Pickaxe: a Python library for the prediction of novel metabolic reactions
Background Biochemical reaction prediction tools leverage enzymatic promiscuity rules to generate reaction networks containing novel compounds and reactions. The resulting reaction networks can be used for multiple applications such as designing novel biosynthetic pathways and annotating untargeted...
Saved in:
| Published in: | BMC bioinformatics Vol. 24; no. 1; pp. 106 - 15 |
|---|---|
| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
London
BioMed Central
22.03.2023
BioMed Central Ltd Springer Nature B.V BMC |
| Subjects: | |
| ISSN: | 1471-2105, 1471-2105 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | Background
Biochemical reaction prediction tools leverage enzymatic promiscuity rules to generate reaction networks containing novel compounds and reactions. The resulting reaction networks can be used for multiple applications such as designing novel biosynthetic pathways and annotating untargeted metabolomics data. It is vital for these tools to provide a robust, user-friendly method to generate networks for a given application. However, existing tools lack the flexibility to easily generate networks that are tailor-fit for a user’s application due to lack of exhaustive reaction rules, restriction to pre-computed networks, and difficulty in using the software due to lack of documentation.
Results
Here we present Pickaxe, an open-source, flexible software that provides a user-friendly method to generate novel reaction networks. This software iteratively applies reaction rules to a set of metabolites to generate novel reactions. Users can select rules from the prepackaged JN1224min ruleset, derived from MetaCyc, or define their own custom rules. Additionally, filters are provided which allow for the pruning of a network on-the-fly based on compound and reaction properties. The filters include chemical similarity to target molecules, metabolomics, thermodynamics, and reaction feasibility filters. Example applications are given to highlight the capabilities of Pickaxe: the expansion of common biological databases with novel reactions, the generation of industrially useful chemicals from a yeast metabolome database, and the annotation of untargeted metabolomics peaks from an
E. coli
dataset.
Conclusion
Pickaxe predicts novel metabolic reactions and compounds, which can be used for a variety of applications. This software is open-source and available as part of the MINE Database python package (
https://pypi.org/project/minedatabase/
) or on GitHub (
https://github.com/tyo-nu/MINE-Database
). Documentation and examples can be found on Read the Docs (
https://mine-database.readthedocs.io/en/latest/
). Through its documentation, pre-packaged features, and customizable nature, Pickaxe allows users to generate novel reaction networks tailored to their application. |
|---|---|
| AbstractList | Biochemical reaction prediction tools leverage enzymatic promiscuity rules to generate reaction networks containing novel compounds and reactions. The resulting reaction networks can be used for multiple applications such as designing novel biosynthetic pathways and annotating untargeted metabolomics data. It is vital for these tools to provide a robust, user-friendly method to generate networks for a given application. However, existing tools lack the flexibility to easily generate networks that are tailor-fit for a user's application due to lack of exhaustive reaction rules, restriction to pre-computed networks, and difficulty in using the software due to lack of documentation. Here we present Pickaxe, an open-source, flexible software that provides a user-friendly method to generate novel reaction networks. This software iteratively applies reaction rules to a set of metabolites to generate novel reactions. Users can select rules from the prepackaged JN1224min ruleset, derived from MetaCyc, or define their own custom rules. Additionally, filters are provided which allow for the pruning of a network on-the-fly based on compound and reaction properties. The filters include chemical similarity to target molecules, metabolomics, thermodynamics, and reaction feasibility filters. Example applications are given to highlight the capabilities of Pickaxe: the expansion of common biological databases with novel reactions, the generation of industrially useful chemicals from a yeast metabolome database, and the annotation of untargeted metabolomics peaks from an E. coli dataset. Pickaxe predicts novel metabolic reactions and compounds, which can be used for a variety of applications. This software is open-source and available as part of the MINE Database python package (https://pypi.org/project/minedatabase/) or on GitHub (https://github.com/tyo-nu/MINE-Database). Documentation and examples can be found on Read the Docs (https://mine-database.readthedocs.io/en/latest/). Through its documentation, pre-packaged features, and customizable nature, Pickaxe allows users to generate novel reaction networks tailored to their application. Biochemical reaction prediction tools leverage enzymatic promiscuity rules to generate reaction networks containing novel compounds and reactions. The resulting reaction networks can be used for multiple applications such as designing novel biosynthetic pathways and annotating untargeted metabolomics data. It is vital for these tools to provide a robust, user-friendly method to generate networks for a given application. However, existing tools lack the flexibility to easily generate networks that are tailor-fit for a user's application due to lack of exhaustive reaction rules, restriction to pre-computed networks, and difficulty in using the software due to lack of documentation.BACKGROUNDBiochemical reaction prediction tools leverage