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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...

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Deep learning enables the use of ultra-high-density array in DNBSEQ.

Junfeng Li^1,2, Zhiwei Zhai^3,4, Hao Zhang⁵

¹BGI Research, Shenzhen, 518083, China.

Scientific Reports

|November 13, 2024

Summary

A new deep learning model, DNBSRN, enhances DNA nanoball sequencing (DNBSEQ) by improving image resolution for ultra-high-density arrays. This boosts sequencing throughput and reduces costs, overcoming previous limitations.

Keywords:

DNBSEQ Deep learning Super resolution Ultra-high-density array

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Ultra-long Read Sequencing for Whole Genomic DNA Analysis

Ultra-long Read Sequencing for Whole Genomic DNA Analysis

Published on: March 15, 2019

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

DNBSEQ technology uses patterned arrays for high-throughput DNA sequencing.
Ultra-high-density (UHD) arrays increase binding sites but face resolution limits in imaging.
Current imaging systems struggle to resolve adjacent DNA nanoballs (DNBs) in UHD arrays, impacting base-calling accuracy.

Purpose of the Study:

To develop a deep learning solution for DNB image super-resolution.
To overcome the resolution limitations of UHD arrays in DNBSEQ.
To improve base-calling performance and enable practical application of UHD arrays.

Main Methods:

Proposed DNBSRN, a deep learning network specifically designed for DNB images.
Implemented a histogram-matching-based preprocessing approach.
Evaluated DNBSRN on DNBSEQ UHD array datasets (360 nm pitch).

Main Results:

DNBSRN significantly improved base-calling performance on UHD array data.
Reconstructed image quality reached levels comparable to regular density arrays.
DNBSRN achieved fast reconstruction speeds (7.61 ms for 500x500 images).
Outperformed state-of-the-art super-resolution networks in quality and speed.

Conclusions:

DNBSRN effectively addresses the DNB image super-resolution challenge.
Integration of DNBSRN enables the use of UHD arrays in DNBSEQ.
This leads to substantial improvements in sequencing throughput and significant cost savings.