Computational methods for functional interpretation of diverse omics data
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2020
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| Online Access: | https://hdl.handle.net/1721.1/124115 |
| _version_ | 1811084195824402432 |
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| author | Nazeen, Sumaiya. |
| author2 | Bonnie Berger. |
| author_facet | Bonnie Berger. Nazeen, Sumaiya. |
| author_sort | Nazeen, Sumaiya. |
| collection | MIT |
| description | This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. |
| first_indexed | 2024-09-23T12:46:53Z |
| format | Thesis |
| id | mit-1721.1/124115 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T12:46:53Z |
| publishDate | 2020 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1241152020-03-10T03:16:59Z Computational methods for functional interpretation of diverse omics data Nazeen, Sumaiya. Bonnie Berger. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Electrical Engineering and Computer Science. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019 Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 199-218). Recent technological advances have resulted in an explosive growth of various types of "omics" data, including genomic, transcriptomic, proteomic, and metagenomic data. Functional interpretation of these data is key to elucidating the potential role of different molecular levels (e.g., genome, transcriptome, proteome, metagenome) in human health and disease. However, the massive size and heterogeneity of raw data pose substantial computational and statistical challenges in integrating and interpreting these data. To overcome these challenges, we need sophisticated approaches and scalable analytical frameworks. This thesis outlines two research efforts along these lines. First, we develop a novel three-tiered integrative omics framework for integrating and functionally analyzing heterogeneous omics datasets across a group of co-occurring diseases. We demonstrate the effectiveness of this framework in investigating the shared pathophysiology of autism spectrum disorder (ASD) and its multi-organ-system co-morbid diseases (e.g., inflammatory bowel disease, asthma, muscular dystrophy, cerebral palsy) and uncover a novel innate immunity connection between them. Second, we develop a new end-to-end computational tool, Carnelian, for robust, alignment-free functional profiling of whole metagenome sequencing reads, that is uniquely suited to finding hidden functional trends across diverse data sets in comparative analysis. Carnelian can find shared metabolic pathways, concordant functional dysbioses, and distinguish microbial metabolic function missed by state-of- the-art functional annotation tools. We demonstrate Carnelian's effectiveness on large-scale metagenomic studies of type-2 diabetes, Crohn's disease, Parkinson's disease, and industrialized versus non-industrialized cohorts. by Sumaiya Nazeen. Ph. D. Ph.D. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science 2020-03-09T18:58:39Z 2020-03-09T18:58:39Z 2019 2019 Thesis https://hdl.handle.net/1721.1/124115 1142186996 eng MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 218 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Nazeen, Sumaiya. Computational methods for functional interpretation of diverse omics data |
| title | Computational methods for functional interpretation of diverse omics data |
| title_full | Computational methods for functional interpretation of diverse omics data |
| title_fullStr | Computational methods for functional interpretation of diverse omics data |
| title_full_unstemmed | Computational methods for functional interpretation of diverse omics data |
| title_short | Computational methods for functional interpretation of diverse omics data |
| title_sort | computational methods for functional interpretation of diverse omics data |
| topic | Electrical Engineering and Computer Science. |
| url | https://hdl.handle.net/1721.1/124115 |
| work_keys_str_mv | AT nazeensumaiya computationalmethodsforfunctionalinterpretationofdiverseomicsdata |