A test for comparing two groups of samples when analyzing multiple omics profiles

Genome-Wide Transcription Start Site Mapping and Promoter Assignments to a Sigma Factor in the Human Enteropathogen Clostridioides difficile

The emerging human enteropathogen Clostridioides difficile is the main cause of diarrhea associated with antibiotherapy. Regulatory pathways underlying the adaptive responses remain understudied and the global view of C. difficile promoter structure is still missing. In the genome of C. difficile 630, 22 genes encoding sigma factors are present suggesting a complex pattern of transcription in this bacterium. We present here the first transcriptional map of the C. difficile genome resulting from the identification of transcriptional start sites (TSS), promoter motifs and operon structures. By 5′-end RNA-seq approach, we mapped more than 1000 TSS upstream of genes. In addition to these primary TSS, this analysis revealed complex structure of transcriptional units such as alternative and internal promoters, potential RNA processing events and 5′ untranslated regions. By following an in silico iterative strategy that used as an input previously published consensus sequences and transcriptomic analysis, we identified candidate promoters upstream of most of protein-coding and non-coding RNAs genes. This strategy also led to refine consensus sequences of promoters recognized by major sigma factors of C. difficile. Detailed analysis focuses on the transcription in the pathogenicity locus and regulatory genes, as well as regulons of transition phase and sporulation sigma factors as important components of C. difficile regulatory network governing toxin gene expression and spore formation. Among the still uncharacterized regulons of the major sigma factors of C. difficile, we defined the SigL regulon by combining transcriptome and in silico analyses. We showed that the SigL regulon is largely involved in amino-acid degradation, a metabolism crucial for C. difficile gut colonization. Finally, we combined our TSS mapping, in silico identification of promoters and RNA-seq data to improve gene annotation and to suggest operon organization in C. difficile. These data will considerably improve our knowledge of global regulatory circuits controlling gene expression in C. difficile and will serve as a useful rich resource for scientific community both for the detailed analysis of specific genes and systems biology studies.

Controlling false discoveries in high-dimensional situations: boosting with stability selection

BACKGROUND

Modern biotechnologies often result in high-dimensional data sets with many more variables than observations (n≪p). These data sets pose new challenges to statistical analysis: Variable selection becomes one of the most important tasks in this setting. Similar challenges arise if in modern data sets from observational studies, e.g., in ecology, where flexible, non-linear models are fitted to high-dimensional data. We assess the recently proposed flexible framework for variable selection called stability selection. By the use of resampling procedures, stability selection adds a finite sample error control to high-dimensional variable selection procedures such as Lasso or boosting. We consider the combination of boosting and stability selection and present results from a detailed simulation study that provide insights into the usefulness of this combination. The interpretation of the used error bounds is elaborated and insights for practical data analysis are given.

RESULTS

Stability selection with boosting was able to detect influential predictors in high-dimensional settings while controlling the given error bound in various simulation scenarios. The dependence on various parameters such as the sample size, the number of truly influential variables or tuning parameters of the algorithm was investigated. The results were applied to investigate phenotype measurements in patients with autism spectrum disorders using a log-linear interaction model which was fitted by boosting. Stability selection identified five differentially expressed amino acid pathways.

CONCLUSION

Stability selection is implemented in the freely available R package stabs (http://CRAN.R-project.org/package=stabs). It proved to work well in high-dimensional settings with more predictors than observations for both, linear and additive models. The original version of stability selection, which controls the per-family error rate, is quite conservative, though, this is much less the case for its improvement, complementary pairs stability selection. Nevertheless, care should be taken to appropriately specify the error bound.