Profile hidden Markov models trained on aligned KEGG Orthology sequences for enzyme annotation

Datacite citation style:
Rodenburg, S.Y.A. (Sander) (2019): Profile hidden Markov models trained on aligned KEGG Orthology sequences for enzyme annotation. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/uuid:9b6e6aa0-b815-409f-9e96-04828b03290b
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

Profile hidden Markov models trained on aligned KEGG Orthology sequences for enzyme annotation. These HMMs were used to reconstruct metabolic networks for the manuscript: The genome of Peronospora belbahrii reveals high heterozygosity, a low number of canonical effectors and CT-rich promoters Marco Thines, Rahul Sharma, Sander Y. A. Rodenburg, Anna Gogleva, Howard S. Judelson, Xiaojuan Xia, Johan van den Hoogen, Miloslav Kitner, Joël Klein, Manon Neilen, Dick de Ridder, Michael F. Seidl, Guido Van den Ackerveken, Francine Govers, Sebastian Schornack, David J. Studholme Draft version of the manuscript can be found with https://doi.org/10.1101/721027 A peer-reviewed version will appear online soon. NOTE: These HMMs are for reviewing purposes, and are based on an old version of KEGG (2016).

History

  • 2019-10-07 first online, published, posted

Publisher

4TU.Centre for Research Data

Format

media types: application/octet-stream, application/x-gzip, text/markdown

Contributors

  • Govers, F. (Francine); Laboratory of Phytopathology, Wageningen University
  • Seidl, M.F. (Michael); Theoretical Biology & Bioinformatics, Utrecht University
  • de Ridder, D. (Dick); Bioinformatics Group, Wageningen university

DATA

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