Informed prediction and analysis of bacterial metabolic pathways and genome-scale networks
gapseq is designed to combine metabolic pathway analysis with metabolic network reconstruction and curation. Based on genomic information and databases for pathways and reactions, gapseq can be used for:
- prediction of metabolic pathways from various databases
- transporter inference
- metabolic model construction
- multi-step gap filling
Zimmermann, J., Kaleta, C. & Waschina, S. gapseq: informed prediction of bacterial metabolic pathways and reconstruction of accurate metabolic models. Genome Biology 22, 81 (2021)
The latest release can be downloaded here. Besides this, the current development version can be accessed via:
git clone https://github.com/jotech/gapseq
Detailed information on installation and troubleshooting.
For detailed use cases and full tutorials, see the documentation.
Prediction of network candidate reactions, building of a draft model and gap filling:
./gapseq doall toy/myb71.fna
Do the same but with a defined medium for gap filling:
./gapseq doall toy/ecoli.fna.gz dat/media/MM_glu.csv
Copyright 2020 Johannes Zimmermann, Christoph Kaleta, & Silvio Waschina; University of Kiel, Germany
GNU General Public License version 3.0 (GPLv3) is applied to all copyrightable parts of gapseq. gapseq uses information on biochemical reactions, compounds, compartments, enzymes, and biological sequences from different external sources. The copyright and licensing terms for each of the resources are listed and cross-linked below. Identifiers for reactions, enzymes, compounds, and compartments may be identical to the external sources but can also differ to those. In both cases, the data from gapseq may be considered to be subject to the original copyright and licensing restrictions of the external resource.
- MNXref: Copyright 2011-2019 SystemsX, SIB Swiss Institute of Bioinformatics. Licensed under a Creative Commons Attribution 4.0 International License. Link to license: https://creativecommons.org/licenses/by/4.0/ Website: https://www.metanetx.org/
- MetaCyc: Copyright © SRI International 1999-2020, Marine Biological Laboratory 1998-2001, DoubleTwist Inc 1998-1999.
Link to license: https://metacyc.org/ptools-academic-license.shtml . Website: https://metacyc.org/ - MODELSEED: Copyright 2015 ModelSEED. Licensed under Creative Commons Attribution 4.0 International License. Link to license: https://creativecommons.org/licenses/by/4.0/ Website: https://modelseed.org/
- KEGG: Copyright 1995-2020 Kanehisa Laboratories.
For license terms see file
dat/licenses/LICENSE.kegg. Website: http://www.kegg.jp - BRENDA: Copyright 2020 Prof. Dr. D. Schomburg, Technische Universität Braunschweig, BRICS, Department of Bioinformatics and Biochemistry, Rebenring 56, 38106 Braunschweig, Germany. Licensed under the Creative Commons Attribution License CC BY 4.0 is applied to all copyrightable parts of BRENDA. Link to license: https://creativecommons.org/licenses/by/4.0/ Website: https://www.brenda-enzymes.org/
- UNIPROT: Copyright 2002 –2020 UniProt Consortium. Licensed under the Creative Commons Attribution License CC BY 4.0 is applied to all copyrightable parts of UNIPROT. Link to license: https://creativecommons.org/licenses/by/4.0/ Website: https://www.uniprot.org/
- TCDB: Copyright 2005 - 2020 Saier Lab. The text of the TCDB website (TCDB.ORG) is available for modification and reuse under the terms of the Creative Commons Attribution-Sharealike 3.0 Unported License and the GNU Free Documentation License. Link to license: https://creativecommons.org/licenses/by-sa/3.0/de/ Website: http://www.tcdb.org/
- BIGG: Copyright © 2019 The Regents of the University of California.
All Rights Reserved by the licenser. For license terms and conditions see file
dat/licenses/LICENSE.bigg. Website: http://bigg.ucsd.edu/ - GTDBtk: Copyright 2017 Pierre-Alain Chaumeil. Licensed under the GPL-3.0 license. Link to license: https://github.com/Ecogenomics/GTDBTk/blob/master/LICENSE Website: https://ecogenomics.github.io/GTDBTk/
- Zimmermann, J., Kaleta, C. & Waschina, S. gapseq: informed prediction of bacterial metabolic pathways and reconstruction of accurate metabolic models. Genome Biol 22, 81 (2021). https://doi.org/10.1186/s13059-021-02295-1
- De Bernardini, N., Zampieri, G., Campanaro, S., Zimmermann, J., Waschina, S. & Treu, L. pan-Draft: Automated reconstruction of species-representative metabolic models from multiple genomes. Currently under revision