Institution
Victorian Life Sciences Computation Initiative
Education•Melbourne, Victoria, Australia•
About: Victorian Life Sciences Computation Initiative is a education organization based out in Melbourne, Victoria, Australia. It is known for research contribution in the topics: Population & Massive parallel sequencing. The organization has 47 authors who have published 141 publications receiving 15559 citations.
Papers
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TL;DR: Prokka is introduced, a command line software tool to fully annotate a draft bacterial genome in about 10 min on a typical desktop computer, and produces standards-compliant output files for further analysis or viewing in genome browsers.
Abstract: UNLABELLED: The multiplex capability and high yield of current day DNA-sequencing instruments has made bacterial whole genome sequencing a routine affair. The subsequent de novo assembly of reads into contigs has been well addressed. The final step of annotating all relevant genomic features on those contigs can be achieved slowly using existing web- and email-based systems, but these are not applicable for sensitive data or integrating into computational pipelines. Here we introduce Prokka, a command line software tool to fully annotate a draft bacterial genome in about 10 min on a typical desktop computer. It produces standards-compliant output files for further analysis or viewing in genome browsers. AVAILABILITY AND IMPLEMENTATION: Prokka is implemented in Perl and is freely available under an open source GPLv2 license from http://vicbioinformatics.com/.
10,432 citations
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TL;DR: The advantages of open source to achieve the goals of the scikit-image library are highlighted, and several real-world image processing applications that use scik it-image are showcased.
Abstract: scikit-image is an image processing library that implements algorithms and utilities for use in research, education and industry applications. It is released under the liberal Modified BSD open source license, provides a well-documented API in the Python programming language, and is developed by an active, international team of collaborators. In this paper we highlight the advantages of open source to achieve the goals of the scikit-image library, and we showcase several real-world image processing applications that use scikit-image. More information can be found on the project homepage, http://scikit-image.org.
3,903 citations
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QIMR Berghofer Medical Research Institute1, University of Queensland2, Peter MacCallum Cancer Centre3, University of Melbourne4, University of Glasgow5, South Australia Pathology6, Imperial College London7, Royal Women's Hospital8, University of Sydney9, University of New South Wales10, Victorian Life Sciences Computation Initiative11, La Trobe University12, Harvard University13, University of Chicago14, University of Western Australia15
TL;DR: It is shown that gene breakage commonly inactivates the tumour suppressors RB1, NF1, RAD51B and PTEN in HGSC, and contributes to acquired chemotherapy resistance.
Abstract: Patients with high-grade serous ovarian cancer (HGSC) have experienced little improvement in overall survival, and standard treatment has not advanced beyond platinum-based combination chemotherapy, during the past 30 years. To understand the drivers of clinical phenotypes better, here we use whole-genome sequencing of tumour and germline DNA samples from 92 patients with primary refractory, resistant, sensitive and matched acquired resistant disease. We show that gene breakage commonly inactivates the tumour suppressors RB1, NF1, RAD51B and PTEN in HGSC, and contributes to acquired chemotherapy resistance. CCNE1 amplification was common in primary resistant and refractory disease. We observed several molecular events associated with acquired resistance, including multiple independent reversions of germline BRCA1 or BRCA2 mutations in individual patients, loss of BRCA1 promoter methylation, an alteration in molecular subtype, and recurrent promoter fusion associated with overexpression of the drug efflux pump MDR1.
1,195 citations
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TL;DR: This work presents SRST2, a read mapping-based tool for fast and accurate detection of genes, alleles and multi-locus sequence types (MLST) from WGS data, which is highly accurate and outperforms assembly-based methods in terms of both gene detection and allele assignment.
Abstract: 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. We include validation of SRST2 within a public health laboratory, and demonstrate its use for microbial genome surveillance in the hospital setting. In the face of rising threats of antimicrobial resistance and emerging virulence among 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/.
820 citations
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29 Apr 2016
TL;DR: SNPs can be extracted from a 8.3 GB alignment file using 59 MB of RAM and 1 CPU core, making it feasible to run on modest computers, and results in multiple formats for downstream analysis are output.
Abstract: Rapidly decreasing genome sequencing costs have led to a proportionate increase in the number of samples used in prokaryotic population studies. Extracting single nucleotide polymorphisms (SNPs) from a large whole genome alignment is now a routine task, but existing tools have failed to scale efficiently with the increased size of studies. These tools are slow, memory inefficient and are installed through non-standard procedures. We present SNP-sites which can rapidly extract SNPs from a multi-FASTA alignment using modest resources and can output results in multiple formats for downstream analysis. SNPs can be extracted from a 8.3 GB alignment file (1842 taxa, 22 618 sites) in 267 seconds using 59 MB of RAM and 1 CPU core, making it feasible to run on modest computers. It is easy to install through the Debian and Homebrew package managers, and has been successfully tested on more than 20 operating systems. SNP-sites is implemented in C and is available under the open source license GNU GPL version 3.
816 citations
Authors
Showing all 47 results
Name | H-index | Papers | Citations |
---|---|---|---|
Paul Harrison | 133 | 1400 | 80539 |
Antony W. Burgess | 79 | 339 | 23211 |
Gary F. Egan | 75 | 431 | 16962 |
Benedikt Brors | 71 | 258 | 22486 |
Alicia Oshlack | 49 | 149 | 17971 |
Torsten Seemann | 47 | 209 | 16278 |
Dieter M. Bulach | 38 | 133 | 4788 |
Nathan E. Hall | 32 | 73 | 3713 |
Daniel J. Park | 26 | 71 | 2776 |
David R. Powell | 25 | 91 | 2170 |
John Wagner | 24 | 83 | 5720 |
Michael J. Kuiper | 23 | 46 | 2955 |
Govinda Poudel | 22 | 75 | 1309 |
Fernando J. Rossello | 21 | 52 | 1545 |
Matthew T. Downton | 20 | 47 | 975 |