PhD Dissertation:Bekir Ergüner
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COMPUTATIONAL METHODS FOR ANALYZING NGS DATA TO DISCOVER CLINICALLY RELEVANT MUTATIONS

 

 

 

Bekir Ergüner
Molecular Biology, Genetics and Bioengineering
, PhD Dissertation 2017

 

Thesis Jury

Prof.İsmail Çakmak (Thesis Advisor)

Prof.Osman Uğur Sezerman

Prof.Yücel Saygın

Assoc. Prof.Devrim Gözüaçık

Assoc. Prof.Muhammed Oğuzhan Külekci

 

 

Date & Time: July 24th, 2017 –  10:00 AM

Place: FENS L030

Keywords : Genome, next generation sequencing, structural variation, mutation, Mendelian disorders

 

Abstract

 

The advent of Next Generation Sequencing platforms started a new era of genomics where affordable genomewide sequencing is available for everyone. These technologies are capable of generating huge amounts of raw sequence data creating an urgent demand for new computational analysis tools and methods. Even the most simple NGS study requires many analysis steps and each step has unique challenges and ambiguities. Therefore efficiently processing raw NGS data and eliminating false-positive signals have become the most challenging issue in genomics. It has been shown that NGS is very effective identifying disease-causing mutations if the data is processed and interpreted properly. In this dissertation, we have presented an efficient and effective whole genome/exome analysis strategy which had been successful in identifying 4 novel rare disease-causing mutations. We also presented a new method for finely mapping genomic structural variations by utilizing de novoassembly and local alignment. Our method is capable of resolving complete structures of complex rearrangements which had not been accomplished before.