A comparative genomic framework for the in silico design and assessment of molecular typing methods using whole-genome sequence data with application to Listeria monocytogenes

xiii, 100 leaves : ill. ; 29 cmAlthough increased genome sequencing e orts have increased our understanding of genomic variability within many bacterial species, there has been limited application of this knowledge towards assessing current molecular typing methods and developing novel molecular typing methods. This thesis reports a novel in silico comparative genomic framework where the performance of typing methods is assessed on the basis of the discriminatory power of the method as well as the concordance of the method with a whole-genome phylogeny. Using this framework, we designed a comparative genomic ngerprinting (CGF) assay for Listeria monocytogenes through optimized molecular marker selection. In silico validation and assessment of the CGF assay against two other molecular typing methods for L. monocytogenes (multilocus sequence typing (MLST) and multiple virulence locus sequence typing (MVLST)) revealed that the CGF assay had better performance than these typing methods. Hence, optimized molecular marker selection can be used to produce highly discriminatory assays with high concordance to whole-genome phylogenies. The framework described in this thesis can be used to assess current molecular typing methods against whole-genome phylogenies and design the next generation of high-performance molecular typing methods from whole-genome sequence data

Development of a comparative genomic fingerprinting assay for rapid and high resolution genotyping of Arcobacter butzleri

Sherpa Romeo green journal. Open access, distributed under the terms of the Creative Commons Attribution (CC-BY) License.Background Molecular typing methods are critical for epidemiological investigations, facilitating disease outbreak detection and source identification. Study of the epidemiology of the emerging human pathogen Arcobacter butzleri is currently hampered by the lack of a subtyping method that is easily deployable in the context of routine epidemiological surveillance. In this study we describe a comparative genomic fingerprinting (CGF) method for high-resolution and high-throughput subtyping of A. butzleri. Comparative analysis of the genome sequences of eleven A. butzleri strains, including eight strains newly sequenced as part of this project, was employed to identify accessory genes suitable for generating unique genetic fingerprints for high-resolution subtyping based on gene presence or absence within a strain. Results A set of eighty-three accessory genes was used to examine the population structure of a dataset comprised of isolates from various sources, including human and non-human animals, sewage, and river water (n=156). A streamlined assay (CGF40) based on a subset of 40 genes was subsequently developed through marker optimization. High levels of profile diversity (121 distinct profiles) were observed among the 156 isolates in the dataset, and a high Simpson’s Index of Diversity (ID) observed (ID > 0.969) indicate that the CGF40 assay possesses high discriminatory power. At the same time, our observation that 115 isolates in this dataset could be assigned to 29 clades with a profile similarity of 90% or greater indicates that the method can be used to identify clades comprised of genetically similar isolates. Conclusions The CGF40 assay described herein combines high resolution and repeatability with high throughput for the rapid characterization of A. butzleri strains. This assay will facilitate the study of the population structure and epidemiology of A. butzleri.Ye

The Global Flood Partnership Conference 2017 - From hazards to impacts

From 27 – 29 June 2017, the 2017 Global Flood Partnership Conference was held at the University of Alabama, U.S.A. More than 90 participants attended the conference coming from 11 different countries in 5 continents. They represented 56 institutions including international organisations, the private sector, national authorities, universities, governmental research agencies and non-profit organisations. Each year, floods cause devastating losses and damage across the world. Growing populations in ill-planned flood-prone coastal and riverine areas are increasingly exposed to more extreme rainfall events. With more population and economic assets at risk, governments, banks, international development and relief agencies, and private firms are investing in flood reduction measures. However, in many countries, the flood risk is not managed optimally because of a lack of scientific data and methods or a communication gap between science and risk managers. The Global Flood Partnership was launched in 2014 and is a cooperation framework between scientific organisations and flood disaster managers worldwide to develop flood observational and modeling infrastructure, leveraging on existing initiatives for better predicting and managing flood disaster impacts and flood risk globally. The conference theme was “From hazards to impacts” and participants had the opportunity to showcase their latest relevant research and activities. As usual, the advances and success stories of the Partnership were reviewed and the next steps to further strengthen the GFP were discussed. As in past meetings, participants had numerous opportunities to present their work, exchange ideas, and turn it into a lively and successful meeting. This included a "Marketplace of Ideas" session, "Ignite" talks, Problem-solving session, workshops, poster pitch session and breakout groups.JRC.E.1-Disaster Risk Managemen

A global network for operational flood risk reduction

Every year riverine flooding affects millions of people in developing countries, due to the large population exposure in the floodplains and the lack of adequate flood protection measures. Preparedness and monitoring are effective ways to reduce flood risk. State-of-the-art technologies relying on satellite remote sensing as well as numerical hydrological and weather predictions can detect and monitor severe flood events at a global scale. This paper describes the emerging role of the Global Flood Partnership (GFP), a global network of scientists, users, private and public organizations active in global flood risk management. Currently, a number of GFP member institutes regularly share results from their experimental products, developed to predict and monitor where and when flooding is taking place in near real-time. GFP flood products have already been used on several occasions by national environmental agencies and humanitarian organizations to support emergency operations and to reduce the overall socio-economic impacts of disasters. This paper describes a range of global flood products developed by GFP partners, and how these provide complementary information to support and improve current global flood risk management for large scale catastrophes. We also discuss existing challenges and ways forward to turn current experimental products into an integrated flood risk management platform to improve rapid access to flood information and increase resilience to flood events at global scale

