Project Details
Projekt
Multi-sample structural variant calling with linked-read sequencing data
Applicant
Professorin Dr. Birte Kehr
Subject Area
Human Genetics
Bioinformatics and Theoretical Biology
Bioinformatics and Theoretical Biology
Term
since 2019
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 400728090
A human genome carries tens of thousands of structural variants (SVs) that can impact phenotypic traits and cause genetic diseases. SVs are, however, notoriously difficult to call from short read sequencing data due to their length and their frequent location within repetitive regions of the genome. Therefore, we are using linked-read sequencing data for preparing a comprehensive SV call set of the RU’s rare disease cohort. Taking advantage of the long-range information contained in linked-read data, we successfully developed the software tool bcmap in the first funding period for ultrafast extraction of reads from a given genomic interval without computing a read alignment (Lüpken et al. submitted). Until the end of the first funding period, we will further develop bcmap into a genome-wide local assembly approach that can detect SVs more comprehensively than current short-read or linked-read analysis tools, and prepare a comprehensive SV call set for the RU’s disease cohorts. In addition, we recently developed the software tools PopDel (Niehus et al. Nat Commun 2021) and PopIns2 (Krannich et al. Bioinformatics 2022) for identifying SVs in short read sequencing data. PopDel and PopIns2 call SVs in data of many individuals simultaneously, thereby achieving more reliable SV callsets compared to previous approaches. In the second funding period we want to extend our linked-read SV calling approach to take into account data from multiple individuals simultaneously. To our knowledge, no other linked-read SV caller is currently able to do so. The resulting call sets promise to be more reliable and will allow better filtering for disease-relevant variants. This is particularly relevant in the context of the RU’s rare disease cohorts and can reduce the length of the diagnostic odyssey for patients with rare diseases.
DFG Programme
Research Units
