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National Institute of Environmental Health Sciences

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Publication Detail

Title: An Automated Method To Predict Mouse Gene and Protein Sequences Using Variant Data.

Authors: Dornbos, Peter; Arkatkar, Anooj A; LaPres, John J

Published In G3 (Bethesda), (2020 03 05)

Abstract: With recent advances in sequencing technologies, the scientific community has begun to probe the potential genetic bases behind complex phenotypes in humans and model organisms. In many cases, the genomes of genetically distinct strains of model organisms, such as the mouse (Mus musculus), have not been fully sequenced. Here, we report on a tool designed to use single-nucleotide polymorphism (SNP) and insertion-deletion (indel) data to predict gene, mRNA, and protein sequences for up to 36 genetically distinct mouse strains. By automated querying of freely accessible databases through a graphical interface, the software requires no data and little computational experience. As a proof of concept, we predicted the gene and amino acid sequence of the aryl hydrocarbon receptor (Ahr) for all inbred mouse strains of which variant data were currently available through Mouse Genome Project. Predicted sequences were compared with fully sequenced genomes to show that the tool is effective in predicting gene and protein sequences.

PubMed ID: 31911484 Exiting the NIEHS site

MeSH Terms: No MeSH terms associated with this publication

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