Metaphor-A workflow for streamlined assembly and binning of metagenomes.

Autor: Salazar VW; Melbourne Integrative Genomics, School of Mathematics & Statistics, University of Melbourne, Parkville, VIC 3052, Victoria, Australia., Shaban B; Melbourne Data Analytics Platform (MDAP), University of Melbourne, Carlton, VIC 3053, Victoria, Australia., Quiroga MDM; Melbourne Data Analytics Platform (MDAP), University of Melbourne, Carlton, VIC 3053, Victoria, Australia., Turnbull R; Melbourne Data Analytics Platform (MDAP), University of Melbourne, Carlton, VIC 3053, Victoria, Australia., Tescari E; Melbourne Data Analytics Platform (MDAP), University of Melbourne, Carlton, VIC 3053, Victoria, Australia., Rossetto Marcelino V; Department of Molecular and Translational Sciences, Monash University, Clayton, VIC 3168, Victoria, Australia.; Centre for Innate Immunity and Infectious Diseases, Hudson Institute of Medical Research, Clayton, VIC 3168, Victoria, Australia.; School of BioSciences, University of Melbourne, Parkville, VIC 3052, Victoria, Australia.; Department of Microbiology and Immunology, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Parkville, VIC 3052, Victoria, Australia., Verbruggen H; School of BioSciences, University of Melbourne, Parkville, VIC 3052, Victoria, Australia., Lê Cao KA; Melbourne Integrative Genomics, School of Mathematics & Statistics, University of Melbourne, Parkville, VIC 3052, Victoria, Australia.
Jazyk: angličtina
Zdroj: GigaScience [Gigascience] 2022 Dec 28; Vol. 12. Date of Electronic Publication: 2023 Jul 31.
DOI: 10.1093/gigascience/giad055
Abstrakt: Recent advances in bioinformatics and high-throughput sequencing have enabled the large-scale recovery of genomes from metagenomes. This has the potential to bring important insights as researchers can bypass cultivation and analyze genomes sourced directly from environmental samples. There are, however, technical challenges associated with this process, most notably the complexity of computational workflows required to process metagenomic data, which include dozens of bioinformatics software tools, each with their own set of customizable parameters that affect the final output of the workflow. At the core of these workflows are the processes of assembly-combining the short-input reads into longer, contiguous fragments (contigs)-and binning, clustering these contigs into individual genome bins. The limitations of assembly and binning algorithms also pose different challenges depending on the selected strategy to execute them. Both of these processes can be done for each sample separately or by pooling together multiple samples to leverage information from a combination of samples. Here we present Metaphor, a fully automated workflow for genome-resolved metagenomics (GRM). Metaphor differs from existing GRM workflows by offering flexible approaches for the assembly and binning of the input data and by combining multiple binning algorithms with a bin refinement step to achieve high-quality genome bins. Moreover, Metaphor generates reports to evaluate the performance of the workflow. We showcase the functionality of Metaphor on different synthetic datasets and the impact of available assembly and binning strategies on the final results.
(© The Author(s) 2023. Published by Oxford University Press GigaScience.)
Databáze: MEDLINE
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje