MGnify: the microbiome analysis resource in 2020

By Alex L. Mitchell, Alexandre Almeida, Martin Beracochea, M. Boland, Josephine Burgin, Guy Cochrane, M. Crusoe, Varsha Kale, Simon C. Potter, Lorna J. Richardson and 7 more. 2019

565 citations recorded by the source corpus.

Abstract MGnify (http://www.ebi.ac.uk/metagenomics) provides a free to use platform for the assembly, analysis and archiving of microbiome data derived from sequencing microbial populations that are present in particular environments. Over the past 2 years, MGnify (formerly EBI Metagenomics) has more than doubled the number of publicly available analysed datasets held within the resource. Recently, an updated approach to data analysis has been unveiled (version 5.0), replacing the previous single pipeline with multiple analysis pipelines that are tailored according to the input data, and that are formally described using the Common Workflow Language, enabling greater provenance, reusability, and reproducibility. MGnify's new analysis pipelines offer additional approaches for taxonomic assertions based on ribosomal internal transcribed spacer regions (ITS1/2) and expanded protein functional annotations. Biochemical pathways and systems predictions have also been added for assembled contigs. MGnify's growing focus on the assembly of metagenomic data has also seen the number of datasets it has assembled and analysed increase six-fold. The non-redundant protein database constructed from the proteins encoded by these assemblies now exceeds 1 billion sequences. Meanwhile, a newly developed contig viewer provides fine-grained visualisation of the assembled contigs and their enriched annotations.

Subfields: Ecology, Molecular Biology, Computational Biology & Bioinformatics

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Citations in the corpus

Evolutionary-scale prediction of atomic level protein structure with a language model (2022, background): “[25] Alex L Mitchell, Alexandre Almeida, Martin Beracochea, Miguel Boland, Josephine Burgin, Guy Cochrane, Michael R Crusoe, Varsha Kale, Simon C Potter, Lorna J Richardson, Ekaterina Sakharova, Maxim Scheremetjew, Anton Korobeynikov, Alex Shlemov, Olga Kunyavskaya, Alla Lapidus, and Robert D Finn.”
Highly accurate protein structure prediction with AlphaFold (2021, neutral): “Through an enormous experimental effort 1–4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6,7 .”
Deep learning-based prediction of protein structure using learned representations of multiple sequence alignments (2020, methodology): “MSAs were built using an approach similar to that taken by our group in CASP13 [5], using the UniClust30 2020_03 database, plus protein sequences from the UniRef100 [17], EBI MGnify [18], IMG/M [19], MetaEuk [20], and NCBI Transcriptome Shotgun Assembly databases.”
PrankWeb 3: accelerated ligand-binding site predictions for experimental and modelled protein structures (2022, background): “Although the number of resolved protein structures keeps increasing, it is still far behind the number of known protein sequences (7).”
The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences (2021, background): “In addition, we aim to increasingly perform in-house data reuse (including data re-analysis) and disseminate high-quality proteomics data from PRIDE into the already mentioned added-value resources (Ensembl, UniProt, Expression Atlas, and MGnify in the near future).”
A practical guide to amplicon and metagenomic analysis of microbiome data (2020, methodology): “For users who are not familiar with command lines, webservers such as Qiita (Gonzalez et al., 2018), MGnify (Mitchell et al., 2020), and gcMeta (Shi et al., 2019b) could be used to perform analysis.”
Pfam: The protein families database in 2021 (2020, background): “Ad-ditionally, we have made 37 new families based on clusters of sequences from the MGnify metagenomic protein database (11).”
PANTHER: Making genome‐scale phylogenetics accessible to all (2021, methodology): “PANTHER HMMs and trees are utilized in several steps of the MGnify 44 metagenomics data processing pipeline.”
The ProteomeXchange consortium at 10 years: 2023 update (2022, unknown): “Additionally, in a wider context of proteogenomics approaches, some pilot work has been performed by PRIDE to link and integrate metaproteomics datasets with the corresponding metagenomics and metatranscriptomics data in the EMBLEBI’s resource MGnify (43).”
MGnify: the microbiome sequence data analysis resource in 2023 (2022, background): “Since our last update (2), we have continued to expand the content of MGnify through a combination of userrequested analyses and analyses of targeted public datasets.”