We present a hierarchical genome-assembly process (HGAP) for high-quality de novo microbial genome assemblies using only a single, long-insert shotgun DNA library in conjunction with Single Molecule, Real-Time (SMRT) DNA sequencing. Our method uses the longest reads as seeds to recruit all other reads for construction of highly accurate preassembled reads through a directed acyclic graph-based consensus procedure, which we follow with assembly using off-the-shelf long-read assemblers. In contrast to hybrid approaches, HGAP does not require highly accurate raw reads for error correction. We demonstrate efficient genome assembly for several microorganisms using as few as three SMRT Cell zero-mode waveguide arrays of sequencing and for BACs using just one SMRT Cell. Long repeat regions can be successfully resolved with this workflow. We also describe a consensus algorithm that incorporates SMRT sequencing primary quality values to produce de novo genome sequence exceeding 99.999% accuracy.

BACKGROUND A large outbreak of diarrhea and the hemolytic–uremic syndrome caused by an unusual serotype of Shiga-toxin–producing Escherichia coli (O104:H4) began in Germany in May 2011. As of July 22, a large number of cases of diarrhea caused by Shiga-toxin–producing E. coli have been reported — 3167 without the hemolytic–uremic syndrome (16 deaths) and 908 with the hemolytic–uremic syndrome (34 deaths) — indicating that this strain is notably more virulent than most of the Shiga-toxin–producing E. coli strains. Preliminary genetic characterization of the outbreak strain suggested that, unlike most of these strains, it should be classified within the enteroaggregative pathotype of E. coli. METHODS We used third-generation, single-molecule, real-time DNA sequencing to determine the complete genome sequence of the German outbreak strain, as well as the genome sequences of seven diarrhea-associated enteroaggregative E. coli serotype O104:H4 strains from Africa and four enteroaggregative E. coli reference strains belonging to other serotypes. Genomewide comparisons were performed with the use of these enteroaggregative E. coli genomes, as well as those of 40 previously sequenced E. coli isolates. RESULTS The enteroaggregative E. coli O104:H4 strains are closely related and form a distinct clade among E. coli and enteroaggregative E. coli strains. However, the genome of the German outbreak strain can be distinguished from those of other O104:H4 strains because it contains a prophage encoding Shiga toxin 2 and a distinct set of additional virulence and antibiotic-resistance factors. CONCLUSIONS Our findings suggest that horizontal genetic exchange allowed for the emergence of the highly virulent Shiga-toxin–producing enteroaggregative E. coli O104:H4 strain that caused the German outbreak. More broadly, these findings highlight the way in which the plasticity of bacterial genomes facilitates the emergence of new pathogens.

Mass spectrometry, the core technology in the field of proteomics, promises to enable scientists to identify and quantify the entire complement of proteins in a complex biological sample. Currently, the primary bottleneck in this type of experiment is computational. Existing algorithms for interpreting mass spectra are slow and fail to identify a large proportion of the given spectra. We describe a database search program called Crux that reimplements and extends the widely used database search program SEQUEST. For speed, Crux uses a peptide indexing scheme to rapidly retrieve candidate peptides for a given spectrum. For each peptide in the target database, Crux generates shuffled decoy peptides on the fly, providing a good null model and, hence, accurate false discovery rate estimates. Crux also implements two recently described postprocessing methods: a p value calculation based upon fitting a Weibull distribution to the observed scores, and a semisupervised method that learns to discriminate between target and decoy matches. Both methods significantly improve the overall rate of peptide identification. Crux is implemented in C and is distributed with source code freely to noncommercial users.

Dramatic improvements in DNA sequencing technology have revolutionized our ability to characterize most genomic diversity. However, accurate resolution of large structural events has remained challenging due to the comparatively shorter read lengths of second-generation technologies. Emerging third-generation sequencing technologies, which yield markedly increased read length on rapid time scales and for low cost, have the potential to address assembly limitations. Here we combine sequencing data from second- and third-generation DNA sequencing technologies to assemble the two-chromosome genome of a recent Haitian cholera outbreak strain into two nearly finished contigs at > 99.9% accuracy. Complex regions with clinically significant structure were completely resolved. In separate control assemblies on experimental and simulated data for the canonical N16961 reference we obtain 14 and 8 scaffolds greater than 1kb, respectively, correcting several errors in the underlying source data. This work provides a blueprint for the next generation of rapid microbial identification and full-genome assembly.

In shotgun proteomics, a complex protein mixture is digested to peptides, separated and identified by microcapillary liquid chromatography followed by tandem mass spectrometry (LC-MS-MS). In this technology, complete protein digestion is often assumed. We show that, to the contrary, modifications to a standard digestion protocol demonstrate large, reproducible improvements in protein identification, a result consistent with digestion being a limiting factor in the efficiency of protein identification.

Most algorithms for identifying peptides from tandem mass spectra use information only from the final spectrum, ignoring non-mass-based information acquired routinely in liquid chromatography tandem mass spectrometry analyses. One physiochemical property that is always obtained but rarely exploited is peptide chromatographic retention time. Efforts to use chromatographic retention time to improve peptide identification are complicated because of the variability of retention time in different experimental conditions-making retention time calculations nongeneralizable. We show that peptide retention time can be reliably predicted by training and testing a support vector regressor on a small collection of data from a single liquid chromatography run. This model can be used to filter peptide identifications with observed retention time that deviates from predicted retention time. After filtering, positive peptide identifications increase by as much as 50% at a false discovery rate of 3%. We demonstrate that our dynamically trained model generalizes well across diverse chromatography conditions and methods for generating peptides, in particular improving peptide identification using nonspecific proteases.

Obtaining accurate peptide identifications from shotgun proteomics liquid chromatography tandem mass spectrometry (LC-MS/MS) experiments requires a score function that consistently ranks correct peptide-spectrum matches (PSMs) above incorrect matches. We have observed that, for the SEQUEST score function X corr, the inability to discriminate between correct and incorrect PSMs is due in part to spectrum-specific properties of the score distribution. In other words, some spectra score well regardless of which peptides they are scored against, and other spectra score well because they are scored against a large number of peptides. We describe a protocol for calibrating PSM score functions, and we demonstrate its application to X corr and the preliminary SEQUEST score function Sp. The protocol accounts for spectrum-and peptide-specific effects by calculating p values for each spectrum individually, using only that spectrum's score distribution. We demonstrate that these calculated p values are uniform under a null distribution and therefore accurately measure significance. These p values can be used to estimate the false discovery rate, therefore eliminating the need for an extra search against a decoy database. In addition, we show that the p values are better calibrated than their underlying scores; consequently, when ranking top-scoring PSMs from multiple spectra, p values are better at discriminating between correct and incorrect PSMs. The calibration protocol is generally applicable to any PSM score function for which an appopriate parametric family can be identified.

A 2D ion trap has a greater ion trapping efficiency, greater ion capacity before observing space-charging effects, and a faster ion ejection rate than a traditional 3D ion trap mass spectrometer. These hardware improvements should result in a significant increase in protein identifications from complex mixtures analyzed using shotgun proteomics. In this study, we compare the quality and quantity of peptide identifications using data-dependent acquisition of tandem mass spectra of peptides between two commercially available ion trap mass spectrometers (an LTQ and an LCQ XP Max). We demonstrate that the increased trapping efficiency, increased ion capacity, and faster ion ejection rate of the LTQ results in greater than 5-fold more protein identifications, better identification of low-abundance proteins, and higher confidence protein identifications when compared with a LCQ XP Max.