Evidence map›Paper›PMID 17175534›Full record

ArticleNucleic acids research2007

GISMO--gene identification using a support vector machine for ORF classification.

Lutz Krause, Alice C McHardy, Tim W Nattkemper, Alfred Pühler, Jens Stoye, Folker Meyer

Abstract readValidation Study
In one paragraph

Article in Nucleic acids research, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing 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

22 citing papers in PubMed.

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  3. Analysis of metagenomic data.Nature reviews. Methods primers · 2025
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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

6 authors.

Lutz KrauseCenter for Biotechnology, Bielefeld University (CeBiTec), D-33594 Bielefeld, Germany. lutz.krause@cebitec.uni-bielefeld.de
Alice C McHardy
Tim W Nattkemper
Alfred Pühler
Jens Stoye
Folker Meyer

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We present the novel prokaryotic gene finder GISMO, which combines searches for protein family domains with composition-based classification based on a support vector machine. GISMO is highly accurate; exhibiting high sensitivity and specificity in gene identification. We found that it performs well for complete prokaryotic chromosomes, irrespective of their GC content, and also for plasmids as short as 10 kb, short genes and for genes with atypical sequence composition. Using GISMO, we found several thousand new predictions for the published genomes that are supported by extrinsic evidence, which strongly suggest that these are very likely biologically active genes. The source code for GISMO is freely available under the GPL license.

Indexed as

Artificial IntelligenceGenes, ArchaealGenes, BacterialOpen Reading FramesAlgorithmsChromosomes, ArchaealChromosomes, BacterialClassificationGene Transfer, HorizontalGenomicsPlasmidsReproducibility of ResultsSoftware

Identifiers

PMID17175534
PMCPMC1802617

What OpenQuestion holds

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LicenceCC BY-NC
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.