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BAGS.v1.1: BAltic Gene Set gene catalogue

Version 2 2024-01-09, 09:45
Version 1 2021-10-01, 12:48
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posted on 2024-01-09, 09:45 authored by Luis Fernando Delgado ZambranoLuis Fernando Delgado Zambrano, Anders F. Andersson

The BAltic Gene Set gene catalogue v1.1 encompasses 66,530,673 genes.

The 66 million genes are based on metagenomic data from Alneberg at al. (2020) from 124 seawater samples, that span the salinity and oxygen gradients of the Baltic Sea and capture seasonal dynamics at two locations. To obtain the gene catalogue, we used a mix-assembly approach described in Delgado et al. (2022).

The gene catalogue has been functionally and taxonomically annotated, using the Mix-assembly Gene Catalog pipeline (https://github.com/EnvGen/mix_assembly_pipeline). The taxonomy annotation was performed using Mmseqs2[1] (uniref90[2]) and CAT[3] (GTDB[4]).

Here you find representative mix-assembly gene and protein sequences, and different types of annotations for the proteins. Also, contigs for the co-assembly are included (see Delgado et al. 2022), gene and protein sequences from each individual assembly and the co-assembly, and a table containing the genes in each of the clusters. See README for details.

When using the BAGSv1.1 gene catalogue, please cite:

1. Delgado LF, Andersson AF. Evaluating metagenomic assembly approaches for biome-specific gene catalogues. Microbiome 10, 72 (2022)

2. Alneberg J, Bennke C, Beier S, Bunse C, Quince C, Ininbergs K, Riemann L, Ekman M, Jürgens K, Labrenz M, Pinhassi J, Andersson AF (2020) Ecosystem-wide metagenomic binning enables prediction of ecological niches from genomes. Commun Biol 3, 119 (2020)


References:

  1. M Mirdita, M Steinegger, F Breitwieser, J Söding, E Levy Karin, Fast and sensitive taxonomic assignment to metagenomic contigs, Bioinformatics, Volume 37, Issue 18, September 2021, Pages 3029–3031, https://doi.org/10.1093/bioinformatics/btab184
  2. Baris E. Suzek, Yuqi Wang, Hongzhan Huang, Peter B. McGarvey, Cathy H. Wu, the UniProt Consortium, UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches, Bioinformatics, Volume 31, Issue 6, March 2015, Pages 926–932, https://doi.org/10.1093/bioinformatics/btu739
  3. von Meijenfeldt, F.A.B., Arkhipova, K., Cambuy, D.D. et al. Robust taxonomic classification of uncharted microbial sequences and bins with CAT and BAT. Genome Biol 20, 217 (2019). https://doi.org/10.1186/s13059-019-1817-x
  4. Donovan H Parks, Maria Chuvochina, Christian Rinke, Aaron J Mussig, Pierre-Alain Chaumeil, Philip Hugenholtz, GTDB: an ongoing census of bacterial and archaeal diversity through a phylogenetically consistent, rank normalized and complete genome-based taxonomy, Nucleic Acids Research, Volume 50, Issue D1, 7 January 2022, Pages D785–D794, https://doi.org/10.1093/nar/gkab776


Funding

Swedish Biodiversity Data Infrastructure

Swedish Research Council

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Swedish Biodiversity Data Infrastructure (SBDI)

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