Future-proofing and maximizing the utility of metadata: The PHA4GE SARS-CoV-2 contextual data specification package.

BACKGROUND: The Public Health Alliance for Genomic Epidemiology (PHA4GE) (https://pha4ge.org) is a global coalition that is actively working to establish consensus standards, document and share best practices, improve the availability of critical bioinformatics tools and resources, and advocate for greater openness, interoperability, accessibility, and reproducibility in public health microbial bioinformatics. In the face of the current pandemic, PHA4GE has identified a need for a fit-for-purpose, open-source SARS-CoV-2 contextual data standard. RESULTS: As such, we have developed a SARS-CoV-2 contextual data specification package based on harmonizable, publicly available community standards. The specification can be implemented via a collection template, as well as an array of protocols and tools to support both the harmonization and submission of sequence data and contextual information to public biorepositories. CONCLUSIONS: Well-structured, rich contextual data add value, promote reuse, and enable aggregation and integration of disparate datasets. Adoption of the proposed standard and practices will better enable interoperability between datasets and systems, improve the consistency and utility of generated data, and ultimately facilitate novel insights and discoveries in SARS-CoV-2 and COVID-19. The package is now supported by the NCBI's BioSample database

Future-proofing and maximizing the utility of metadata: The PHA4GE SARS-CoV-2 contextual data specification package

Background The Public Health Alliance for Genomic Epidemiology (PHA4GE) (https://pha4ge.org) is a global coalition that is actively working to establish consensus standards, document and share best practices, improve the availability of critical bioinformatics tools and resources, and advocate for greater openness, interoperability, accessibility, and reproducibility in public health microbial bioinformatics. In the face of the current pandemic, PHA4GE has identified a need for a fit-for-purpose, open-source SARS-CoV-2 contextual data standard. Results As such, we have developed a SARS-CoV-2 contextual data specification package based on harmonizable, publicly available community standards. The specification can be implemented via a collection template, as well as an array of protocols and tools to support both the harmonization and submission of sequence data and contextual information to public biorepositories. Conclusions Well-structured, rich contextual data add value, promote reuse, and enable aggregation and integration of disparate datasets. Adoption of the proposed standard and practices will better enable interoperability between datasets and systems, improve the consistency and utility of generated data, and ultimately facilitate novel insights and discoveries in SARS-CoV-2 and COVID-19. The package is now supported by the NCBI’s BioSample database

Mitochondrial genome variation in healthy aging

Mitochondria are thought to play a role in the aging process through their production of reactive oxygen species (ROS), and their regulation of cell fate via senescence and apoptosis. We hypothesize that genetic variation in the mitochondrial genome may explain a portion of the phenotypic variance in the development of long-term good health. To test this hypothesis, we have performed genetic association tests on a set of common mitochondrial polymorphisms, in a study of 419 exceptionally healthy seniors (cases) and 415 population-based mid-life individuals (controls). Variant discovery was performed using Sanger sequencing of 834 individuals for the 1.1 kb non-coding mitochondrial control region, and identified 277 SNPs present in at least one individual. A set of 92 mitochondrial coding-region SNPs were chosen via pooled high-throughput sequencing, combined with a previously-published set of European-specific mitochondrial tag SNPs. After filtering for minor-allele frequency of > 10%, a set of nine control-region SNPs and seven coding-region SNPs were tested for association with healthy aging. None showed a statistically-significant association signal. Additionally, one control-region variant that had shown association in an Italian centenarian population was tested in our sample set, but the association was not replicated.Medicine, Faculty ofMedical Genetics, Department ofGraduat

MentaLiST – A fast MLST caller for large MLST schemes

MLST (multi-locus sequence typing) is a classic technique for genotyping bacteria, widely applied for pathogen outbreak surveillance. Traditionally, MLST is based on identifying sequence types from a small number of housekeeping genes. With the increasing availability of whole-genome sequencing data, MLST methods have evolved towards larger typing schemes, based on a few hundred genes [core genome MLST (cgMLST)] to a few thousand genes [whole genome MLST (wgMLST)]. Such large-scale MLST schemes have been shown to provide a finer resolution and are increasingly used in various contexts such as hospital outbreaks or foodborne pathogen outbreaks. This methodological shift raises new computational challenges, especially given the large size of the schemes involved. Very few available MLST callers are currently capable of dealing with large MLST schemes. We introduce MentaLiST, a new MLST caller, based on a k-mer voting algorithm and written in the Julia language, specifically designed and implemented to handle large typing schemes. We test it on real and simulated data to show that MentaLiST is faster than any other available MLST caller while providing the same or better accuracy, and is capable of dealing with MLST schemes with up to thousands of genes while requiring limited computational resources. MentaLiST source code and easy installation instructions using a Conda package are available at https://github.com/WGS-TB/MentaLiST