Biology subjects

Hunt, M.

Publications and source records attributed to Hunt, M..

3 recordsLinked to original sources

bioRxiv2017

SeroBA: rapid high-throughput serotyping of Streptococcus pneumoniae from whole genome sequence data

Streptococcus pneumoniae is responsible for 240,000 - 460,000 deaths in children under 5 years of age each year. Accurate identification of pneumococcal serotypes is important for tracking the distribution and evolution of serotypes following the introduction of effective vaccines. Recent efforts have been made to infer serotypes directly from genomic data but current software approaches are limited and do not scale well. Here, we introduce a novel method, SeroBA, which uses a hybrid assembly and mapping approach. We compared SeroBA against real and simulated data and present results on the concordance and computational performance against a validation dataset, the robustness and scalability when analysing a large dataset, and the impact of varying the depth of coverage in the cps locus region on sequence-based serotyping. SeroBA can predict serotypes, by identifying the cps locus, directly from raw whole genome sequencing read data with 98% concordance using a k-mer based method, can process 10,000 samples in just over 1 day using a standard server and can call serotypes at a coverage as low as 10x. SeroBA is implemented in Python3 and is freely available under an open source GPLv3 license from: https://github.com/sanger-pathogens/seroba\n\nDATA SUMMARYO_LIThe reference genome Streptococcus pneumoniae ATCC 700669 is available from National Center for Biotechnology Information (NCBI) with the accession number: FM211187\nC_LIO_LISimulated paired end reads for experiment 2 have been deposited in FigShare: https://doi.org/10.6084/m9.figshare.5086054.v1\nC_LIO_LIAccession numbers for all other experiments are listed in Supplementary Table S1 and Supplementary Table S2.\nC_LI\n\nI/We confirm all supporting data, code and protocols have been provided within the article or through supplementary data files. {boxtimes}\n\nIMPACT STATEMENTThis article describes SeroBA, a k-mer based method for predicting the serotypes of Streptococcus pneumoniae from Whole Genome Sequencing (WGS) data. SeroBA can identify 92 serotypes and 2 subtypes with constant memory usage and low computational costs. We showed that SeroBA is able to reliably predict serotypes at a depth of coverage as low as 10x and is scalable to large datasets.

bioinformatics↗

bioRxiv2017

Inferring synteny between genome assemblies: a systematic evaluation

Identification of synteny between genomes of closely related species is an important aspect of comparative genomics. However, it is unknown to what extent draft assemblies lead to errors in such analysis. To investigate this, we fragmented genome assemblies of model nematodes to various extents and conducted synteny identification and downstream analysis. We first show that synteny between species can be underestimated up to 40% and find disagreements between popular tools that infer synteny blocks. This inconsistency and further demonstration of erroneous gene ontology enrichment tests throws into question the robustness of previous synteny analysis when gold standard genome sequences remain limited. In addition, determining the true evolutionary relationship is compromised by assembly improvement using a reference guided approach with a closely related species. Annotation quality, however, has minimal effect on synteny if the assembled genome is highly contiguous. Our results highlight the need for gold standard genome assemblies for synteny identification and accurate downstream analysis.\n\nAuthor summaryGenome assemblies across all domains of life are currently produced routinely. Initial analysis of any new genome usually includes annotation and comparative genomics. Synteny provides a framework in which conservation of homologous genes and gene order is identified between genomes of different species. The availability of human and mouse genomes paved the way for algorithm development in large-scale synteny mapping, which eventually became an integral part of comparative genomics. Synteny analysis is regularly performed on assembled sequences that are fragmented, neglecting the fact that most methods were developed using complete genomes. Here, we systematically evaluate this interplay by inferring synteny in genome assemblies with different degrees of contiguation. As expected, our investigation reveals that assembly quality can drastically affect synteny analysis, from the initial synteny identification to downstream analysis. Importantly, we found that improving a fragmented assembly using synteny with the genome of a related species can be dangerous, as this a priori assumes a potentially false evolutionary relationship between the species. The results presented here re-emphasize the importance of gold standard genomes to the science community, and should be achieved given the current progress in sequencing technology.

bioinformatics↗

bioRxiv2017

ARIBA: rapid antimicrobial resistance genotyping directly from sequencing reads

Antimicrobial resistance (AMR) is one of the major threats to human and animal health worldwide, yet few high-throughput tools exist to analyse and predict the resistance of a bacterial isolate from sequencing data. Here we present a new tool, ARIBA, that identifies AMR-associated genes and single nucleotide polymorphisms directly from short reads, and generates detailed and customisable output. The accuracy and advantages of ARIBA over other tools are demonstrated on three datasets from Gram-positive and Gram-negative bacteria, with ARIBA outperforming existing methods. ARIBA is available at https://github.com/sanger-pathogens/ariba.

bioinformatics↗