HMS-S-S: A tool for the identification of Sulphur metabolism-related genes and analysis of operon structures in genome and metagenome assemblies.

Sulphur compounds are used in a variety of biological processes including respiration and photosynthesis. Sulphide and sulphur compounds of intermediary oxidation state can serve as electron donors for lithotrophic growth while sulphate, thiosulphate and sulphur are used as electron acceptors in anaerobic respiration. The biochemistry underlying the manifold transformations of inorganic sulphur compounds occurring in sulphur metabolizing prokaryotes is astonishingly complex and knowledge about it has immensely increased over the last years. The advent of next-generation sequencing approaches as well as the significant increase of data availability in public databases has driven focus of environmental microbiology to probing the metabolic capacity of microbial communities by analysis of this sequence data. To facilitate these analyses, we created HMS-S-S, a comprehensive equivalogous hidden Markov model (HMM)-supported tool. Protein sequences related to sulphur compound oxidation, reduction, transport and intracellular transfer are efficiently detected and related enzymes involved in dissimilatory sulphur oxidation as opposed to sulphur compound reduction can be confidently distinguished. HMM search results are coupled to corresponding genes, which allows analysis of co-occurrence, synteny and genomic neighbourhood. The HMMs were validated on an annotated test data set and by cross-validation. We also proved its performance by exploring meta-assembled genomes isolated from samples from environments with active sulphur cycling, including members of the cable bacteria, novel Acidobacteria and assemblies from a sulphur-rich glacier, and were able to replicate and extend previous reports.

FeGenie: A Comprehensive Tool for the Identification of Iron Genes and Iron Gene Neighborhoods in Genome and Metagenome Assemblies.

Iron is a micronutrient for nearly all life on Earth. It can be used as an electron donor and electron acceptor by iron-oxidizing and iron-reducing microorganisms and is used in a variety of biological processes, including photosynthesis and respiration. While it is the fourth most abundant metal in the Earth's crust, iron is often limiting for growth in oxic environments because it is readily oxidized and precipitated. Much of our understanding of how microorganisms compete for and utilize iron is based on laboratory experiments. However, the advent of next-generation sequencing and surge in publicly available sequence data has made it possible to probe the structure and function of microbial communities in the environment. To bridge the gap between our understanding of iron acquisition, iron redox cycling, iron storage, and magnetosome formation in model microorganisms and the plethora of sequence data available from environmental studies, we have created a comprehensive database of hidden Markov models (HMMs) based on genes related to iron acquisition, storage, and reduction/oxidation in Bacteria and Archaea. Along with this database, we present FeGenie, a bioinformatics tool that accepts genome and metagenome assemblies as input and uses our comprehensive HMM database to annotate provided datasets with respect to iron-related genes and gene neighborhood. An important contribution of this tool is the efficient identification of genes involved in iron oxidation and dissimilatory iron reduction, which have been largely overlooked by standard annotation pipelines. We validated FeGenie against a selected set of 28 isolate genomes and showcase its utility in exploring iron genes present in 27 metagenomes, 4 isolate genomes from human oral biofilms, and 17 genomes from candidate organisms, including members of the candidate phyla radiation. We show that FeGenie accurately identifies iron genes in isolates. Furthermore, analysis of metagenomes using FeGenie demonstrates that the iron gene repertoire and abundance of each environment is correlated with iron richness. While this tool will not replace the reliability of culture-dependent analyses of microbial physiology, it provides reliable predictions derived from the most up-to-date genetic markers. FeGenie's database will be maintained and continually updated as new genes are discovered. FeGenie is freely available: https://github.com/Arkadiy-Garber/FeGenie.