Applying Machine Learning for VCF file filtering

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dc.contributor.advisorPfliegler, Valter Péter
dc.contributor.advisorNémeth, Bálint
dc.contributor.authorAllouh, Fares Raja Farah
dc.contributor.departmentDE--Természettudományi és Technológiai Kar--Biotechnológiai Intézet
dc.date.accessioned2024-12-17T07:38:10Z
dc.date.available2024-12-17T07:38:10Z
dc.date.created2024-11-14
dc.description.abstractAn in silico experiment in which the sequencing data of chromosome 1 from a multitude of S. cerevisiae strains was utilized. The compendium of yeasts created by the Department of Molecular Biotechnology and Microbiology provided the data for many S. cerevisiae strains and their different sequencing runs (replicates). Variants from same-strain replicates were called and combined into VCF files which were then subjected to hard-filtering, and filtering by a convolutional neural network. The study describes a pipeline which utilizes the Genome Analysis Toolkit (GATK) for variant calling and filtration. Finally, the efficacies of both methods are compared, and their strengths and weaknesses are highlighted.
dc.description.courseBiochemical Engineering
dc.description.degreeBSc/BA
dc.format.extent39
dc.identifier.urihttps://hdl.handle.net/2437/383183
dc.language.isoen
dc.rights.accessHozzáférhető a 2022 decemberi felsőoktatási törvénymódosítás értelmében.
dc.subjectBioinformatics
dc.subjectSaccharomyces cerevisiae
dc.subjectNext-generation sequencing
dc.subjectGATK
dc.subject.dspaceBiology::Biotechnology
dc.titleApplying Machine Learning for VCF file filtering
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