Background: Environmental DNA and metabarcoding allow the identification of a mixture of species and launch a new era in bio- and eco-assessment. Many steps are required to obtain taxonomically assigned matrices from raw data. For most of these, a plethora of tools are available; each tool's execution parameters need to be tailored to reflect each experiment's idiosyncrasy. Adding to this complexity, the computation capacity of high-performance computing systems is frequently required for such analyses. To address the difficulties, bioinformatic pipelines need to combine state-of-the art technologies and algorithms with an easy to get-set-use framework, allowing researchers to tune each study. Software containerization technologies ease the sharing and running of software packages across operating systems; thus, they strongly facilitate pipeline development and usage. Likewise programming languages specialized for big data pipelines incorporate features like roll-back checkpoints and on-demand partial pipeline execution. Findings: PEMA is a containerized assembly of key metabarcoding analysis tools that requires low effort in setting up, running, and customizing to researchers' needs. Based on third-party tools, PEMA performs read pre-processing, (molecular) operational taxonomic unit clustering, amplicon sequence variant inference, and taxonomy assignment for 16S and 18S ribosomal RNA, as well as ITS and COI marker gene data. Owing to its simplified parameterization and checkpoint support, PEMA allows users to explore alternative algorithms for specific steps of the pipeline without the need of a complete re-execution. PEMA was evaluated against both mock communities and previously published datasets and achieved results of comparable quality. Conclusions: A high-performance computing–based approach was used to develop PEMA; however, it can be used in personal computers as well. PEMA's time-efficient performance and good results will allow it to be used for accurate environmental DNA metabarcoding analysis, thus enhancing the applicability of next-generation biodiversity assessment studies.

Background: Given a genome-scale metabolic model (GEM) of a microorganism and criteria for optimization, flux balance analysis (FBA) predicts the optimal growth rate and its corresponding flux distribution for a specific medium. FBA has been extended to microbial consortia and thus can be used to predict interactions by comparing in-silico growth rates for co- and monocultures. Although FBA-based methods for microbial interaction prediction are becoming popular, a systematic evaluation of their accuracy has not yet been performed. Results: Here, we evaluate the accuracy of FBA-based predictions of human and mouse gut bacterial interactions using growth data from the literature. For this, we collected 26 GEMs from the semi-curated AGORA database as well as four previously published curated GEMs. We tested the accuracy of three tools (COMETS, Microbiome Modeling Toolbox and MICOM) by comparing growth rates predicted in mono- and co-culture to growth rates extracted from the literature and also investigated the impact of different tool settings and media. We found that except for curated GEMs, predicted growth rates and their ratios (i.e. interaction strengths) do not correlate with growth rates and interaction strengths obtained from in vitro data. Conclusions: Prediction of growth rates with FBA using semi-curated GEMs is currently not sufficiently accurate to predict interaction strengths reliably.

High-performance computing (HPC) systems have become indispensable for modern marine research, providing support to an increasing number and diversity of users. Pairing with the impetus offered by high-throughput methods to key areas such as non-model organism studies, their operation continuously evolves to meet the corresponding computational challenges. Here, we present a Tier 2 (regional) HPC facility, operating for over a decade at the Institute of Marine Biology, Biotechnology, and Aquaculture of the Hellenic Centre for Marine Research in Greece. Strategic choices made in design and upgrades aimed to strike a balance between depth (the need for a few high-memory nodes) and breadth (a number of slimmer nodes), as dictated by the idiosyncrasy of the supported research. Qualitative computational requirement analysis of the latter revealed the diversity of marine fields, methods, and approaches adopted to translate data into knowledge. In addition, hardware and software architectures, usage statistics, policy, and user management aspects of the facility are presented. Drawing upon the last decade’s experience from the different levels of operation of the Institute of Marine Biology, Biotechnology, and Aquaculture HPC facility, a number of lessons are presented; these have contributed to the facility’s future directions in light of emerging distribution technologies (e.g., containers) and Research Infrastructure evolution. In combination with detailed knowledge of the facility usage and its upcoming upgrade, future collaborations in marine research and beyond are envisioned.

The mitochondrial cytochrome C oxidase subunit I gene (COI) is commonly used in environmental DNA (eDNA) metabarcoding studies, especially for assessing metazoan diversity. Yet, a great number of COI operational taxonomic units (OTUs) or/and amplicon sequence variants (ASVs) retrieved from such studies do not get a taxonomic assignment with a reference sequence. To assess and investigate such sequences, we have developed the Dark mAtteR iNvestigator (DARN) software tool. For this purpose, a reference COI-oriented phylogenetic tree was built from 1,593 consensus sequences covering all the three domains of life. With respect to eukaryotes, consensus sequences at the family level were constructed from 183,330 sequences retrieved from the Midori reference 2 database, which represented 70% of the initial number of reference sequences. Similarly, sequences from 431 bacterial and 15 archaeal taxa at the family level (29% and 1% of the initial number of reference sequences respectively) were retrieved from the BOLD and the PFam databases. DARN makes use of this phylogenetic tree to investigate COI pre-processed sequences of amplicon samples to provide both a tabular and a graphical overview of their phylogenetic assignments. To evaluate DARN, both environmental and bulk metabarcoding samples from different aquatic environments using various primer sets were analysed. We demonstrate that a large proportion of non-target prokaryotic organisms, such as bacteria and archaea, are also amplified in eDNA samples and we suggest prokaryotic COI sequences to be included in the reference databases used for the taxonomy assignment to allow for further analyses of dark matter. DARN source code is available on GitHub at https://github.com/hariszaf/darn and as a Docker image at https://hub.docker.com/r/hariszaf/darn.

To elucidate ecosystem functioning, it is fundamental to recognize what processes occur in which environments (where) and which microorganisms carry them out (who). Here, we present PREGO, a one-stop-shop knowledge base providing such associations. PREGO combines text mining and data integration techniques to mine such what-where-who associations from data and metadata scattered in the scientific literature and in public omics repositories. Microorganisms, biological processes, and environment types are identified and mapped to ontology terms from established community resources. Analyses of comentions in text and co-occurrences in metagenomics data/metadata are performed to extract associations and a level of confidence is assigned to each of them thanks to a scoring scheme. The PREGO knowledge base contains associations for 364,508 microbial taxa, 1090 environmental types, 15,091 biological processes, and 7971 molecular functions with a total of almost 58 million associations. These associations are available through a web portal, an Application Programming Interface (API), and bulk download. By exploring environments and/or processes associated with each other or with microbes, PREGO aims to assist researchers in design and interpretation of experiments and their results. To demonstrate PREGO’s capabilities, a thorough presentation of its web interface is given along with a meta-analysis of experimental results from a lagoon-sediment study of sulfur-cycle related microbes.

Deciphering microbial metabolism is essential for understanding ecosystem functions. Genome-scale metabolic models (GSMMs) predict metabolic traits from genomic data, but constructing GSMMs for uncultured bacteria is challenging due to incomplete metagenome-assembled genomes, resulting in many gaps. We introduce the deep neural network guided imputation of reactomes (DNNGIOR), which uses AI to improve gap-filling by learning from the presence and absence of metabolic reactions across diverse bacterial genomes. Key factors for prediction accuracy are: (1) reaction frequency across all bacteria and (2) phylogenetic distance of the query to the training genomes. DNNGIOR predictions achieve an average F1 score of 0.85 for reactions present in over 30% of training genomes. DNNGIOR guided gap-filling was 14 times more accurate for draft reconstructions and 2-9 times for curated models than unweighted gap-filling.