attention was given to allopolyploid study systems, species resulting from an hybridization event accompanied by WGD. Investigating the mechanisms behind the survival of a newly formed allopolyploid highlighted the key role of DNA methylation. With the improvement of high-throughput methods, such as whole genome bisulfite sequencing (WGBS), an opportunity opened to further understand the role of DNA methylation at a larger scale and higher resolution. However, only a few studies have applied WGBS to allopolyploids, which might be due to lack of genomic resources combined with a burdensome data analysis process. To overcome these problems, we developed the Automated Reproducible Polyploid EpiGenetic GuIdance workflOw (ARPEGGIO): the first workflow for the analysis of epigenetic data in polyploids. This workflow analyzes WGBS data from allopolyploid species via the genome assemblies of the allopolyploid’s parent species. ARPEGGIO utilizes an updated read classification algorithm (EAGLE-RC), to tackle the challenge of sequence similarity amongst parental genomes. ARPEGGIO offers automation, but more importantly, a complete set of analyses including spot checks starting from raw WGBS data: quality checks, trimming, alignment, methylation extraction, statistical analyses and downstream analyses. A full run of ARPEGGIO outputs a list of genes showing differential methylation. ARPEGGIO was made simple to set up, run and interpret, and its implementation ensures reproducibility by including both package management and containerization.ResultsWe evaluated ARPEGGIO in two ways. First, we tested EAGLE-RC’s performance with publicly available datasets given a ground truth, and we show that EAGLE-RC decreases the error rate by 3 to 4 times compared to standard approaches. Second, using the same initial dataset, we show agreement between ARPEGGIO’s output and published results. Compared to other similar workflows, ARPEGGIO is the only one supporting polyploid data.ConclusionsThe goal of ARPEGGIO is to promote, support and improve polyploid research with a reproducible and automated set of analyses in a convenient implementation. ARPEGGIO is available at https://github.com/supermaxiste/ARPEGGIO.

【 授权许可】

CC BY