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Algorithmic Methods for Multi-Omics Biomarker Discovery

Li, Yichao
http://orcid.org/0000-0001-5791-096X
http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1541609328071533

Abstract Details

2018, Doctor of Philosophy (PhD), Ohio University, Electrical Engineering & Computer Science (Engineering and Technology).
The central dogma of molecular biology states that DNA is transcribed into RNA, which is then translated into proteins. The flow of genetic information in time and space is orchestrated by complex regulatory mechanisms. With the advent of modern biotechnology, our understanding of genomics, transcriptomics, and proteomics has deepened. However, bioinformatic tools for biomarker discovery in the different types of omics are still lacking. To address these issues, we developed novel algorithmic methods for three primary omics. Proteins are the main executor of cellular functions. In the proteomic level, we developed machine learning models for early diagnosis of type 2 diabetes based on the abundance of post-translational modifications (PTMs). Our models can interpret mass spectrometry data and perform integrative analysis together with clinical parameters such as HbA1C and fasting plasma glucose. In the results, we identified glycated lysine-141 of haptoglobin to be a potential biomarker. Gene regulation is conducted by cis-regulatory elements and transcription factors. In the transcriptomic level, we developed Emotif Alpha bioinformatic pipeline for DNA motif discovery and selection using RNA-seq, ChIP-seq, and gene homology data. We applied this pipeline to multiple species, including human, mouse, plants, and nematodes. The discovered motifs were validated using Gaussia Luciferase (GLuc) reporter. The 3D genome architecture in the nucleus involves spatial organization of nuclear bodies such as the histone locus body (HLB). In the 3D genomics level, we developed a bioinformatic pipeline for characterizing locus-specific chromatin interactions. Specifically, we integrated Hi-C, GAM, and SPRITE data and identified complex chromatin organization signature of the Hist1 cluster in mouse embryonic stem cell (mESC). In addition, we performed network hub analysis and identified hubs of diverse functions. These hubs contained not only histone genes and other active genes, but also lamina-associated domains (LADs) and polycomb domains. Motif and motif pair analyses further revealed putative transcription factors that might play important roles for each interaction hub.
Lonnie Welch (Advisor)
Razvan Bunescu (Committee Member)
Liu Jundong (Committee Member)
Frank Drews (Committee Member)
Allan Showalter (Committee Member)
Shiyong Wu (Committee Member)
138 p.
Bioinformatics; Computer Science
Motif; Diabetes; Transcription Factor; HiC; Set Cover; Machine Learning; Ensemble Learning; HbA1C; Glycated Peptide; Motif Discovery; Motif Pair; 3D Genome Organization; DREAM challenge; Python; Data Analytics; Hist1; Clustering Analysis; Cross Validation

Recommended Citations

Citations

  • Li, Y. (2018). Algorithmic Methods for Multi-Omics Biomarker Discovery [Doctoral dissertation, Ohio University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1541609328071533

    APA Style (7th edition)

  • Li, Yichao. Algorithmic Methods for Multi-Omics Biomarker Discovery. 2018. Ohio University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1541609328071533.

    MLA Style (8th edition)

  • Li, Yichao. "Algorithmic Methods for Multi-Omics Biomarker Discovery." Doctoral dissertation, Ohio University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1541609328071533

    Chicago Manual of Style (17th edition)

ohiou1541609328071533
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© 2018, all rights reserved.
This open access ETD is published by Ohio University and OhioLINK.