Christopher J Neely's research while affiliated with University of Southern California and other places

Metagenomics is a powerful method for interpreting the ecological roles and physiological capabilities of mixed microbial communities. Yet, many tools for processing metagenomic data are neither designed to consider eukaryotes nor are they built for an increasing amount of sequence data. EukHeist is an automated pipeline to retrieve eukaryotic and prokaryotic metagenome-assembled genomes (MAGs) from large-scale metagenomic sequence data sets. We developed the EukHeist workflow to specifically process large amounts of both metagenomic and/or metatranscriptomic sequence data in an automated and reproducible fashion. Here, we applied EukHeist to the large-size fraction data (0.8–2,000 µm) from Tara Oceans to recover both eukaryotic and prokaryotic MAGs, which we refer to as TOPAZ (Tara Oceans Particle-Associated MAGs). The TOPAZ MAGs consisted of >900 environmentally relevant eukaryotic MAGs and >4,000 bacterial and archaeal MAGs. The bacterial and archaeal TOPAZ MAGs expand upon the phylogenetic diversity of likely particle- and host-associated taxa. We use these MAGs to demonstrate an approach to infer the putative trophic mode of the recovered eukaryotic MAGs. We also identify ecological cohorts of co-occurring MAGs, which are driven by specific environmental factors and putative host-microbe associations. These data together add to a number of growing resources of environmentally relevant eukaryotic genomic information. Complementary and expanded databases of MAGs, such as those provided through scalable pipelines like EukHeist, stand to advance our understanding of eukaryotic diversity through increased coverage of genomic representatives across the tree of life. IMPORTANCE Single-celled eukaryotes play ecologically significant roles in the marine environment, yet fundamental questions about their biodiversity, ecological function, and interactions remain. Environmental sequencing enables researchers to document naturally occurring protistan communities, without culturing bias, yet metagenomic and metatranscriptomic sequencing approaches cannot separate individual species from communities. To more completely capture the genomic content of mixed protistan populations, we can create bins of sequences that represent the same organism (metagenome-assembled genomes [MAGs]). We developed the EukHeist pipeline, which automates the binning of population-level eukaryotic and prokaryotic genomes from metagenomic reads. We show exciting insight into what protistan communities are present and their trophic roles in the ocean. Scalable computational tools, like EukHeist, may accelerate the identification of meaningful genetic signatures from large data sets and complement researchers’ efforts to leverage MAG databases for addressing ecological questions, resolving evolutionary relationships, and discovering potentially novel biodiversity.

Microbial eukaryotes are ubiquitous in the environment and play important roles in key ecosystem processes, including accounting for a significant portion of global primary production. Yet, our tools for assessing the functional capabilities of eukaryotic microbes in the environment are quite limited because many microbes have yet to be grown in culture. Maximum growth rate is a fundamental parameter of microbial lifestyle that reveals important information about an organism's functional role in a community. We developed and validated a genomic estimator of maximum growth rate for eukaryotic microbes, enabling the assessment of growth potential for both cultivated and yet-to-be-cultivated organisms. We produced a database of over 700 growth predictions from genomes, transcriptomes, and metagenome-assembled genomes, and found that closely related and/or functionally similar organisms tended to have similar maximal growth rates. By comparing the maximal growth rates of existing culture collections with environmentally-derived genomes we found that, unlike for prokaryotes, culture collections of microbial eukaryotes are only minimally biased in terms of growth potential. We then extended our tool to make community-wide estimates of growth potential from over 500 marine metagenomes, mapping growth potential across the global oceans. We found that prokaryotic and eukaryotic communities have highly correlated growth potentials near the ocean surface, but that this relationship disappears deeper in the water column. This suggests that fast growing eukaryotes and prokaryotes thrive under similar conditions at the ocean surface, but that there is a decoupling of these communities as resources become scarce deeper in the water column.

Gene prediction and annotation for eukaryotic genomes is challenging with large data demands and complex computational requirements. For most eukaryotes, genomes are recovered from specific target taxa. However, it is now feasible to reconstruct or sequence hundreds of metagenome-assembled genomes (MAGs) or single-amplified genomes directly from the environment. To meet this forthcoming wave of eukaryotic genome generation, we introduce EukMetaSanity, which combines state-of-the-art tools into three pipelines that have been specifically designed for extensive parallelization on high-performance computing infrastructure. EukMetaSanity performs an automated taxonomy search against a protein database of 1,482 species to identify phylogenetically compatible proteins to be used in downstream gene prediction. We present the results for intron, exon, and gene locus prediction for 112 genomes collected from NCBI, including fungi, plants, and animals, along with 1,669 MAGs and demonstrate that EukMetaSanity can provide reliable preliminary gene predictions for a single target taxon or at scale for hundreds of MAGs. EukMetaSanity is freely available at https://github.com/cjneely10/EukMetaSanity.

