UNLABELLED: Transposon insertion sequencing is a high-throughput technique for assaying large libraries of otherwise isogenic transposon mutants providing insight into gene essentiality, gene function and genetic interactions. We previously developed the Transposon Directed Insertion Sequencing (TraDIS) protocol for this purpose, which utilizes shearing of genomic DNA followed by specific PCR amplification of transposon-containing fragments and Illumina sequencing. Here we describe an optimized high-yield library preparation and sequencing protocol for TraDIS experiments and a novel software pipeline for analysis of the resulting data. The Bio-Tradis analysis pipeline is implemented as an extensible Perl library which can either be used as is, or as a basis for the development of more advanced analysis tools. This article can serve as a general reference for the application of the TraDIS methodology. AVAILABILITY AND IMPLEMENTATION: The optimized sequencing protocol is included as supplementary information. The Bio-Tradis analysis pipeline is available under a GPL license at https://github.com/sanger-pathogens/Bio-Tradis CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Next generation sequencing technologies have provided us with a wealth of information on genetic variation, but predicting the functional significance of this variation is a difficult task. While many comparative genomics studies have focused on gene flux and large scale changes, relatively little attention has been paid to quantifying the effects of single nucleotide polymorphisms and indels on protein function, particularly in bacterial genomics. RESULTS: We present a hidden Markov model based approach we call delta-bitscore (DBS) for identifying orthologous proteins that have diverged at the amino acid sequence level in a way that is likely to impact biological function. We benchmark this approach with several widely used datasets and apply it to a proof-of-concept study of orthologous proteomes in an investigation of host adaptation in Salmonella enterica We highlight the value of the method in identifying functional divergence of genes, and suggest that this tool may be a better approach than the commonly used dN/dS metric for identifying functionally significant genetic changes occurring in recently diverged organisms. AVAILABILITY AND IMPLEMENTATION: A program implementing DBS for pairwise genome comparisons is freely available at: https://github.com/UCanCompBio/deltaBS CONTACT: [email protected] or [email protected] information: Supplementary data are available at Bioinformatics online.

Noncoding RNAs are integral to a wide range of biological processes, including translation, gene regulation, host-pathogen interactions and environmental sensing. While genomics is now a mature field, our capacity to identify noncoding RNA elements in bacterial and archaeal genomes is hampered by the difficulty of de novo identification. The emergence of new technologies for characterizing transcriptome outputs, notably RNA-seq, are improving noncoding RNA identification and expression quantification. However, a major challenge is to robustly distinguish functional outputs from transcriptional noise. To establish whether annotation of existing transcriptome data has effectively captured all functional outputs, we analysed over 400 publicly available RNA-seq datasets spanning 37 different Archaea and Bacteria. Using comparative tools, we identify close to a thousand highly-expressed candidate noncoding RNAs. However, our analyses reveal that capacity to identify noncoding RNA outputs is strongly dependent on phylogenetic sampling. Surprisingly, and in stark contrast to protein-coding genes, the phylogenetic window for effective use of comparative methods is perversely narrow: aggregating public datasets only produced one phylogenetic cluster where these tools could be used to robustly separate unannotated noncoding RNAs from a null hypothesis of transcriptional noise. Our results show that for the full potential of transcriptomics data to be realized, a change in experimental design is paramount: effective transcriptomics requires phylogeny-aware sampling.

UNLABELLED: We describe handalign, a software package for Bayesian reconstruction of phylogenetic history. The underlying model of sequence evolution describes indels and substitutions. Alignments, trees and model parameters are all treated as jointly dependent random variables and sampled via Metropolis-Hastings Markov chain Monte Carlo (MCMC), enabling systematic statistical parameter inference and hypothesis testing. handalign implements several different MCMC proposal kernels, allows sampling from arbitrary target distributions via Hastings ratios, and uses standard file formats for trees, alignments and models. AVAILABILITY AND IMPLEMENTATION: Installation and usage instructions are at http://biowiki.org/HandAlign.