Evidence map›Paper›PMID 25887214›Full record

ArticleBMC bioinformatics2015

PathwayBooster: a tool to support the curation of metabolic pathways.

Rodrigo Liberal, Beata K Lisowska, David J Leak, John W Pinney

Abstract read
In one paragraph

Article in BMC bioinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
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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.

Rodrigo LiberalCentre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London, SW7 2AZ, UK. r.liberal@imperial.ac.uk.
Beata K LisowskaDepartment of Biology and Biochemistry, University of Bath, Claverton Down, Bath BA2, 7AY, UK. b.k.lisowska@bath.ac.uk.
David J LeakDepartment of Biology and Biochemistry, University of Bath, Claverton Down, Bath BA2, 7AY, UK. d.j.leak@bath.ac.uk.
John W PinneyCentre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London, SW7 2AZ, UK. j.pinney@imperial.ac.uk.

Funding

Biotechnology and Biological Sciences Research Council BB/J001120/1
6 · The paper itself

Abstract

backgroundDespite several recent advances in the automated generation of draft metabolic reconstructions, the manual curation of these networks to produce high quality genome-scale metabolic models remains a labour-intensive and challenging task.

resultsWe present PathwayBooster, an open-source software tool to support the manual comparison and curation of metabolic models. It combines gene annotations from GenBank files and other sources with information retrieved from the metabolic databases BRENDA and KEGG to produce a set of pathway diagrams and reports summarising the evidence for the presence of a reaction in a given organism's metabolic network. By comparing multiple sources of evidence within a common framework, PathwayBooster assists the curator in the identification of likely false positive (misannotated enzyme) and false negative (pathway hole) reactions. Reaction evidence may be taken from alternative annotations of the same genome and/or a set of closely related organisms.

conclusionsBy integrating and visualising evidence from multiple sources, PathwayBooster reduces the manual effort required in the curation of a metabolic model. The software is available online at http://www.theosysbio.bio.ic.ac.uk/resources/pathwaybooster/ .

Indexed as

SoftwareCysteineDatabases, FactualEnzymesGenomeMetabolic Networks and PathwaysMethionineModels, BiologicalMolecular Sequence AnnotationCysteineEnzymesMethionine

Identifiers

PMID25887214
PMCPMC4367891

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LicenceCC BY
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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.