Targeted Enrichment for Pathogen Detection and Characterization in Three Felid Species.

Traditional diagnostic assays often lack sensitivity and can be difficult to multiplex across many pathogens. Next-generation sequencing (NGS) can overcome some of these problems but has limited application in the detection of low-copy-number pathogens in complex samples. Targeted genome capture (TGC) utilizes oligonucleotide probes to enrich specific nucleic acids in heterogeneous extracts and can therefore increase the proportion of NGS reads for low-abundance targets. While earlier studies have demonstrated the utility of this technology for detection of novel pathogens in human clinical samples, the capacity and practicality of TGC-NGS in a veterinary diagnostic setting have not yet been evaluated. Here we report the use of TGC-NGS assays for the detection and characterization of diverse feline pathogen taxa. We detected 31 pathogens comprising nine pathogen taxa in 28 felid samples analyzed. This included 20 pathogens detected via traditional PCR and 11 additional pathogens that had not been previously detected in the same samples. Most of the pathogens detected were sequenced at sufficient breadth and depth to confidently classify them at the species or subspecies level. Target nucleic acids were enriched from a low of 58-fold to 56 million-fold relative to host nucleic acids. Despite the promising performance of these assays, a number of pathogens detected by conventional PCR or serology were not isolated by TGC-NGS, suggesting that further validation is required before this technology can be used in lieu of quality-controlled standard assays. We conclude that TGC-NGS offers great potential as a broad multiplex pathogen characterization assay in veterinary diagnostic and research settings.

HyDA-Vista: towards optimal guided selection of k-mer size for sequence assembly.

MOTIVATION: Intimately tied to assembly quality is the complexity of the de Bruijn graph built by the assembler. Thus, there have been many paradigms developed to decrease the complexity of the de Bruijn graph. One obvious combinatorial paradigm for this is to allow the value of k to vary; having a larger value of k where the graph is more complex and a smaller value of k where the graph would likely contain fewer spurious edges and vertices. One open problem that affects the practicality of this method is how to predict the value of k prior to building the de Bruijn graph. We show that optimal values of k can be predicted prior to assembly by using the information contained in a phylogenetically-close genome and therefore, help make the use of multiple values of k practical for genome assembly. RESULTS: We present HyDA-Vista, which is a genome assembler that uses homology information to choose a value of k for each read prior to the de Bruijn graph construction. The chosen k is optimal if there are no sequencing errors and the coverage is sufficient. Fundamental to our method is the construction of the maximal sequence landscape, which is a data structure that stores for each position in the input string, the largest repeated substring containing that position. In particular, we show the maximal sequence landscape can be constructed in O(n+n log n)-time and O(n)-space. HyDA-Vista first constructs the maximal sequence landscape for a homologous genome. The reads are then aligned to this reference genome, and values of k are assigned to each read using the maximal sequence landscape and the alignments. Eventually, all the reads are assembled by an iterative de Bruijn graph construction method. Our results and comparison to other assemblers demonstrate that HyDA-Vista achieves the best assembly of E. coli before repeat resolution or scaffolding. AVAILABILITY: HyDA-Vista is freely available 1. The code for constructing the maximal sequence landscape and choosing the optimal value of k for each read is also separately available on the website and could be incorporated into any genome assembler.