Evidence map›Paper›PMID 23746312›Full record

ReviewFEMS microbiology letters2013

The challenge of constructing, classifying, and representing metabolic pathways.

Ron Caspi, Kate Dreher, Peter D Karp

Abstract readReview
In one paragraph

Review in FEMS microbiology letters, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.

0numbers the graph read from it
0cells of the map it votes in
49citing 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

49 citing papers in PubMed.

  1. Trial
  2. Article
  3. The genome-scale sugar metabolic model fromCurrent research in microbial sciences · 2026
    Article
  4. Article
  5. Microbiome Single Cell Atlases Generated with a Commercial Instrument.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Article
  6. Proteomic profiling of zinc homeostasis mechanisms inbioRxiv : the preprint server for biology · 2025
    Article
  7. Article
  8. Article
  9. Article
  10. Review
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Sparse tree-based clustering of microbiome data to characterize microbiome heterogeneity in pancreatic cancer.Journal of the Royal Statistical Society. Series C, Applied statistics · 2023
    Article
  18. Frontiers in plant science · 2023
    Article
  19. Article
  20. Biochemical Insights into the Ability ofPlants (Basel, Switzerland) · 2022
    Article
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

3 authors.

Ron CaspiBioinformatics Research Group, SRI International, Menlo Park, CA 94025, USA. ron.caspi@sri.com
Kate Dreher
Peter D Karp

Funding

The MetaCyc & BioCyc Pathway/Genome Databases (SRI Proposal ECU 14-630)R01GM080746 · NIGMS · SRI INTERNATIONAL · PI CASPI, RON, KARP, PETER D · 2007 to 2021
$15.6M
The EcoCyc Model Organism Database for Escherichia coli [SRI Proposal ECU 14-631]U24GM077678 · NIGMS · SRI INTERNATIONAL · PI KARP, PETER D · 2006 to 2018
$12.3M
Robust Software Components for Model Organism DatabasesR01GM075742 · NIGMS · SRI INTERNATIONAL · PI KARP, PETER D · 2005 to 2019
$12.0M
The EcoCyc Model Organism Database for Escherichia coliR01GM077678 · NIGMS · SRI INTERNATIONAL · PI KARP, PETER D · 2019 to 2022
$4.6M
EcoliHub2.0: A next-generation E. coli model organism resource (SRI Proposal ECU U24GM088849 · NIGMS · UNIVERSITY OF SOUTHERN CALIFORNIA · PI HU, JAMES C, SHERLOCK, GAVIN J · 2009 to 2012
$4.4M
NIGMS NIH HHS GM075742NIGMS NIH HHS GM077678NIGMS NIH HHS GM080746NIGMS NIH HHS GM088849NIGMS NIH HHS R01 GM075742NIGMS NIH HHS R01 GM077678NIGMS NIH HHS R01 GM080746NIGMS NIH HHS U24 GM077678NIGMS NIH HHS U24 GM088849
6 · The paper itself

Abstract

Scientists, educators, and students benefit from having free and centralized access to the wealth of metabolic information that has been gathered over the decades. Curators of the MetaCyc database work to present this information in an easily understandable pathway-based framework. MetaCyc is used not only as an encyclopedic resource for metabolic information but also as a template for the pathway prediction software that generates pathway/genome databases for thousands of organisms with sequenced genomes (available at www.biocyc.org). Curators need to define pathway boundaries and classify pathways within a broader pathway ontology to maximize the utility of the pathways to both users and the pathway prediction software. These seemingly simple tasks pose several challenges. This review describes these challenges as well as the criteria that need to be considered, and the rules that have been developed by MetaCyc curators as they make decisions regarding the representation and classification of metabolic pathway information in MetaCyc. The functional consequences of these decisions in regard to pathway prediction in new species are also discussed.

Indexed as

Databases, FactualMetabolic Networks and PathwaysBacteriaBacterial ProteinsSoftwareBacterial Proteinsmetabolic databasemetabolic pathwayMetaCycpathway prediction

Identifiers

PMID23746312
PMCPMC4026850

What OpenQuestion holds

Textmetadata
LicenceTDM
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.