MetaFunc: taxonomic and functional analyses of high throughput sequencing for microbiomes.

Metatranscriptomics functional annotation host correlation microbiome

Journal

Gut microbiome (Cambridge, England)
ISSN: 2632-2897
Titre abrégé: Gut Microbiome (Camb)
Pays: England
ID NLM: 9918556685506676

Informations de publication

Date de publication:
2023
Historique:
received: 26 07 2022
revised: 06 11 2022
accepted: 13 12 2022
medline: 12 1 2023
pubmed: 12 1 2023
entrez: 19 9 2024
Statut: epublish

Résumé

The identification of functional processes taking place in microbiome communities augment traditional microbiome taxonomic studies, giving a more complete picture of interactions taking place within the community. While there are applications that perform functional annotation on metagenomes or metatranscriptomes, very few of these are able to link taxonomic identity to function or are limited by their input types or databases used. Here we present MetaFunc, a workflow which takes RNA sequences as input reads, and from these (1) identifies species present in the microbiome sample and (2) provides gene ontology annotations associated with the species identified. In addition, MetaFunc allows for host gene analysis, mapping the reads to a host genome, and separating these reads, prior to microbiome analyses. Differential abundance analysis for microbe taxonomies, and differential gene expression analysis and gene set enrichment analysis may then be carried out through the pipeline. A final correlation analysis between microbial species and host genes can also be performed. Finally, MetaFunc builds an R shiny application that allows users to view and interact with the microbiome results. In this paper, we showed how MetaFunc can be applied to metatranscriptomic datasets of colorectal cancer.

Identifiants

pubmed: 39295912
doi: 10.1017/gmb.2022.12
pii: S2632289722000123
pmc: PMC11406379
doi:

Types de publication

Journal Article

Langues

eng

Pagination

e4

Informations de copyright

© The Author(s) 2023.

Déclaration de conflit d'intérêts

The authors have no competing interests.

Auteurs

Arielle Kae Sulit (AK)

Department of Surgery, University of Otago, Christchurch, New Zealand.
School of Natural Sciences, Massey University, Auckland, New Zealand.

Tyler Kolisnik (T)

School of Natural Sciences, Massey University, Auckland, New Zealand.

Frank Antony Frizelle (FA)

Department of Surgery, University of Otago, Christchurch, New Zealand.

Rachel Purcell (R)

Department of Surgery, University of Otago, Christchurch, New Zealand.

Sebastian Schmeier (S)

School of Natural Sciences, Massey University, Auckland, New Zealand.

Classifications MeSH