The use of whole-genome sequencing (WGS) for routine typing of bacterial isolates has increased substantially in recent years. For (MTB), in particular, WGS has the benefit of drastically reducing the time required to generate results compared to most conventional phenotypic methods. Consequently, a multitude of solutions for analyzing WGS MTB data have been developed, but their successful integration in clinical and national reference laboratories is hindered by the requirement for their validation, for which a consensus framework is still largely absent. We developed a bioinformatics workflow for (Illumina) WGS-based routine typing of MTB complex (MTBC) member isolates allowing complete characterization, including (sub)species confirmation and identification (16S, /RD, ), single nucleotide polymorphism (SNP)-based antimicrobial resistance (AMR) prediction, and pathogen typing (spoligotyping, SNP barcoding, and core genome multilocus sequence typing). Workflow performance was validated on a per-assay basis using a collection of 238 in-house-sequenced MTBC isolates, extensively characterized with conventional molecular biology-based approaches supplemented with public data. For SNP-based AMR prediction, results from molecular genotyping methods were supplemented with modified data sets, allowing us to greatly increase the set of evaluated mutations. The workflow demonstrated very high performance with performance metrics of >99% for all assays, except for spoligotyping, where sensitivity dropped to ∼90%. The validation framework for our WGS-based bioinformatics workflow can aid in the standardization of bioinformatics tools by the MTB community and other SNP-based applications regardless of the targeted pathogen(s). The bioinformatics workflow is available for academic and nonprofit use through the Galaxy instance of our institute at https://galaxy.sciensano.be.
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http://dx.doi.org/10.1128/JCM.00202-21 | DOI Listing |
Sci Adv
January 2025
Department of Cell Biology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA.
Lysosomal storage diseases (LSDs) comprise ~50 monogenic disorders marked by the buildup of cellular material in lysosomes, yet systematic global molecular phenotyping of proteins and lipids is lacking. We present a nanoflow-based multiomic single-shot technology (nMOST) workflow that quantifies HeLa cell proteomes and lipidomes from over two dozen LSD mutants. Global cross-correlation analysis between lipids and proteins identified autophagy defects, notably the accumulation of ferritinophagy substrates and receptors, especially in and mutants, where lysosomes accumulate cholesterol.
View Article and Find Full Text PDFCurr Protoc
January 2025
New England Biolabs, Ipswich, Massachusetts.
Functional genomic approaches have been effective at uncovering the function of uncharacterized genes and identifying new functions for known genes. Often these approaches rely on an in vivo screen or selection to associate genes with a phenotype of interest. These selections and screens are dependent upon the expression of proteins encoded in genomic DNA from an expression vector, such as a plasmid.
View Article and Find Full Text PDFJ Allergy Clin Immunol
January 2025
Department of Dermatology, University of Michigan Medical School, Ann Arbor, Michigan, USA; Taubman Medical Research Institute, University of Michigan Medical School, Ann Arbor, Michigan, USA. Electronic address:
Spatial profiling, through single-cell gene level expression data paired with cell localization, offers unprecedented biological insights within the intact spatial context of cells in healthy and diseased tissue, adding a novel dimension to data interpretation. This review summarizes recent developments in this field, its application to allergy and inflammation, and recent single-cell resolution platforms designed for spatial transcriptomics with a focus on data processing and analyses for efficient biological interpretation of data. By preserving spatial context, these technologies provide critical insights into tissue architecture and cellular interactions unattainable with traditional transcriptomics methods, such as revealing localized inflammatory cell network in atopic dermatitis, and T-cell interactions in the lung in chronic obstructive pulmonary disease.
View Article and Find Full Text PDFClin Oncol (R Coll Radiol)
December 2024
Department of Pathology and Laboratory Medicine, Medical College of Wisconsin, 9200 W Wisconsin Ave, Milwaukee, WI 53226, USA; Department of Pathology, Yale School of Medicine, 20 York Street, Ste East Pavilion 2-631, New Haven, CT 06510, USA. Electronic address:
Aims: The recent widespread use of electronic health records (EHRs) has opened the possibility for innumerable artificial intelligence (AI) tools to aid in genomics, phenomics, and other research, as well as disease prevention, diagnosis, and therapy. Unfortunately, much of the data contained in EHRs are not optimally structured for even the most sophisticated AI approaches. There are very few published efforts investigating methods for recording discrete data in EHRs that would not slow current clinical workflows or ways to prioritise patient characteristics worth recording.
View Article and Find Full Text PDFBioinformatics
January 2025
Federal Research Institute for Animal Health, Institute of Novel and Emerging Infectious Diseases, Friedrich-Loeffler-Institut, 17493, Greifswald-Insel Riems, Germany.
Summary: Virus surveillance programmes are designed to counter the growing threat of viral outbreaks to human health. Nanopore sequencing, in particular, has proven to be suitable for this purpose, as it is readily available and provides rapid results. However, as special bioinformatic programmes are required to extract the relevant information from the sequencing data, applications are needed that allow users without extensive bioinformatics knowledge to carry out the relevant analysis steps.
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