From sequencing to knowledge: Design and implementation of tools for genomic and transcriptomic data analysis and visualization

Advances in next-generation sequencing (NGS) technologies after the completion of the Human Genome Project have increased the sequencing speed and brought down the cost of sequencing leading to an influx of large scale genomic and transcriptomic studies involving whole-genome sequencing (WGS), whole-exome sequencing (WES) and RNA-sequencing (RNA-seq). Prior to 2005, before the advent of massively parallel sequencing, the primary bottleneck was sequence generation. With widespread adoption of NGS technologies, this has shifted to computational analysis, where storage and computing capacity have become key challenges. Numerous workflows exist for analysing sequencing data but each with its own set of advantages and disadvantages. To address this, I present an integrated bioinformatics framework that unites three modular tools – Kuura, Sampo, and BioCPR – to facilitate comprehensive multi-omics analysis. Kuura performs end-to-end WES and WGS analysis with no user intervention. Kuura uses a consensus-based variant calling approach where it normalizes and integrates variant calls from multiple callers to enhance reliability and reduce bias. The output of Kuura is an annotated Variant Call Format (VCF) containing high-confidence variant calls. Sampo uses a combinational approach for transcriptomic data where preprocessing and alignment are handled through Nextflow, while differential expression analysis and visualization are performed in R, to improve workflow organisation. BioCPR, implemented in R, provides an interactive platform for analysis of expression data with correlation-based clustering, enabling users to identify co-expression and regulatory networks. The framework allows seamless integration, with expression matrices from Sampo directly analysed in BioCPR, and variants identified by Kuura cross-referenced with expression networks. Its reproducible design makes it applicable to germline cancer susceptibility and other complex diseases, enabling discovery of disease-relevant genes and pathways.