2026-09-19T20:23:27Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/836312022-01-13T07:54:04Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022
Bonnie Berger.
Loh, Po-Ru
Massachusetts Institute of Technology. Department of Mathematics.
Massachusetts Institute of Technology. Department of Mathematics
2014-01-09T18:54:20Z
2014-01-09T18:54:20Z
2013
http://hdl.handle.net/1721.1/83631
864152524
Thesis (Ph. D.)--Massachusetts Institute of Technology, Department of Mathematics, 2013.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 133-139).
Rapid advances in next-generation sequencing technologies are revolutionizing genomics, with data sets at the scale of thousands of human genomes fast becoming the norm. These technological leaps promise to enable corresponding advances in biology and medicine, but the deluge of raw data poses substantial mathematical, computational and statistical challenges that must first be overcome. This thesis consists of two research thrusts along these lines. First, we propose an algorithmic framework, "compressive genomics," that accelerates bioinformatic computations through analysis-aware compression. We demonstrate this methodology with proof-of-concept implementations of compression-accelerated search (CaBLAST and CaBLAT). Second, we develop new computational tools for investigating population admixture, a phenomenon of importance in understanding demographic histories of human populations and facilitating association mapping of disease genes. Our recently released ALDER and MixMapper software packages provide fast, sensitive, and robust methods for detecting and analyzing signatures of admixture created by genetic drift and recombination on genome-wide, large-sample scales.
by Po-Ru Loh.
Ph.D.
139 pages
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
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http://dspace.mit.edu/handle/1721.1/7582
Mathematics.
Algorithms for genomics and genetics : compression-accelerated search and admixture analysis
Thesis
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<Abstract>Rapid advances in next-generation sequencing technologies are revolutionizing genomics, with data sets at the scale of thousands of human genomes fast becoming the norm. These technological leaps promise to enable corresponding advances in biology and medicine, but the deluge of raw data poses substantial mathematical, computational and statistical challenges that must first be overcome. This thesis consists of two research thrusts along these lines. First, we propose an algorithmic framework, "compressive genomics," that accelerates bioinformatic computations through analysis-aware compression. We demonstrate this methodology with proof-of-concept implementations of compression-accelerated search (CaBLAST and CaBLAT). Second, we develop new computational tools for investigating population admixture, a phenomenon of importance in understanding demographic histories of human populations and facilitating association mapping of disease genes. Our recently released ALDER and MixMapper software packages provide fast, sensitive, and robust methods for detecting and analyzing signatures of admixture created by genetic drift and recombination on genome-wide, large-sample scales.</Abstract>
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