Digitalization & Sustainability Review

Peer Reviewed Article

Vol. 1 No. 1 (2021)

Bioinformatics Tools and IT Infrastructure for High-Throughput Genomic Data Analysis

Published
2021-05-25

Abstract

This work assesses the present status of IT infrastructure and bioinformatics tools necessary for high-throughput genetic data analysis to identify developments, constraints, and policy impacts. The study includes IT solutions, integrated platforms, variant calling, quality control, sequence alignment, and data annotation tools using a secondary data review technique. Principal results show notable advancements in tool capabilities, including the thorough quality evaluations of FastQC and the effective alignment offered by BWA and Minimap2. However, there are still issues like the intricacy of GATK, Bioconductor's high learning curve, and QC tools' scalability. While cloud computing and HPC clusters optimize IT infrastructure and show improved scalability and performance, persistent problems remain with data security and cost management. The policy implications emphasize the necessity of investing in safe and user-friendly tools, standardizing protocols, and putting strong data protection measures in place. Future improvements should prioritize using AI and machine learning, enhancing interoperability, and resolving ethical problems to fully achieve the potential of genomic data analysis in personalized treatment and collaborative research. This thorough analysis highlights the importance of making deliberate policy decisions and maintaining ongoing development to advance genomic science.

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