Evidence map›Paper›PMID 29773659›Full record

ArticleGenome research2018

Modeling leaderless transcription and atypical genes results in more accurate gene prediction in prokaryotes.

Alexandre Lomsadze, Karl Gemayel, Shiyuyun Tang, Mark Borodovsky

Abstract read
In one paragraph

Article in Genome research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 143 papers.

0numbers the graph read from it
0cells of the map it votes in
143citing 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

143 citing papers in PubMed.

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  10. International journal of systematic and evolutionary microbiology · 2026
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  13. Imported, not invented, genes prevail amongProceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  14. Article
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  16. Genomic and proteomic analyses of the maize root isolateApplied and environmental microbiology · 2026
    Article
  17. Review
  18. Article
  19. Article
  20. Article

83 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

4 authors.

Alexandre LomsadzeWallace H. Coulter Department of Biomedical Engineering, Georgia Tech, Atlanta, Georgia 30332, USA.
Karl GemayelSchool of Computational Science and Engineering, Georgia Tech, Atlanta, Georgia 30332, USA.
Shiyuyun TangSchool of Biological Sciences, Georgia Tech, Atlanta, Georgia 30332, USA.
Mark BorodovskyWallace H. Coulter Department of Biomedical Engineering, Georgia Tech, Atlanta, Georgia 30332, USA.

Funding

PREDICTION OF GENE LOCATIONS USING STOCHASTIC MODELSR01HG000783 · NHGRI · GEORGIA INSTITUTE OF TECHNOLOGY · PI BORODOVSKY, MARK · 1993 to 2014
$4.7M
NHGRI NIH HHS R01 HG000783
6 · The paper itself

Abstract

In a conventional view of the prokaryotic genome organization, promoters precede operons and ribosome binding sites (RBSs) with Shine-Dalgarno consensus precede genes. However, recent experimental research suggesting a more diverse view motivated us to develop an algorithm with improved gene-finding accuracy. We describe GeneMarkS-2, an ab initio algorithm that uses a model derived by self-training for finding species-specific (native) genes, along with an array of precomputed "heuristic" models designed to identify harder-to-detect genes (likely horizontally transferred). Importantly, we designed GeneMarkS-2 to identify several types of distinct sequence patterns (signals) involved in gene expression control, among them the patterns characteristic for leaderless transcription as well as noncanonical RBS patterns. To assess the accuracy of GeneMarkS-2, we used genes validated by COG (Clusters of Orthologous Groups) annotation, proteomics experiments, and N-terminal protein sequencing. We observed that GeneMarkS-2 performed better on average in all accuracy measures when compared with the current state-of-the-art gene prediction tools. Furthermore, the screening of ∼5000 representative prokaryotic genomes made by GeneMarkS-2 predicted frequent leaderless transcription in both archaea and bacteria. We also observed that the RBS sites in some species with leadered transcription did not necessarily exhibit the Shine-Dalgarno consensus. The modeling of different types of sequence motifs regulating gene expression prompted a division of prokaryotic genomes into five categories with distinct sequence patterns around the gene starts.

Indexed as

AlgorithmsArchaeaBacteriaBinding SitesComputational BiologyGenes, BacterialMolecular Sequence AnnotationOperonProkaryotic CellsProtein BiosynthesisProteomicsRibosomesTranscription, Genetic

Identifiers

PMID29773659
PMCPMC6028130

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Registered trials

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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.