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Screening insertion libraries for mutations in many genes simultaneously using DNA microarrays.

ABSTRACT: We describe a method to screen pools of DNA from multiple transposon lines for insertions in many genes simultaneously. We use thermal asymmetric interlaced-PCR, a hemispecific PCR amplification protocol that combines nested, insertion-specific primers with degenerate primers, to amplify DNA flanking the transposons. In reconstruction experiments with previously characterized Arabidopsis lines carrying insertions of the maize Dissociation (Ds) transposon, we show that fluorescently labeled, transposon-flanking fragments overlapping ORFs hybridize to cognate expressed sequence tags (ESTs) on a DNA microarray. We further show that insertions can be detected in DNA pools from as many as 100 plants representing different transposon lines and that all of the tested, transposon-disrupted genes whose flanking fragments can be amplified individually also can be detected when amplified from the pool. The ability of a transposon-flanking fragment to hybridize declines rapidly with decreasing homology to the spotted DNA fragment, so that only ESTs with >90% homology to the transposon-disrupted gene exhibit significant cross-hybridization. Because thermal asymmetric interlaced-PCR fragments tend to be short, use of the present method favors recovery of insertions in and near genes. We apply the technique to screening pools of new Ds lines using cDNA microarrays containing ESTs for approximately 1,000 stress-induced and -repressed Arabidopsis genes.

SUBMITTER: Mahalingam R

PROVIDER: S-EPMC34684 | biostudies-literature | 2001 Jun

REPOSITORIES: biostudies-literature

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Screening insertion libraries for mutations in many genes simultaneously using DNA microarrays.

Mahalingam R R Fedoroff N N

Proceedings of the National Academy of Sciences of the United States of America 20010601 13

We describe a method to screen pools of DNA from multiple transposon lines for insertions in many genes simultaneously. We use thermal asymmetric interlaced-PCR, a hemispecific PCR amplification protocol that combines nested, insertion-specific primers with degenerate primers, to amplify DNA flanking the transposons. In reconstruction experiments with previously characterized Arabidopsis lines carrying insertions of the maize Dissociation (Ds) transposon, we show that fluorescently labeled, tran ...[more]

PMID: 11416215

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Project description:MotivationProbabilistic Identification of bacterial essential genes using transposon-directed insertion-site sequencing (TraDIS) data based on Tn5 libraries has received relatively little attention in the literature; most methods are designed for mariner transposon insertions. Analysis of Tn5 transposon-based genomic data is challenging due to the high insertion density and genomic resolution. We present a novel probabilistic Bayesian approach for classifying bacterial essential genes using transposon insertion density derived from transposon insertion sequencing data. We implement a Markov chain Monte Carlo sampling procedure to estimate the posterior probability that any given gene is essential. We implement a Bayesian decision theory approach to selecting essential genes. We assess the effectiveness of our approach via analysis of both simulated data and three previously published Escherichia coli, Salmonella Typhimurium and Staphylococcus aureus datasets. These three bacteria have relatively well characterized essential genes which allows us to test our classification procedure using receiver operating characteristic curves and area under the curves. We compare the classification performance with that of Bio-Tradis, a standard tool for bacterial gene classification.ResultsOur method is able to classify genes in the three datasets with areas under the curves between 0.967 and 0.983. Our simulated synthetic datasets show that both the number of insertions and the extent to which insertions are tolerated in the distal regions of essential genes are both important in determining classification accuracy. Importantly our method gives the user the option of classifying essential genes based on the user-supplied costs of false discovery and false non-discovery.Availability and implementationAn R package that implements the method presented in this paper is available for download from https://github.com/Kevin-walters/insdens.Supplementary informationSupplementary data are available at Bioinformatics online.

Project description:Motivation: Identification of eQTL, the genetic loci that contribute to heritable variation in gene expression, can be obstructed by factors that produce variation in expression profiles if these factors are unmeasured or hidden from direct analysis. Methods: We have developed a method for Hidden Expression Factor analysis (HEFT) that identifies individual and pleiotropic effects of eQTL in the presence of hidden factors. The HEFT model simultaneously accounts for the effects of genotypes while learning hidden factors, where we make use of the complete likelihood of a unified multivariate regression and factor analysis model to derive a ridge estimator for combined factor learning and detection of eQTL. HEFT requires no pre-estimation of hidden factor effects, no iterative model selection, it provides p-values, and is extremely fast, requiring just a few hours to complete an eQTL analysis of thousands of expression variables when analyzing hundreds of thousands of SNPs on a standard 8 core 2.6G desktop. Results: By analyzing simulated data, we demonstrate that HEFT can correct for an unknown number of hidden factors and outperforms related hidden factor methods for eQTL analysis, where the improved performance is particularly evident in the detection of eQTL with multivariate effects. To demonstrate a real-world application, we applied HEFT to identify eQTL affecting gene expression in human lung tissue for a study that included presumptive hidden factors. The analysis identified a number of eQTL with direct relevance to lung disease that could not be found without a hidden factor analysis, including cis-eQTL for GTF2H1 and MTRR, genes that have been independently associated with lung cancer. We have developed HEFT, a fast multivariate method that detect eQTLs by analyzing thousands of traits simultaneously in the presence of hidden factors. HEFT employs a combined regression factor analysis approach to analyze the gene expression and genotype data sets, looking for univariate or multivariate eQTLs that regulate gene expression, while simultaneously controlling for both orthogonal or non-orthogonal hidden factors. We show by extensive simulation that HEFT outperforms competing methods, and by applying HEFT to a study that included presumptive hidden factors, we identified a number of eQTL with direct relevance to lung disease that could not be found without a hidden factor analysis. HEFT analysis results file (includes all results, not just top hits) linked below as supplementary file.

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Screening insertion libraries for mutations in many genes simultaneously using DNA microarrays.

Publications

Screening insertion libraries for mutations in many genes simultaneously using DNA microarrays.

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