Citation:

LaLone, C., Dan Villeneuve, H. Helgen, D. Lane, S. Walata, K. Nelson, S. Robinson, J. Swintek, AND G. Ankley. SeqAPASS (Sequence Alignment to Predict Across Species Susceptibility) software and documentation. U.S. EPA Office of Research and Development, Washington, DC, 2014.

Impact/Purpose:

Cross-species extrapolation of biological effects of chemicals is an important uncertainty for both human health and ecological risk assessments. There is a compelling need for innovative, quantitative, scientifically-based approaches to inform decisions regarding data relevance across taxa, particularly when testing resources are limited and/or rapid turn-around is required. As chemical evaluations rely more heavily on mechanistically-oriented data generated via high-throughput screening (e.g., EPA’s ToxCast program) it is important to develop complementary understanding of the extent to which these data can be extrapolated across species, based on considerations anchored to an AOP framework. The SeqAPASS tool provides a platform to rapidly, systematically, and strategically compare the similarity of toxicologically-significant molecular targets across species. The ability to efficiently and quantitatively assess the taxonomic conservation of molecular targets is expected to support informed extrapolation of mechanistic toxicology data across species, particularly in instances where the regulatory mandate and/or resources needed to generate empirical toxicity data for a broad range of species is limited. Current on-line tools that allow for such analyses are dispersed and do not effortlessly link together to provide relevant output that can be rapidly and easily disseminated. Development of the SeqAPASS tool represents a significant leap forward that can facilitate routine application of molecular target similarity considerations in research and regulatory decision-making.

Description:

SeqAPASS is a software application facilitates rapid and streamlined, yet transparent, comparisons of the similarity of toxicologically-significant molecular targets across species. The present application facilitates analysis of primary amino acid sequence similarity (including ortholog identification), functional domains, and structurally important individual residue positions for predictions of relative intrinsic susceptibility across species. Further, a data visualization method and tutorial to compliment the SeqAPASS tool using a R workspace for generation of customizable box-plots to enhance interpretation of SeqAPASS results has been developed.

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