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BARM and BalticMicrobeDB, a reference metagenome and interface to meta-omic data for the Baltic Sea
- Source :
- Alneberg, J, Sundh, J, Bennke, C, Beier, S, Lundin, D, Hugerth, L W, Pinhassi, J, Kisand, V, Riemann, L, Jürgens, K, Labrenz, M & Andersson, A F 2018, ' BARM and BalticMicrobeDB, a reference metagenome and interface to meta-omic data for the Baltic Sea ', Scientific Data, vol. 5, 180146 . https://doi.org/10.1038/sdata.2018.146, Scientific Data, 5 . p. 180146.
- Publication Year :
- 2018
- Publisher :
- Springer Science and Business Media LLC, 2018.
-
Abstract
- The Baltic Sea is one of the world’s largest brackish water bodies and is characterised by pronounced physicochemical gradients where microbes are the main biogeochemical catalysts. Meta-omic methods provide rich information on the composition of, and activities within microbial ecosystems, but are computationally heavy to perform. We here present the BAltic Sea Reference Metagenome (BARM), complete with annotated genes to facilitate further studies with much less computational effort. The assembly is constructed using 2.6 billion metagenomic reads from 81 water samples, spanning both spatial and temporal dimensions, and contains 6.8 million genes that have been annotated for function and taxonomy. The assembly is useful as a reference, facilitating taxonomic and functional annotation of additional samples by simply mapping their reads against the assembly. This capability is demonstrated by the successful mapping and annotation of 24 external samples. In addition, we present a public web interface, BalticMicrobeDB, for interactive exploratory analysis of the dataset. QC 20180516
- Subjects :
- Ekologi
Statistics and Probability
Bioinformatics (Computational Biology)
Bioinformatics and Systems Biology
Ecology
Bioinformatik och systembiologi
Library and Information Sciences
Microbiology
Computer Science Applications
Education
Mikrobiologi
Bioinformatik (beräkningsbiologi)
Statistics, Probability and Uncertainty
Information Systems
Subjects
Details
- Language :
- English
- ISSN :
- 20524463
- Volume :
- 5
- Database :
- OpenAIRE
- Journal :
- Scientific Data
- Accession number :
- edsair.dedup.wf.001..9f20248c0c368dfc9a12ca88934a75c3
- Full Text :
- https://doi.org/10.1038/sdata.2018.146