- Research group
Leggett Group
Developing algorithms to enable new technologies.
Group Leader
Richard Leggett
Group activities.
The Technology Algorithms Group is focussed on developing computational methods to enhance new sequencing technologies, particularly in the area of real-time and in-field metagenomics and surveillance.
Much of our current projects are focussed on nanopore sequencing, but we are also interested in new applications of short read technologies and other long-read technologies.
Our work usually involves software engineering, data science, algorithm development and data analysis, but members of the group are also active in the wet lab, developing DNA extraction protocols and carrying out sequencing.
Some of our current projects include:
• Developing tools for In-field and real-time sequencing.
• Development of the NanoOK tool for nanopore data analysis.
• Developing real-time metagenomic analysis software.
• Development of MetaCortex, an assembly tool for metagenomic data.
• Clinical microbiome diagnostics: monitoring pathogens and AMR resistance in human microbiomes.
• In-field environmental sequencing.
• Ocean metagenomics.
• Viral metagenomics.
• Real-time, low-power, efficient analysis.
Tools.
MARTi
MARTi is a software package designed for performing real-time analysis of metagenomic samples using nanopore sequencing.
ALVIS
Alvis is a tool that can generate visualisations for a number of common long read/contig alignment analysis tasks.
NextClip
A tool for comprehensive quality analysis and read preparation for Nextera long mate pair (LMP) libraries.
NanoOK
A tool for comprehensive analysis of Oxford Nanopore MinION sequence data.
Kontaminant
Kmer-based screening of sequencing data.
Bubbleparse
Identification of SNPs from sequencing data without a reference.
Publications.
Identifying and Classifying Trait Linked Polymorphisms in Non-Reference Species by Walking Coloured de Bruijn Graphs
Leggett R.M., Ramirez-Gonzalez R.H., Verweij W., Kawashima C.G., Iqbal Z., Jones J.D.G., Caccamo M., MacLean D. (2013).
PLoS ONE 8(3): e60058.