enzymatic promiscuity rules to generate reaction networks containing novel compounds and reactions. The resulting reaction networks can be used for multiple applications such as designing novel biosynthetic pathways and annotating untargeted metabolomics data. It is vital for these tools to provide a robust, user-friendly method to generate networks for a given application. However, existing tools lack the flexibility to easily generate networks that are tailor-fit for a user's application due to lack of exhaustive reaction rules, restriction to pre-computed networks, and difficulty in using the software due to lack of documentation.Here we present Pickaxe, an open-source, flexible software that provides a user-friendly method to generate novel reaction networks. This software iteratively applies reaction rules to a set of metabolites to generate novel reactions. Users can select rules from the prepackaged JN1224min ruleset, derived from MetaCyc, or define their own custom rules. Additionally, filters are provided which allow for the pruning of a network on-the-fly based on compound and reaction properties. The filters include chemical similarity to target molecules, metabolomics, thermodynamics, and reaction feasibility filters. Example applications are given to highlight the capabilities of Pickaxe: the expansion of common biological databases with novel reactions, the generation of industrially useful chemicals from a yeast metabolome database, and the annotation of untargeted metabolomics peaks from an E. coli dataset.RESULTSHere we present Pickaxe, an open-source, flexible software that provides a user-friendly method to generate novel reaction networks. This software iteratively applies reaction rules to a set of metabolites to generate novel reactions. Users can select rules from the prepackaged JN1224min ruleset, derived from MetaCyc, or define their own custom rules. Additionally, filters are provided which allow for the pruning of a network on-the-fly based on compound and reaction properties. The filters include chemical similarity to target molecules, metabolomics, thermodynamics, and reaction feasibility filters. Example applications are given to highlight the capabilities of Pickaxe: the expansion of common biological databases with novel reactions, the generation of industrially useful chemicals from a yeast metabolome database, and the annotation of untargeted metabolomics peaks from an E. coli dataset.Pickaxe predicts novel metabolic reactions and compounds, which can be used for a variety of applications. This software is open-source and available as part of the MINE Database python package ( https://pypi.org/project/minedatabase/ ) or on GitHub ( https://github.com/tyo-nu/MINE-Database ). Documentation and examples can be found on Read the Docs ( https://mine-database.readthedocs.io/en/latest/ ). Through its documentation, pre-packaged features, and customizable nature, Pickaxe allows users to generate novel reaction networks tailored to their application.CONCLUSIONPickaxe predicts novel metabolic reactions and compounds, which can be used for a variety of applications. This software is open-source and available as part of the MINE Database python package ( https://pypi.org/project/minedatabase/ ) or on GitHub ( https://github.com/tyo-nu/MINE-Database ). Documentation and examples can be found on Read the Docs ( https://mine-database.readthedocs.io/en/latest/ ). Through its documentation, pre-packaged features, and customizable nature, Pickaxe allows users to generate novel reaction networks tailored to their application. Biochemical reaction prediction tools leverage enzymatic promiscuity rules to generate reaction networks containing novel compounds and reactions. The resulting reaction networks can be used for multiple applications such as designing novel biosynthetic pathways and annotating untargeted metabolomics data. It is vital for these tools to provide a robust, user-friendly method to generate networks for a given application. However, existing tools lack the flexibility to easily generate networks that are tailor-fit for a user’s application due to lack of exhaustive reaction rules, restriction to pre-computed networks, and difficulty in using the software due to lack of documentation. Here we present Pickaxe, an open-source, flexible software that provides a user-friendly method to generate novel reaction networks. This software iteratively applies reaction rules to a set of metabolites to generate novel reactions. Users can select rules from the prepackaged JN1224min ruleset, derived from MetaCyc, or define their own custom rules. Additionally, filters are provided which allow for the pruning of a network on-the-fly based on compound and reaction properties. The filters include chemical similarity to target molecules, metabolomics, thermodynamics, and reaction feasibility filters. Example applications are given to highlight the capabilities of Pickaxe: the expansion of common biological databases with novel reactions, the generation of industrially useful chemicals from a yeast metabolome database, and the annotation of untargeted metabolomics peaks from an E. coli dataset. Pickaxe predicts novel metabolic reactions and compounds, which can be used for a variety of applications. This software is open-source and available as part of the MINE Database python package (https://pypi.org/project/minedatabase/) or on GitHub (https://github.com/tyo-nu/MINE-Database). Documentation and examples can be found on Read the Docs (https://mine-database.readthedocs.io/en/latest/). Through its documentation, pre-packaged features, and customizable nature, Pickaxe allows users to generate novel reaction networks tailored to their application. Background Biochemical reaction prediction tools leverage