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Analysis of next-generation sequencing for the diagnosis of Mendelian rare neurological disorders
University of Skövde, School of Bioscience.
2020 (English)Independent thesis Advanced level (degree of Master (One Year)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

In recent years, there has been a huge advance in our ability to determine the genetic cause of Mendelian rare conditions through the utilization of next generation sequencing (NGS) techniques. In a clinical setting, the most common application of NGS technology is to detect single nucleotide variants, small insertions and deletions with respect to a reference genome. Although NGS offers cheaper and faster molecular genetic diagnosis, analysis and interpretation of the data are challenging and highly time consuming. Additionally, approximately 75% of patients with Mendelian disorders evaluated by clinical whole exome sequencing remain undiagnosed. Therefore, an urgent need for a user-friendly and fast NGS data analysis and interpretation of data in clinical use is accelerating. In this thesis project, application of two software packages, QIAGEN’s Ingenuity® Variant Analysis™ respectively Moon, for analysing variant call format (VCF) files to identify accurate disease-causing variants was compared. In addition, the quality of the output data was evaluated using a number of specified criteria. The results indicated that both software packages give equally accurate variant identifications. The Ingenuity® Variant Analysis™ is, however, facilitated by a larger number of combined analytical tools, which provides a rapidly comprehensive interpretation of identified variants and thereby helps to make sense of the data in a more time-efficient and userfriendly manner.

Place, publisher, year, edition, pages
2020. , p. 57
National Category
Bioinformatics and Systems Biology
Identifiers
URN: urn:nbn:se:his:diva-18599OAI: oai:DiVA.org:his-18599DiVA, id: diva2:1446188
Subject / course
Bioinformatics
Educational program
Bioinformatics - Master’s Programme
Supervisors
Examiners
Available from: 2020-06-24 Created: 2020-06-24 Last updated: 2020-06-24Bibliographically approved

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