2009 Swine-Origin Influenza A (H1N1) Resembles Previous Influenza Isolates

Background: In April 2009, novel swine-origin influenza viruses (S-OIV) were identified in patients from Mexico and the United States. The viruses were genetically characterized as a novel influenza A (H1N1) strain originating in swine, and within a very short time the S-OIV strain spread across the globe via human-to-human contact. Methodology: We conducted a comprehensive computational search of all available sequences of the surface proteins of H1N1 swine influenza isolates and found that a similar strain to S-OIV appeared in Thailand in 2000. The earlier isolates caused infections in pigs but only one sequenced human case, A/Thailand/271/2005 (H1N1). Significance: Differences between the Thai cases and S-OIV may help shed light on the ability of the current outbreak strain to spread rapidly among humans

OPERA-LG: efficient and exact scaffolding of large, repeat-rich eukaryotic genomes with performance guarantees

10.1186/s13059-016-0951-yGenome Biology17110

Species Identification and Profiling of Complex Microbial Communities Using Shotgun Illumina Sequencing of 16S rRNA Amplicon Sequences

The high throughput and cost-effectiveness afforded by short-read sequencing technologies, in principle, enable researchers to perform 16S rRNA profiling of complex microbial communities at unprecedented depth and resolution. Existing Illumina sequencing protocols are, however, limited by the fraction of the 16S rRNA gene that is interrogated and therefore limit the resolution and quality of the profiling. To address this, we present the design of a novel protocol for shotgun Illumina sequencing of the bacterial 16S rRNA gene, optimized to capture more than 90% of sequences in the Greengenes database and with nearly twice the resolution of existing protocols. Using several in silico and experimental datasets, we demonstrate that despite the presence of multiple variable and conserved regions, the resulting shotgun sequences can be used to accurately quantify the diversity of complex microbial communities. The reconstruction of a significant fraction of the 16S rRNA gene also enabled high precision (>90%) in species-level identification thereby opening up potential application of this approach for clinical microbial characterization.Comment: 17 pages, 2 tables, 2 figures, supplementary materia

Alignment and clustering of phylogenetic markers - implications for microbial diversity studies

<p>Abstract</p> <p>Background</p> <p>Molecular studies of microbial diversity have provided many insights into the bacterial communities inhabiting the human body and the environment. A common first step in such studies is a survey of conserved marker genes (primarily 16S rRNA) to characterize the taxonomic composition and diversity of these communities. To date, however, there exists significant variability in analysis methods employed in these studies.</p> <p>Results</p> <p>Here we provide a critical assessment of current analysis methodologies that cluster sequences into operational taxonomic units (OTUs) and demonstrate that small changes in algorithm parameters can lead to significantly varying results. Our analysis provides strong evidence that the species-level diversity estimates produced using common OTU methodologies are inflated due to overly stringent parameter choices. We further describe an example of how semi-supervised clustering can produce OTUs that are more robust to changes in algorithm parameters.</p> <p>Conclusions</p> <p>Our results highlight the need for systematic and open evaluation of data analysis methodologies, especially as targeted 16S rRNA diversity studies are increasingly relying on high-throughput sequencing technologies. All data and results from our study are available through the JGI FAMeS website <url>http://fames.jgi-psf.org/</url>.</p

No evidence for a common blood microbiome based on a population study of 9,770 healthy humans

Human blood is conventionally considered sterile but recent studies suggest the presence of a blood microbiome in healthy individuals. Here we characterized the DNA signatures of microbes in the blood of 9,770 healthy individuals using sequencing data from multiple cohorts. After filtering for contaminants, we identified 117 microbial species in blood, some of which had DNA signatures of microbial replication. They were primarily commensals associated with the gut (n = 40), mouth (n = 32) and genitourinary tract (n = 18), and were distinct from pathogens detected in hospital blood cultures. No species were detected in 84% of individuals, while the remainder only had a median of one species. Less than 5% of individuals shared the same species, no co-occurrence patterns between different species were observed and no associations between host phenotypes and microbes were found. Overall, these results do not support the hypothesis of a consistent core microbiome endogenous to human blood. Rather, our findings support the transient and sporadic translocation of commensal microbes from other body sites into the bloodstream

Fast and sensitive mapping of nanopore sequencing reads with GraphMap

Realizing the democratic promise of nanopore sequencing requires the development of new bioinformatics approaches to deal with its specific error characteristics. Here we present GraphMap, a mapping algorithm designed to analyse nanopore sequencing reads, which progressively refines candidate alignments to robustly handle potentially high-error rates and a fast graph traversal to align long reads with speed and high precision (&gt;95%). Evaluation on MinION sequencing data sets against short- and long-read mappers indicates that GraphMap increases mapping sensitivity by 10–80% and maps &gt;95% of bases. GraphMap alignments enabled single-nucleotide variant calling on the human genome with increased sensitivity (15%) over the next best mapper, precise detection of structural variants from length 100 bp to 4 kbp, and species and strain-specific identification of pathogens using MinION reads. GraphMap is available open source under the MIT license at https://github.com/isovic/graphmap

Finishing genomes with limited resources: lessons from an ensemble of microbial genomes

While new sequencing technologies have ushered in an era where microbial genomes can be easily sequenced, the goal of routinely producing high-quality draft and finished genomes in a cost-effective fashion has still remained elusive. Due to shorter read lengths and limitations in library construction protocols, shotgun sequencing and assembly based on these technologies often results in fragmented assemblies. Correspondingly, while draft assemblies can be obtained in days, finishing can take many months and hence the time and effort can only be justified for high-priority genomes and in large sequencing centers. In this work, we revisit this issue in light of our own experience in producing finished and nearly-finished genomes for a range of microbial species in a small-lab setting. These genomes were finished with surprisingly little investments in terms of time, computational effort and lab work, suggesting that the increased access to sequencing might also eventually lead to a greater proportion of finished genomes from small labs and genomics cores

GiRaF: robust, computational identification of influenza reassortments via graph mining

Reassortments in the influenza virus—a process where strains exchange genetic segments—have been implicated in two out of three pandemics of the 20th century as well as the 2009 H1N1 outbreak. While advances in sequencing have led to an explosion in the number of whole-genome sequences that are available, an understanding of the rate and distribution of reassortments and their role in viral evolution is still lacking. An important factor in this is the paucity of automated tools for confident identification of reassortments from sequence data due to the challenges of analyzing large, uncertain viral phylogenies. We describe here a novel computational method, called GiRaF (Graph-incompatibility-based Reassortment Finder), that robustly identifies reassortments in a fully automated fashion while accounting for uncertainties in the inferred phylogenies. The algorithms behind GiRaF search large collections of Markov chain Monte Carlo (MCMC)-sampled trees for groups of incompatible splits using a fast biclique enumeration algorithm coupled with several statistical tests to identify sets of taxa with differential phylogenetic placement. GiRaF correctly finds known reassortments in human, avian, and swine influenza populations, including the evolutionary events that led to the recent ‘swine flu’ outbreak. GiRaF also identifies several previously unreported reassortments via whole-genome studies to catalog events in H5N1 and swine influenza isolates