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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.

Genetic diversity and population distribution are influenced by environmental factors, yet information is scarce on the interrelations that define dispersal of populations. Transitional water ecosystems are hosting habitats with temporally and spatially variable conditions, which make them very useful in understanding the mechanisms affecting population establishment. For the study, seasonal samples were collected from four lagoons in the lagoonal complex of Amvrakikos Gulf (W Greece). The mitochondrial DNA (COI gene) was analysed from polychaetes ofNephtys hombergii, as it is considered an important component of these assemblages and shows great dispersal ability. The results of the genetic analysis showed intraspecific variability in all the lagoons, with genetic structuring tending to follow a seasonal pattern rather than a spatial one. The results of BIOENV analysis indicated correlation of the observed pattern with the water pH levels, the redox potential and the concentration of phosphate in the sediment. The complexity of the network suggested the enhancement of the local population with more recently established haplotypes. The findings of the study support the necessity of designing management strategies by taking into account genetic diversity and population demography approaches in addition to those based on species and habitats.

BackgroundEnvironmental DNA (eDNA) and metabarcoding, allow the identification of a mixture of individuals and launch a new era in bio- and eco-assessment. A number of steps are required to obtain taxonomically assigned (Molecular) Operational Taxonomic Unit ((M)OTU) tables from raw data. For most of these, a plethora of tools is available; each tool’s execution parameters need to be tailored to reflect each experiment’s idiosyncrasy. Adding to this complexity, for such analyses, the computation capacity of High Performance Computing (HPC) systems is frequently required.Software containerization technologies ease the sharing and running of software packages across operating systems; thus, they strongly facilitate pipeline development and usage. Likewise are programming languages specialized for big data pipelines, incorporating features like roll-back checkpoints and on-demand partial pipeline execution.FindingsPEMA is a containerized assembly of key metabarcoding analysis tools with a low effort in setting up, running and customizing to researchers’ needs. Based on third party tools, PEMA performs reads’ pre-processing, clustering to (M)OTUs and taxonomy assignment for 16S rRNA and COI marker gene data. Due to its simplified parameterisation 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 previously published datasets and achieved comparable quality results.ConclusionsGiven its time-efficient performance and its quality results, it is suggested that PEMA can be used for accurate eDNA metabarcoding analysis, thus enhancing the applicability of next-generation biodiversity assessment studies.

Several models have been developed for the description of diversity in estuaries and other brackish habitats, with the most recognized being Remane’s Artenminimum (“species minimum”) concept. It was developed for the Baltic Sea, one of the world’s largest semi-enclosed brackish water body with a unique permanent salinity gradient, and it argues that taxonomic diversity of macrobenthic organisms is lowest within the horohalinicum (5 to 8 psu). The aim of the present study was to investigate the relationship between salinity and sediment microbial diversity at a freshwater-marine transect in Amvrakikos Gulf (Ionian Sea, Western Greece) and assess whether species composition and community function follow a generalized concept such as Remane’s. DNA was extracted from sediment samples from six stations along the aforementioned transect and sequenced for the 16S rRNA gene using high-throughput sequencing. The metabolic functions of the OTUs were predicted and the most abundant metabolic pathways were extracted. Key abiotic variables, i.e., salinity, temperature, chlorophyll-a and oxygen concentration etc., were measured and their relation with diversity and functional patterns was explored. Microbial communities were found to differ in the three habitats examined (river, lagoon and sea) with certain taxonomic groups being more abundant in the freshwater and less in the marine environment, andvice versa. Salinity was the environmental factor with the highest correlation to the microbial community pattern, while oxygen concentration was highly correlated to the metabolic functional pattern. The total number of OTUs showed a negative relationship with increasing salinity, thus the sediment microbial OTUs in this study area do not follow Remane’s concept.

Sulfate-reducing microorganisms (SRMs) are a phylogenetically and physiologically diverse group of microorganisms, responsible for the dissimilatory reduction of sulfate. SRMs thrive under anaerobic conditions with high availability of organic matter. Such conditions characterize lagoonal ecosystems which experience regular dystrophic crises. The aim of the present study was to explore the biodiversity patterns of SRMs and to examine the extent to which these patterns are associated with biogeographic and environmental factors. Sediment samples were collected from 5 lagoons in the Amvrakikos Gulf (Ionian Sea, western Greece). DNA was extracted from the sediment and was further processed through pyrosequencing of a region of the dissimilatory sulfite reductase β-subunit (dsrB). The results of this exploratory study show that the majority of the observed operational taxonomic units (OTUs) belong to the Deltaproteobacteria supercluster and more specifically, to the Desulfobacteraceae family. Salinity and ammonium ions are the environmental factors that best correlated with the SRM community pattern. Furthermore, the SRM community of the brackish lagoons is differentiated from that of the brackish-marine lagoons and the studied lagoons have distinct SRM communities.

