Biology subjects

Mark B Schultz

Publications and source records attributed to Mark B Schultz.

2 recordsLinked to original sources

bioRxiv2015

Bandage: interactive visualisation of de novo genome assemblies

SummaryWhile de novo assembly graphs contain assembled contigs (nodes), the connections between those contigs (edges) are difficult for users to access. Bandage (a Bioinformatics Application for Navigating De novo Assembly Graphs Easily) is a tool for visualising assembly graphs with connections. Users can zoom in to specific areas of the graph and interact with it by moving nodes, adding labels, changing colours and extracting sequences. BLAST searches can be performed within the Bandage GUI and the hits are displayed as highlights in the graph. By displaying connections between contigs, Bandage presents new possibilities for analysing de novo assemblies that are not possible through investigation of contigs alone.\n\nAvailability and implementationSource code and binaries are freely available at https://github.com/rrwick/Bandage. Bandage is implemented in C++ and supported on Linux, OS X and Windows.\n\nContactrrwick@gmail.com\n\nSupplementary informationA full feature list and screenshots are available at Bioinformatics online and http://rrwick.github.io/Bandage.

Bioinformatics↗

bioRxiv2014

SRST2: Rapid genomic surveillance for public health and hospital microbiology labs

Rapid molecular typing of bacterial pathogens is critical for public health epidemiology, surveillance and infection control, yet routine use of whole genome sequencing (WGS) for these purposes poses significant challenges. Here we present SRST2, a read mapping-based tool for fast and accurate detection of genes, alleles and multi-locus sequence types (MLST) from WGS data. Using >900 genomes from common pathogens, we show SRST2 is highly accurate and outperforms assembly-based methods in terms of both gene detection and allele assignment. Here we have demonstrated the use of SRST2 for microbial genome surveillance in a variety of public health and hospital settings. In the face of rising threats of antimicrobial resistance and emerging virulence amongst bacterial pathogens, SRST2 represents a powerful tool for rapidly extracting clinically useful information from raw WGS data. Source code is available from http://katholt.github.io/srst2/.

Genomics↗