Analysis of codon selection in viral genomes is an important step for comprehension of the coevolution of a virus and its hosts. In this work, we developed a computational tool to simplify the analyses of codon usage. This tool, CUV or the Codon Usage Viewer, allows users to calculate and compare codon usage between viral and bacterial species including both graphical visualization and statistical analyses. To test our tool, the codon usage of homologous genes from the bacterial virus species PhiX174, G4 e alpha3, all belonging to the Microviridae family, were examined. The viruses codon usage was then compared to that of its common host, Escherichia coli. The results showed a conservation between the species codon usage.

Through high-throughput RNA sequencing (RNAseq), transcriptomes for a single cell, tissue, or organism(s) can be ascertained at a high resolution. While a number of bioinformatic tools have been developed for transcriptome analyses, significant challenges exist for studies of non-model organisms. Without a reference sequence available, raw reads must first be assembled de novo followed by the tedious task of BLAST searches and data mining for functional information. We have created a pipeline, PyRanger, to automate this process. The pipeline includes functionality to assess a single transcriptome and also facilitate comparative transcriptomic studies.

Contemporary DNA sequencing technologies are continuously increasing throughput at ever decreasing costs. Moreover, due to recent advances in sequencing technology new platforms are emerging. As such computational challenges persist. The average read length possible has taken a giant leap forward with the PacBio and Nanopore solutions. Regardless of the platform used, impurities within the DNA preparation of the sample - be it from unintentional contaminants or pervasive symbiots - remains an issue. We have developed a new tool, HAsh-MaP-ERadicator (HAMPER), for the detection and removal of non-target, contaminating DNA sequences. Integrating hash-based and mapping-based strategies, HAMPER is both memory and time efficient while maintaining a high level of sensitivity. Moreover, HAMPER was designed for flexibility: reads of any size can be efficiently examined and the user can set parameters specific for the analysis of reads produced by a particular sequencer. To evaluate our method, mock sequencing runs were generated including various contaminating species and with variable rates of mutation revealing a high level of sensitivity and specificity. Reads that are not of interest can quickly be removed using HAMPER thus improving downstream analyses.

BACKGROUND: In many genomes, a clear preference in the usage of particular codons exists. The mechanisms that induce codon biases remain an open question; studies have attributed codon usage to translational selection, mutational bias and drift. Furthermore, correlations between codon usage within host genomes and their viral pathogens have been observed for a myriad of host-virus systems. As such, numerous studies have investigated codon usage and codon bias in an effort to better understand how species evolve. Numerous metrics have been developed to identify biases in codon usage. In addition, a few data repositories of codon bias data are available, differing in the metrics reported as well as the number and taxonomy of strains examined. DESCRIPTION: We have created a new web resource called the Codon Bias Database (CBDB) which provides information regarding the codon bias within the set of highly expressed genes for 300+ bacterial genomes. CBDB was developed to provide a resource for researchers investigating codon bias in bacteria, facilitating comparisons between strains and species. Furthermore, the site was created to serve those studying adaptation in phage; the genera selected for this first release of CBDB all have sequenced, annotated bacteriophages. The annotations and sequences for the highly expressed gene set are available for each strain in addition to the strain's codon bias measurements. CONCLUSIONS: Comparing species and strains provides a comprehensive look at how codon usage has been shaped over evolutionary time and can elucidate the putative mechanisms behind it. The Codon Bias Database provides a centralized repository of look-up tables and codon usage bias measures for a wide variety of genera, species and strains. Through our analysis of the variation in codon usage within the strains presently available, we find that most members of a genus have a codon composition most similar to other members of its genus, although not necessarily other members of its species.

MOTIVATION: Genomic-based methods have significant potential for fast and accurate identification of organisms or even genes of interest in complex environmental samples (air, water, soil, food, etc.), especially when isolation of the target organism cannot be performed by a variety of reasons. Despite this potential, the presence of the unknown, variable and usually large quantities of background DNA can cause interference resulting in false positive outcomes. RESULTS: In order to estimate how the genomic diversity of the background (total length of all of the different genomes present in the background), target length and target mutation rate affect the probability of misidentifications, we introduce a mathematical definition for the quality of an individual signature in the presence of a background based on its length and number of mismatches needed to transform the signature into the closest subsequence present in the background. This definition, in conjunction with a probabilistic framework, allows one to predict the minimal signature length required to identify the target in the presence of different sizes of backgrounds and the effect of the target's mutation rate on the quality of its identification. The model assumptions and predictions were validated using both Monte Carlo simulations and real genomic data examples. The proposed model can be used to determine appropriate signature lengths for various combinations of target and background genome sizes. It also predicted that any genomic signatures will be unable to identify target if its mutation rate is >5%. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.