Browsing by Author "Ebren, Ezgi"

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    Large structural variation discovery using long reads with several degrees of error
    (2020-12) Ebren, Ezgi
    Genomic structural variations (SVs) are briefly defined as large-scale alterations of DNA content, copy, and organization. Although significant progress has been made since the introduction of high throughput sequencing (HTS) in character-izing SVs, accurate detection of complex SVs and balanced rearrangements still remains elusive due to the sequence complexity at the breakpoints. Until very recently, the difficulty of read mapping in such regions when the reads were short and the high error rates of long read platforms kept the problem challenging. However, with the introduction of the Pacific Biosciences’ High Fidelity (HiFi) sequencing methodology, powerful SV detection and breakpoint resolution be-came possible as a result of its capability to produce highly accurate (> 99%) long reads (10 − 20 kbps). Here, we introduce DALEK, a novel algorithm that aims to use long-read tech-nologies to discover large structural variations with high break-point resolution. DALEK uses split read and read depth signatures from long read data to dis-cover large (≥ 10 kbps) deletions, inversions and segmental duplications. We also develop methods to detect large SVs in existing high-error Oxford Nanopore Technologies data.
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    Türkiye Esnaf ve Sanatkârlar Konfederasyonu’nun tarihi ve Türk Esnaf ve Sanatkârları için önemi
    (Bilkent University, 2016) Elvan, Taha; Ebren, Ezgi; Dikbayır, Doğa; Tuncayengin, Göksun; Erdoğan, Ramazan Tufan
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    VALOR2: characterization of large-scale structural variants using linked-reads
    (BioMed Central Ltd., 2020-03) Karaoğlanoğlu, Fatih; Ricketts, C.; Ebren, Ezgi; Rasekh, M. E.; Hajirasouliha, I.; Alkan, Can
    Most existing methods for structural variant detection focus on discovery and genotyping of deletions, insertions, and mobile elements. Detection of balanced structural variants with no gain or loss of genomic segments, for example, inversions and translocations, is a particularly challenging task. Furthermore, there are very few algorithms to predict the insertion locus of large interspersed segmental duplications and characterize translocations. Here, we propose novel algorithms to characterize large interspersed segmental duplications, inversions, deletions, and translocations using linked-read sequencing data. We redesign our earlier algorithm, VALOR, and implement our new algorithms in a new software package, called VALOR2.