Taxonomic analysis of metagenomic data with kASA.

The taxonomic analysis of sequencing data has become important in many areas of life sciences. However, currently available tools for that purpose either consume large amounts of RAM or yield insufficient quality and robustness. Here, we present kASA, a k-mer based tool capable of identifying and profiling metagenomic DNA or protein sequences with high computational efficiency and a user-definable memory footprint. We ensure both high sensitivity and precision by using an amino acid-like encoding of k-mers together with a range of multiple k's. Custom algorithms and data structures optimized for external memory storage enable a full-scale taxonomic analysis without compromise on laptop, desktop, and HPCC.

Elevated virulence of an emerging viral genotype as a driver of honeybee loss.

Emerging infectious diseases (EIDs) have contributed significantly to the current biodiversity crisis, leading to widespread epidemics and population loss. Owing to genetic variation in pathogen virulence, a complete understanding of species decline requires the accurate identification and characterization of EIDs. We explore this issue in the Western honeybee, where increasing mortality of populations in the Northern Hemisphere has caused major concern. Specifically, we investigate the importance of genetic identity of the main suspect in mortality, deformed wing virus (DWV), in driving honeybee loss. Using laboratory experiments and a systematic field survey, we demonstrate that an emerging DWV genotype (DWV-B) is more virulent than the established DWV genotype (DWV-A) and is widespread in the landscape. Furthermore, we show in a simple model that colonies infected with DWV-B collapse sooner than colonies infected with DWV-A. We also identify potential for rapid DWV evolution by revealing extensive genome-wide recombination in vivo The emergence of DWV-B in naive honeybee populations, including via recombination with DWV-A, could be of significant ecological and economic importance. Our findings emphasize that knowledge of pathogen genetic identity and diversity is critical to understanding drivers of species decline.

Enhancing European capabilities for application of multi-omics studies in biology and biomedicine space research.

Following on from the NASA twins' study, there has been a tremendous interest in the use of omics techniques in spaceflight. Individual space agencies, NASA's GeneLab, JAXA's ibSLS, and the ESA-funded Space Omics Topical Team and the International Standards for Space Omics Processing (ISSOP) groups have established several initiatives to support this growth. Here, we present recommendations from the Space Omics Topical Team to promote standard application of space omics in Europe. We focus on four main themes: i) continued participation in and coordination with international omics endeavors, ii) strengthening of the European space omics infrastructure including workforce and facilities, iii) capitalizing on the emerging opportunities in the commercial space sector, and iv) capitalizing on the emerging opportunities in human subjects research.

NASA GeneLab RNA-seq consensus pipeline: standardized processing of short-read RNA-seq data.

With the development of transcriptomic technologies, we are able to quantify precise changes in gene expression profiles from astronauts and other organisms exposed to spaceflight. Members of NASA GeneLab and GeneLab-associated analysis working groups (AWGs) have developed a consensus pipeline for analyzing short-read RNA-sequencing data from spaceflight-associated experiments. The pipeline includes quality control, read trimming, mapping, and gene quantification steps, culminating in the detection of differentially expressed genes. This data analysis pipeline and the results of its execution using data submitted to GeneLab are now all publicly available through the GeneLab database. We present here the full details and rationale for the construction of this pipeline in order to promote transparency, reproducibility, and reusability of pipeline data; to provide a template for data processing of future spaceflight-relevant datasets; and to encourage cross-analysis of data from other databases with the data available in GeneLab.