CFIA-NCFAD/Delta-SARS-CoV-2-WTD-QC: 1.0.0 - Zenodo release

Release for Zenodo archiving and DOI generation

sistr_cmd v1.0.2 serotyping databases

<p><strong><a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0147101">Salmonella In Silico Typing Resource (SISTR)</a> <a href="https://github.com/peterk87/sistr_cmd">sistr_cmd</a> version <a href="https://github.com/peterk87/sistr_cmd/releases/tag/v1.0.2">1.0.2</a> serotyping databases</strong></p> <p>File structure tree for <code>sistr_cmd</code> <code>data</code> folder:</p> <pre><code>. |-- [4.0K] antigens | |-- [1.0M] fliC.fasta | |-- [210K] fljB.fasta | |-- [126K] wzx.fasta | `-- [ 60K] wzy.fasta |-- [4.0K] cgmlst | |-- [7.4M] cgmlst-centroid.fasta | |-- [ 96M] cgmlst-full.fasta | |-- [134M] cgmlst-profiles.hdf | `-- [ 803] README.md |-- [1.1M] genomes-to-serovar.txt |-- [1.0M] genomes-to-subspecies.txt |-- [118K] Salmonella-serotype_serogroup_antigen_table-WHO_2007.csv `-- [ 92M] sistr.msh 2 directories, 12 files</code></pre> <p><strong>Description of files:</strong></p> <ul> <li><code>genomes-to-serovar.txt</code>: Each genome id to serovar designation delimited by tab character for the 52,790 Salmonella genomes.</li> <li><code>genomes-to-subspecies.txt</code>: Each genome id to subspecies designation delimited by tab character for the 52,790 Salmonella genomes.</li> <li><code>Salmonella-serotype_serogroup_antigen_table-WHO_2007.csv</code>: Serovar and antigenic formula information table used by `sistr_cmd` for looking up serovar designations from antigen results</li> <li><code>sistr.msh</code>: <a href="https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0997-x">Mash</a> sketch file of 11840 Salmonella genomes for Mash-based serotyping</li> <li><code>antigens</code>: for antigen gene search-based serotyping <ul> <li><code>fliC.fasta</code>: fliC gene alleles for H1-antigen typing</li> <li><code>fljB.fasta</code>: fljB gene alleles for H2-antigen typing</li> <li><code>wzx.fasta</code>: wzx gene alleles for O-antigen typing</li> <li><code>wzy.fasta</code>: wzy gene alleles for O-antigen typing</li> </ul> </li> <li><code>cgmlst</code> for core-genome multilocus sequence typing (cgMLST) and cgMLST-based serotyping <ul> <li><code>cgmlst-profiles.hdf</code>: HDF5 file with cgMLST allelic profiles of 52,790 Salmonella genomes <ul> <li>read in with Pandas, i.e. <pre><code>pd.read_hdf(CGMLST_PROFILES_PATH, key='cgmlst')</code></pre> </li> </ul> </li> <li><code>cgmlst-centroid.fasta</code>: "Centroid" or representative alleles of 52,790 Salmonella genomes for rapid NCBI BLAST+ blastn searching. Centroid alleles were defined from the full set of alleles for the 52,790 Salmonella genomes as the alleles for each locus: <ul> <li>group alleles by length</li> <li>group length grouped alleles by ends (28bp at allele start and end; 28 is word size of blastn megablast)</li> <li>hierarchical clustering of length+end grouped alleles</li> <li>flat clusters at 2.5% distance</li> <li>within each cluster, pick allele with least distance to others in cluster</li> </ul> </li> </ul> </li> <li><code>cgmlst-full.fasta</code>: alleles for the 52,790 Salmonella genomes</li> </ul

CFIA-NCFAD/nf-flu v3.3.5

Fixes &lt;ul&gt; &lt;li&gt;handling of empty IRMA &lt;code&gt;amended_consensus/&lt;/code&gt; when running a negative control or blank sequence (#47)&lt;/li&gt; &lt;/ul&gt

CFIA-NCFAD/nf-flu v3.3.6

&lt;h3&gt;Fixes&lt;/h3&gt; &lt;ul&gt; &lt;li&gt;docs updated to show proper profile to run test profiles for Illumina and Nanopore locally (#52)&lt;/li&gt; &lt;li&gt;&lt;code&gt;test_nanopore&lt;/code&gt; profile has been updated to run locally with &lt;a href="https://github.com/CFIA-NCFAD/nf-test-datasets/blob/nf-flu/samplesheet/samplesheet_test_nanopore_influenza.csv"&gt;the test samplesheet.csv updated with URLs to FASTQ files at CFIA-NCFAD/nf-test-datasets&lt;/a&gt;&lt;/li&gt; &lt;li&gt;read samplesheet CSV in &lt;code&gt;parse_influenza_blast_results.py&lt;/code&gt; with all columns read as string rather than inferred (#54)&lt;/li&gt; &lt;li&gt;handle cloud storage paths and non-HTTP/FTP URLs in user samplesheets (#55)&lt;/li&gt; &lt;/ul&gt