2026-09-18T17:42:26Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1061132026-06-06T01:03:15Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Pardis Sabeti and Daniel Park
Krasilnikova, Lydia A
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2016-12-22T16:29:32Z
2016-12-22T16:29:32Z
2016
2016
http://hdl.handle.net/1721.1/106113
965796036
Thesis: M. Eng. in Computer Science and Molecular Biology, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.
Cataloged from PDF version of thesis.
Includes bibliographical references (page 37).
Sequencing followed by metagenomic analysis is an extremely promising method for broad, unbiased disease profiling of patients for disease surveillance and diagnosis. Here, we use two popular metagenomics tools in union, k-mer-based Kraken with reads and BLAST- and LCAbased MEGAN with assembled contiguous sequence. We analyze sequence from 463 febrile and afebrile patients from Sierra Leone before and during the 2014 Ebola virus outbreak. We find that co-infection with malaria is correlated with increased survival of Ebola virus patients, from 18% survival rate to 53%. We also explore the utility of and emphasize the need for both positive and negative controls to distinguish and remove noise and contaminants from real signal, especially to keep up with increasing sensitivity in sequencing.
by Lydia A. Krasilnikova.
M.Eng. in Computer Science and Molecular Biology
384 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.
Metagenomic investigation of co-infections of Ebola virus disease and Lassa fever patients
Thesis
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<Abstract>Sequencing followed by metagenomic analysis is an extremely promising method for broad, unbiased disease profiling of patients for disease surveillance and diagnosis. Here, we use two popular metagenomics tools in union, k-mer-based Kraken with reads and BLAST- and LCAbased MEGAN with assembled contiguous sequence. We analyze sequence from 463 febrile and afebrile patients from Sierra Leone before and during the 2014 Ebola virus outbreak. We find that co-infection with malaria is correlated with increased survival of Ebola virus patients, from 18% survival rate to 53%. We also explore the utility of and emphasize the need for both positive and negative controls to distinguish and remove noise and contaminants from real signal, especially to keep up with increasing sensitivity in sequencing.</Abstract>
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