Published November 2, 2021 | Version 0.1.0
Dataset Open

MetaFunc Databases: nr-go database

Authors/Creators

  • 1. School of Natural and Computational Sciences, Massey University, Auckland, New Zealand
  • 2. Department of Surgery, University of Otago, Christchurch, New Zealand
  • 3. School of Natural and Computational Sciences, Massey University, Auckland, New Zealand; Evotec SE, Hamburg, Germany

Contributors

Supervisor:

  • 1. School of Natural and Computational Sciences, Massey University, Auckland, New Zealand

Description

MetaFunc is a computational pipeline that can take input reads and pass it through a pipeline that will then analyse host genes from the reads on one side, and microbiome taxonomies and gene ontology annotations on the other, and finally allowing for microbe-host gene correlations. This dataset contains databases used for analysing the microbiome component of the pipeline. Full description of the pipeline can be found at https://metafunc.readthedocs.io/en/latest/#.

Notes

Funding: Maurice and Phyllis Paykel Trust Gut Cancer Foundation (NZ), with support from the Hugh Green Foundation Colorectal Surgical Society of Australia and New Zealand (CSSANZ) The Health Research Council of New Zealand

Files

202001_nrgo_md5sums.txt

Files (3.3 GB)

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md5:3e2371e5a480dfa257d0999f9086c61a
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md5:9868a01fc9b593e1373736a6490a73b2
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Additional details

Related works

Is compiled by
https://gitlab.com/schmeierlab/metafunc/metafunc-nrgo.git (URL)
Is supplement to
Preprint: 10.1101/2020.09.02.271098 (DOI)
Software documentation: https://metafunc.readthedocs.io/en/latest/ (URL)

References

  • Menzel, P., Ng, K.L., Krogh, A., 2016. Fast and sensitive taxonomic classification for metagenomics with Kaiju. Nature Communications 7, 11257. https://doi.org/10.1038/ncomms11257
  • Shen, W., Ren, H., TaxonKit: a practical and efficient NCBI Taxonomy toolkit, Journal of Genetics and Genomics, https://doi.org/10.1016/j.jgg.2021.03.006
  • https://dx.doi.org/10.5281/zenodo.2529950; release date: 2020-01-01
  • Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., Harris, M. A., Hill, D. P., Issel-Tarver, L., Kasarskis, A., Lewis, S., Matese, J. C., Richardson, J. E., Ringwald, M., Rubin, G. M., & Sherlock, G. (2000). Gene Ontology: Tool for the unification of biology. Nature Genetics, 25(1), 25–29. https://doi.org/10.1038/75556
  • Huang, H., McGarvey, P.B., Suzek, B.E., Mazumder, R., Zhang, J., Chen, Y., Wu, C.H., 2011. A comprehensive protein-centric ID mapping service for molecular data integration. Bioinformatics 27, 1190–1191. https://doi.org/10.1093/bioinformatics/btr101
  • The UniProt Consortium, 2017. UniProt: the universal protein knowledgebase. Nucleic Acids Research 45, D158–D169. https://doi.org/10.1093/nar/gkw1099
  • Camon, E., Magrane, M., Barrell, D., Lee, V., Dimmer, E., Maslen, J., Binns, D., Harte, N., Lopez, R., Apweiler, R., 2004. The Gene Ontology Annotation (GOA) Database: sharing knowledge in Uniprot with Gene Ontology. Nucleic Acids Res 32, D262–D266. https://doi.org/10.1093/nar/gkh021