Sažimanje genoma korištenjem referentnog genoma

undergraduate thesis

• Title (english): Referential genome compression
• Author: Kristijan Čeple
• Mentor: Mirjana Domazet-Lošo (mentor)
• Committee member: Mirjana Domazet-Lošo (predsjednik povjerenstva)
• Committee member: Ivica Botički (član povjerenstva)
• Committee member: Fran Tonković (član povjerenstva)
• Granter: University of Zagreb; Faculty of Electrical Engineering and Computing; Zagreb
• Defense date and country: 2020-07-07, Croatia
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Computing

Abstract

HiRGC je algoritam referencijske kompresije genoma iz 2017. Prije kompresiranja DNA sekvence kao string, prvo se DNA string preprocesira, uzimajuci u obzir određena 'građena' svojstva, kvalitete i odlike DNA sekvenci koji proizlaze iz njihove strukture i funkcije. Jedan takav način je spremiti DNA u vremenski sortiranoj listi sličnosti i razlika izmedu jedne referentne sekvence i druge(ili više!) ciljnih sekvenci.

Ovo je moguće zbog toga što su DNA sekvence međusobno u ljudi 99.9% slične, te algoritam iskorištava ovo svojstvo. Nakon što je DNA procesiran i spremljen na ovaj način, možemo ponovo na vrh ovoga novoga stringa koristiti tradicionalne metode kompresiranja stringova.

Konačan rezultat je spektakularan - npr. DNA ljudski genom se kompresira sa neka 3 GB dolje na jedan file od 200-300 MB.

Link na gitlab repozitorij:
https://gitlab.com/kristijan_ceple/hash-based-referential-genome-compression

Abstract (english)

HiRGC is a 2017 genome compression algorithm. Before compressing the DNA sequences as raw textual data, one can first pursue certain DNA qualities to enhance the compression before-hand the traditional text compression methods. One such way is to process and store the sequences as a list of similarities and differences between a reference sequence, and 1(or more!) target sequences. Human DNA is mutually(between 2 units) 99.9% similar, and this algorithm takes advantage of that. After the DNA is processed in such a manner, it can then be converted using traditional text compression methods. This produces outstanding results – such as reducing a ~3GB human genome into a 200-300MB file.

The link to the gitlab repository:
https://gitlab.com/kristijan_ceple/hash-based-referential-genome-compression

• Keywords: HiRGC; DNA; kompresija; dekompresija; sličnosti; genom; gen; geni; brzina; performanse; performansa; veličina; memorija; vrijeme; RAM; tehnologija; hardver; softver; hardware; software; big data; bioinformatika; računarska biologija
• Keywords (english): HiRGC; DNA; compression; decompression; similarity; genome; gene; genes; speed; performance; size; memory; time; RAM; technology; hardware; software; storage; big data; bioinformatics; biology; Computational biology
• Language: english
• URN:NBN: urn:nbn:hr:168:763324
• Study programme: Title: Computing; Study programme type: university; Study level: undergraduate; Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Closed access
• Created on: 2023-01-14 08:38:48