Characterization of large structural variation using linked-reads

Simple item page
buir.advisorAlkan, Can
dc.contributor.authorKaraoğlanoğlu, Fatih
dc.date.accessioned2018-09-13T13:57:00Z
dc.date.available2018-09-13T13:57:00Z
dc.date.copyright2018-08
dc.date.issued2018-08
dc.date.submitted2018-09-03
dc.departmentDepartment of Computer Engineeringen_US
dc.descriptionCataloged from PDF version of article.en_US
dc.descriptionThesis (M.S.): Bilkent University, Department of Computer Engineering, İhsan Doğramacı Bilkent University, 2018.en_US
dc.descriptionIncludes bibliographical references (leaves 35-43).en_US
dc.description.abstractMany algorithms aimed at characterizing genomic structural variation (SV) have been developed since the inception of high-throughput sequencing. However, the full spectrum of SVs in the human genome is not yet assessed. Most of the existing methods focus on discovery and genotyping of deletions, insertions, and mobile elements. Detection of balanced SVs with no gain or loss of genomic segments (e.g. inversions) is particularly a challenging task. Long read sequencing has been leveraged to find short inversions but there is still a need to develop methods to detect large genomic inversions. Furthermore, currently there are no algorithms to predict the insertion locus of large interspersed segmental duplications. Here we propose novel algorithms to characterize large (>40Kbp) interspersed segmental duplications and (>80Kbp) inversions using Linked-Read sequencing data. Linked-Read sequencing provides long range information, where Illumina reads are tagged with barcodes that can be used to assign short reads to pools of larger (30-50 Kbp) molecules. Our methods rely on split molecule sequence signature that we have previously described. Similar to the split read, split molecules refer to large segments of DNA that span an SV breakpoint. Therefore, when mapped to the reference genome, the mapping of these segments would be discontinuous. We redesign our earlier algorithm, VALOR, to specifically leverage Linked-Read sequencing data to discover large inversions and characterize interspersed segmental duplications. We implement our new algorithms in a new software package, called VALOR2.en_US
dc.description.degreeM.S.en_US
dc.description.statementofresponsibilityby Fatih Karaoğlanoğlu.en_US
dc.format.extentxi, 46 leaves : charts (some color) ; 30 cm.en_US
dc.identifier.itemidB158945
dc.identifier.urihttp://hdl.handle.net/11693/47871
dc.language.isoEnglishen_US
dc.publisherBilkent Universityen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectStructural Variationen_US
dc.subjectSegmental Duplicationen_US
dc.subjectInversionen_US
dc.subjectLinked Readsen_US
dc.titleCharacterization of large structural variation using linked-readsen_US
dc.title.alternativeBüyük yapısal varyasyonların bağlı okumalar kullanılarak karakterize edilmesien_US
dc.typeThesisen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Fatih_Karaoglanoglu-M.Sc.-Thesis.pdf
Size:
410.65 KB
Format:
Adobe Portable Document Format
Description:
Full printable version
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Dept. of Computer Engineering - Master's degree