By analyzing the genomes of hundreds of Shigella bacteria, a team led by scientists from the University of Liverpool has uncovered new details about the pathogen that may help in disease prevention and control. Shigella infection is a leading cause of severe childhood diarrhea in low- and middle-income countries and is responsible for an estimated 212,000 deaths worldwide each year. While it can be treated with antibiotics, the pathogen is increasingly antimicrobial resistant, and development of a vaccine is hampered by Shigella's considerable genomic and phenotypic diversity. In their study, which appears in this week's Nature Microbiology, the researchers performed whole-genome sequencing on 1,246 Shigella isolates sampled from seven countries in sub-Saharan Africa and South Asia. Among their findings is a Shigella species that is significantly more responsible for causing disease than others and a number of genes involved in antibiotic resistance. The findings, the authors write, "demonstrates the urgent need to integrate existing genomic diversity into vaccine and treatment plans for Shigella." The study also helps establish a framework for that is translatable to other bacterial pathogens, they add.
A new computational approach for analysis of spatially resolved omics data is presented in Nature Methods this week. Developed by scientists at the Helmholtz Center Munich, the framework — dubbed spatial quantification of molecular data in Python, or Squidpy — combines various omics and image analysis tools to enable the scalable description of spatial molecular data such as transcriptome or multivariate proteins. Squidpy provides an infrastructure and analysis methods that allows for the efficient storage, manipulation, and interactive visualization of spatial omics data, its developers write. It can also interface with a variety of already existing libraries for the scalable analysis of spatial omics data. "We hope that Squidpy will serve as a bridge between the molecular omics community and the image analysis and computer vision community to develop the next generation of computational methods for spatial omics technologies," the researchers conclude.
Finally, a knowledge graph framework that facilitates harmonization of proteomics with other omics data, biomedical data, and scientific literature is described in this week's Nature Biotechnology. Precision medicine requires the integration of omics data into clinical decision making, but this process is hindered by the diversity of such data and its spread across multiple databases and publications. To address this issue, investigators from the University of Copenhagen developed the Clinical Knowledge Graph, an open-source platform of nearly 20 million nodes and 220 million relationships that represent relevant experimental data, public databases, and literature. The platform allows clinically meaningful queries and advanced statistical analyses, enabling automated data analysis, knowledge mining, and visualization, its developers write. It also incorporates community efforts by building on scientific Python libraries, which also makes the platform reliable, maintainable, and continuously improving.