The sourmash software package uses MinHash-based sketching to create “signatures”, compressed representations of DNA, RNA, and protein sequences, that can be stored, searched, explored, and taxonomically annotated. sourmash signatures can be used to estimate sequence similarity between very large data sets quickly and in low memory, and can be used to search large databases of genomes for matches to query genomes and metagenomes. sourmash is implemented in C++, Rust, and Python, and is freely available under the BSD license at http://github.com/dib-lab/sourmash.

The identification of reference genomes and taxonomic labels from metagenome data underlies many microbiome studies. Here we describe two algorithms for compositional analysis of metagenome sequencing data. We first investigate the FracMinHash sketching technique, a derivative of modulo hash that supports Jaccard containment estimation between sets of different sizes. We implement FracMinHash in the sourmash software, evaluate its accuracy, and demonstrate large-scale containment searches of metagenomes using 700,000 microbial reference genomes. We next frame shotgun metagenome compositional analysis as the problem of finding a minimum collection of reference genomes that "cover" the known k-mers in a metagenome, a minimum set cover problem. We implement a greedy approximate solution using FracMinHash sketches, and evaluate its accuracy for taxonomic assignment using a CAMI community benchmark. Finally, we show that the minimum metagenome cover can be used to guide the selection of reference genomes for read mapping. sourmash is available as open source software under the BSD 3-Clause license at github.com/dib-lab/sourmash/.

D-arginine dehydrogenase from Pseudomonas aeruginosa catalyzes the oxidation of D-arginine to iminoarginine, which is hydrolyzed in solution to ketoarginine and ammonia. In the present study, we have genetically engineered an untagged form of the enzyme that was purified to high levels and characterized in its kinetic properties. The enzyme is a true dehydrogenase that does not react with molecular oxygen. Steady-state kinetic studies with D-arginine or D-histidine as substrate and PMS as the electron acceptor established a ping-pong bi-bi kinetic mechanism. With the fast substrate D-arginine a dead-end complex of the reduced enzyme and the substrate occurs at high concentrations of D-arginine yielding substrate inhibition, while the overall turnover is partially limited by the release of the iminoarginine product. With the slow substrate D-histidine the initial Michaelis complex undergoes an isomerization involving multiple conformations that are not all equally catalytically competent for the subsequent oxidation reaction, while the overall turnover is at least partially limited by flavin reduction. The kinetic data are interpreted in view of the high-resolution crystal structures of the iminoarginine--and iminohistidine--enzyme complexes.

As the scale of biological data generation has increased, the bottleneck of research has shifted from data generation to analysis. Researchers commonly need to build computational workflows that include multiple analytic tools and require incremental development as experimental insights demand tool and parameter modifications. These workflows can produce hundreds to thousands of intermediate files and results that must be integrated for biological insight. Data-centric workflow systems that internally manage computational resources, software, and conditional execution of analysis steps are reshaping the landscape of biological data analysis and empowering researchers to conduct reproducible analyses at scale. Adoption of these tools can facilitate and expedite robust data analysis, but knowledge of these techniques is still lacking. Here, we provide a series of strategies for leveraging workflow systems with structured project, data, and resource management to streamline large-scale biological analysis. We present these practices in the context of high-throughput sequencing data analysis, but the principles are broadly applicable to biologists working beyond this field.

The sourmash software package uses MinHash-based sketching to create "signatures", compressed representations of DNA, RNA, and protein sequences, that can be stored, searched, explored, and taxonomically annotated. sourmash signatures can be used to estimate sequence similarity between very large data sets quickly and in low memory, and can be used to search large databases of genomes for matches to query genomes and metagenomes. sourmash is implemented in C++, Rust, and Python, and is freely available under the BSD license at http://github.com/dib-lab/sourmash. bioinformatics, sequence analysis, MinHash, k-mer, sourmash