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bioRxiv · 10.1101/2024.06.10.598220

Unveiling Genomic Complexity: A Framework for Genome Graph Structural Analysis and Optimised Variant Calling Workflows

Kamaraj, V.·Gupta, A.·Narayanan, M.·Raman, K.·Sinha, H.
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Abstract

MotivationGenome graphs represent genetic diversity by highlighting polymorphic regions, but current methods lack the ability to characterize and compare their complex structures effectively. ResultsOur study introduces GViNC: a framework for Genome graph Visualisation, Navigation, and Comparison. GViNC maps genomic coordinates onto genome graph nodes, facilitating subgraph partitioning by regions, which aids in navigating and comparing genetic data. Applied to multiple genome graphs from the 1,000 Genomes Project, we observed that genomic complexity varies by ancestry and chromosomes, with rare variants increasing variability significantly. GViNC identified key regions like HLA and DEFB loci, revealing population-specific heterogeneity linked to essential biological functions. Its versatility and scalability support extensive research on genetic diversity across different cohorts or species. Availability and ImplementationGViNC, automated with Snakemake, is available at https://github.com/IBSE-IITM/GViNC. Contact(K.R.) kraman@iitm.ac.in, (M.N) nmanik@cse.iitm.ac.in, (H.S.) sinha@iitm.ac.in Supplementary informationA supplementary document with tables and figures accompanies this manuscript.

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Kamaraj, V., Gupta, A., Narayanan, M., Raman, K., Sinha, H.. 2024-06-11. Unveiling Genomic Complexity: A Framework for Genome Graph Structural Analysis and Optimised Variant Calling Workflows. https://doi.org/10.1101/2024.06.10.598220

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