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ExTaxsI: an exploration tool of biodiversity molecular data.

Authors :
Agostinetto G
Brusati A
Sandionigi A
Chahed A
Parladori E
Balech B
Bruno A
Pescini D
Casiraghi M
Source :
GigaScience [Gigascience] 2022 Jan 25; Vol. 11.
Publication Year :
2022

Abstract

Background: The increasing availability of multi-omics data is leading to regularly revised estimates of existing biodiversity data. In particular, the molecular data enable novel species to be characterized and the information linked to those already observed to be increased with new genomics data. For this reason, the management and visualization of existing molecular data, and their related metadata, through the implementation of easy-to-use IT tools have become a key point to design future research. The more users are able to access biodiversity-related information, the greater the ability of the scientific community to expand its knowledge in this area.<br />Results: In this article we focus on the development of ExTaxsI (Exploring Taxonomy Information), an IT tool that can retrieve biodiversity data stored in NCBI databases and provide a simple and explorable visualization. We use 3 case studies to show how an efficient organization of the available data can lead to obtaining new information that is fundamental as a starting point for new research. Using this approach highlights the limits in the distribution of data availability, a key factor to consider in the experimental design phase of broad-spectrum studies such as metagenomics.<br />Conclusions: ExTaxsI can easily retrieve molecular data and its metadata with an explorable visualization, with the aim of helping researchers to improve experimental designs and highlight the main gaps in the coverage of available data.<br /> (© The Author(s) 2022. Published by Oxford University Press GigaScience.)

Details

Language :
English
ISSN :
2047-217X
Volume :
11
Database :
MEDLINE
Journal :
GigaScience
Publication Type :
Academic Journal
Accession number :
35077538
Full Text :
https://doi.org/10.1093/gigascience/giab092