Por favor, use este identificador para citar o enlazar a este item: http://hdl.handle.net/10261/158368
COMPARTIR / EXPORTAR:
logo share SHARE logo core CORE BASE
Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL | DATACITE

Invitar a revisión por pares abierta
Título

MEBS, a software platform to evaluate large (meta)genomic collections according to their metabolic machinery: unraveling the sulfur cycle

AutorDe Anda, Valerie; Zapata-Peñasco, Icoquih; Poot-Hernández, Augusto César; Eguiarte, Luis E.; Contreras-Moreira, Bruno CSIC ORCID ; Souza, Valeria
Palabras clavemetabolic machinery
Metagenomics
omic-datasets
Pfam domains
relative entropy
sulfur cycle
multigenomic entropy-based score
Fecha de publicaciónnov-2017
EditorBioMed Central
Oxford University Press
CitaciónDe Anda V, Zapata-Peñasco I, Poot-Hernández AC, Eguiarte LE, Contreras-Moreira B, Souza V. MEBS, a software platform to evaluate large (meta)genomic collections according to their metabolic machinery: unraveling the sulfur cycle. GigaScience 6 (11): 1–17 (2017)
ResumenThe increasing number of metagenomic and genomic sequences has dramatically improved our understanding of microbial diversity, yet our ability to infer metabolic capabilities in such datasets remains challenging. We describe the Multigenomic Entropy Based Score pipeline (MEBS), a software platform designed to evaluate, compare, and infer complex metabolic pathways in large “omic” datasets, including entire biogeochemical cycles. MEBS is open source and available through https://github.com/eead-csic-compbio/metagenome_Pfam_score. To demonstrate its use, we modeled the sulfur cycle by exhaustively curating the molecular and ecological elements involved (compounds, genes, metabolic pathways, and microbial taxa). This information was reduced to a collection of 112 characteristic Pfam protein domains and a list of complete-sequenced sulfur genomes. Using the mathematical framework of relative entropy (H΄), we quantitatively measured the enrichment of these domains among sulfur genomes. The entropy of each domain was used both to build up a final score that indicates whether a (meta)genomic sample contains the metabolic machinery of interest and to propose marker domains in metagenomic sequences such as DsrC (PF04358). MEBS was benchmarked with a dataset of 2107 non-redundant microbial genomes from RefSeq and 935 metagenomes from MG-RAST. Its performance, reproducibility, and robustness were evaluated using several approaches, including random sampling, linear regression models, receiver operator characteristic plots, and the area under the curve metric (AUC). Our results support the broad applicability of this algorithm to accurately classify (AUC = 0.985) hard-to-culture genomes (e.g., Candidatus Desulforudis audaxviator), previously characterized ones, and metagenomic environments such as hydrothermal vents, or deep-sea sediment. Our benchmark indicates that an entropy-based score can capture the metabolic machinery of interest and can be used to efficiently classify large genomic and metagenomic datasets, including uncultivated/unexplored taxa.
Descripción17 pags.- 7 Figs.- 1 Tabl. © The Authors 2017. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Versión del editorhttps://doi.org/10.1093/gigascience/gix096
URIhttp://hdl.handle.net/10261/158368
DOI10.1093/gigascience/gix096
E-ISSN2047-217X
Aparece en las colecciones: (EEAD) Artículos




Ficheros en este ítem:
Fichero Descripción Tamaño Formato
Contreras-MoreiraB_GigaScience_2017.pdf2,56 MBAdobe PDFVista previa
Visualizar/Abrir
Mostrar el registro completo

CORE Recommender

PubMed Central
Citations

17
checked on 18-feb-2024

SCOPUSTM   
Citations

19
checked on 12-mar-2024

WEB OF SCIENCETM
Citations

25
checked on 25-feb-2024

Page view(s)

753
checked on 18-mar-2024

Download(s)

309
checked on 18-mar-2024

Google ScholarTM

Check

Altmetric

Altmetric


Artículos relacionados:


NOTA: Los ítems de Digital.CSIC están protegidos por copyright, con todos los derechos reservados, a menos que se indique lo contrario.