Molecular Evolution of Odonata Opsins, Odonata Phylogenomics and Detection of False Positive Sequence Homology Using Machine Learning
My dissertation comprises three related topics of evolutionary and computational biology, which correspond to the three Chapters. Chapter 1 focuses on tempo and mode of evolution in visual genes, namely opsins, via duplication events and subsequent molecular adaptation in Odonata (dragonflies and damselflies). Gene duplication plays a central role in adaptation to novel environments by providing new genetic material for functional divergence and evolution of biological complexity. Odonata have the largest opsin repertoire of any insect currently known. In particular our results suggest that both the blue sensitive (BS) and long-wave sensitive (LWS) opsin classes were subjected to strong positive selection that greatly weakens after multiple duplication events, a pattern that is consistent with the permanent heterozygote model. Due to the immense interspecific variation and duplicability potential of opsin genes among odonates, they represent a unique model system to test hypotheses regarding opsin gene duplication and diversification at the molecular level. Chapter 2 primarily focuses on reconstruction of the phylogenetic backbone of Odonata using RNA-seq data. In order to reconstruct the evolutionary history of Odonata, we performed comprehensive phylotranscriptomic analyses of 83 species covering 75% of all extant odonate families. Using maximum likelihood, Bayesian, coalescent-based and alignment free tree inference frameworks we were able to test, refine and resolve previously controversial relationships within the order. In particular, we confirmed the monophyly of Zygoptera, recovered Gomphidae and Petaluridae as sister groups with high confidence and identified Calopterygoidea as monophyletic. Fossil calibration coupled with diversification analyses provided insight into key events that influenced the evolution of Odonata. Specifically, we determined that there was a possible mass extinction of ancient odonate diversity during the P-Tr crisis and a single odonate lineage persisted following this extinction event. Lastly, Chapter 3 focuses on identification of erroneously assigned sequence homology using the intelligent agents of machine learning techniques. Accurate detection of homologous relationships of biological sequences (DNA or amino acid) amongst organisms is an important and often difficult task that is essential to various evolutionary studies, ranging from building phylogenies to predicting functional gene annotations. We developed biologically informative features that can be extracted from multiple sequence alignments of putative homologous genes (orthologs and paralogs) and further utilized in context of guided experimentation to verify false positive outcomes.
College and Department
Life Sciences; Biology
BYU ScholarsArchive Citation
Suvorov, Anton, "Molecular Evolution of Odonata Opsins, Odonata Phylogenomics and Detection of False Positive Sequence Homology Using Machine Learning" (2018). Theses and Dissertations. 7320.
molecular evolution, vision, insects, Bayesian modeling, phylogenetic inference, big data, next-generation sequencing, artificial intelligence, homology