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        <datestamp>2025-05-12</datestamp>
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          <dc:identifier>https://doi.org/10.17044/SCILIFELAB.16677252</dc:identifier>
          <dc:title xml:lang="en">BAGS.v1.1: BAltic Gene Set gene catalogue</dc:title>
          <dc:creator>https://orcid.org/0000-0001-7850-5285</dc:creator>
          <dc:creator>Anders Andersson</dc:creator>
          <dc:subject xml:lang="en">Medical and Health Sciences</dc:subject>
          <dc:subject xml:lang="sv">Medicin och hälsovetenskap</dc:subject>
          <dc:subject xml:lang="en">Bioinformatics (Computational Biology)</dc:subject>
          <dc:subject xml:lang="sv">Bioinformatik (beräkningsbiologi)</dc:subject>
          <dc:description xml:lang="en">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)</dc:description>
          <dc:rights>https://creativecommons.org/licenses/by/4.0/</dc:rights>
          <dc:publisher xml:lang="en">Royal Institute of Technology</dc:publisher>
          <dc:publisher xml:lang="sv">Kungliga Tekniska högskolan</dc:publisher>
          <dc:date>2025-05-12T00:00:00Z</dc:date>
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