enzymatic promiscuity rules to generate reaction networks containing novel compounds and reactions. The resulting reaction networks can be used for multiple applications such as designing novel biosynthetic pathways and annotating untargeted metabolomics data. It is vital for these tools to provide a robust, user-friendly method to generate networks for a given application. However, existing tools lack the flexibility to easily generate networks that are tailor-fit for a user's application due to lack of exhaustive reaction rules, restriction to pre-computed networks, and difficulty in using the software due to lack of documentation. Results Here we present Pickaxe, an open-source, flexible software that provides a user-friendly method to generate novel reaction networks. This software iteratively applies reaction rules to a set of metabolites to generate novel reactions. Users can select rules from the prepackaged JN1224min ruleset, derived from MetaCyc, or define their own custom rules. Additionally, filters are provided which allow for the pruning of a network on-the-fly based on compound and reaction properties. The filters include chemical similarity to target molecules, metabolomics, thermodynamics, and reaction feasibility filters. Example applications are given to highlight the capabilities of Pickaxe: the expansion of common biological databases with novel reactions, the generation of industrially useful chemicals from a yeast metabolome database, and the annotation of untargeted metabolomics peaks from an E. coli dataset. Conclusion Pickaxe predicts novel metabolic reactions and compounds, which can be used for a variety of applications. This software is open-source and available as part of the MINE Database python package ( Keywords: Enzyme promiscuity, Network generation, Biosynthetic design, Retrobiosynthesis, Metabolite identification Background Biochemical reaction prediction tools leverage enzymatic promiscuity rules to generate reaction networks containing novel compounds and reactions. The resulting reaction networks can be used for multiple applications such as designing novel biosynthetic pathways and annotating untargeted metabolomics data. It is vital for these tools to provide a robust, user-friendly method to generate networks for a given application. However, existing tools lack the flexibility to easily generate networks that are tailor-fit for a user’s application due to lack of exhaustive reaction rules, restriction to pre-computed networks, and difficulty in using the software due to lack of documentation. Results Here we present Pickaxe, an open-source, flexible software that provides a user-friendly method to generate novel reaction networks. This software iteratively applies reaction rules to a set of metabolites to generate novel reactions. Users can select rules from the prepackaged JN1224min ruleset, derived from MetaCyc, or define their own custom rules. Additionally, filters are provided which allow for the pruning of a network on-the-fly based on compound and reaction properties. The filters include chemical similarity to target molecules, metabolomics, thermodynamics, and reaction feasibility filters. Example applications are given to highlight the capabilities of Pickaxe: the expansion of common biological databases with novel reactions, the generation of industrially useful chemicals from a yeast metabolome database, and the annotation of untargeted metabolomics peaks from an E. coli dataset. Conclusion Pickaxe predicts novel metabolic reactions and compounds, which can be used for a variety of applications. This software is open-source and available as part of the MINE Database python package ( https://pypi.org/project/minedatabase/ ) or on GitHub ( https://github.com/tyo-nu/MINE-Database ). Documentation and examples can be found on Read the Docs ( https://mine-database.readthedocs.io/en/latest/ ). Through its documentation, pre-packaged features, and customizable nature, Pickaxe allows users to generate novel reaction networks tailored to their application. BackgroundBiochemical reaction prediction tools leverage enzymatic promiscuity rules to generate reaction networks containing novel compounds and reactions. The resulting reaction networks can be used for multiple applications such as designing novel biosynthetic pathways and annotating untargeted metabolomics data. It is vital for these tools to provide a robust, user-friendly method to generate networks for a given application. However, existing tools lack the flexibility to easily generate networks that are tailor-fit for a user’s application due to lack of exhaustive reaction rules, restriction to pre-computed networks, and difficulty in using the software due to lack of documentation.ResultsHere we present Pickaxe, an open-source, flexible software that provides a user-friendly method to generate novel reaction networks. This software iteratively applies reaction rules to a set of metabolites to generate novel reactions. Users can select rules from the prepackaged JN1224min ruleset, derived from MetaCyc, or define their own custom rules. Additionally, filters are provided which allow for the pruning of a network on-the-fly based on compound and reaction properties. The filters include chemical similarity to target molecules, metabolomics, thermodynamics, and reaction feasibility filters. Example applications are given to highlight the capabilities of Pickaxe: the expansion of common biological databases with novel reactions, the generation of industrially useful chemicals from a yeast metabolome database, and the annotation of untargeted metabolomics peaks from an E. coli dataset.ConclusionPickaxe predicts novel metabolic reactions and compounds, which can be used for a variety of applications. This software is open-source