MGnify: the microbiome sequence data analysis resource in 2023

Richardson, Lorna; Allen, Ben; Baldi, Germana; Beracochea, Martín; Bileschi, Maxwell L; Burdett, Tony; Burgin, Josephine; Caballero-Pérez, Juan; Cochrane, Guy; Colwell, Lucy; Curtis, Tom; Escobar‐Zepeda, Alejandra; Gurbich, Tatiana A.; Kale, Varsha; Korobeynikov, Anton; Raj, Shriya; Rogers, A. B.; Sakharova, Ekaterina; Sanchez, Santiago; Wilkinson, Darren J.; Finn, Robert D.

doi:10.1093/nar/gkac1080

Cited by 172 publications

(128 citation statements)

References 29 publications

(33 reference statements)

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“…Sequence dataset of Class II effector proteins Initial datasets of Cas12 and Cas9 were taken from (a recently accepted paper, to be cited upon publication) and (17), respectively. Cas13 sequence dataset was constructed by building HMMER (25) sequence profiles for Cas13 groups (26) and using them to search NR (27), UniRef100 (28), MGnify (29), and IMG/VR v4 (30) databases with hmmsearch (25). Only sequences with E-value ≤ 1e-20 were extracted.…”

Section: Methodsmentioning

confidence: 99%

TemStaPro: protein thermostability prediction using sequence representations from protein language models

Pudžiuvelytė

Olechnovič

Godliauskaite³

et al. 2023

Preprint

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Reliable prediction of protein thermostability from its sequence is valuable for both academic and industrial research. This prediction problem can be tackled using machine learning and by taking advantage of the recent blossoming of deep learning methods for sequence analysis. We propose applying the principle of transfer learning to predict protein thermostability using embeddings generated by protein language models (pLMs) from an input protein sequence. We used large pLMs that were pre-trained on hundreds of millions of known sequences. The embeddings from such models allowed us to efficiently train and validate a high-performing prediction method using over 2 million sequences that we collected from organisms with annotated growth temperatures. Our method, TemStaPro (Temperatures of Stability for Proteins), was used to predict thermostability of CRISPR-Cas Class II effector proteins (C2EPs). Predictions indicated sharp differences among groups of C2EPs in terms of thermostability and were largely in tune with previously published and our newly obtained experimental data. TemStaPro software is freely available from https://github.com/ievapudz/TemStaPro.

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Section: Methodsmentioning

confidence: 99%

TemStaPro: protein thermostability prediction using sequence representations from protein language models

Pudžiuvelytė

Olechnovič

Godliauskaite³

et al. 2023

Preprint

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“…New features of the former include an improved genome context viewer while the latter has fresh tools for detection of metagenome-derived viral genomes and prediction of their hosts. The update from the popular MGnify resource for metagenomics data ( 52 ), recognising the scale and continued growth of the database, reports interestingly on the adoption of Deep Learning methods to annotate protein sequences with Pfam families ( 53 ). The importance of the microbiome to the colonised host is demonstrated by two databases.…”

Section: New and Updated Databasesmentioning

confidence: 99%

The 2023 Nucleic Acids Research Database Issue and the online molecular biology database collection

Rigden

Fernández

2023

Nucleic Acids Research

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The 2023 Nucleic Acids Research Database Issue contains 178 papers ranging across biology and related fields. There are 90 papers reporting on new databases and 82 updates from resources previously published in the Issue. Six more papers are updates from databases most recently published elsewhere. Major nucleic acid databases reporting updates include Genbank, ENA, ChIPBase, JASPAR, mirDIP and the Issue's first Breakthrough Article, NACDDB for Circular Dichroism data. Updates from BMRB and RCSB cover experimental protein structural data while AlphaFold 2 computational structure predictions feature widely. STRING and REBASE are stand-out updates in the signalling and enzymes section. Immunology-related databases include CEDAR, the second Breakthrough Article, for cancer epitopes and receptors alongside returning IPD-IMGT/HLA and the new PGG.MHC. Genomics-related resources include Ensembl, GWAS Central and UCSC Genome Browser. Major returning databases for drugs and their targets include Open Targets, DrugCentral, CTD and Pubchem. The EMPIAR image archive appears in the Issue for the first time. The entire database Issue is freely available online on the Nucleic Acids Research website (https://academic.oup.com/nar). The NAR online Molecular Biology Database Collection has been updated, revisiting 463 entries, adding 92 new resources and eliminating 96 discontinued URLs so bringing the current total to 1764 databases. It is available at http://www.oxfordjournals.org/nar/database/c/.

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“…For example, the latest release of the UniProtKB database, containing 227M sequences, expands the 2019 version by 90% [30,7]. Metagenomic databases grow even faster: the EMBL-EBI MGnify, including hundreds of millions of sequences produced from high-quality assembled genomes, reported a 48-fold growth since its first release in 2017 [24] [19].…”

Section: Background and Summarymentioning

confidence: 99%

“…Furthermore, the proliferation of metagenomic projects will likely induce a further drop in Pfam coverage. In fact, family annotation levels of metagenomic protein sequence databases are low, typically around 46% [24].…”

Section: Background and Summarymentioning

confidence: 99%

Protein family annotation for the Unified Human Gastrointestinal Proteome by DPCfam clustering

Barone

Russo

Garcia

et al. 2023

Preprint

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Technological advances in massively parallel sequencing have led to an exponential growth in the number of known protein sequences. Much of this growth originates from metagenomic projects producing new sequences from environmental and clinical samples. The Unified Human Gastrointestinal Proteome (UHGP) catalogue is one of the most relevant metagenomic datasets with applications ranging from medicine to biology. However, the lack of sequence annotation impairs its usability. This work aims to produce a family classification of UHGP sequences to facilitate downstream structural and functional annotation. This is achieved through the release of theDPCfam-UHGP50 datasetcontaining 10,778 putative protein families generated using DPCfam clustering, an unsupervised pipeline grouping sequences into multi-domain architectures. DPCfam-UHGP50 considerably improves family coverage at protein and residue levels compared to the manually curated repository Pfam. It is our hope that DPCfam-UHGP50 will foster future discoveries in the field of metagenomics of the human gut by the release of a FAIR-compliant database easily accessible via a searchable web server and Zenodo repository.

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MGnify: the microbiome sequence data analysis resource in 2023

Cited by 172 publications

References 29 publications

TemStaPro: protein thermostability prediction using sequence representations from protein language models

TemStaPro: protein thermostability prediction using sequence representations from protein language models

The 2023 Nucleic Acids Research Database Issue and the online molecular biology database collection

Protein family annotation for the Unified Human Gastrointestinal Proteome by DPCfam clustering

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