Metagenomics is a powerful method for interpreting the ecological roles and physiological capabilities of mixed microbial communities. Yet, many tools for processing metagenomic data are not designed to consider eukaryotes, nor are they built for an increasing amount of sequence data. EukHeist is an automated pipeline to retrieve eukaryotic and prokaryotic metagenome assembled genomes (MAGs) from large-scale metagenomic datasets. We developed the EukHeist workflow to specifically process large amounts of both metagenomic and/or metatranscriptomic sequence data in an automated and reproducible fashion. Here, we applied EukHeist to the large-size fraction data (0.8-2000 µm ) from Tara Oceans to recover both eukaryotic and prokaryotic MAGs, which we refer to as TOPAZ ( Tara Oceans Particle-Associated MAGs). The TOPAZ MAGs consisted of >900 environmentally-relevant eukaryotic MAGs and >4,000 bacterial and archaeal MAGs. The bacterial and archaeal TOPAZ MAGs expand the known marine phylogenetic diversity through the increase in coverage of likely particle- and host-associated taxa. We also demonstrate an approach to infer the putative functional mode of the recovered eukaryotic MAGs. A global survey of the TOPAZ MAGs enabled the identification of ecological cohorts, driven by specific environmental factors, and putative host-microbe associations. Importance Despite the ecological importance of single-celled eukaryotic organisms in marine environments, the majority are difficult to cultivate in the lab. Sequencing genetic material extracted from environmental samples enables researchers to document naturally-occurring protistan communities. However, conventional sequencing methodologies cannot separate out the genomes of individual organisms. To more completely capture the entire genomic content of mixed protistan community, we can create bins of sequences that represent the same organism. We developed a pipeline that enables scientists to bin individual organisms out of metagenomic reads, and show results that provide exciting insights into what protistan communities are present in the ocean and what roles they play in the ecosystem. Here, a global survey of both eukaryotic and prokaryotic MAGs enabled the identification of ecological cohorts, driven by specific environmental factors, and putative host-microbe associations. Accessible and scalable computational tools, such as EukHeist, are likely to accelerate the identification of meaningful genetic signatures from large datasets, ultimately expanding the eukaryotic tree of life.

As the importance of microbiome research continues to become more prevalent and essential to understanding a wide variety of ecosystems (e.g., marine, built, host-associated, etc.), there is a need for researchers to be able to perform highly reproducible and quality analysis of microbial genomes. MetaSanity incorporates analyses from eleven existing and widely used genome evaluation and annotation suites into a single, distributable workflow, thereby decreasing the workload of microbiologists by allowing for a flexible, expansive data analysis pipeline. MetaSanity has been designed to provide separate, reproducible workflows, that (1) can determine the overall quality of a microbial genome, while providing a putative phylogenetic assignment, and (2) can assign structural and functional gene annotations with varying degrees of specificity to suit the needs of the researcher. The software suite combines the results from several tools to provide broad insights into overall metabolic function. Importantly, this software provides built-in optimization for "big data" analysis by storing all relevant outputs in an SQL database, allowing users to query all the results for the elements that will most impact their research. Availability: MetaSanity is provided under the GNU General Public License v.3.0 and is available for download at https://github.com/cjneely10/MetaSanity. This application is distributed as a Docker image. MetaSanity is implemented in Python3/Cython and C ++. Instructions for its installation and use are available within the GitHub wiki page at https://github.com/cjneely10/MetaSanity/wiki, and additional instructions are available at https://cjneely10.github.io/year-archive/. MetaSanity is optimized for users with limited programming experience. Supplementary information: Supplementary data are available at Bioinformatics online.

As the importance of microbiome research continues to become more prevalent and essential to understanding a wide variety of ecosystems (e.g., marine, built, host-associated, etc.), there is a need for researchers to be able to perform highly reproducible and quality analysis of microbial genomes. MetaSanity incorporates analyses from eleven existing and widely used genome evaluation and annotation suites into a single, distributable workflow, thereby decreasing the workload of microbiologists by allowing for a flexible, expansive data analysis pipeline. MetaSanity has been designed to provide separate, reproducible workflows, that (1) can determine the overall quality of a microbial genome, while providing a putative phylogenetic assignment, and (2) can assign structural and functional gene annotations with varying degrees of specificity to suit the needs of the researcher. The software suite combines the results from several tools to provide broad insights into overall metabolic function and putative extracellular localization of peptidases and carbohydrate-active enzymes. Importantly, this software provides built-in optimization for “big data” analysis by storing all relevant outputs in an SQL database, allowing users to query all the results for the elements that will most impact their research. Availability and implementation MetaSanity is provided under the GNU General Public License v.3.0 and is available for download at https://github.com/cjneely10/MetaSanity . This application is distributed as a Docker image. MetaSanity is implemented in Python3/Cython and C++. Supplementary information Supplementary data are available below.

Hydrogenovibrio sp. strain SC-1 was isolated from pyrrhotite incubatedin situin the marine surface sediment of Catalina Island, CA. Strain SC-1 has demonstrated autotrophic growth through the oxidation of thiosulfate and iron. Here, we present the 2.45-Mb genome sequence of SC-1, which contains 2,262 protein-coding genes.