Biology subjects

Alikhan, N.-F.

Publications and source records attributed to Alikhan, N.-F..

4 recordsLinked to original sources

bioRxiv2017

GrapeTree: Visualization of core genomic relationships among 100,000 bacterial pathogens

O_LICurrent methods struggle to reconstruct and visualise the genomic relationships of [≥]100,000 bacterial genomes.\nC_LIO_LIGrapeTree facilitates the analyses of allelic profiles from 10,000s of core genomes within a web browser window.\nC_LIO_LIGrapeTree implements a novel minimum spanning tree algorithm to reconstruct genetic relationships despite missing data together with a static \"GrapeTree Layout\" algorithm to render interactive visualisations of large trees.\nC_LIO_LIGrapeTree is a stand-along package for investigating Newick trees plus associated metadata and is also integrated into EnteroBase to facilitate cutting edge navigation of genomic relationships among >160,000 genomes from bacterial pathogens.\nC_LIO_LIThe GrapeTree package was released under the GPL v3.0 Licence.\nC_LI

bioinformatics↗

bioRxiv2017

Accurate Reconstruction of Microbial Strains Using Representative Reference Genomes

Exploring the genetic diversity of microbes within the environment through metagenomic sequencing first requires classifying these reads into taxonomic groups. Current methods compare these sequencing data with existing biased and limited reference databases. Several recent evaluation studies demonstrate that current methods either lack sufficient sensitivity for species-level assignments or suffer from false positives, overestimating the number of species in the metagenome. Both are especially problematic for the identification of low-abundance microbial species, e. g. detecting pathogens in ancient metagenomic samples. We present a new method, SPARSE, which improves taxonomic assignments of metagenomic reads. SPARSE balances existing biased reference databases by grouping reference genomes into similarity-based hierarchical clusters, implemented as an efficient incremental data structure. SPARSE assigns reads to these clusters using a probabilistic model, which specifically penalizes non-specific mappings of reads from unknown sources and hence reduces false-positive assignments. Our evaluation on simulated datasets from two recent evaluation studies demonstrated the improved precision of SPARSE in comparison to other methods for species-level classification. In a third simulation, our method successfully differentiated multiple co-existing Escherichia coli strains from the same sample. In real archaeological datasets, SPARSE identified ancient pathogens with[≤] 0.02% abundance, consistent with published findings that required additional sequencing data. In these datasets, other methods either missed targeted pathogens or reported non-existent ones. SPARSE and all evaluation scripts are available at https://github.com/zheminzhou/SPARSE.

bioinformatics↗

bioRxiv2017

Comparison Of Multi-locus Sequence Typing Software For Next Generation Sequencing Data

Multi-locus sequence typing (MLST) is a widely used method for categorising bacteria. Increasingly MLST is being performed using next generation sequencing data by reference labs and for clinical diagnostics. Many software applications have been developed to calculate sequence types from NGS data; however, there has been no comprehensive review to date on these methods. We have compared six of these applications against real and simulated data and present results on: 1. the accuracy of each method against traditional typing methods, 2. the performance on real outbreak datasets, 3. in the impact of contamination and varying depth of coverage, and 4. the computational resource requirements.\n\nDATA SUMMARYO_LISimulated reads for datasets testing coverage and mixed samples have been deposited in Figshare; DOI: https://doi.org/10.6084/m9.figshare.4602301.vl\nC_LIO_LIOutbreak databases are available from Github; url - https://github.com/WGS-standards-and-analysis/datasets\nC_LIO_LIDocker containers used to run each of the applications are available from Github; url - https://tinyurl.com/z7ks2ft\nC_LIO_LIAccession numbers for the data used in this paper are available in the Supplementary material.\nC_LI\n\nWe confirm all supporting data, code and protocols have been provided within the article or through supplementary data files. {ballotx}\n\nIMPACT STATEMENTSequence typing is rapidly transitioning from traditional sequencing methods to using whole genome sequencing. A number of in silico prediction methods have been developed on an ad hoc basis and aim to replicate Multi-locus sequence typing (MLST). This is the first study to comprehensively evaluate multiple MLST software applications on real validated datasets and on common simulated difficult cases. It will give researchers a clearer understanding of the accuracy, limitations and computational performance of the methods they use, and will assist future researchers to choose the most appropriate method for their experimental goals.

bioinformatics↗

bioRxiv2017

Millennia of genomic stability within the invasive Para C Lineage of Salmonella enterica

Salmonella enterica serovar Paratyphi C is the causative agent of enteric (paratyphoid) fever. While today a potentially lethal infection of humans that occurs in Africa and Asia, early 20th century observations in Eastern Europe suggest it may once have had a wider-ranging impact on human societies. We recovered a draft Paratyphi C genome from the 800-year-old skeleton of a young woman in Trondheim, Norway, who likely died of enteric fever. Analysis of this genome against a new, significantly expanded database of related modern genomes demonstrated that Paratyphi C is descended from the ancestors of swine pathogens, serovars Choleraesuis and Typhisuis, together forming the Para C Lineage. Our results indicate that Paratyphi C has been a pathogen of humans for at least 1,000 years, and may have evolved after zoonotic transfer from swine during the Neolithic period.\n\nOne Sentence SummaryThe combination of an 800-year-old Salmonella enterica Paratyphi C genome with genomes from extant bacteria reshapes our understanding of this pathogens origins and evolution.

microbiology↗