and available as part of the MINE Database python package (https://pypi.org/project/minedatabase/) or on GitHub (https://github.com/tyo-nu/MINE-Database). Documentation and examples can be found on Read the Docs (https://mine-database.readthedocs.io/en/latest/). Through its documentation, pre-packaged features, and customizable nature, Pickaxe allows users to generate novel reaction networks tailored to their application. Abstract Background Biochemical reaction prediction tools leverage enzymatic promiscuity rules to generate reaction networks containing novel compounds and reactions. The resulting reaction networks can be used for multiple applications such as designing novel biosynthetic pathways and annotating untargeted metabolomics data. It is vital for these tools to provide a robust, user-friendly method to generate networks for a given application. However, existing tools lack the flexibility to easily generate networks that are tailor-fit for a user’s application due to lack of exhaustive reaction rules, restriction to pre-computed networks, and difficulty in using the software due to lack of documentation. Results Here we present Pickaxe, an open-source, flexible software that provides a user-friendly method to generate novel reaction networks. This software iteratively applies reaction rules to a set of metabolites to generate novel reactions. Users can select rules from the prepackaged JN1224min ruleset, derived from MetaCyc, or define their own custom rules. Additionally, filters are provided which allow for the pruning of a network on-the-fly based on compound and reaction properties. The filters include chemical similarity to target molecules, metabolomics, thermodynamics, and reaction feasibility filters. Example applications are given to highlight the capabilities of Pickaxe: the expansion of common biological databases with novel reactions, the generation of industrially useful chemicals from a yeast metabolome database, and the annotation of untargeted metabolomics peaks from an E. coli dataset. Conclusion Pickaxe predicts novel metabolic reactions and compounds, which can be used for a variety of applications. This software is open-source and available as part of the MINE Database python package ( https://pypi.org/project/minedatabase/ ) or on GitHub ( https://github.com/tyo-nu/MINE-Database ). Documentation and examples can be found on Read the Docs ( https://mine-database.readthedocs.io/en/latest/ ). Through its documentation, pre-packaged features, and customizable nature, Pickaxe allows users to generate novel reaction networks tailored to their application. |
| ArticleNumber | 106 |
| Audience | Academic |
| Author | Shebek, Kevin M. Strutz, Jonathan Broadbelt, Linda J. Tyo, Keith E. J. |
| Author_xml | – sequence: 1 givenname: Kevin M. surname: Shebek fullname: Shebek, Kevin M. organization: Department of Chemical and Biological Engineering, Northwestern University, Center for Synthetic Biology, Northwestern University, Chemistry of Life Processes Institute, Northwestern University – sequence: 2 givenname: Jonathan surname: Strutz fullname: Strutz, Jonathan organization: Department of Chemical and Biological Engineering, Northwestern University, Center for Synthetic Biology, Northwestern University, Chemistry of Life Processes Institute, Northwestern University – sequence: 3 givenname: Linda J. surname: Broadbelt fullname: Broadbelt, Linda J. organization: Department of Chemical and Biological Engineering, Northwestern University, Center for Synthetic Biology, Northwestern University – sequence: 4 givenname: Keith E. J. orcidid: 0000-0002-2342-0687 surname: Tyo fullname: Tyo, Keith E. J. email: k-tyo@northwestern.edu organization: Department of Chemical and Biological Engineering, Northwestern University, Center for Synthetic Biology, Northwestern University, Chemistry of Life Processes Institute, Northwestern University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36949401$$D View this record in MEDLINE/PubMed https://www.osti.gov/biblio/1962934$$D View this record in Osti.gov |
| BookMark | eNp9kktv1DAUhSNURB_wB1igCDawSPEjfrFBVcVjpEpUPNaW49zMuGTswfZU7b_HmSltp0KVF7F8v3PuzdU5rPZ88FBVLzE6xljy9wkTyVSDCG0Qw61q5JPqALcCNwQjtnfvvl8dpnSBEBYSsWfVPuWqVS3CB9Xs3Nnf5go-1KY-v86L4OvRddHE63oIsc4LqFcRemezK6Uw1D5cwlgvIZsujM7WEcymlp5XTwczJnhx8z2qfn3-9PP0a3P27cvs9OSssZyr3IBiSvaCSSzooAhAxyxnEizgDvEBD7hvBeoFUEUNAoSR4mhghHS9aCnv6FE12_r2wVzoVXTLMqwOxunNQ4hzbWJ2dgRNpRBECEOKXcunxrh05tCbng6gSPH6uPVarbsl9BZ8jmbcMd2teLfQ83CpMUIUSyaKw-utQ0jZ6WRdBruwwXuwWWPFiaJtgd7etInhzxpS1kuXLIyj8RDWSROhEGol4RP65gF6EdbRl4VqIhFRXLCW3VFzU37T-SGU6exkqk8Ew1wRQifq-D9UOT0sXZkRBlfedwTvdgSFyXCV52adkp79-L7Lvrq_utud_YtWAcgWsDGkFGG4RTDSU371Nr-65Fdv8qtlEckHorJRM-WrjO7Gx6V0K02lj59DvNvcI6q_rRb_tQ |
| CitedBy_id | crossref_primary_10_1093_bib_bbae495 crossref_primary_10_1016_j_xplc_2025_101320 crossref_primary_10_1039_D3ME00162H crossref_primary_10_1021_acs_jcim_5c00265 crossref_primary_10_1038_s41467_025_61160_y crossref_primary_10_1016_j_copbio_2023_102992 crossref_primary_10_1002_cbic_202300577 crossref_primary_10_1021_acs_chemmater_5c00163 crossref_primary_10_1039_D4ME00118D |
| Cites_doi | 10.1002/biot.202000605 10.1016/j.mec.2021.e00170 10.1038/s41467-021-22022-5 10.1093/bioinformatics/btac331 10.1016/j.ymben.2021.02.006 10.1093/nar/gkw1058 10.1186/s13321-015-0087-1 10.1016/j.biotechadv.2012.12.008 10.1093/nar/gkab1106 10.1021/acssynbio.0c00598 10.1186/s13068-018-1135-7 10.1021/acssynbio.7b00072 10.1038/nmeth.4103 10.1186/s12918-015-0241-4 10.1515/jib-2017-0081 10.1073/pnas.1812605115 10.1038/s41929-020-00556-z 10.1016/j.ymeth.2014.08.005 10.3389/fmicb.2021.711077 10.1146/annurev-biochem-030409-143718 10.1371/journal.pcbi.1003483 10.1002/bit.22673 10.1186/s13321-018-0324-5 10.3762/bjoc.15.49 10.1093/NAR/GKAA230 10.1021/acssynbio.9b00105 10.1016/j.coisb.2019.04.004 10.1016/j.tibtech.2018.08.001 10.1021/acssynbio.0c00518 10.1021/acssynbio.0c00058 10.1038/s41467-017-02362-x 10.1016/j.ymben.2020.03.002 10.1126/science.aah5237 10.1016/j.ymben.2017.12.002 10.1093/nar/gkw1092 10.3390/metabo10040160 10.1016/j.ymben.2019.09.001 10.1021/acssynbio.6b00054 |
