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Integrated Assembly and Annotation of Fathead Minnow Genome Towards Prediction of Environmentarl Exposures

Martinson, John W
http://orcid.org/0000-0001-6570-2837
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1592135992030861

Abstract Details

2020, PhD, University of Cincinnati, Engineering and Applied Science: Biomedical Engineering.
The fathead minnow (FHM, Pimephales promelas) is a species of temperate freshwater fish with a geographic range that extends throughout much of North America that is widely used as a model organism for aquatic toxicity testing. Our team at the Environmental Protection Agency produced a new FHM assembly which served as the foundation for accomplishing the aims in this project. Because of the importance of the underlying assembly to being able to achieve the aims, the generation of the new assembly is presented in this dissertation, though it was not a specific aim. The first aim of this research project was to annotate the protein coding genes in a new FHM genome. A comprehensive set (26,150) of gene models that can facilitate the analysis of RNA-seq expression profiles derived from exposures of P. promelas subjects to chemicals and other stressors was produced. The second aim of the project was to demonstrate the application and utility of the new gene models by using RNA-seq data generated in controlled exposure experiments to identify differentially expressed genes (DEGs) as markers of exposure. FHM were exposed to two chemicals with different modes of toxicity, the pyrethroid pesticide bifenthrin and copper. The new gene models were used to quantify mRNA expressions levels and statistical and machine learning techniques were applied to develop lists of DEGs in treated and untreated samples. The third aim of the study was to develop predictors of exposure from the data, using machine and statistical learning methods to combine the obtained markers into exposure signatures and optimize the predictive power of the resulting exposure classifiers. As part of these experiments, five different classifiers were evaluated using a cross-validation framework. Classifiers were able to distinguish treated samples from controls and were then applied to samples treated with the other chemical to evaluate how the classifiers performed when faced with an exposure scenario different from the one for which they were trained. Assessment of the genome and gene models in terms of both BUSCO coverage and RNA-seq mapping rates show that the new assembly and gene models represent not only a significant improvement over the previously published FHM assembly and gene annotations, but also that they compare very favorably with the highly studied and closely related zebrafish (Danio rerio). Given the mature state of the zebrafish genome the FHM results presented here represent a significant success story. Further validation of the success was provided by the successful use of the new gene models in the bifenthrin/copper exposure study. For each of the two toxicants studied, successful classifiers of exposure were able to be developed from a variety of approaches based on mapping RNA-seq data to the new gene models. Functional analysis of the differentially expressed genes (DEGs) leveraged by the classifiers indicated toxicant specific responses at the gene level appeared to drive the ability to correctly classify samples. Glm elastic net (“glmnet”) and random forest showed the most promise of being able to avoid false positive classifications in the cross-chemical testing.
Jaroslaw Meller, Ph.D. (Committee Chair)
Adam Biales, Ph.D. (Committee Member)
Daria Narmoneva, Ph.D. (Committee Member)
Marepalli Rao, Ph.D. (Committee Member)
157 p.
Biology
genome; annotation; fathead minnow; RNA-seq; classifier

Recommended Citations

Citations

  • Martinson, J. W. (2020). Integrated Assembly and Annotation of Fathead Minnow Genome Towards Prediction of Environmentarl Exposures [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1592135992030861

    APA Style (7th edition)

  • Martinson, John. Integrated Assembly and Annotation of Fathead Minnow Genome Towards Prediction of Environmentarl Exposures. 2020. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1592135992030861.

    MLA Style (8th edition)

  • Martinson, John. "Integrated Assembly and Annotation of Fathead Minnow Genome Towards Prediction of Environmentarl Exposures." Doctoral dissertation, University of Cincinnati, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1592135992030861

    Chicago Manual of Style (17th edition)

ucin1592135992030861
542
© 2020, some rights reserved.Creative Commons License Integrated Assembly and Annotation of Fathead Minnow Genome Towards Prediction of Environmentarl Exposures by John W Martinson is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by University of Cincinnati and OhioLINK.