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G2-seq: A High Throughput Sequencing-based Technique for Identifying Late Replicating Regions of the Genome

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Maximizing the potential of high-throughput next-generation sequencing through precise normalization based on read

Caitriona Brennan¹, Rodolfo A Salido², Pedro Belda-Ferre¹

¹Department of Pediatrics, University of California San Diego , La Jolla, California, USA.

|June 23, 2023

Summary

A new normalization method uses shallow sequencing to accurately pool samples for high-throughput sequencing. This approach reduces noise and improves data quality, optimizing next-generation sequencing efficiency and reducing costs.

Keywords:

NGS normalization automation high-throughput sequencing large-scale studies metagenomics multiplexing quantification

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Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

Next-generation sequencing (NGS) advances biology but faces high sample preparation costs.
High-throughput sequencing often suffers from uneven sample representation due to variable sample quality.
This unevenness leads to data misinterpretation, increased noise, and additional costs for resequencing.

Purpose of the Study:

To introduce a novel normalization method for high-throughput NGS.
To address the issue of over- and underrepresentation of samples in sequencing runs.
To improve the efficiency and reduce the cost of NGS experiments.

Main Methods:

Developed a normalization method utilizing shallow iSeq sequencing.
Quantified adapter-ligated molecules for accurate pooling volume determination.
Enabled normalization based on read counts and feature space, including non-ribosomal reads.

Main Results:

The method accurately informs pooling volumes based on read distribution.
It outperforms traditional fluorometry methods by specifically targeting sequencing-relevant molecules.
Achieved reduced noise, higher average reads per sample, and more even sequencing depth.

Conclusions:

The presented normalization method significantly enhances the efficiency of high-throughput NGS.
It provides a more accurate and cost-effective alternative to existing methods.
Optimizes NGS for broader applications in genomics and other biological fields.