Insights into origins and function of the unexplored majority of the reductive dehalogenase gene family as a result of genome assembly and ortholog group classification.

Organohalide respiring bacteria (OHRB) express reductive dehalogenases for energy conservation and growth. Some of these enzymes catalyze the reductive dehalogenation of chlorinated and brominated pollutants in anaerobic subsurface environments, providing a valuable ecosystem service. Dehalococcoides mccartyi strains have been most extensively studied owing to their ability to dechlorinate all chlorinated ethenes - most notably carcinogenic vinyl chloride - to ethene. The genomes of OHRB, particularly obligate OHRB, often harbour multiple putative reductive dehalogenase genes (rdhA), most of which have yet to be characterized. We recently sequenced and closed the genomes of eight new strains, increasing the number of available D. mccartyi genomes in NCBI from 16 to 24. From all available OHRB genomes, we classified predicted translations of reductive dehalogenase genes using a previously established 90% amino acid pairwise identity cut-off to identify Ortholog Groups (OGs). Interestingly, the majority of D. mccartyi dehalogenase gene sequences, once classified into OGs, exhibited a remarkable degree of synteny (gene order) in all genomes sequenced to date. This organization was not apparent without the classification. A high degree of synteny indicates that differences arose from rdhA gene loss rather than recombination. Phylogenetic analysis suggests that most rdhA genes have a long evolutionary history in the Dehalococcoidia with origin prior to speciation of Dehalococcoides and Dehalogenimonas. We also looked for evidence of synteny in the genomes of other species of OHRB. Unfortunately, there are too few closed Dehalogenimonas genomes to compare at this time. There is some partial evidence for synteny in the Dehalobacter restrictus genomes, but here too more closed genomes are needed for confirmation. Interestingly, we found that the rdhA genes that encode enzymes that catalyze dehalogenation of industrial pollutants are the only rdhA genes with strong evidence of recent lateral transfer - at least in the genomes examined herein. Given the utility of the RdhA sequence classification to comparative analyses, we are building a public web server () for the community to use, which allows users to add and classify new sequences, and download the entire curated database of reductive dehalogenases.

PathBank: a comprehensive pathway database for model organisms.

PathBank (www.pathbank.org) is a new, comprehensive, visually rich pathway database containing more than 110 000 machine-readable pathways found in 10 model organisms (Homo sapiens, Bos taurus, Rattus norvegicus, Mus musculus, Drosophila melanogaster, Caenorhabditis elegans, Arabidopsis thaliana, Saccharomyces cerevisiae, Escherichia coli and Pseudomonas aeruginosa). PathBank aims to provide a pathway for every protein and a map for every metabolite. This resource is designed specifically to support pathway elucidation and pathway discovery in transcriptomics, proteomics, metabolomics and systems biology. It provides detailed, fully searchable, hyperlinked diagrams of metabolic, metabolite signaling, protein signaling, disease, drug and physiological pathways. All PathBank pathways include information on the relevant organs, organelles, subcellular compartments, cofactors, molecular locations, chemical structures and protein quaternary structures. Each small molecule is hyperlinked to the rich data contained in public chemical databases such as HMDB or DrugBank and each protein or enzyme complex is hyperlinked to UniProt. All PathBank pathways are accompanied with references and detailed descriptions which provide an overview of the pathway, condition or processes depicted in each diagram. Every PathBank pathway is downloadable in several machine-readable and image formats including BioPAX, SBML, PWML, SBGN, RXN, PNG and SVG. PathBank also supports community annotations and submissions through the web-based PathWhiz pathway illustrator. The vast majority of PathBank's pathways (>95%) are not found in any other public pathway database.

Assessing the performance of genome-wide association studies for predicting disease risk.

To date more than 3700 genome-wide association studies (GWAS) have been published that look at the genetic contributions of single nucleotide polymorphisms (SNPs) to human conditions or human phenotypes. Through these studies many highly significant SNPs have been identified for hundreds of diseases or medical conditions. However, the extent to which GWAS-identified SNPs or combinations of SNP biomarkers can predict disease risk is not well known. One of the most commonly used approaches to assess the performance of predictive biomarkers is to determine the area under the receiver-operator characteristic curve (AUROC). We have developed an R package called G-WIZ to generate ROC curves and calculate the AUROC using summary-level GWAS data. We first tested the performance of G-WIZ by using AUROC values derived from patient-level SNP data, as well as literature-reported AUROC values. We found that G-WIZ predicts the AUROC with <3% error. Next, we used the summary level GWAS data from GWAS Central to determine the ROC curves and AUROC values for 569 different GWA studies spanning 219 different conditions. Using these data we found a small number of GWA studies with SNP-derived risk predictors that have very high AUROCs (>0.75). On the other hand, the average GWA study produces a multi-SNP risk predictor with an AUROC of 0.55. Detailed AUROC comparisons indicate that most SNP-derived risk predictions are not as good as clinically based disease risk predictors. All our calculations (ROC curves, AUROCs, explained heritability) are in a publicly accessible database called GWAS-ROCS (http://gwasrocs.ca). The G-WIZ code is freely available for download at https://github.com/jonaspatronjp/GWIZ-Rscript/.