| ContentType | Journal Article |
| Copyright | The Author(s) 2023 2023. The Author(s). COPYRIGHT 2023 BioMed Central Ltd. 2023. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: The Author(s) 2023 – notice: 2023. The Author(s). – notice: COPYRIGHT 2023 BioMed Central Ltd. – notice: 2023. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| CorporateAuthor | Northwestern Univ., Evanston, IL (United States) |
| CorporateAuthor_xml | – sequence: 0 name: Northwestern Univ., Evanston, IL (United States) |
| DBID | C6C AAYXX CITATION CGR CUY CVF ECM EIF NPM ISR 3V. 7QO 7SC 7X7 7XB 88E 8AL 8AO 8FD 8FE 8FG 8FH 8FI 8FJ 8FK ABUWG AEUYN AFKRA ARAPS AZQEC BBNVY BENPR BGLVJ BHPHI CCPQU DWQXO FR3 FYUFA GHDGH GNUQQ HCIFZ JQ2 K7- K9. L7M LK8 L~C L~D M0N M0S M1P M7P P5Z P62 P64 PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS Q9U 7X8 OTOTI 5PM DOA |
| DOI | 10.1186/s12859-023-05149-8 |
| DatabaseName | SpringerOpen Free (Free internet resource, activated by CARLI) CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Gale In Context: Science ProQuest Central (Corporate) Biotechnology Research Abstracts Computer and Information Systems Abstracts Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) Computing Database (Alumni Edition) ProQuest Pharma Collection Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Natural Science Collection Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni Edition) ProQuest One Sustainability ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials - QC Biological Science Collection ProQuest Central Technology Collection Natural Science Collection ProQuest One Community College ProQuest Central Korea Engineering Research Database Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student SciTech Premium Collection ProQuest Computer Science Collection Computer Science Database ProQuest Health & Medical Complete (Alumni) Advanced Technologies Database with Aerospace ProQuest Biological Science Collection Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Computing Database Health & Medical Collection (Alumni Edition) Medical Database Biological Science Database Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection Biotechnology and BioEngineering Abstracts Proquest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China ProQuest Central Basic MEDLINE - Academic OSTI.GOV PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Publicly Available Content Database Computer Science Database ProQuest Central Student ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection Computer and Information Systems Abstracts SciTech Premium Collection ProQuest Central China ProQuest One Applied & Life Sciences ProQuest One Sustainability Health Research Premium Collection Natural Science Collection Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) Advanced Technologies & Aerospace Collection ProQuest Biological Science Collection ProQuest One Academic Eastern Edition ProQuest Hospital Collection ProQuest Technology Collection Health Research Premium Collection (Alumni) Biological Science Database ProQuest Hospital Collection (Alumni) Biotechnology and BioEngineering Abstracts ProQuest Health & Medical Complete ProQuest One Academic UKI Edition Engineering Research Database ProQuest One Academic ProQuest One Academic (New) Technology Collection Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest One Academic Middle East (New) ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Pharma Collection ProQuest Central ProQuest Health & Medical Research Collection Biotechnology Research Abstracts Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Advanced Technologies Database with Aerospace ProQuest Computing ProQuest Central Basic ProQuest Computing (Alumni Edition) ProQuest SciTech Collection Computer and Information Systems Abstracts Professional Advanced Technologies & Aerospace Database ProQuest Medical Library ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic Publicly Available Content Database MEDLINE |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: PIMPY name: Proquest Open Access Content url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Biology |
| EISSN | 1471-2105 |
| EndPage | 15 |
| ExternalDocumentID | oai_doaj_org_article_3877277a2d7e4695981d756edad3fe92 PMC10031857 1962934 A751692235 36949401 10_1186_s12859_023_05149_8 |
| Genre | Journal Article |
| GeographicLocations | United States |
| GeographicLocations_xml | – name: United States |
| GrantInformation_xml | – fundername: Biological and Environmental Research grantid: DE-SC0018249 funderid: http://dx.doi.org/10.13039/100006206 – fundername: Biological and Environmental Research grantid: DE-SC0018249 – fundername: ; grantid: DE-SC0018249 |
| GroupedDBID | --- 0R~ 23N 2WC 53G 5VS 6J9 7X7 88E 8AO 8FE 8FG 8FH 8FI 8FJ AAFWJ AAJSJ AAKPC AASML ABDBF ABUWG ACGFO ACGFS ACIHN ACIWK ACPRK ACUHS ADBBV ADMLS ADUKV AEAQA AENEX AEUYN AFKRA AFPKN AFRAH AHBYD AHMBA AHYZX ALMA_UNASSIGNED_HOLDINGS AMKLP AMTXH AOIJS ARAPS AZQEC BAPOH BAWUL BBNVY BCNDV BENPR BFQNJ BGLVJ BHPHI BMC BPHCQ BVXVI C6C CCPQU CS3 DIK DU5 DWQXO E3Z EAD EAP EAS EBD EBLON EBS EMB EMK EMOBN ESX F5P FYUFA GNUQQ GROUPED_DOAJ GX1 HCIFZ HMCUK HYE IAO ICD IHR INH INR ISR ITC K6V K7- KQ8 LK8 M1P M48 M7P MK~ ML0 M~E O5R O5S OK1 OVT P2P P62 PGMZT PHGZM PHGZT PIMPY PJZUB PPXIY PQGLB PQQKQ PROAC PSQYO PUEGO RBZ RNS ROL RPM RSV SBL SOJ SV3 TR2 TUS UKHRP W2D WOQ WOW XH6 XSB AAYXX AFFHD CITATION ALIPV CGR CUY CVF ECM EIF NPM 3V. 7QO 7SC 7XB 8AL 8FD 8FK FR3 JQ2 K9. L7M L~C L~D M0N P64 PKEHL PQEST PQUKI PRINS Q9U 7X8 -A0 ACRMQ ADINQ C24 OTOTI 5PM |
