2026-09-18T23:22:39Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/998292021-07-05T14:03:20Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
David K. Gifford.
Zeng, Haoyang, Ph.D. Massachusetts Institute of Technology
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2015-11-09T19:51:33Z
2015-11-09T19:51:33Z
2015
2015
http://hdl.handle.net/1721.1/99829
927347507
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 41-44).
With the advent of high-throughput sequencing technology, Genome Wide Association Studies (GWAS) have identified thousands of genetic variants that are associated with disease and complex traits. Many of these variants reside in the non-coding region of the genome, and affect gene expression and downstream cellular phenotype by disrupting the regulatory machinery of the cell. For example these variants can alter the binding of the transcription factors (TF). In this thesis we present Whole-genome regulAtory Variant Evaluation (WAVE), a computational method that models the TF binding ChIP-seq signal solely from DNA sequence and predicts genetic a variant's effect on TF binding. Applying WAVE to two important transcription factors, NFnB and CTCF, we show that WAVE accurately predicts ChIP-seq signal on held-out chromosome. WAVE discovers the DNA motif of the target TF as well as the binding co-factors, displaying substantially greater expressiveness in modeling TF binding than conventional motif-based approaches. Furthermore, with AUC larger than 0.7 in the most stringent control scenario, WAVE outperformed existing motif-based approaches in predicting genetic variants associated with allele-specific binding.
by Haoyang Zeng.
S.M.
44 pages
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Whole genome regulatory variant evaluation for transcription factor binding
Whole genome WAVE for TF binding
Thesis
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<Abstract>With the advent of high-throughput sequencing technology, Genome Wide Association Studies (GWAS) have identified thousands of genetic variants that are associated with disease and complex traits. Many of these variants reside in the non-coding region of the genome, and affect gene expression and downstream cellular phenotype by disrupting the regulatory machinery of the cell. For example these variants can alter the binding of the transcription factors (TF). In this thesis we present Whole-genome regulAtory Variant Evaluation (WAVE), a computational method that models the TF binding ChIP-seq signal solely from DNA sequence and predicts genetic a variant's effect on TF binding. Applying WAVE to two important transcription factors, NFnB and CTCF, we show that WAVE accurately predicts ChIP-seq signal on held-out chromosome. WAVE discovers the DNA motif of the target TF as well as the binding co-factors, displaying substantially greater expressiveness in modeling TF binding than conventional motif-based approaches. Furthermore, with AUC larger than 0.7 in the most stringent control scenario, WAVE outperformed existing motif-based approaches in predicting genetic variants associated with allele-specific binding.</Abstract>
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