M&Ms: a versatile software for building microbial mock communities.


Journal

Bioinformatics (Oxford, England)
ISSN: 1367-4811
Titre abrégé: Bioinformatics
Pays: England
ID NLM: 9808944

Informations de publication

Date de publication:
28 03 2022
Historique:
received: 04 05 2021
revised: 20 12 2021
pubmed: 14 1 2022
medline: 3 2 2023
entrez: 13 1 2022
Statut: ppublish

Résumé

Advances in sequencing technologies have triggered the development of many bioinformatic tools aimed to analyze 16S rDNA sequencing data. As these tools need to be tested, it is important to simulate datasets that resemble samples from different environments. Here, we introduce M&Ms, a user-friendly open-source bioinformatic tool to produce different 16S rDNA datasets from reference sequences, based on pragmatic ecological parameters. It creates sequence libraries for 'in silico' microbial communities with user-controlled richness, evenness, microdiversity and source environment. M&Ms allows the user to generate simple to complex read datasets based on real parameters that can be used in developing bioinformatic software or in benchmarking current tools. The source code of M&Ms is freely available at https://github.com/ggnatalia/MMs (GPL-3.0 License). Supplementary data are available at Bioinformatics online.

Identifiants

pubmed: 35022654
pii: 6505202
doi: 10.1093/bioinformatics/btab882
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

2057-2059

Subventions

Organisme : Spanish Ministerio de Economía, Industria y Competitividad
ID : CTM2016-80095-C2-1-R
Organisme : Spanish Ministerio de Ciencia e Innovación
ID : PID2019-110011RB-C31
Organisme : Severo Ochoa Program at CNB
ID : SEV-2013-0347-17-2
Organisme : European Union's Horizon 2020
Organisme : Marie Skłodowska-Curie
ID : 892961

Informations de copyright

© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Auteurs

Natalia García-García (N)

Department of Systems Biology, Address Centro Nacional de Biotecnología (CNB-CSIC), 28049 Madrid, Spain.

Javier Tamames (J)

Department of Systems Biology, Address Centro Nacional de Biotecnología (CNB-CSIC), 28049 Madrid, Spain.

Fernando Puente-Sánchez (F)

Department of Systems Biology, Address Centro Nacional de Biotecnología (CNB-CSIC), 28049 Madrid, Spain.

Articles similaires

Selecting optimal software code descriptors-The case of Java.

Yegor Bugayenko, Zamira Kholmatova, Artem Kruglov et al.
1.00
Software Algorithms Programming Languages
Populus Soil Microbiology Soil Microbiota Fungi

Exploring blood-brain barrier passage using atomic weighted vector and machine learning.

Yoan Martínez-López, Paulina Phoobane, Yanaima Jauriga et al.
1.00
Blood-Brain Barrier Machine Learning Humans Support Vector Machine Software
Coal Metagenome Phylogeny Bacteria Genome, Bacterial

Classifications MeSH