| ID | FETCH-LOGICAL-c669t-e9598d758173f92eeb5c658ece1b06f1f1d470d7e393a0e010960f522bd7436b3 |
| IEDL.DBID | K7- |
| ISICitedReferencesCount | 10 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000957578700004&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1471-2105 |
| IngestDate | Mon Nov 10 04:31:47 EST 2025 Tue Nov 04 02:08:18 EST 2025 Thu Dec 05 06:19:58 EST 2024 Thu Sep 04 17:43:42 EDT 2025 Mon Oct 06 18:35:56 EDT 2025 Tue Nov 11 10:39:07 EST 2025 Tue Nov 04 17:56:52 EST 2025 Wed Nov 26 11:01:15 EST 2025 Mon Jul 21 06:04:08 EDT 2025 Tue Nov 18 21:13:17 EST 2025 Sat Nov 29 05:40:14 EST 2025 Sat Sep 06 07:27:30 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | Network generation Retrobiosynthesis Enzyme promiscuity Biosynthetic design Metabolite identification |
| Language | English |
| License | 2023. The Author(s). Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c669t-e9598d758173f92eeb5c658ece1b06f1f1d470d7e393a0e010960f522bd7436b3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 USDOE Office of Science (SC), Biological and Environmental Research (BER) SC0018249 |
| ORCID | 0000-0002-2342-0687 0000000223420687 |
| OpenAccessLink | https://www.proquest.com/docview/2802967545?pq-origsite=%requestingapplication% |
| PMID | 36949401 |
| PQID | 2802967545 |
| PQPubID | 44065 |
| PageCount | 15 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_3877277a2d7e4695981d756edad3fe92 pubmedcentral_primary_oai_pubmedcentral_nih_gov_10031857 osti_scitechconnect_1962934 proquest_miscellaneous_2790048264 proquest_journals_2802967545 gale_infotracmisc_A751692235 gale_infotracacademiconefile_A751692235 gale_incontextgauss_ISR_A751692235 pubmed_primary_36949401 crossref_primary_10_1186_s12859_023_05149_8 crossref_citationtrail_10_1186_s12859_023_05149_8 springer_journals_10_1186_s12859_023_05149_8 |
| PublicationCentury | 2000 |
| PublicationDate | 2023-03-22 |
| PublicationDateYYYYMMDD | 2023-03-22 |
| PublicationDate_xml | – month: 03 year: 2023 text: 2023-03-22 day: 22 |
| PublicationDecade | 2020 |
| PublicationPlace | London |
| PublicationPlace_xml | – name: London – name: England – name: United States |
| PublicationTitle | BMC bioinformatics |
| PublicationTitleAbbrev | BMC Bioinformatics |
| PublicationTitleAlternate | BMC Bioinformatics |
| PublicationYear | 2023 |
| Publisher | BioMed Central BioMed Central Ltd Springer Nature B.V BMC |
| Publisher_xml | – sequence: 0 name: BioMed Central – name: BioMed Central – name: BioMed Central Ltd – name: Springer Nature B.V – name: BMC |
| References | O Motwalli (5149_CR27) 2020; 9 E Noor (5149_CR34) 2014; 10 N Hadadi (5149_CR26) 2016; 5 JD Tyzack (5149_CR28) 2019; 8 JG Jeffryes (5149_CR19) 2015; 7 Y Djoumbou-Feunang (5149_CR14) 2019 5149_CR31 5149_CR30 J Hafner (5149_CR18) 2021; 12 Z Ni (5149_CR20) 2021; 65 Y Kim (5149_CR35) 2021; 16 A Vila-Santa (5149_CR21) 2021; 10 DC Sévin (5149_CR40) 2017; 14 M Kanehisa (5149_CR37) 2017; 45 J Strutz (5149_CR29) 2022; 38 M Yousofshahi (5149_CR15) 2015; 9 IM Keseler (5149_CR39) 2021; 12 V Porokhin (5149_CR10) 2021; 12 EJ Yun (5149_CR9) 2018; 11 GM Lin (5149_CR7) 2019; 14 X Yang (5149_CR8) 2019; 56 CS Henry (5149_CR22) 2010; 106 O Khersonsky (5149_CR1) 2010; 79 S Ding (5149_CR17) 2020; 48 TJ Erb (5149_CR11) 2019; 15 B Delépine (5149_CR16) 2018; 45 N Hassanpour (5149_CR23) 2020 JH Shin (5149_CR2) 2013; 31 JR King (5149_CR6) 2017; 6 J Rosenberg (5149_CR12) 2019; 37 DL Trudeau (5149_CR5) 2018; 115 M Ramirez-Gaona (5149_CR38) 2017; 45 H He (5149_CR4) 2020; 60 L Wang (5149_CR24) 2021; 10 A Kumar (5149_CR13) 2018 W Finnigan (5149_CR25) 2021; 4 A Cereto-Massagué (5149_CR32) 2015; 71C ME Beber (5149_CR33) 2022; 50 T Schwander (5149_CR3) 2016; 354 M Hucka (5149_CR36) 2018 |
| References_xml | – volume: 16 start-page: 1 year: 2021 ident: 5149_CR35 publication-title: Biotechnol J doi: 10.1002/biot.202000605 – volume: 12 start-page: e00170 year: 2021 ident: 5149_CR10 publication-title: Metab Eng Commun doi: 10.1016/j.mec.2021.e00170 – volume: 12 start-page: 1 year: 2021 ident: 5149_CR18 publication-title: Nat Commun doi: 10.1038/s41467-021-22022-5 – volume: 38 start-page: 3484 year: 2022 ident: 5149_CR29 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btac331 – volume: 65 start-page: 79 year: 2021 ident: 5149_CR20 publication-title: Metab Eng doi: 10.1016/j.ymben.2021.02.006 – volume: 45 start-page: D440 year: 2017 ident: 5149_CR38 publication-title: Nucleic Acids Res doi: 10.1093/nar/gkw1058 – volume: 7 start-page: 1 year: 2015 ident: 5149_CR19 publication-title: J Cheminform doi: 10.1186/s13321-015-0087-1 – volume: 31 start-page: 925 year: 2013 ident: 5149_CR2 publication-title: Biotechnol Adv doi: 10.1016/j.biotechadv.2012.12.008 – volume: 50 start-page: D603 year: 2022 ident: 5149_CR33 publication-title: Nucleic Acids Res doi: 10.1093/nar/gkab1106 – volume: 10 start-page: 1064 year: 2021 ident: 5149_CR24 publication-title: ACS Synth Biol doi: 10.1021/acssynbio.0c00598 – volume: 11 start-page: 1 year: 2018 ident: 5149_CR9 publication-title: Biotechnol Biofuels doi: 10.1186/s13068-018-1135-7 – ident: 5149_CR30 – volume: 6 start-page: 1416 year: 2017 ident: 5149_CR6 publication-title: ACS Synth Biol doi: 10.1021/acssynbio.7b00072 – volume: 14 start-page: 187 year: 2017 ident: 5149_CR40 publication-title: Nat Methods doi: 10.1038/nmeth.4103 – volume: 9 start-page: 1 year: 2015 ident: 5149_CR15 publication-title: BMC Syst Biol doi: 10.1186/s12918-015-0241-4 – year: 2018 ident: 5149_CR36 publication-title: J Integr Bioinform doi: 10.1515/jib-2017-0081 – volume: 115 start-page: E11455 year: 2018 ident: 5149_CR5 publication-title: Proc Natl Acad Sci USA doi: 10.1073/pnas.1812605115 – volume: 4 start-page: 98 year: 2021 ident: 5149_CR25 