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The development of Genotyping-By-Sequencing (GBS) technologies enables cost-effective analysis of large numbers of Single Nucleotide Polymorphisms (SNPs), especially in “non-model” species. Nevertheless, as such technologies enter a mature phase, biases and errors inherent to GBS are becoming evident. Here, we evaluated the performance of double digest Restriction enzyme Associated DNA (ddRAD) sequencing in SNP genotyping studies including high number of samples. Datasets of sequence data were generated from three marine teleost species (>5500 samples, >2.5 × 10 12 bases in total), using a standardized protocol. A common bioinformatics pipeline based on STACKS was established, with and without the use of a reference genome. We performed analyses throughout the production and analysis of ddRAD data in order to explore (i) the loss of information due to heterogeneous raw read number across samples; (ii) the discrepancy between expected and observed tag length and coverage; (iii) the performances of reference based vs. de novo approaches; (iv) the sources of potential genotyping errors of the library preparation/bioinformatics protocol, by comparing technical replicates. Our results showed use of a reference genome and a posteriori genotype correction improved genotyping precision. Individual read coverage was a key variable for reproducibility; variance in sequencing depth between loci in the same individual was also identified as an important factor and found to correlate to tag length. A comparison of downstream analysis carried out with ddRAD vs single SNP allele specific assay genotypes provided information about the levels of genotyping imprecision that can have a significant impact on allele frequency estimations and population assignment. The results and insights presented here will help to select and improve approaches to the analysis of large datasets based on RAD-like methodologies. [ABSTRACT FROM AUTHOR]

Abstract: The availability of a rapid and accurate method for the diagnosis of Photobacterium damselae subsp. piscicida (Phdp), able to discriminate its strictly correlated subsp. damselae (Phdd), formally known as Vibrio damsela, is essential for managing fish pasteurellosis outbreaks in farmed fish. A single‐step, high‐sensitivity real‐time PCR assay for simultaneous detection and quantification of P. damselae was designed targeting partial of the sequence of the bamB gene and tested for specificity and sensitivity on laboratory‐generated samples as well as on experimentally infected seabream tissue samples. With a limit of detection (LOD) of one copy in pure bacterial DNA, the sensitivity was higher than all methods previously reported. Validation in target and non‐target bacterial species proved the assay was able to discriminate Phdd‐Phdp subspecies from diverse hosts/geographical origins and between non‐target species. In addition, two SNPs in the target amplicon region determine two distinctive qPCR dissociation curves distinguishing between Phdp‐Phdd. This is the first time that a molecular method for P. damselae diagnosis combines detection, quantification and subspecies identification in one step. The assay holds the potential to improve the knowledge of infection dynamics and the development of better strategies to control an important fish disease. [ABSTRACT FROM AUTHOR]

Presents a statistical framework to address questions that rise in general problems involving collaboration of several contributors and applies the statistical framework to the problem of routing a functional unit of the IBM POWER4 microprocessor. Method for external intervention; Statistical model of effectiveness; Application to POWER4 instruction fetch unit.