Understanding the distribution of taxa and associated traits across different environments is one of the central questions in microbial ecology. High-throughput sequencing (HTS) studies are presently generating huge volumes of data to address this biogeographical topic. However, these studies are often focused on specific environment types or processes leading to the production of individual, unconnected datasets. The large amounts of legacy sequence data with associated metadata that exist can be harnessed to better place the genetic information found in these surveys into a wider environmental context. Here we introduce a software program, seqenv, to carry out precisely such a task. It automatically performs similarity searches of short sequences against the “nt” nucleotide database provided by NCBI and, out of every hit, extracts–if it is available–the textual metadata field. After collecting all the isolation sources from all the search results, we run a text mining algorithm to identify and parse words that are associated with the Environmental Ontology (EnvO) controlled vocabulary. This, in turn, enables us to determine both in which environments individual sequences or taxa have previously been observed and, by weighted summation of those results, to summarize complete samples. We present two demonstrative applications of seqenv to a survey of ammonia oxidizing archaea as well as to a plankton paleome dataset from the Black Sea. These demonstrate the ability of the tool to reveal novel patterns in HTS and its utility in the fields of environmental source tracking, paleontology, and studies of microbial biogeography. To install seqenv, go to: https://github.com/xapple/seqenv.

Parallel data manipulation using R has previously been addressed by members of the R community, however most of these studies produce ad hoc solutions that are not readily available to the average R user. Our targeted users, ranging from the expert ecologist/microbiologists to computational biologists, often experience difficulties in finding optimal ways to exploit the full capacity of their computational resources. In addition, improving performance of commonly used R scripts becomes increasingly difficult especially with large datasets. Furthermore, the implementations described here can be of significant interest to expert bioinformaticians or R developers. Therefore, our goals can be summarized as: (i) description of a complete methodology for the analysis of large datasets by combining capabilities of diverse R packages, (ii) presentation of their application through a virtual R laboratory (RvLab) that makes execution of complex functions and visualization of results easy and readily available to the end-user. In this paper, the novelty stems from implementations of parallel methodologies which rely on the processing of data on different levels of abstraction and the availability of these processes through an integrated portal. Parallel implementation R packages, such as the pbdMPI (Programming with Big Data – Interface to MPI) package, are used to implement Single Program Multiple Data (SPMD) parallelization on primitive mathematical operations, allowing for interplay with functions of the vegan package. The dplyr and RPostgreSQL R packages are further integrated offering connections to dataframe like objects (databases) as secondary storage solutions whenever memory demands exceed available RAM resources. The RvLab is running on a PC cluster, using version 3.1.2 (2014-10-31) on a x86_64-pc-linux-gnu (64-bit) platform, and offers an intuitive virtual environmet interface enabling users to perform analysis of ecological and microbial communities based on optimized vegan functions. A beta version of the RvLab is available after registration at: https://portal.lifewatchgreece.eu/

Summary: The association of organisms to their environments is a key issue in exploring biodiversity patterns. This knowledge has traditionally been scattered, but textual descriptions of taxa and their habitats are now being consolidated in centralized resources. However, structured annotations are needed to facilitate large-scale analyses. Therefore, we developed ENVIRONMENTS, a fast dictionary-based tagger capable of identifying Environment Ontology (ENVO) terms in text. We evaluate the accuracy of the tagger on a new manually curated corpus of 600 Encyclopedia of Life (EOL) species pages. We use the tagger to associate taxa with environments by tagging EOL text content monthly, and integrate the results into the EOL to disseminate them to a broad audience of users. Availability and implementation: The software and the corpus are available under the open-source BSD and the CC-BY-NC-SA 3.0 licenses, respectively, at http://environments.hcmr.gr Contact: pafilis@hcmr.gr or lars.juhl.jensen@cpr.ku.dk Supplementary information: Supplementary data are available at Bioinformatics online.

