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The Global Invertebrate Genomics Alliance (GIGA): developing community resources to study diverse invertebrate genomes.

ABSTRACT: Over 95% of all metazoan (animal) species comprise the "invertebrates," but very few genomes from these organisms have been sequenced. We have, therefore, formed a "Global Invertebrate Genomics Alliance" (GIGA). Our intent is to build a collaborative network of diverse scientists to tackle major challenges (e.g., species selection, sample collection and storage, sequence assembly, annotation, analytical tools) associated with genome/transcriptome sequencing across a large taxonomic spectrum. We aim to promote standards that will facilitate comparative approaches to invertebrate genomics and collaborations across the international scientific community. Candidate study taxa include species from Porifera, Ctenophora, Cnidaria, Placozoa, Mollusca, Arthropoda, Echinodermata, Annelida, Bryozoa, and Platyhelminthes, among others. GIGA will target 7000 noninsect/nonnematode species, with an emphasis on marine taxa because of the unrivaled phyletic diversity in the oceans. Priorities for selecting invertebrates for sequencing will include, but are not restricted to, their phylogenetic placement; relevance to organismal, ecological, and conservation research; and their importance to fisheries and human health. We highlight benefits of sequencing both whole genomes (DNA) and transcriptomes and also suggest policies for genomic-level data access and sharing based on transparency and inclusiveness. The GIGA Web site (http://giga.nova.edu) has been launched to facilitate this collaborative venture.

SUBMITTER: GIGA Community of Scientists

PROVIDER: S-EPMC4072906 | biostudies-other | 2014 Jan-Feb

REPOSITORIES: biostudies-other

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The Global Invertebrate Genomics Alliance (GIGA): developing community resources to study diverse invertebrate genomes.

Bracken-Grissom Heather H Collins Allen G AG Collins Timothy T Crandall Keith K Distel Daniel D Dunn Casey C Giribet Gonzalo G Haddock Steven S Knowlton Nancy N Martindale Mark M Medina Mónica M Messing Charles C O'Brien Stephen J SJ Paulay Gustav G Putnam Nicolas N Ravasi Timothy T Rouse Greg W GW Ryan Joseph F JF Schulze Anja A Wörheide Gert G Adamska Maja M Bailly Xavier X Breinholt Jesse J Browne William E WE Diaz M Christina MC Evans Nathaniel N Flot Jean-François JF Fogarty Nicole N Johnston Matthew M Kamel Bishoy B Kawahara Akito Y AY Laberge Tammy T Lavrov Dennis D Michonneau François F Moroz Leonid L LL Oakley Todd T Osborne Karen K Pomponi Shirley A SA Rhodes Adelaide A Santos Scott R SR Satoh Nori N Thacker Robert W RW Van de Peer Yves Y Voolstra Christian R CR Welch David Mark DM Winston Judith J Zhou Xin X

The Journal of heredity 20140101 1

Over 95% of all metazoan (animal) species comprise the "invertebrates," but very few genomes from these organisms have been sequenced. We have, therefore, formed a "Global Invertebrate Genomics Alliance" (GIGA). Our intent is to build a collaborative network of diverse scientists to tackle major challenges (e.g., species selection, sample collection and storage, sequence assembly, annotation, analytical tools) associated with genome/transcriptome sequencing across a large taxonomic spectrum. We ...[more]

PMID: 24336862

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Project description:BackgroundThe goal of the Global Health in Preconception, Pregnancy and Postpartum (HiPPP) Alliance, comprising consumers and leading international multidisciplinary academics and clinicians, is to generate research and translation priorities and build international collaboration around healthy lifestyle and obesity prevention among women across the reproductive years. In doing so, we actively seek to involve consumers in research, implementation and translation initiatives. There are limited frameworks specifically designed to involve women across the key obesity prevention windows before (preconception), during and after pregnancy (postpartum). The aim of this paper is to outline our strategy for the development of the HiPPP Consumer and Community (CCI) Framework, with consumers as central to co-designed, co-implemented and co-disseminated research and translation.MethodThe development of the framework involved three phases: In Phase 1, 21 Global HiPPP Alliance members participated in a CCI workshop to propose and discuss values and approaches for framework development; Phase 2 comprised a search of peer-reviewed and grey literature for existing CCI frameworks and resources; and Phase 3 entailed collaboration with consumers (i.e., members of the public with lived experience of weight/lifestyle issues in preconception, pregnancy and postpartum) and international CCI experts to workshop and refine the HiPPP CCI Framework (guided by Phases 1 and 2).ResultsThe HiPPP CCI Framework's values and approaches identified in Phases 1-2 and further refined in Phase 3 were summarized under the following five key principles: 1. Inclusive, 2. Flexible, 3. Transparent, 4. Equitable, and 5. Adaptable. The HiPPP Framework describes values and approaches for involving consumers in research initiatives from design to translation that focus on improving healthy lifestyles and preventing obesity specifically before, during and after pregnancy; importantly it takes into consideration common barriers to partnering in obesity research during perinatal life stages, such as limited availability associated with family caregiving responsibilities.ConclusionThe HiPPP CCI Framework aims to describe approaches for implementing meaningful CCI initiatives with women in preconception, pregnancy and postpartum periods. Evaluation of the framework is now needed to understand how effective it is in facilitating meaningful involvement for consumers, researchers and clinicians, and its impact on research to improve healthy lifestyle outcomes.

Dataset Information

The Global Invertebrate Genomics Alliance (GIGA): developing community resources to study diverse invertebrate genomes.

Publications

The Global Invertebrate Genomics Alliance (GIGA): developing community resources to study diverse invertebrate genomes.

Similar Datasets

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