CNV detection from exome sequencing data using a generative probabilistic model

Activity: Talk or presentationContributed talkunknown

Description

Next generation sequencing (NGS) has emerged to one of the key technologies for analyzing genome variations. In particular exome sequencing is widely used as a cost and time efficient technology to identify disease-causing genetic variants as about 85% are located around coding regions. One important category of genetic variants are copy number variants (CNVs) typically detected by whole genome sequencing (WGS). However, most methods finding CNVs in WGS data are not applicable to exome sequencing data, since their read distributions differ substantially due to enrichment effects. The problem of read variations across targeted regions can be circumvented by locally modeling the read counts. For more see http://www.bioinf.jku.at/publications/2012/HGV2012_Klambauer.pdf
Period06 Sept 2012
Event title13th International Meeting on Human Genome Variation and Complex Genome Analysis (HGV2012)
Event typeConference
LocationChinaShow on map

Fields of science

  • 106005 Bioinformatics
  • 305 Other Human Medicine, Health Sciences
  • 102018 Artificial neural networks
  • 102 Computer Sciences
  • 106041 Structural biology
  • 101029 Mathematical statistics
  • 106023 Molecular biology
  • 106013 Genetics
  • 106002 Biochemistry
  • 102001 Artificial intelligence
  • 101004 Biomathematics
  • 102015 Information systems

JKU Focus areas

  • Computation in Informatics and Mathematics
  • Nano-, Bio- and Polymer-Systems: From Structure to Function