Expanding the Galaxy's reference data.

Summary: Properly and effectively managing reference datasets is an important task for many bioinformatics analyses. Refgenie is a reference asset management system that allows users to easily organize, retrieve and share such datasets. Here, we describe the integration of refgenie into the Galaxy platform. Server administrators are able to configure Galaxy to make use of reference datasets made available on a refgenie instance. In addition, a Galaxy Data Manager tool has been developed to provide a graphical interface to refgenie's remote reference retrieval functionality. A large collection of reference datasets has also been made available using the CVMFS (CernVM File System) repository from GalaxyProject.org, with mirrors across the USA, Canada, Europe and Australia, enabling easy use outside of Galaxy. Availability and implementation: The ability of Galaxy to use refgenie assets was added to the core Galaxy framework in version 22.01, which is available from https://github.com/galaxyproject/galaxy under the Academic Free License version 3.0. The refgenie Data Manager tool can be installed via the Galaxy ToolShed, with source code managed at https://github.com/BlankenbergLab/galaxy-tools-blankenberg/tree/main/data_managers/data_manager_refgenie_pull and released using an MIT license. Access to existing data is also available through CVMFS, with instructions at https://galaxyproject.org/admin/reference-data-repo/. No new data were generated or analyzed in support of this research.

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.

Influenza Classification Suite: An automated Galaxy workflow for rapid influenza sequence analysis.

Influenza viruses continually evolve to evade population immunity, and the different lineages are assigned into clades based on shared mutations. We have developed a publicly available computational workflow, the Influenza Classification Suite, for rapid clade mapping of sequenced influenza viruses. This suite provides a user-friendly workflow implemented in Galaxy to automate clade calling and antigenic site extraction. Workflow input includes clade definition and amino acid index array files, which can be customized to identify any clades of interest. The Influenza Classification Suite provides rapid, high-resolution understanding of circulating influenza strain evolution to inform influenza vaccine effectiveness and the need for potential vaccine reformulation.

Reduced telomere length variation in healthy oldest old.

Telomeres protect against DNA degradation at the ends of linear chromosomes. The number of telomere repeats is reduced over time in human aging. Using flow FISH we have assessed telomere length in 134 exceptionally healthy seniors aged 85 or older who have never been diagnosed with cancer, cardiovascular disease, major pulmonary disease, diabetes or Alzheimer disease (the 'Super-seniors') and 47 randomly-ascertained mid-life individuals aged 40-50 years. We compared their telomere lengths to a reference interval based on 400 individuals aged 1-100 years and show that Super-seniors do not have exceptionally long telomeres for their age. Consistent with the known trend of telomere shortening over time; however, they have shorter telomeres than the younger control group. Furthermore, we show that variability in telomere length was lower in the Super-seniors than in the mid-life controls or the reference data. Reduced telomere length variation was observed for lymphocytes, CD45RA-positive T-cells and memory T-cells. These results suggest that individuals, some types of their somatic cells, or both, may be selected for an optimal rather than extreme telomere length. Selection of individuals and/or cells that have an optimal telomere repeat length could contribute to disease resistance and promote healthy aging.

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.