Browsing by Author "Exter, Katrina"
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- European marine omics biodiversity observation network: a strategic outline for the implementation of omics approaches in ocean observationPublication . Santi, Ioulia; Beluche, Odette; Beraud, Mélanie; Buttigieg, Pier Luigi; Casotti, Raffaella; Cox, Cymon J.; Cunliffe, Michael; Davies, Neil; de Cerio, Oihane Diaz; Exter, Katrina; Kervella, Anne Emmanuelle; Kotoulas, Georgios; Lagaisse, Rune; Laroquette, Arnaud; Louro, Bruno; Not, Fabrice; Obst, Matthias; Pavloudi, Christina; Poulain, Julie; Præbel, Kim; Vanaverbeke, Jan; Pade, NicolasMarine ecosystems, ranging from coastal seas and wetlands to the open ocean, accommodate a wealth of biological diversity from small microorganisms to large mammals. This biodiversity and its associated ecosystem function occurs across complex spatial and temporal scales and is not yet fully understood. Given the wide range of external pressures on the marine environment, this knowledge is crucial for enabling effective conservation measures and defining the limits of sustainable use. The development and application of omics-based approaches to biodiversity research has helped overcome hurdles, such as allowing the previously hidden community of microbial life to be identified, thereby enabling a holistic view of an entire ecosystem's biodiversity and functioning. The potential of omics-based approaches for marine ecosystems observation is enormous and their added value to ecosystem monitoring, management, and conservation is widely acknowledged. Despite these encouraging prospects, most omics-based studies are short-termed and typically cover only small spatial scales which therefore fail to include the full spatio-temporal complexity and dynamics of the system. To date, few attempts have been made to establish standardised, coordinated, broad scaled, and long-term omics observation networks. Here we outline the creation of an omics-based marine observation network at the European scale, the European Marine Omics Biodiversity Observation Network (EMO BON). We illustrate how linking multiple existing individual observation efforts increases the observational power in large-scale assessments of status and change in biodiversity in the oceans. Such large-scale observation efforts have the added value of cross-border cooperation, are characterised by shared costs through economies of scale, and produce structured, comparable data. The key components required to compile reference environmental datasets and how these should be linked are major challenges that we address.
- First release of the European marine omics biodiversity observation network (EMO BON) shotgun metagenomics data from water and sediment samplesPublication . Pavloudi, Christina; Santi, Ioulia; Azua, Iñigo; Baña, Zuriñe; Bastianini, Mauro; Belser, Caroline; Bilbao, Jone; Bitz-Thorsen, Julie; Broudin, Caroline; Camusat, Mathieu; Cancio, Ibon; Caray-Counil, Louis; Casotti, Raffaella; Castel, Jade; Comtet, Thierry; Cox, Cymon; Daguin, Claire; Cerio, Oihane Díaz de; Exter, Katrina; Fauvelot, Cécile; Frada, Miguel; Galand, Pierre; Garczarek, Laurence; Fernández, Jose González; Guillou, Laure; Hablützel, Pascal; Heynderickx, Hanneloor; Houbin, Céline; Kervella, Anne; Krystallas, Apostolos; Lagaisse, Rune; Laroquette, Arnaud; Lescure, Lyvia; Lopes, Eva; Loulakaki, Melina; Louro, Bruno; Magalhaes, Catarina; Maidanou, Maria; Margiotta, Francesca; Montresor, Marina; Not, Fabrice; Paredes, Estefanía; Percopo, Isabella; Péru, Erwan; Poulain, Julie; Præbel, Kim; Rigaut-Jalabert, Fabienne; Romac, Sarah; Stavroulaki, Melanthia; Troncoso, Jesús Souza; Thiébaut, Eric; Thomas, Wilfried; Tkacz, Andrzej; Trano, Anna Chiara; Wincker, Patrick; Pade, NicolasThe European Marine Omics Biodiversity Observation Network (EMO BON) is an initiative of the European Marine Biological Resource Centre (EMBRC) to establish a persistent genomic observatory amongst designated European coastal marine sites, sharing the same protocols for sampling and data curation. Environmental samples are collected from the water column and, at some sites, soft sediments and hard substrates (Autonomous Reef Monitoring Structures- ARMS), together with a set of mandatory and discretionary metadata (including Essential Ocean Variables- EOVs). Samples are collected following standardised protocols at regular and specified intervals and sequenced in large six-monthly batches at a centralised sequencing facility. The use of standard operating procedures (SOPs) during data collection, library preparation and sequencing aims to provide uniformity amongstthe data collected from the sites. Coupled with strict adherence to open and FAIR (Findable, Accessible, Interoperable, Reusable) data principles, this ensures maximum comparability amongst samples and enhances reusability and interoperability of the data with other data sources. The observatory networkwas launched in June 2021, when the first sampling campaign took place.
- metaGOflow: a workflow for the analysis of marine genomic observatories shotgun metagenomics dataPublication . Zafeiropoulos, Haris; Beracochea, Martin; Ninidakis, Stelios; Exter, Katrina; Potirakis, Antonis; De Moro, Gianluca; Richardson, Lorna; Corre, Erwan; Machado, João Paulo; Pafilis, Evangelos; Kotoulas, Georgios; Santi, Ioulia; Finn, Robert D; J. Cox, Cymon; Pavloudi, ChristinaBackground: Genomic Observatories (GOs) are sites of long-term scientific study that undertake regular assessments of the genomic biodiversity. The European Marine Omics Biodiversity Observation Network (EMO BON) is a network of GOs that conduct regular biological community samplings to generate environmental and metagenomic data of microbial communities from designated marine stations around Europe. The development of an effective workflow is essential for the analysis of the EMO BON metagenomic data in a timely and reproducible manner. Findings: Based on the established MGnify resource, we developed metaGOflow. meta GOflow supports the fast inference of taxonomic profiles from GO-derived data based on ribosomal RNA genes and their functional annotation using the raw reads. Thanks to the Research Object Crate packaging, relevant metadata about the sample under study, and the details of the bioinformatics analysis it has been subjected to, are inherited to the data product while its modular implementation allows running the workflow partially. The analysis of 2 EMO BON samples and 1 Tara Oceans sample was performed as a use case. Conclusions: metaGOflow is an efficient and robust workflow that scales to the needs of projects producing big metagenomic data such as EMO BON. It highlights how containerization technologies along with modern workflow languages and metadata package approaches can support the needs of researchers when dealing with ever-increasing volumes of biological data. Despite being initially oriented to address the needs of EMO BON, metaGOflowis a flexible and easy-to-use workflow that can be broadly used for one-sample-at-a-time analysis of shotgun metagenomics data.