Evidence map›Paper›PMID 26380633›Full record

ArticleStandards in genomic sciences2015

Toward a standard in structural genome annotation for prokaryotes.

H James Tripp, Granger Sutton, Owen White, Jennifer Wortman, Amrita Pati, Natalia Mikhailova, Galina Ovchinnikova, Samuel H Payne, Nikos C Kyrpides, Natalia Ivanova

Abstract read
In one paragraph

Article in Standards in genomic sciences, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Prokaryotic Genome Annotation.Methods in molecular biology (Clifton, N.J.) · 2022
    Article
  5. Comparative Genomics of RumenApplied and environmental microbiology · 2019
    Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Methods, Tools and Current Perspectives in Proteogenomics.Molecular & cellular proteomics : MCP · 2017
    Review
  11. Article
  12. 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

10 authors.

H James Tripp *DOE Joint Genome Institute, Walnut Creek, California USA.
Granger SuttonJ. Craig Venter Institute, Rockville, MD USA.
Owen WhiteInstitute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD USA.
Jennifer WortmanBroad Institute, Cambridge, MA USA.
Amrita PatiDOE Joint Genome Institute, Walnut Creek, California USA.
Natalia MikhailovaDOE Joint Genome Institute, Walnut Creek, California USA.
Galina OvchinnikovaDOE Joint Genome Institute, Walnut Creek, California USA.
Samuel H PaynePacific Northwest National Laboratory, Richland, WA USA.
Nikos C KyrpidesDOE Joint Genome Institute, Walnut Creek, California USA.
Natalia Ivanova *DOE Joint Genome Institute, Walnut Creek, California USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn an effort to identify the best practice for finding genes in prokaryotic genomes and propose it as a standard for automated annotation pipelines, 1,004,576 peptides were collected from various publicly available resources, and were used as a basis to evaluate various gene-calling methods. The peptides came from 45 bacterial replicons with an average GC content from 31 % to 74 %, biased toward higher GC content genomes. Automated, manual, and semi-manual methods were used to tally errors in three widely used gene calling methods, as evidenced by peptides mapped outside the boundaries of called genes.

resultsWe found that the consensus set of identical genes predicted by the three methods constitutes only about 70 % of the genes predicted by each individual method (with start and stop required to coincide). Peptide data was useful for evaluating some of the differences between gene callers, but not reliable enough to make the results conclusive, due to limitations inherent in any proteogenomic study.

conclusionsA single, unambiguous, unanimous best practice did not emerge from this analysis, since the available proteomics data were not adequate to provide an objective measurement of differences in the accuracy between these methods. However, as a result of this study, software, reference data, and procedures have been better matched among participants, representing a step toward a much-needed standard. In the absence of sufficient amount of exprimental data to achieve a universal standard, our recommendation is that any of these methods can be used by the community, as long as a single method is employed across all datasets to be compared.

Identifiers

PMID26380633
PMCPMC4572445

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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.