Evidence map›Paper›PMID 24387194›Full record

ArticleBMC genomics2014

ITEP: an integrated toolkit for exploration of microbial pan-genomes.

Matthew N Benedict, James R Henriksen, William W Metcalf, Rachel J Whitaker, Nathan D Price

Abstract read
In one paragraph

Article in BMC genomics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 papers.

0numbers the graph read from it
0cells of the map it votes in
70citing 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

70 citing papers in PubMed.

  1. Article
  2. RefiningMicrobial genomics · 2026
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  4. Unique growth and morphology properties of Clade 5bioRxiv : the preprint server for biology · 2025
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  5. Article
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  8. Sequencing-based analysis of microbiomes.Nature reviews. Genetics · 2024
    Review
  9. Article
  10. Metabolic capabilities are highly conserved among human nasal-associatedbioRxiv : the preprint server for biology · 2024
    Article
  11. Article
  12. Genomic analysis ofMicrobial genomics · 2023
    Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article

10 more citing papers are in PubMed but not listed here.

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

5 authors.

Matthew N Benedict
James R Henriksen
William W Metcalf
Rachel J Whitaker
Nathan D PriceInstitute for Systems Biology, 401 Terry Ave, N,, Seattle, WA 98109, USA. Nathan.Price@systemsbiology.org.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundComparative genomics is a powerful approach for studying variation in physiological traits as well as the evolution and ecology of microorganisms. Recent technological advances have enabled sequencing large numbers of related genomes in a single project, requiring computational tools for their integrated analysis. In particular, accurate annotations and identification of gene presence and absence are critical for understanding and modeling the cellular physiology of newly sequenced genomes. Although many tools are available to compare the gene contents of related genomes, new tools are necessary to enable close examination and curation of protein families from large numbers of closely related organisms, to integrate curation with the analysis of gain and loss, and to generate metabolic networks linking the annotations to observed phenotypes.

resultsWe have developed ITEP, an Integrated Toolkit for Exploration of microbial Pan-genomes, to curate protein families, compute similarities to externally-defined domains, analyze gene gain and loss, and generate draft metabolic networks from one or more curated reference network reconstructions in groups of related microbial species among which the combination of core and variable genes constitute the their "pan-genomes". The ITEP toolkit consists of: (1) a series of modular command-line scripts for identification, comparison, curation, and analysis of protein families and their distribution across many genomes; (2) a set of Python libraries for programmatic access to the same data; and (3) pre-packaged scripts to perform common analysis workflows on a collection of genomes. ITEP's capabilities include de novo protein family prediction, ortholog detection, analysis of functional domains, identification of core and variable genes and gene regions, sequence alignments and tree generation, annotation curation, and the integration of cross-genome analysis and metabolic networks for study of metabolic network evolution.

conclusionsITEP is a powerful, flexible toolkit for generation and curation of protein families. ITEP's modular design allows for straightforward extension as analysis methods and tools evolve. By integrating comparative genomics with the development of draft metabolic networks, ITEP harnesses the power of comparative genomics to build confidence in links between genotype and phenotype and helps disambiguate gene annotations when they are evaluated in both evolutionary and metabolic network contexts.

Indexed as

SoftwareAlgorithmsBacterial ProteinsClostridiumCluster AnalysisDatabases, GeneticGenome, BacterialGenomicsGenotypeInternetMetabolic Networks and PathwaysPhenotypePhylogenyRibosomal ProteinsUser-Computer InterfaceBacterial ProteinsRibosomal Proteins

Identifiers

PMID24387194
PMCPMC3890548

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.