MetaCerberus: distributed highly parallelized HMM-based processing for robust functional annotation across the tree of life.

MOTIVATION: MetaCerberus is a massively parallel, fast, low memory, scalable annotation tool for inference gene function across genomes to metacommunities. MetaCerberus provides an elusive HMM/HMMER-based tool at a rapid scale with low memory. It offers scalable gene elucidation to major public databases, including KEGG (KO), COGs, CAZy, FOAM, and specific databases for viruses, including VOGs and PHROGs, from single genomes to metacommunities. RESULTS: MetaCerberus is 1.3× as fast on a single node than eggNOG-mapper v2 on 5× less memory using an exclusively HMM/HMMER mode. In a direct comparison, MetaCerberus provides better annotation of viruses, phages, and archaeal viruses than DRAM, Prokka, or InterProScan. MetaCerberus annotates more KOs across domains when compared to DRAM, with a 186× smaller database, and with 63× less memory. MetaCerberus is fully integrated for automatic analysis of statistics and pathways using differential statistic tools (i.e. DESeq2 and edgeR), pathway enrichment (GAGE R), and pathview R. MetaCerberus provides a novel tool for unlocking the biosphere across the tree of life at scale. AVAILABILITY AND IMPLEMENTATION: MetaCerberus is written in Python and distributed under a BSD-3 license. The source code of MetaCerberus is freely available at https://github.com/raw-lab/metacerberus compatible with Python 3 and works on both Mac OS X and Linux. MetaCerberus can also be easily installed using bioconda: mamba create -n metacerberus -c bioconda -c conda-forge metacerberus.

Viral enumeration using cost-effective wet-mount epifluorescence microscopy for aquatic ecosystems and modern microbialites.

IMPORTANCE: Low-cost and robust viral enumeration is a critical first step toward understanding the global virome. Our method is a deep drive integration providing a window into viral dark matter within aquatic ecosystems. We enumerated the viruses within Green Lake and Great Salt Lake microbialites, EPS, and water column. The entire weight of all the viruses in Green Lake and Great Salt Lake are ~598 g and ~2.2 kg, respectively.

NFixDB (Nitrogen Fixation DataBase)-a comprehensive integrated database for robust 'omics analysis of diazotrophs.

Biological nitrogen fixation is a fundamental biogeochemical process that transforms molecular nitrogen into biologically available nitrogen via diazotrophic microbes. Diazotrophs anaerobically fix nitrogen using the nitrogenase enzyme which is arranged in three different gene clusters: (i) molybdenum nitrogenase (nifHDK) is the most abundant, followed by it's alternatives, (ii) vanadium nitrogenase (vnfHDK) and (iii) iron nitrogenase (anfHDK). Multiple databases have been constructed as resources for diazotrophic 'omics analysis; however, an integrated database based on whole genome references does not exist. Here, we present NFixDB (Nitrogen Fixation DataBase), a comprehensive integrated whole genome based database for diazotrophs, which includes all nitrogenases (nifHDK, vnfHDK, anfHDK) and nitrogenase-like enzymes (e.g. nflHD) linked to ribosomal RNA operons (16S-5S-23S). NFixDB was computed using Hidden Markov Models (HMMs) against the entire whole genome based Genome Taxonomy Database (GTDB R214), providing searchable reference HMMs for all nitrogenase and nitrogenase-like genes, complete ribosomal RNA operons, both GTDB and NCBI/RefSeq taxonomy, and an SQL database for querying matches. We compared NFixDB to nifH databases from Buckley, Zehr, Mise and FunGene finding extensive evidence of nifH, in addition to vnfH and nflH. NFixDB contains >4000 verified nifHDK sequences contained on 50 unique phyla of bacteria and archaea. NFixDB provides the first comprehensive nitrogenase database available to researchers unlocking diazotrophic microbial potential.