publication-title: Nat Catal doi: 10.1038/s41929-020-00556-z – volume: 71C start-page: 58 year: 2015 ident: 5149_CR32 publication-title: Methods doi: 10.1016/j.ymeth.2014.08.005 – volume: 12 start-page: 1 year: 2021 ident: 5149_CR39 publication-title: Front Microbiol doi: 10.3389/fmicb.2021.711077 – volume: 79 start-page: 471 year: 2010 ident: 5149_CR1 publication-title: Annu Rev Biochem doi: 10.1146/annurev-biochem-030409-143718 – volume: 10 start-page: e1003483 year: 2014 ident: 5149_CR34 publication-title: PLoS Comput Biol doi: 10.1371/journal.pcbi.1003483 – volume: 106 start-page: 462 year: 2010 ident: 5149_CR22 publication-title: Biotechnol Bioeng doi: 10.1002/bit.22673 – year: 2019 ident: 5149_CR14 publication-title: J Cheminform doi: 10.1186/s13321-018-0324-5 – volume: 15 start-page: 551 year: 2019 ident: 5149_CR11 publication-title: Beilstein J Org Chem doi: 10.3762/bjoc.15.49 – volume: 48 start-page: W477 year: 2020 ident: 5149_CR17 publication-title: Nucleic Acids Res doi: 10.1093/NAR/GKAA230 – volume: 8 start-page: 2494 year: 2019 ident: 5149_CR28 publication-title: ACS Synth Biol doi: 10.1021/acssynbio.9b00105 – volume: 14 start-page: 82 year: 2019 ident: 5149_CR7 publication-title: Curr Opin Syst Biol doi: 10.1016/j.coisb.2019.04.004 – volume: 37 start-page: 29 year: 2019 ident: 5149_CR12 publication-title: Trends Biotechnol doi: 10.1016/j.tibtech.2018.08.001 – volume: 10 start-page: 724 year: 2021 ident: 5149_CR21 publication-title: ACS Synth Biol doi: 10.1021/acssynbio.0c00518 – volume: 9 start-page: 3217 year: 2020 ident: 5149_CR27 publication-title: ACS Synth Biol doi: 10.1021/acssynbio.0c00058 – year: 2018 ident: 5149_CR13 publication-title: Nat Commun doi: 10.1038/s41467-017-02362-x – volume: 60 start-page: 1 year: 2020 ident: 5149_CR4 publication-title: Metab Eng doi: 10.1016/j.ymben.2020.03.002 – volume: 354 start-page: 900 year: 2016 ident: 5149_CR3 publication-title: Science (80-) doi: 10.1126/science.aah5237 – volume: 45 start-page: 158 year: 2018 ident: 5149_CR16 publication-title: Metab Eng doi: 10.1016/j.ymben.2017.12.002 – ident: 5149_CR31 – volume: 45 start-page: D353 year: 2017 ident: 5149_CR37 publication-title: Nucleic Acids Res doi: 10.1093/nar/gkw1092 – year: 2020 ident: 5149_CR23 publication-title: Metabolites doi: 10.3390/metabo10040160 – volume: 56 start-page: 142 year: 2019 ident: 5149_CR8 publication-title: Metab Eng doi: 10.1016/j.ymben.2019.09.001 – volume: 5 start-page: 1155 year: 2016 ident: 5149_CR26 publication-title: ACS Synth Biol doi: 10.1021/acssynbio.6b00054 |
| SSID | ssj0017805 |
| Score | 2.467361 |
| Snippet | Background
Biochemical reaction prediction tools leverage enzymatic promiscuity rules to generate reaction networks containing novel compounds and reactions.... Biochemical reaction prediction tools leverage enzymatic promiscuity rules to generate reaction networks containing novel compounds and reactions. The... Background Biochemical reaction prediction tools leverage enzymatic promiscuity rules to generate reaction networks containing novel compounds and reactions.... BackgroundBiochemical reaction prediction tools leverage enzymatic promiscuity rules to generate reaction networks containing novel compounds and reactions.... Abstract Background Biochemical reaction prediction tools leverage enzymatic promiscuity rules to generate reaction networks containing novel compounds and... |
| SourceID | doaj pubmedcentral osti proquest gale pubmed crossref springer |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 106 |
| SubjectTerms | Algorithms Annotations Applications programs BASIC BIOLOGICAL SCIENCES Biochemical Phenomena Biochemistry Biochemistry & Molecular Biology Bioinformatics Biological research Biology, Experimental Biomedical and Life Sciences Biosynthetic design Biotechnology & Applied Microbiology Chemical fingerprinting Chemical properties Chemical reactions Computational Biology/Bioinformatics Computer Appl. in Life Sciences Documentation E coli Enzyme promiscuity Enzymes Escherichia coli - genetics Filters Identification and classification Life Sciences Mathematical & Computational Biology Metabolic regulation Metabolism Metabolite identification Metabolites Metabolome Metabolomics Microarrays Molecular weight Network generation Networks Open source software Public software Python Python (Programming language) Retrobiosynthesis Software Yeast |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9QwELbQCiQuqLxDCzIIiQNETewktrkVREUlVK14SL1ZjmOXFUtSbbJV---ZcR4QnheOG4-jzTfj8Tex85mQp8wYz7gVccEEFCgpz2NjRR5bprwVJb4lDJ5-J46P5cmJWv5w1BfuCevlgXvg9rkE_ieEYZVwUMrlCgiWyAtXmYp7p0L2BdYzFlPD-gEq9Y-fyMhiv01Rpy2G-SlGvW8Vy9k0FNT6p5y8aGBw_Y5w_rpv8qfF0zAnHe6QGwOZpAf9Q9wkV1x9i1zrj5e8vE2Oliv7xVy4l9TQ5SWKBNDhpQ0FqkqB-tGzDS7UoHNo42ndnLs1_eo6iIz1ylIglKGtvUM-Hb75-PptPBydENuiUF3sECjASaaCe8WcK3MLXMNZl5ZJ4VOfVplIAFKuuEkcro8ViQcuVlZAKYqS3yWLuqndfUJLZkSSS2vA21kpvJGMu0qmPmPww_mIpCOS2g664ni8xVqH-kIWukdfA_o6oK9lRJ5Pfc56VY2_Wr9CB02WqIgdLkCc6CFO9L_iJCJP0L0aNS9q3FRzarZtq48-vNcHAhcLgSflEXk2GPkGnsGa4RsFQAJlsmaWezNLGJR21ryLUaSBxqAWr8VNS7bTkO6AXmXQeQwuPaSMVjOZMAXlWwadH0_NeF_cBle7Zgs2QmHKBRIbkXt9LE6o8EJlCqrliMhZlM5gm7fUq89BUDwNqT0XEXkxBvT3__Vnvzz4H37ZJddZGJc8ZmyPLLrN1j0kV-15t2o3j8Ko_gbcQ0vj priority: 102 providerName: Directory of Open Access Journals – databaseName: SpringerLink Contemporary (1997 - Present) dbid: RSV link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1Lb9QwELZgAYkL70doQQYhcYCoiZP4wa0gKiqhatUC6s1yHLusWJJqk63ov2fGmwQFChIcNx5HO-MZ-5uM_ZmQ58wYzzIrYs4EJChpVsTGiiK2THkrSvxKGEb6gzg4kMfHat4fCmuH3e5DSTLM1CGsJd9pU-Rai2GNiZGzW8XyMrkCy53EcDw8-jzWDpClfzgec2G_yRIUmPrH-XjWQGBdBDZ_3zP5S-E0rEd7N_9Pk1vkRo8_6e7GYW6TS66-Q65tbqQ8v0v25wv71Xx3r6mh83PkFaD9dx4K6JYCWqSnK6zt4HjSxtO6OXNL-s114EzLhaWAQUNbe4982nv38e37uL9tIbacqy52qlCygvQhFZlXzLmysABPnHVpmXCf-rTKRVIJl6nMJA5LajzxAN_KClAIL7P7ZFY3tXtIaMmMSAppDThIXgpvJMtcJVOfM_jhfETSYQC07anI8UaMpQ4pieR6YyINJtLBRFpG5OXY53RDxPFX6Tc4rqMkkmiHB83qRPcxqTMJqYUQhoFSOUf1U9Cfu8pUmXeKReQZeoVGmowa9-GcmHXb6v2jQ70rsL4I0KqIyIteyDeggzX9sQawBDJrTSS3J5IQx3bSvIXOpwH5IH2vxX1OttMwQwIiy6Hz4JO6n2VazWTCFGR8OXR-Ojbje3HnXO2aNcgIhbM04N6IPNi48GiVjKtcQYIdETlx7onZpi314kvgIE_DalCIiLwafPzn__rzuDz6N_Etcp2FMMlixrbJrFut3WNy1Z51i3b1JIT9D9nSUJw priority: 102 providerName: Springer Nature |
