title: MetaGeniE: characterizing human clinical samples using deep metagenomic sequencing.
abstract: With the decreasing cost of next-generation sequencing, deep sequencing of clinical samples provides unique opportunities to understand host-associated microbial communities. Among the primary challenges of clinical metagenomic sequencing is the rapid filtering of human reads to survey for pathogens with high specificity and sensitivity. Metagenomes are inherently variable due to different microbes in the samples and their relative abundance, the size and architecture of genomes, and factors such as target DNA amounts in tissue samples (i.e. human DNA versus pathogen DNA concentration). This variation in metagenomes typically manifests in sequencing datasets as low pathogen abundance, a high number of host reads, and the presence of close relatives and complex microbial communities. In addition to these challenges posed by the composition of metagenomes, high numbers of reads generated from high-throughput deep sequencing pose immense computational challenges. Accurate identification of pathogens is confounded by individual reads mapping to multiple different reference genomes due to gene similarity in different taxa present in the community or close relatives in the reference database. Available global and local sequence aligners also vary in sensitivity, specificity, and speed of detection. The efficiency of detection of pathogens in clinical samples is largely dependent on the desired taxonomic resolution of the organisms. We have developed an efficient strategy that identifies "all against all" relationships between sequencing reads and reference genomes. Our approach allows for scaling to large reference databases and then genome reconstruction by aggregating global and local alignments, thus allowing genetic characterization of pathogens at higher taxonomic resolution. These results were consistent with strain level SNP genotyping and bacterial identification from laboratory culture.
authors.affiliation: Pathogen Genomics Division, Translational Genomics Research Institute, Flagstaff, Arizona, United States of America.
authors.forename: Arun
authors.initials: A
authors.lastname: Rawat
authors.affiliation: Pathogen Genomics Division, Translational Genomics Research Institute, Flagstaff, Arizona, United States of America.
authors.forename: David M
authors.initials: DM
authors.lastname: Engelthaler
authors.affiliation: Pathogen Genomics Division, Translational Genomics Research Institute, Flagstaff, Arizona, United States of America.
authors.forename: Elizabeth M
authors.initials: EM
authors.lastname: Driebe
authors.affiliation: Pathogen Genomics Division, Translational Genomics Research Institute, Flagstaff, Arizona, United States of America; Center for Microbial Genetics and Genomics, Northern Arizona University, Flagstaff, Arizona, United States of America.
authors.forename: Paul
authors.initials: P
authors.lastname: Keim
authors.affiliation: Center for Microbial Genetics and Genomics, Northern Arizona University, Flagstaff, Arizona, United States of America; Department of Molecular, Cellular, and Biomedical Sciences, University of New Hampshire, Durham, New Hampshire, United States of America.
authors.forename: Jeffrey T
authors.initials: JT
authors.lastname: Foster
mesh_terms: D044822:Biodiversity; D030561:Databases, Nucleic Acid; D066264:Datasets as Topic; D005838:Genotype; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D054892:Metagenome; D056186:Metagenomics / Q000379:methods; D064307:Microbiota; D020641:Polymorphism, Single Nucleotide; D015203:Reproducibility of Results; D012984:Software