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Computational Methods for Discovering Genetic Functions of Conserved Non-coding Elements with Comparative Genomics

Saputra, Elysia Cecilia (2023) Computational Methods for Discovering Genetic Functions of Conserved Non-coding Elements with Comparative Genomics. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Unveiling the genetic encodings of complex phenotypes is a fundamental goal of biology. With the increasing availability of sequenced genomes, it has become possible to elucidate molecular adaptations that engender species diversity with evolutionary-based methods. Although morphological differences arise from changes in transcriptional regulation, regulatory non-coding elements are still insufficiently characterized, and there is a lack of phylogenetic tools that account for their evolutionary properties. This dissertation addresses this gap by developing new tools to perform unbiased genome-wide predictions of regulatory element adaptations that underlie convergent phenotypes. This dissertation introduces three new tools, discussed in the following chapters.

In Chapter 1, we introduce empirical strategies for calibrating phylogenetic signals against statistical biases that arise from phylogenetic, technical, and biological sources. We develop phylogenetically-constrained trait permutation strategies for binary and continuous traits, and benchmark them systematically on various methods and convergent phenotypes. This study demonstrates the effectiveness of phylogeny-aware permutation strategies for improving the statistical behavior and prediction specificity from phylogenetic analysis.

In Chapter 2, we build a maximum likelihood-based phylogenetic method tailored to characterizing the adaptation of conserved regulatory elements associated with phenotypic convergence. We benchmark the method using the classical case of convergent evolution of mammalian lineages to subterranean habitats and demonstrate the ability of the method to modularly identify phenotype-relevant local segments of regulatory elements. In Chapter 3, we apply the method to study the regulatory changes underlying the convergent adaptation of mammals to life at altitude. We release the tool as a software package that can be used by the research community.

In Chapter 4, we develop an alignment-free phylogenetic method for characterizing regulatory motif adaptations that underlie phenotypic convergence from orthologous, but not necessarily alignable sequences. Using a reference-free alignment dataset, we benchmark the method against competing alignment-based and alignment-free methods using the convergence case of vision loss in mammals and demonstrate the superior performance of the method. We finally apply the method to investigate the regulatory motif adaptation underlying the convergent evolution of longevity and increased body size in mammals. We make the tool publicly available to use for scalable computations of motif-level convergence signals.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Saputra, Elysia Ceciliaelysia.saputra@pitt.eduels1910000-0002-2572-393X
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Thesis AdvisorChikina, Mariamchikina@pitt.edu0000-0003-2550-5403
Committee ChairKostka, Denniskostka@pitt.edu0000-0002-1460-5487
Committee MemberClark, Nathannclark@utah.edu0000-0003-0006-8374
Committee MemberSiepel, Adamasiepel@cshl.edu0000-0002-3557-7219
Committee MemberPfenning, Andreasapfenning@cmu.edu0000-0002-3447-9801
Date: 8 December 2023
Date Type: Publication
Defense Date: 17 July 2023
Approval Date: 8 December 2023
Submission Date: 2 August 2023
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 193
Institution: University of Pittsburgh
Schools and Programs: School of Medicine > Computational Biology
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: evolutionary genetics, convergent evolution, transcriptional regulation, transcription factor, comparative genomics
Date Deposited: 08 Dec 2023 14:59
Last Modified: 08 Dec 2023 14:59


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