Evidence map›Paper›PMID 27342282›Full record

ArticleNucleic acids research2016

NCBI prokaryotic genome annotation pipeline.

Tatiana Tatusova, Michael DiCuccio, Azat Badretdin, Vyacheslav Chetvernin, Eric P Nawrocki, Leonid Zaslavsky, Alexandre Lomsadze, Kim D Pruitt, Mark Borodovsky, James Ostell

Abstract read
In one paragraph

Article in Nucleic acids research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4,970 papers.

0numbers the graph read from it
0cells of the map it votes in
4,970citing 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

4,970 citing papers in PubMed.

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4,910 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

10 authors.

Tatiana TatusovaNational Center for Biotechnology Information, U.S. National Library of Medicine, Bethesda, MD 20894, USA.
Michael DiCuccioNational Center for Biotechnology Information, U.S. National Library of Medicine, Bethesda, MD 20894, USA.
Azat BadretdinNational Center for Biotechnology Information, U.S. National Library of Medicine, Bethesda, MD 20894, USA.
Vyacheslav ChetverninNational Center for Biotechnology Information, U.S. National Library of Medicine, Bethesda, MD 20894, USA.
Eric P NawrockiNational Center for Biotechnology Information, U.S. National Library of Medicine, Bethesda, MD 20894, USA.
Leonid ZaslavskyNational Center for Biotechnology Information, U.S. National Library of Medicine, Bethesda, MD 20894, USA.
Alexandre LomsadzeWallace H. Coulter Department of Biomedical Engineering, Georgia Tech, Atlanta, GA 30332, USA.
Kim D PruittNational Center for Biotechnology Information, U.S. National Library of Medicine, Bethesda, MD 20894, USA.
Mark BorodovskyWallace H. Coulter Department of Biomedical Engineering, Georgia Tech, Atlanta, GA 30332, USA School of Computational Science and Engineering, Georgia Tech, Atlanta, GA 30332, USA borodovsky@gatech.edu.
James OstellNational Center for Biotechnology Information, U.S. National Library of Medicine, Bethesda, MD 20894, USA.

Funding

PREDICTION OF GENE LOCATIONS USING STOCHASTIC MODELSR01HG000783 · NHGRI · GEORGIA INSTITUTE OF TECHNOLOGY · PI BORODOVSKY, MARK · 1993 to 2014
$4.7M
Biological Sequence Quality Control and OrganizationZIALM622435 · NLM · NATIONAL LIBRARY OF MEDICINE · PI LIPMAN, DAVID J. · 2016 to 2016
$338k
NHGRI NIH HHS R01 HG000783
6 · The paper itself

Abstract

Recent technological advances have opened unprecedented opportunities for large-scale sequencing and analysis of populations of pathogenic species in disease outbreaks, as well as for large-scale diversity studies aimed at expanding our knowledge across the whole domain of prokaryotes. To meet the challenge of timely interpretation of structure, function and meaning of this vast genetic information, a comprehensive approach to automatic genome annotation is critically needed. In collaboration with Georgia Tech, NCBI has developed a new approach to genome annotation that combines alignment based methods with methods of predicting protein-coding and RNA genes and other functional elements directly from sequence. A new gene finding tool, GeneMarkS+, uses the combined evidence of protein and RNA placement by homology as an initial map of annotation to generate and modify ab initio gene predictions across the whole genome. Thus, the new NCBI's Prokaryotic Genome Annotation Pipeline (PGAP) relies more on sequence similarity when confident comparative data are available, while it relies more on statistical predictions in the absence of external evidence. The pipeline provides a framework for generation and analysis of annotation on the full breadth of prokaryotic taxonomy. For additional information on PGAP see https://www.ncbi.nlm.nih.gov/genome/annotation_prok/ and the NCBI Handbook, https://www.ncbi.nlm.nih.gov/books/NBK174280/.

Indexed as

Genome, BacterialMolecular Sequence AnnotationBacteriaBacterial ProteinsDatabases, Nucleic AcidGenes, BacterialProkaryotic CellsBacterial Proteins

Identifiers

PMID27342282
PMCPMC5001611

What OpenQuestion holds

Textmetadata
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.