Evidence map›Paper›PMID 36949401›Full record

ArticleBMC bioinformatics2023

Pickaxe: a Python library for the prediction of novel metabolic reactions.

Kevin M Shebek, Jonathan Strutz, Linda J Broadbelt, Keith E J Tyo

Full text read
In one paragraph

Article in BMC bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. RC-GNN: A predictive model of enzyme-reaction pairs.bioRxiv : the preprint server for biology · 2025
    Article
  7. Article
  8. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Kevin M ShebekDepartment of Chemical and Biological Engineering, Northwestern University, Evanston, IL, 60208, USA.
Jonathan StrutzDepartment of Chemical and Biological Engineering, Northwestern University, Evanston, IL, 60208, USA.
Linda J BroadbeltDepartment of Chemical and Biological Engineering, Northwestern University, Evanston, IL, 60208, USA.
Keith E J TyoDepartment of Chemical and Biological Engineering, Northwestern University, Evanston, IL, 60208, USA. k-tyo@northwestern.edu.ORCID http://orcid.org/0000-0002-2342-0687

Funding

Biological and Environmental Research DE-SC0018249
6 · The paper itself

Abstract

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.

Indexed as

Biochemical PhenomenaEscherichia coliMetabolomeMetabolomicsSoftwareBiosynthetic designEnzyme promiscuityMetabolite identificationNetwork generationRetrobiosynthesis

Identifiers

PMID36949401
PMCPMC10031857

What OpenQuestion holds

Textfull text, public
LicenceCC BY
measurements read24
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.