Unsupervised Learning Approach for Comparing Multiple Transposon Insertion Sequencing Studies

MSphere
T P HubbardMatthew K Waldor

Abstract

Transposon insertion sequencing (TIS) is a widely used technique for conducting genome-scale forward genetic screens in bacteria. However, few methods enable comparison of TIS data across multiple replicates of a screen or across independent screens, including screens performed in different organisms. Here, we introduce a post hoc analytic framework, comparative TIS (CompTIS), which utilizes unsupervised learning to enable meta-analysis of multiple TIS data sets. CompTIS first implements screen-level principal-component analysis (PCA) and clustering to identify variation between the TIS screens. This initial screen-level analysis facilitates the selection of related screens for additional analyses, reveals the relatedness of complex environments based on growth phenotypes measured by TIS, and provides a useful quality control step. Subsequently, PCA is performed on genes to identify loci whose corresponding mutants lead to concordant/discordant phenotypes across all or in a subset of screens. We used CompTIS to analyze published intestinal colonization TIS data sets from two vibrio species. Gene-level analyses identified both pan-vibrio genes required for intestinal colonization and conserved genes that displayed species-specif...Continue Reading

References

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Citations

Jun 14, 2020·Nature Reviews. Genetics·Amy K CainTim van Opijnen
Sep 5, 2020·Annual Review of Genetics·Tim van Opijnen, Henry L Levin
Apr 14, 2020·Computational and Structural Biotechnology Journal·Anel MahmutovicSören Abel

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Methods Mentioned

BETA
PCA
RNA-seq

Software Mentioned

ARTIST
GraphPad Prism
Curve Fitting Toolbox
CompTIS
Matlab
Con
pvclust
WebMGA
R

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