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dc.contributor.authorGifford, David K.
dc.contributor.authorRolfe, Philip Alexander
dc.contributor.authorFink, Gerald R.
dc.contributor.authorBernstein, Douglas A.
dc.contributor.authorGrisafi, Paula
dc.date.accessioned2012-11-09T19:19:12Z
dc.date.available2012-11-09T19:19:12Z
dc.date.issued2012-08
dc.date.submitted2012-02
dc.identifier.issn1932-6203
dc.identifier.urihttp://hdl.handle.net/1721.1/74619
dc.description.abstractDespite the known relevance of genomic structural variants to pathogen behavior, cancer, development, and evolution, certain repeat based structural variants may evade detection by existing high-throughput techniques. Here, we present ruler arrays, a technique to detect genomic structural variants including insertions and deletions (indels), duplications, and translocations. A ruler array exploits DNA polymerase’s processivity to detect physical distances between defined genomic sequences regardless of the intervening sequence. The method combines a sample preparation protocol, tiling genomic microarrays, and a new computational analysis. The analysis of ruler array data from two genomic samples enables the identification of structural variation between the samples. In an empirical test between two closely related haploid strains of yeast ruler arrays detected 78% of the structural variants larger than 100 bp.en_US
dc.description.sponsorshipUnited States. National Institutes of Health (Grant R01GM069676)en_US
dc.language.isoen_US
dc.publisherPublic Library of Scienceen_US
dc.relation.isversionofhttp://dx.doi.org/10.1371/journal.pone.0043210en_US
dc.rightsCreative Commons Attributionen_US
dc.rights.urihttp://creativecommons.org/licenses/by/2.5/en_US
dc.sourcePLoSen_US
dc.titleRuler Arrays Reveal Haploid Genomic Structural Variationen_US
dc.typeArticleen_US
dc.identifier.citationRolfe, P. Alexander et al. “Ruler Arrays Reveal Haploid Genomic Structural Variation.” Ed. Gualtiero Colombo. PLoS ONE 7.8 (2012).en_US
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratoryen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Biologyen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.mitauthorGifford, David K.
dc.contributor.mitauthorRolfe, Philip Alexander
dc.contributor.mitauthorFink, Gerald R.
dc.relation.journalPLoS ONEen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.orderedauthorsRolfe, P. Alexander; Bernstein, Douglas A.; Grisafi, Paula; Fink, Gerald R.; Gifford, David K.en
dc.identifier.orcidhttps://orcid.org/0000-0003-3704-2899
dc.identifier.orcidhttps://orcid.org/0000-0003-1709-4034
dspace.mitauthor.errortrue
mit.licensePUBLISHER_CCen_US
mit.metadata.statusComplete


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