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


RESULTS
Using 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.
Department of Computer Science, Texas A&M University, College Station, TX 77843, USA. michael.dejesus@tamu.edu
Michael A
MA
DeJesus
Yanjia J
YJ
Zhang
Christopher M
CM
Sassetti
Eric J
EJ
Rubin
James C
JC
Sacchettini
Thomas R
TR
Ioerger
D000465:Algorithms; D001499:Bayes Theorem; D004251:DNA Transposable Elements; D015723:Gene Library; D005798:Genes, Bacterial; D020043:Genes, Essential; D023281:Genomics; D059014:High-Throughput Nucleotide Sequencing; D015233:Models, Statistical; D016254:Mutagenesis, Insertional; D009169:Mycobacterium tuberculosis / Q000235:genetics; D017434:Protein Structure, Tertiary
D004251:DNA Transposable Elements