| Title | Pickaxe: a Python library for the prediction of novel metabolic reactions |
| URI | https://link.springer.com/article/10.1186/s12859-023-05149-8 https://www.ncbi.nlm.nih.gov/pubmed/36949401 https://www.proquest.com/docview/2802967545 https://www.proquest.com/docview/2790048264 https://www.osti.gov/biblio/1962934 https://pubmed.ncbi.nlm.nih.gov/PMC10031857 https://doaj.org/article/3877277a2d7e4695981d756edad3fe92 |
| Volume | 24 |
| WOSCitedRecordID | wos000957578700004&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVADU databaseName: BioMed Central customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: RBZ dateStart: 20000101 isFulltext: true titleUrlDefault: https://www.biomedcentral.com/search/ providerName: BioMedCentral – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: DOA dateStart: 20000101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: M~E dateStart: 20000101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: P5Z dateStart: 20090101 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: Biological Science Database customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: M7P dateStart: 20090101 isFulltext: true titleUrlDefault: http://search.proquest.com/biologicalscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: Computer Science Database customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: K7- dateStart: 20090101 isFulltext: true titleUrlDefault: http://search.proquest.com/compscijour providerName: ProQuest – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: 7X7 dateStart: 20090101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: BENPR dateStart: 20090101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Proquest Open Access Content customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: PIMPY dateStart: 20090101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest – providerCode: PRVAVX databaseName: SpringerLINK Contemporary 1997-Present customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: RSV dateStart: 20001201 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3db9MwELdYBxIvfH-EjSogJB7AWuJ82OYFbWgTFVBFHaCOF8txnFFRktK0E_vvuXPTTuFjL7xESn2pcr7z-Wff5WdCnjGtSxYZTlPGYYESRgnVhifUMFkanuMuobP0ez4civFYZu2GW9OWVa5jogvURW1wj3yPiYBJQLdx8nr2g-KpUZhdbY_Q2CLbIWMh-vk7TjdZBOTrX38oI9K9JkS2NgqzFEXWb0lFZzJynP2byNyrYYj9DXb-WT35WwrVzUxHN_9Xp1vkRotJ_f2VE90mV2x1h1xbnVJ5fpcMson5pn_aV772s3PkGvDbvR8fEK8PCNKfzTHfgzb269Kv6jM79b_bBTjYdGJ8wKWurblHPh0dfnzzlrYnMFCTpnJBrUykKGBJEfKolMzaPDEAWayxYR6kZViGRcyDgttIRjqwmGZLgxIgXV4AMknz6D7pVXVlHxI_Z5oHiTAanCbOeakFi2whwjJmcGNLj4RrUyjT0pPjKRlT5ZYpIlUr8ykwn3LmU8IjLzbPzFbkHJdKH6CFN5JIrO1-qOenqh2nKhKw3OBcM1AqTlH9EPRPbaGLqLSSeeQp-odC6owKa3NO9bJp1OB4pPY55hwBbiUeed4KlTXoYHT7qQP0BLJtdSR3O5Iwtk2neQfdUAEaQkpfg7VPZqEgagJKi-HhtVupNvI06sKnPPJk04z_i9V0la2XIMMlRm7Awh55sHLmTa9EqYwlLLo9Ijpu3um2bks1-ep4yUM3QyTcIy_XI-Livf5tl0eXq7FDrjM3ZCPK2C7pLeZL-5hcNWeLSTPvky0-5u4q-mT74HCYjfpuX6XvQkEfa3kzuGbJF2jPBh-yE7gbHX_-BUfiY0g |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Zj9MwELaWAoIX7iPsAgaBeIBoE-ewjYTQcqy2aqkqWKR9M47jLBUlKU270D_Fb2QmR1fh2Ld94LHxJOrYc3z2jGcIecS0zlhguBszDhsUP4hcbXjkGiYzwxM8JaxWeshHI3FwIMcb5Gd7FwbTKlubWBnqtDB4Rr7NhMckoNswejn75mLXKIyuti00arEY2NV32LKVL_pvYH0fM7b7dv_1ntt0FXBNHMuFa2UkRQow2edBJpm1SWTADVtj_cSLMz_z05B7KbeBDLRnMXQUexnAlCQFbxsnAXz3DDkbBoJjrf4Bd9dRC-wP0F7MEfF26WN1OBe8ootVxqUrOs6v6hGw9gS9AlT6bzD3z2zN30K2lSfcvfy_zeEVcqnB3HSnVpKrZMPm18j5ugvn6jrpjyfmi_5hn1NNxyuspUCbsy0KiJ4CQqazOcazUIZpkdG8OLJT-tUuQIGmE0MBd1dj5Q3y8VT4uEl6eZHb24QmTHMvEkaDUoQJz7RggU2Fn4UMftjMIX679Mo05dexC8hUVdswEataXBSIi6rERQmHPF2_M6uLj5xI_Qolak2JhcOrB8X8UDV2SIHUAmLlmgFTYYzs-8B_bFOdBpmVzCEPUR4VlgbJMffoUC_LUvU_vFc7HGOqACcjhzxpiLICeDC6ucoBM4HVxDqUWx1KsF2mM7yJYq8A7WHJYoO5XWahwCsACg3h5VaMVWNZS3Usww55sB7G72K2YG6LJdBwiZ4JsL5DbtXKs56VIJahDD3fIaKjVp1p647kk89V3XW_8oARd8izVgOP_9e_1-XOyWzcJxf29t8N1bA_GmySi6wyF4HL2BbpLeZLe5ecM0eLSTm_VxkbSj6dtmb-ApP7tfM |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Zj9MwELagHOKF-wi7gEFIPCzRJs5hm7flWFGxqioW0L5ZjmMvFSWpmnTF_ntmnLQQWJAQj43HUWc8tr_JjD8T8pRp7VhieJgzDgFKnGShNjwLDZPO8AK_EvqRPuCTiTg6ktOfTvH7avd1SrI704AsTVW7uyhdN8VFvtvEyLsWwn4TIn-3DMV5ciHFS4MwXj_8tMkjIGP_-qjMmf0G25Fn7d-szaMaJtlZwPP3-slfkqh-b9q_9v9aXSdXe1xK9zpHukHO2eomudTdVHl6i4ynM_NFf7MvqKbTU-QboP33HwqolwKKpIsl5nxwnGntaFWf2Dn9altwsvnMUMCmvq25TT7uv_nw6m3Y38IQmjyXbWhlJkUJYUXMEyeZtUVmALZYY-Miyl3s4jLlUcltIhMdWUy15ZEDWFeUgE7yIrlDRlVd2XuEFkzzKBNGg-OkBXdasMSWInYpgx_WBSReD4YyPUU53pQxVz5UEbnqTKTARMqbSImA7Gz6LDqCjr9Kv8Qx3kgiubZ_UC-PVT9XVSIg5OBcM1AqzVH9GPTPbanLxFnJAvIEPUQhfUaF9TnHetU0anz4Xu1xzDsC5MoC8qwXcjXoYHR_3AEsgYxbA8ntgSTMbzNo3kJHVICIkNbXYP2TaRWsnIDUUui89k_Vrz6NYiJiEiLBFDo_3jTje7GirrL1CmS4xNUb8HBA7nbuvLFKkstUQuAdEDFw9IHZhi3V7LPnJo_9LpHxgDxf-_uP__Xncbn_b-KPyOXp6311MJ682yJXmJ8xScjYNhm1y5V9QC6ak3bWLB_61eA7GWpcZA |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Pickaxe%3A+a+Python+library+for+the+prediction+of+novel+metabolic+reactions&rft.jtitle=BMC+bioinformatics&rft.au=Shebek%2C+Kevin+M.&rft.au=Strutz%2C+Jonathan&rft.au=Broadbelt%2C+Linda+J.&rft.au=Tyo%2C+Keith+E.+J.&rft.date=2023-03-22&rft.issn=1471-2105&rft.eissn=1471-2105&rft.volume=24&rft.issue=1&rft_id=info:doi/10.1186%2Fs12859-023-05149-8&rft.externalDBID=n%2Fa&rft.externalDocID=10_1186_s12859_023_05149_8 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1471-2105&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1471-2105&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1471-2105&client=summon |