Protein Sequence Annotation Tool (PSAT): a centralized web-based meta-server for high-throughput sequence annotations.

BACKGROUND: Here we introduce the Protein Sequence Annotation Tool (PSAT), a web-based, sequence annotation meta-server for performing integrated, high-throughput, genome-wide sequence analyses. Our goals in building PSAT were to (1) create an extensible platform for integration of multiple sequence-based bioinformatics tools, (2) enable functional annotations and enzyme predictions over large input protein fasta data sets, and (3) provide a web interface for convenient execution of the tools. RESULTS: In this paper, we demonstrate the utility of PSAT by annotating the predicted peptide gene products of Herbaspirillum sp. strain RV1423, importing the results of PSAT into EC2KEGG, and using the resulting functional comparisons to identify a putative catabolic pathway, thereby distinguishing RV1423 from a well annotated Herbaspirillum species. This analysis demonstrates that high-throughput enzyme predictions, provided by PSAT processing, can be used to identify metabolic potential in an otherwise poorly annotated genome. CONCLUSIONS: PSAT is a meta server that combines the results from several sequence-based annotation and function prediction codes, and is available at http://psat.llnl.gov/psat/. PSAT stands apart from other sequence-based genome annotation systems in providing a high-throughput platform for rapid de novo enzyme predictions and sequence annotations over large input protein sequence data sets in FASTA. PSAT is most appropriately applied in annotation of large protein FASTA sets that may or may not be associated with a single genome.

Short communication: Typing and tracking Bacillaceae in raw milk and milk powder using pyroprinting.

Contamination of fluid and processed milk products with endospore-forming bacteria, such as Bacillaceae, affect milk quality and longevity. Contaminants come from a variety of sources, including the dairy farm environment, transportation equipment, or milk processing machinery. Tracking the origin of bacterial contamination to allow specifically targeted remediation efforts depends on a reliable strain-typing method that is reproducible, fast, easy to use, and amenable to computerized analysis. Our objective was to adapt a recently developed genotype-based Escherichia coli strain-typing method, called pyroprinting, for use in a microbial source-tracking study to follow endospore-forming bacillus bacteria from raw milk to powdered milk. A collection of endospores was isolated from both raw milk and its finished powder, and, after germination, the vegetative cells were subject to the pyroprinting protocol. Briefly, a ribosomal DNA intergenic transcribed spacer present in multiple copies in Bacillaceae genomes was amplified by the PCR. This multicopy locus generated a mixed PCR product that was subsequently subject to pyrosequencing, a quantitative real-time sequencing method. Through a series of enzymatic reactions, each nucleotide incorporation event produces a photon of light that is quantified at each nucleotide dispensation. The pattern of light peaks generated from this mixed template reaction is called a pyroprint. Isolates with pyroprints that match with a Pearson correlation of 0.99 or greater are considered to be in the same group. The pyroprint also contains some sequence data useful for presumptive species-level identification. This method identified groups with isolates from raw milk only, from powdered milk only, or from both sources. This study confirms pyroprinting as a rapid, reproducible, automatically digitized tool that can be used to distinguish bacterial strains into taxonomically relevant groups and, thus, indicate probable origins of bacterial contamination in powdered milk.

A variant by any name: quantifying annotation discordance across tools and clinical databases.

BACKGROUND: Clinical genomic testing is dependent on the robust identification and reporting of variant-level information in relation to disease. With the shift to high-throughput sequencing, a major challenge for clinical diagnostics is the cross-identification of variants called on their genomic position to resources that rely on transcript- or protein-based descriptions. METHODS: We evaluated the accuracy of three tools (SnpEff, Variant Effect Predictor, and Variation Reporter) that generate transcript and protein-based variant nomenclature from genomic coordinates according to guidelines by the Human Genome Variation Society (HGVS). Our evaluation was based on transcript-controlled comparisons to a manually curated set of 126 test variants of various types drawn from data sources, each with HGVS-compliant transcript and protein descriptors. We further evaluated the concordance between annotations generated by Snpeff and Variant Effect Predictor and those in major germline and cancer databases: ClinVar and COSMIC, respectively. RESULTS: We find that there is substantial discordance between the annotation tools and databases in the description of insertions and/or deletions. Using our ground truth set of variants, constructed specifically to identify challenging events, accuracy was between 80 and 90% for coding and 50 and 70% for protein changes for 114 to 126 variants. Exact concordance for SNV syntax was over 99.5% between ClinVar and Variant Effect Predictor and SnpEff, but less than 90% for non-SNV variants. For COSMIC, exact concordance for coding and protein SNVs was between 65 and 88% and less than 15% for insertions. Across the tools and datasets, there was a wide range of different but equivalent expressions describing protein variants. CONCLUSIONS: Our results reveal significant inconsistency in variant representation across tools and databases. While some of these syntax differences may be clear to a clinician, they can confound variant matching, an important step in variant classification. These results highlight the urgent need for the adoption and adherence to uniform standards in variant annotation, with consistent reporting on the genomic reference, to enable accurate and efficient data-driven clinical care.

Pyroprinting: a rapid and flexible genotypic fingerprinting method for typing bacterial strains.

Bacterial strain typing is commonly employed in studies involving epidemiology, population ecology, and microbial source tracking to identify sources of fecal contamination. Methods for differentiating strains generally use either a collection of phenotypic traits or rely on some interrogation of the bacterial genotype. This report introduces pyroprinting, a novel genotypic strain typing method that is rapid, inexpensive, and discriminating compared to the most sensitive methods already in use. Pyroprinting relies on the simultaneous pyrosequencing of polymorphic multicopy loci, such as the intergenic transcribed spacer regions of rRNA operons in bacterial genomes. Data generated by sequencing combinations of variable templates are reproducible and intrinsically digitized. The theory and development of pyroprinting in Escherichia coli, including the selection of similarity thresholds to define matches between isolates, are presented. The pyroprint-based strain differentiation limits and phylogenetic relevance compared to other typing methods are also explored. Pyroprinting is unique in its simplicity and, paradoxically, in its intrinsic complexity. This new approach serves as an excellent alternative to more cumbersome or less phylogenetically relevant strain typing methods.