Evidence map›Paper›PMID 23361328›Full record

ArticleBioinformatics (Oxford, England)2013

Bayesian analysis of gene essentiality based on sequencing of transposon insertion libraries.

Michael A DeJesus, Yanjia J Zhang, Christopher M Sassetti, Eric J Rubin, James C Sacchettini, Thomas R Ioerger

Open access · bronzeAbstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers.

0numbers the graph read from it
0cells of the map it votes in
52citing papers in PubMed
6.7field-weighted citation impact, top 3% of its field
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

52 citing papers in PubMed, 89 citations in OpenAlex.

  1. Article
  2. Defining thebioRxiv : the preprint server for biology · 2026
    Article
  3. Article
  4. Analysis of essential genes inJournal of bacteriology · 2025
    Article
  5. Analysis of Essential Genes inbioRxiv : the preprint server for biology · 2025
    Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Bile acid fitness determinants of abioRxiv : the preprint server for biology · 2023
    Article
  11. Article
  12. Article
  13. Pathogens (Basel, Switzerland) · 2022
    Article
  14. Article
  15. Article
  16. Article
  17. Analysis of Gene Essentiality from TnSeq Data Using Transit.Methods in molecular biology (Clifton, N.J.) · 2022
    Article
  18. Article
  19. Article
  20. 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

6 authors at 2 institutions in 1 country.

Michael A DeJesusDepartment of Computer Science, Texas A&M University, College Station, TX 77843, USA. michael.dejesus@tamu.edu
Yanjia J Zhang
Christopher M Sassetti
Eric J Rubin
James C Sacchettini
Thomas R Ioerger
Texas A&M University · USUniversity of Massachusetts Chan Medical School · US

Funding

Medical Scientist Training ProgramT32GM007753 · NIGMS · HARVARD UNIVERSITY (MEDICAL SCHOOL) · PI WALENSKY, LOREN DAVID · 1985 to 2021
$50.0M
The Cloning Protein Production, Crystallization, and X-ray Data Collection CoreP01AI068135 · NIAID · TEXAS A&M AGRILIFE RESEARCH · PI SACCHETTINI, JAMES C · 2006 to 2010
$13.2M
Structures of Mtb proteins conferring susceptibility to known Mtb inhibitorsU01GM094568 · NIGMS · TEXAS A&M UNIVERSITY · PI TERWILLIGER, THOMAS C. · 2010 to 2014
$6.0M
IMMUNOLOGY AND INFECTIOUS DISEASES IN PUBLIC HEALTHT32AI007638 · NIAID · HARVARD UNIVERSITY (SCH OF PUBLIC HLTH) · PI RUBIN, ERIC J. · 2000 to 2014
$2.2M
NIAID NIH HHS P01 AI068135NIAID NIH HHS T32 AI007638NIGMS NIH HHS T32 GM007753NIGMS NIH HHS U01GM094568
6 · The paper itself

Abstract

motivationNext-generation sequencing affords an efficient analysis of transposon insertion libraries, which can be used to identify essential genes in bacteria. To analyse this high-resolution data, we present a formal Bayesian framework for estimating the posterior probability of essentiality for each gene, using the extreme-value distribution to characterize the statistical significance of the longest region lacking insertions within a gene. We describe a sampling procedure based on the Metropolis-Hastings algorithm to calculate posterior probabilities of essentiality while simultaneously integrating over unknown internal parameters.

resultsUsing a sequence dataset from a transposon library for Mycobacterium tuberculosis, we show that this Bayesian approach predicts essential genes that correspond well with genes shown to be essential in previous studies. Furthermore, we show that by using the extreme-value distribution to characterize genomic regions lacking transposon insertions, this method is capable of identifying essential domains within genes. This approach can be used for analysing transposon libraries in other organisms and augmenting essentiality predictions with statistical confidence scores.

Indexed as

DNA Transposable ElementsGenes, BacterialHigh-Throughput Nucleotide SequencingMutagenesis, InsertionalAlgorithmsBayes TheoremGene LibraryGenes, EssentialGenomicsModels, StatisticalMycobacterium tuberculosisProtein Structure, TertiaryDNA Transposable Elements

Identifiers

PMID23361328
PMCPMC3597147
OpenAlexW2111358540

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.