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Next-generation Sequencing

Next-generation Sequencing

The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features....

RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

Maxam-Gilbert Sequencing

Maxam-Gilbert Sequencing

In the same year as the discovery of the Sanger sequencing method, another group of scientists, Allan Maxam and Walter Gilbert, demonstrated their chemical-cleavage method for DNA sequencing. The Maxam-Gilbert method relies on using different chemicals that can cleave the DNA sequence at specific sites, the separation of resulting DNA fragments of variable size using electrophoresis, and deciphering the DNA sequence from the resulting gel bands.
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Related Experiment Video

Updated: Jan 11, 2026

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

Published on: April 11, 2016

Rapid NGS Analysis on Google Cloud Platform: Performance Benchmark and User Tutorial.

Eugenio Franzoso¹, Mariangela Santorsola¹, Francesco Lescai¹

¹Department of Biology and Biotechnology "L. Spallanzani", University of Pavia, Pavia, Italy.

Clinical and Translational Science

|November 19, 2025

Summary

Next-Generation Sequencing (NGS) analysis pipelines, Sentieon and Parabricks, are benchmarked on Google Cloud Platform (GCP). Both offer comparable performance for rapid clinical genetic testing, making advanced genomic tools accessible without extensive local infrastructure.

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Last Updated: Jan 11, 2026

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

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Published on: April 11, 2016

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Targeted DNA Methylation Analysis by Next-generation Sequencing

Targeted DNA Methylation Analysis by Next-generation Sequencing

Published on: February 24, 2015

Area of Science:

Genomics
Bioinformatics
Clinical Diagnostics

Background:

Next-Generation Sequencing (NGS) adoption is growing in clinical settings for genetic pathology diagnosis.
Rapid analysis is critical for critically ill patients, but NGS pipelines have high computational demands.
Cloud platforms like Google Cloud Platform (GCP) offer scalable solutions, but implementation can be complex.

Purpose of the Study:

Benchmark Sentieon DNASeq and Clara Parabricks Germline pipelines on GCP.
Provide a tutorial for implementing these pipelines on GCP.
Offer cost guidance for cloud-based NGS processing.

Main Methods:

Benchmarking of Sentieon and Parabricks on GCP using five whole exome sequencing (WES) and five whole genome sequencing (WGS) samples.
Evaluation of runtime, cost, and resource utilization.
Development of a tutorial for GCP implementation.

Main Results:

Sentieon and Parabricks demonstrated comparable performance in NGS analysis on GCP.
Both pipelines are viable for rapid, cloud-based genomic analysis.
Results provide insights into resource utilization and cost-effectiveness.

Conclusions:

Sentieon and Parabricks are effective solutions for accelerated clinical NGS analysis on GCP.
Cloud-based NGS processing enables healthcare facilities to leverage advanced genomic tools.
This work simplifies cloud implementation for bioinformaticians and aids cost management for healthcare leaders.