DNA Sequencing and Digital Hardware: Pushing the Boundaries of Genomics

The convergence of DNA sequencing and digital hardware technologies represents a transformative frontier in genomics research, accelerating capabilities while reducing costs and increasing accessibility. Next-Generation Sequencing (NGS) technologies have dramatically increased genetic data volume, creating computational challenges that traditional systems struggle to address efficiently. Specialized hardware accelerators, including Graphics Processing Units (GPU) and Field-Programmable Gate Arrays (FPGA), provide customizable acceleration for genomic algorithms with superior energy efficiency. Custom silicon solutions like Application-Specific Integrated Circuits (ASICs) and System-on-Chip (SoC) designs minimize data transfer bottlenecks and enable point-of-care genetic analysis. Additionally, Neural Processing Units (NPUs) embedded in sequencing systems enable real-time pattern recognition while reducing false positives, and machine-learning hardware accelerators improve base-calling accuracy through adaptive error correction. These technological advances have significant impacts on healthcare through personalized medicine advancements, enabling routine whole-genome analysis in clinical settings with faster turnaround times, while research capabilities benefit from dramatically improved processing capabilities that make previously impractical analyses feasible.

Authors

Institutions

Publication Details

Journal
Journal of Computer Science and Technology Studies
Published
2025-05-29
DOI
https://doi.org/10.32996/jcsts.2025.7.5.5
Primary Topic
CRISPR and Genetic Engineering
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

DNA Sequencing and Digital Hardware: Pushing the Boundaries of Genomics

Sruthi Somarouthu
Journal of Computer Science and Technology Studies
CRISPR and Genetic Engineering
article

DNA Sequencing and Digital Hardware: Pushing the Boundaries of Genomics

Sruthi Somarouthu
article en

Abstract

The convergence of DNA sequencing and digital hardware technologies represents a transformative frontier in genomics research, accelerating capabilities while reducing costs and increasing accessibility. Next-Generation Sequencing (NGS) technologies have dramatically increased genetic data volume, creating computational challenges that traditional systems struggle to address efficiently. Specialized hardware accelerators, including Graphics Processing Units (GPU) and Field-Programmable Gate Arrays (FPGA), provide customizable acceleration for genomic algorithms with superior energy efficiency. Custom silicon solutions like Application-Specific Integrated Circuits (ASICs) and System-on-Chip (SoC) designs minimize data transfer bottlenecks and enable point-of-care genetic analysis. Additionally, Neural Processing Units (NPUs) embedded in sequencing systems enable real-time pattern recognition while reducing false positives, and machine-learning hardware accelerators improve base-calling accuracy through adaptive error correction. These technological advances have significant impacts on healthcare through personalized medicine advancements, enabling routine whole-genome analysis in clinical settings with faster turnaround times, while research capabilities benefit from dramatically improved processing capabilities that make previously impractical analyses feasible.

Journal of Computer Science and Technology StudiesVol. 7(5)
The University of Texas at Austin (US)
Openalex Percentile: Top 89%
CRISPR and Genetic Engineering
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.