Comparative genomic analysis and molecular examination of the diversity of enterotoxigenic Escherichia coli isolates from Chile

Enterotoxigenic Escherichia coli (ETEC) is one of the most common diarrheal pathogens in the low- and middle-income regions of the world, however a systematic examination of the genomic content of isolates from Chile has not yet been undertaken. Whole genome sequencing and comparative analysis of a collection of 125 ETEC isolates from three geographic locations in Chile, allowed the interrogation of phylogenomic groups, sequence types and genes specific to isolates from the different geographic locations. A total of 80.8% (101/125) of the ETEC isolates were identified in E. coli phylogroup A, 15.2% (19/125) in phylogroup B, and 4.0% (5/125) in phylogroup E. The over-representation of genomes in phylogroup A was significantly different from other global ETEC genomic studies. The Chilean ETEC isolates could be further subdivided into sub-clades similar to previously defined global ETEC reference lineages that had conserved multi-locus sequence types and toxin profiles. Comparison of the gene content of the Chilean ETEC identified genes that were unique based on geographic location within Chile, phylogenomic classifications or sequence type. Completion of a limited number of genomes provided insight into the ETEC plasmid content, which is conserved in some phylogenomic groups and not conserved in others. These findings suggest that the Chilean ETEC isolates contain unique virulence factor combinations and genomic content compared to global reference ETEC isolates

The Human Oral Microbiome Database: a web accessible resource for investigating oral microbe taxonomic and genomic information

The human oral microbiome is the most studied human microflora, but 53% of the species have not yet been validly named and 35% remain uncultivated. The uncultivated taxa are known primarily from 16S rRNA sequence information. Sequence information tied solely to obscure isolate or clone numbers, and usually lacking accurate phylogenetic placement, is a major impediment to working with human oral microbiome data. The goal of creating the Human Oral Microbiome Database (HOMD) is to provide the scientific community with a body site-specific comprehensive database for the more than 600 prokaryote species that are present in the human oral cavity based on a curated 16S rRNA gene-based provisional naming scheme. Currently, two primary types of information are provided in HOMD—taxonomic and genomic. Named oral species and taxa identified from 16S rRNA gene sequence analysis of oral isolates and cloning studies were placed into defined 16S rRNA phylotypes and each given unique Human Oral Taxon (HOT) number. The HOT interlinks phenotypic, phylogenetic, genomic, clinical and bibliographic information for each taxon. A BLAST search tool is provided to match user 16S rRNA gene sequences to a curated, full length, 16S rRNA gene reference data set. For genomic analysis, HOMD provides comprehensive set of analysis tools and maintains frequently updated annotations for all the human oral microbial genomes that have been sequenced and publicly released. Oral bacterial genome sequences, determined as part of the Human Microbiome Project, are being added to the HOMD as they become available. We provide HOMD as a conceptual model for the presentation of microbiome data for other human body sites

Fast individual ancestry inference from DNA sequence data leveraging allele frequencies for multiple populations.

BackgroundEstimation of individual ancestry from genetic data is useful for the analysis of disease association studies, understanding human population history and interpreting personal genomic variation. New, computationally efficient methods are needed for ancestry inference that can effectively utilize existing information about allele frequencies associated with different human populations and can work directly with DNA sequence reads.ResultsWe describe a fast method for estimating the relative contribution of known reference populations to an individual's genetic ancestry. Our method utilizes allele frequencies from the reference populations and individual genotype or sequence data to obtain a maximum likelihood estimate of the global admixture proportions using the BFGS optimization algorithm. It accounts for the uncertainty in genotypes present in sequence data by using genotype likelihoods and does not require individual genotype data from external reference panels. Simulation studies and application of the method to real datasets demonstrate that our method is significantly times faster than previous methods and has comparable accuracy. Using data from the 1000 Genomes project, we show that estimates of the genome-wide average ancestry for admixed individuals are consistent between exome sequence data and whole-genome low-coverage sequence data. Finally, we demonstrate that our method can be used to estimate admixture proportions using pooled sequence data making it a valuable tool for controlling for population stratification in sequencing based association studies that utilize DNA pooling.ConclusionsOur method is an efficient and versatile tool for estimating ancestry from DNA sequence data and is available from https://sites.google.com/site/vibansal/software/iAdmix