Deciphering the functional potential of a hypersaline swamp microbial mat community
in Publications on Shotgun, Amplicon
The present study combines 16S rRNA amplicon sequencing and shotgun metagenomics on a hypersaline marsh in Tristomo bay (Karpathos, Greece). Samples were collected in July 2018 and November 2019 from microbial mats, deeper sediment, aggregates observed in the water overlying the sediment, as well as sediment samples with no apparent layering. Metagenomic samples’ coassembly and binning revealed 250 bacterial and 39 archaeal metagenome-assembled genomes, with completeness estimates higher than 70% and contamination less than 5%. All MAGs had KEGG Orthology terms related to osmoadaptation, with the ‘salt in’ strategy ones being prominent. Halobacteria and Bacteroidetes were the most abundant taxa in the mats. Photosynthesis was most likely performed by purple sulphur and nonsulphur bacteria. All samples had the capacity for sulphate reduction, dissimilatory arsenic reduction, and conversion of pyruvate to oxaloacetate.
Automating the Curation Process of Historical Literature on Marine Biodiversity Using Text Mining: The DECO Workflow
in Publications on Textmining
This work focuses on information Extraction (IE) from the marine historical biodiversity data perspective. It orchestrates IE tools and provides the curators with a unified view of the methodology; as a result the documentation of the strengths, limitations and dependencies of several tools was drafted. Additionally, the classification of tools into Graphical User Interface (web and standalone) applications and Command Line Interface ones enables the data curators to select the most suitable tool for their needs, according to their specific features
The high volume of already digitised marine documents that await curation is amassed and a demonstration of the methodology, with a new scalable, extendable and containerised tool, DECO (bioDivErsity data Curation programming wOrkflow) is presented. DECO’s usage will provide a solid basis for future curation initiatives and an augmented degree of reliability towards high value data products that allow for the connection between the past and the present, in marine biodiversity research.
dingo
in Software on Metabolic\_networks, Sampling, Fba
dingo is a Python package that supports a variety of methods to sample from the flux space of metabolic models, based on state-of-the-art random walks and rounding methods. It relies on high dimensional sampling with Markov Chain Monte Carlo (MCMC) methods and fast optimization methods to analyze the possible states of a metabolic network. To perform MCMC sampling, dingo relies on the C++ library volesti, which provides several algorithms for sampling convex polytopes. Among the different ways to sample, dingo also implements the Multiphase Monte Carlo Sampling algorithm (see post for relative publication).
Flux sampling provides insgith of strong statistical evidence. For example, pairwise fluxes correlated with one another in a positive or negative way, can be found.
dingo also supports Flux Balance Analysis and Flux Variability Analysis, two standard methods to analyze the flux space of a metabolic network,.
dingo is part of the GeomScale that is over the last year has been an organization of Google Summer of Code.
PREGO: A Literature and Data-Mining Resource to Associate Microorganisms, Biological Processes, and Environment Types
in Publications on Textmining, Data, Integration
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.
Read more for the PREGO software
metabarcoding analysis - an overview
With Dr. Christina Pavloudi and Dr. Ioulia Santi we prepared a class for amplicon analysis for starterts.
So, here is a place to start with if you are interested in that:
The Dark mAtteR iNvestigator (DARN) tool: getting to know the known unknowns in COI amplicon data
in Publications on Amplicon
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. 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.
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
Read more for the darn software
in Publications on Amplicon, Shotgun, Hpc
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.
PREGO
in Software on Metadata
Text-mining and data integration.
Genome-scale metabolic model reconstruction
in Teaching on Model
Really glad about dingo being accepted at BOSC2021.
We will be there with our e-poster!
Thanks for the event @OBF_BOSC!
e-See you all there!
