Metagenomics emerged as an important field of research not only in microbial ecology but also for human health and disease, and metagenomic studies are performed on increasingly larger scales. While recent taxonomic classification programs achieve high speed by comparing genomic k-mers, they often lack sensitivity for overcoming evolutionary divergence, so that large fractions of the metagenomic reads remain unclassified. Here we present the novel metagenome classifier Kaiju, which finds maximum (in-)exact matches on the protein-level using the Burrows–Wheeler transform. We show in a genome exclusion benchmark that Kaiju classifies reads with higher sensitivity and similar precision compared with current k-mer-based classifiers, especially in genera that are underrepresented in reference databases. We also demonstrate that Kaiju classifies up to 10 times more reads in real metagenomes. Kaiju can process millions of reads per minute and can run on a standard PC. Source code and web server are available at http://kaiju.binf.ku.dk.

Correlates of infectiousness: The role that individuals with asymptomatic or mildly symptomatic severe acute respiratory syndrome coronavirus 2 have in transmission of the virus is not well understood. Jones et al. investigated viral load in patients, comparing those showing few, if any, symptoms with hospitalized cases. Approximately 400,000 individuals, mostly from Berlin, were tested from February 2020 to March 2021 and about 6% tested positive. Of the 25,381 positive subjects, about 8% showed very high viral loads. People became infectious within 2 days of infection, and in hospitalized individuals, about 4 days elapsed from the start of virus shedding to the time of peak viral load, which occurred 1 to 3 days before the onset of symptoms. Overall, viral load was highly variable, but was about 10-fold higher in persons infected with the B.1.1.7 variant. Children had slightly lower viral loads than adults, although this difference may not be clinically significant. Science , abi5273, this issue p. eabi5273

Novel hydrolases from hot and other extreme environments showing appropriate performance and/or novel functionalities and new approaches for their systematic screening are of great interest for developing new processes, for improving safety, health and environment issues. Existing processes could benefit as well from their properties. The workflow, based on the HotZyme project, describes a multitude of technologies and their integration from discovery to application, providing new tools for discovering, identifying and characterizing more novel thermostable hydrolases with desired functions from hot terrestrial and marine environments. To this end, hot springs worldwide were mined, resulting in hundreds of environmental samples and thousands of enrichment cultures growing on polymeric substrates of industrial interest. Using high-throughput sequencing and bioinformatics, 15 hot spring metagenomes, as well as several sequenced isolate genomes and transcriptomes were obtained. To facilitate the discovery of novel hydrolases, the annotation platform Anastasia and a whole-cell bioreporter-based functional screening method were developed. Sequence-based screening and functional screening together resulted in about 100 potentially new hydrolases of which more than a dozen have been characterized comprehensively from a biochemical and structural perspective. The characterized hydrolases include thermostable carboxylesterases, enol lactonases, quorum sensing lactonases, gluconolactonases, epoxide hydrolases, and cellulases. Apart from these novel thermostable hydrolases, the project generated an enormous amount of samples and data, thereby allowing the future discovery of even more novel enzymes.

User-driven in silico RNA homology search is still a nontrivial task. In part, this is the consequence of a limited precision of the computational tools in spite of recent exciting progress in this area, and to a certain extent, computational costs are still problematic in practice. An important, and as we argue here, dominating issue is the dependence on good curated (secondary) structural alignments of the RNAs. These are often hard to obtain, not so much because of an inherent limitation in the available data, but because they require substantial manual curation, an effort that is rarely acknowledged. Here, we qualitatively describe a realistic scenario for what a “regular user” (i.e., a nonexpert in a particular RNA family) can do in practice, and what kind of results are likely to be achieved. Despite the indisputable advances in computational RNA biology, the conclusion is discouraging: BLAST still works better or equally good as other methods unless extensive expert knowledge on the RNA family is included. However, when good curated data are available the recent development yields further improvements in finding remote homologs. Homology search beyond the reach of BLAST hence is not at all a routine task.