The exponential growth of the biomedical literature is making the need for efficient, accurate text-mining tools increasingly clear. The identification of named biological entities in text is a central and difficult task. We have developed an efficient algorithm and implementation of a dictionary-based approach to named entity recognition, which we here use to identify names of species and other taxa in text. The tool, SPECIES, is more than an order of magnitude faster and as accurate as existing tools. The precision and recall was assessed both on an existing gold-standard corpus and on a new corpus of 800 abstracts, which were manually annotated after the development of the tool. The corpus comprises abstracts from journals selected to represent many taxonomic groups, which gives insights into which types of organism names are hard to detect and which are easy. Finally, we have tagged organism names in the entire Medline database and developed a web resource, ORGANISMS, that makes the results accessible to the broad community of biologists. The SPECIES software is open source and can be downloaded from http://species.jensenlab.org along with dictionary files and the manually annotated gold-standard corpus. The ORGANISMS web resource can be found at http://organisms.jensenlab.org.

Advances in next-generation sequencing (NGS) have allowed significant breakthroughs in microbial ecology studies. This has led to the rapid expansion of research in the field and the establishment of “metagenomics”, often defined as the analysis of DNA from microbial communities in environmental samples without prior need for culturing. Many metagenomics statistical/computational tools and databases have been developed in order to allow the exploitation of the huge influx of data. In this review article, we provide an overview of the sequencing technologies and how they are uniquely suited to various types of metagenomic studies. We focus on the currently available bioinformatics techniques, tools, and methodologies for performing each individual step of a typical metagenomic dataset analysis. We also provide future trends in the field with respect to tools and technologies currently under development. Moreover, we discuss data management, distribution, and integration tools that are capable of performing comparative metagenomic analyses of multiple datasets using well-established databases, as well as commonly used annotation standards.

Viruses interfere with their host&rsquo, s metabolism through the expression of auxiliary metabolic genes (AMGs) that, until now, are mostly studied under large physicochemical gradients. Here, we focus on coastal marine ecosystems and we sequence the viral metagenome (virome) of samples with discrete levels of human-driven disturbances. We aim to describe the relevance of viromics with respect to ecological quality status, defined by the classic seawater trophic index (TRIX). Neither viral (family level) nor bacterial (family level, based on 16S rRNA sequencing) community structure correlated with TRIX. AMGs involved in the Calvin and tricarboxylic acid cycles were found at stations with poor ecological quality, supporting viral lysis by modifying the host&rsquo, s energy supply. AMGs involved in &ldquo, non-traditional&rdquo, energy-production pathways (3HP, sulfur oxidation) were found irrespective of ecological quality, highlighting the importance of recognizing the prevalent metabolic paths and their intermediate byproducts. Various AMGs explained the variability between stations with poor vs. good ecological quality. Our study confirms the pivotal role of the virome content in ecosystem functioning, acting as a &ldquo, pool&rdquo, of available functions that may be transferred to the hosts. Further, it suggests that AMGs could be used as an ultra-sensitive metric of energy-production pathways with relevance in the vulnerable coastal zone and its ecological quality.

Biomolecular ocean observing and research is a rapidly evolving field that uses omics approaches to describe biodiversity at its foundational level, giving insight into the structure and function of marine ecosystems over time and space. It is an especially effective approach for investigating the marine microbiome. To mature marine microbiome research and operations within a global ocean biomolecular observing network (OBON) for the UN Decade of Ocean Science for Sustainable Development and beyond, research groups will need a system to effectively share, discover, and compare “omic” practices and protocols. While numerous informatic tools and standards exist, there is currently no global, publicly-supported platform specifically designed for sharing marine omics [or any omics] protocols across the entire value-chain from initiating a study to the publication and use of its results. Toward that goal, we propose the development of the Minimum Information for an Omic Protocol (MIOP), a community-developed guide of curated, standardized metadata tags and categories that will orient protocols in the value-chain for the facilitated, structured, and user-driven discovery of suitable protocol suites on the Ocean Best Practices System. Users can annotate their protocols with these tags, or use them as search criteria to find appropriate protocols. Implementing such a curated repository is an essential step toward establishing best practices. Sharing protocols and encouraging comparisons through this repository will be the first steps toward designing a decision tree to guide users to community endorsed best practices.