{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/113121"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/113121","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Fine-Mapping Tools : an interactive framework for dissecting disease-associated genetic loci with functional genomics data","abstract":"Fine mapping causal SNPs from GWAS summary statistics is hard. Although many frame- works exist to support fine mapping, some of which leverage epigenomic contexts to increase predictive power, they fail to provide interactivity. Here, we introduce Fine-Mapping Tools (fm-tools), a framework for doing interactive and iterative fine mapping. Fm-tools provides a harmonized data store and implements a number of algorithms for fine mapping -- one of which is the custom RiVIERA-mini, an efficient Bayesian inference framework -- and exposes them via a rich API that can be plugged into a variety of services (e.g., web applications for visualization). Most importantly, fm-tools allows scientists to interactively and iteratively explore dynamically generated hypotheses, as demonstrated by a case study for celiac disease. In summary, fm-tools standardizes the way fine mapping is done, reduces the overhead of fine mapping for scientists and of algorithm development for researchers, and paves the way towards achieving real-time personalized medicine.","abstract_html":"Fine mapping causal SNPs from GWAS summary statistics is hard. Although many frame- works exist to support fine mapping, some of which leverage epigenomic contexts to increase predictive power, they fail to provide interactivity. Here, we introduce Fine-Mapping Tools (fm-tools), a framework for doing interactive and iterative fine mapping. Fm-tools provides a harmonized data store and implements a number of algorithms for fine mapping -- one of which is the custom RiVIERA-mini, an efficient Bayesian inference framework -- and exposes them via a rich API that can be plugged into a variety of services (e.g., web applications for visualization). Most importantly, fm-tools allows scientists to interactively and iteratively explore dynamically generated hypotheses, as demonstrated by a case study for celiac disease. In summary, fm-tools standardizes the way fine mapping is done, reduces the overhead of fine mapping for scientists and of algorithm development for researchers, and paves the way towards achieving real-time personalized medicine.","abstract_has_math":false,"creators":["Nguyen, Peter H. T. (Peter Hung Trung)"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.","school":null,"contributors":[],"advisors":["Manolis Kellis."],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017","date_published":"2017","updated_at":"2026-07-22T22:21:50Z","subjects":["Electrical Engineering and Computer Science."],"languages":["eng"],"rights":["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."],"rights_urls":["http://dspace.mit.edu/handle/1721.1/7582"],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/1721.1/113121","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Manolis Kellis."]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."]},{"key":"dc:contributor.other","label":"Dc Contributor Other","values":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."]},{"key":"dc:creator","label":"Author","values":["Nguyen, Peter H. T. (Peter Hung Trung)"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2018-01-12T20:57:26Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2018-01-12T20:57:26Z"]},{"key":"dc:date.issued","label":"Date","values":["2017"]},{"key":"dc:publisher","label":"Institution","values":["Massachusetts Institute of Technology"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Electrical Engineering and Computer Science."]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["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."]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://dspace.mit.edu/handle/1721.1/7582"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/1721.1/113121"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.","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 57-58)."]},{"key":"dc:description.abstract","label":"Abstract","values":["Fine mapping causal SNPs from GWAS summary statistics is hard. Although many frame- works exist to support fine mapping, some of which leverage epigenomic contexts to increase predictive power, they fail to provide interactivity. Here, we introduce Fine-Mapping Tools (fm-tools), a framework for doing interactive and iterative fine mapping. Fm-tools provides a harmonized data store and implements a number of algorithms for fine mapping -- one of which is the custom RiVIERA-mini, an efficient Bayesian inference framework -- and exposes them via a rich API that can be plugged into a variety of services (e.g., web applications for visualization). Most importantly, fm-tools allows scientists to interactively and iteratively explore dynamically generated hypotheses, as demonstrated by a case study for celiac disease. In summary, fm-tools standardizes the way fine mapping is done, reduces the overhead of fine mapping for scientists and of algorithm development for researchers, and paves the way towards achieving real-time personalized medicine."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M. Eng."]},{"key":"dc:title","label":"Title","values":["Fine-Mapping Tools : an interactive framework for dissecting disease-associated genetic loci with functional genomics data"]}]}],"canonical_facts":{"dc:contributor.advisor":["Manolis Kellis."],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."],"dc:contributor.other":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."],"dc:creator":["Nguyen, Peter H. T. (Peter Hung Trung)"],"dc:date.accessioned":["2018-01-12T20:57:26Z"],"dc:date.available":["2018-01-12T20:57:26Z"],"dc:date.issued":["2017"],"dc:description":["Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.","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 57-58)."],"dc:description.abstract":["Fine mapping causal SNPs from GWAS summary statistics is hard. Although many frame- works exist to support fine mapping, some of which leverage epigenomic contexts to increase predictive power, they fail to provide interactivity. Here, we introduce Fine-Mapping Tools (fm-tools), a framework for doing interactive and iterative fine mapping. Fm-tools provides a harmonized data store and implements a number of algorithms for fine mapping -- one of which is the custom RiVIERA-mini, an efficient Bayesian inference framework -- and exposes them via a rich API that can be plugged into a variety of services (e.g., web applications for visualization). Most importantly, fm-tools allows scientists to interactively and iteratively explore dynamically generated hypotheses, as demonstrated by a case study for celiac disease. In summary, fm-tools standardizes the way fine mapping is done, reduces the overhead of fine mapping for scientists and of algorithm development for researchers, and paves the way towards achieving real-time personalized medicine."],"dc:description.degree":["M. Eng."],"dc:identifier.uri":["http://hdl.handle.net/1721.1/113121"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["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."],"dc:rights.uri":["http://dspace.mit.edu/handle/1721.1/7582"],"dc:subject":["Electrical Engineering and Computer Science."],"dc:title":["Fine-Mapping Tools : an interactive framework for dissecting disease-associated genetic loci with functional genomics data"],"dc:type":["Thesis"]},"updated_at":"2026-07-22T22:21:50Z"}