ECB-ART-43675Gigascience 2014 Oct 14;3:21. doi: 10.1186/2047-217X-3-21.
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A dataset comprising 141 magnetic resonance imaging scans of 98 extant sea urchin species.
BACKGROUND: Apart from its application in human diagnostics, magnetic resonance imaging (MRI) can also be used to study the internal anatomy of zoological specimens. As a non-invasive imaging technique, MRI has several advantages, such as rapid data acquisition, output of true three-dimensional imagery, and provision of digital data right from the onset of a study. Of particular importance for comparative zoological studies is the capacity of MRI to conduct high-throughput analyses of multiple specimens. In this study, MRI was applied to systematically document the internal anatomy of 98 representative species of sea urchins (Echinodermata: Echinoidea). FINDINGS: The dataset includes raw and derived image data from 141 MRI scans. Most of the whole sea urchin specimens analyzed were obtained from museum collections. The attained scan resolutions permit differentiation of various internal organs, including the digestive tract, reproductive system, coelomic compartments, and lantern musculature. All data deposited in the GigaDB repository can be accessed using open source software. Potential uses of the dataset include interactive exploration of sea urchin anatomy, morphometric and volumetric analyses of internal organs observed in their natural context, as well as correlation of hard and soft tissue structures. CONCLUSIONS: The dataset covers a broad taxonomical and morphological spectrum of the Echinoidea, focusing on ''regular'' sea urchin taxa. The deposited files significantly expand the amount of morphological data on echinoids that are electronically available. The approach chosen here can be extended to various other vertebrate and invertebrate taxa. We argue that publicly available digital anatomical and morphological data gathered during experiments involving non-invasive imaging techniques constitute one of the prerequisites for future large-scale genotype-phenotype correlations.
PubMed ID: 25356198
PMC ID: PMC4212584
Article link: Gigascience
Genes referenced: LOC100887844 LOC115925415
References [+] :
Berquist, The Digital Fish Library: using MRI to digitize, database, and document the morphological diversity of fish. 2012, Pubmed