Interpreting non-coding and structural genomic variation in renal ciliopathies by ML approaches

Renal ciliopathies pose an important health burden. Short-read genome sequencing (srGS) is the upcoming standard in genetic diagnostics of rare disease. However, because the current interpretation is limited to the exome, 30-40% of patients with suspected genetic kidney disease, including renal ciliopathies, remain unsolved. Innovative approaches are required to interpret non-coding and structural genomic variation. We hypothesize that GS will increase diagnostic yield by 10-15% and improve treatment opportunities for patients with genetic kidney disease. We aim to: (1) Identify non-coding variation using srGS data from 30 patient-parent trios complemented by RNAseq; (2) Detect structural variation by performing long-read GS in 10 patients unsolved by srGS (3) Uncover novel therapeutic targets by ML-approaches that incorporate pathway and GO-term analyses for identified loci.

Fellow profile: Master degree in Life Science, Biomedical Science, Biology or related field. This project is very suitable for students with affinity/keen interest in bioinformatics.