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Sanger Sequencing

Sanger Sequencing

DNA sequencing is a fundamental technique that is routinely used in the biological sciences. This method can be applied to a range of questions at different scales - from the sequencing of a cloned DNA fragment or the study of a mutation in a gene up to whole-genome sequencing. However, despite the widespread use of sequencing today, it was not until 1977 that Fredrick Sanger and his collaborators developed the chain-termination method to decode DNA sequences. It relies on the separation of a...

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Related Experiment Video

Updated: May 5, 2026

Fine-tuning the Size and Minimizing the Noise of Solid-state Nanopores

Fine-tuning the Size and Minimizing the Noise of Solid-state Nanopores

Published on: October 31, 2013

SqueezeCall: nanopore basecalling using a Squeezeformer network.

¹Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, 310022, China.

Gigabyte (Hong Kong, China)

|February 26, 2025

Summary

This summary is machine-generated.

SqueezeCall improves nanopore sequencing accuracy by using a novel Squeezeformer model to enhance DNA basecalling. This advanced method effectively reduces noise, leading to more reliable sequencing results.

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Last Updated: May 5, 2026

Fine-tuning the Size and Minimizing the Noise of Solid-state Nanopores

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Published on: October 31, 2013

Nanopore DNA Sequencing for Metagenomic Soil Analysis

Nanopore DNA Sequencing for Metagenomic Soil Analysis

Published on: December 14, 2017

Sequencing of mRNA from Whole Blood using Nanopore Sequencing

Sequencing of mRNA from Whole Blood using Nanopore Sequencing

Published on: June 3, 2019

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Nanopore sequencing offers direct RNA sequencing, real-time analysis, and long reads.
Accurate basecalling in nanopore sequencing is challenging due to molecular variations and noise.
Existing basecalling models face limitations in handling complex sequencing data.

Purpose of the Study:

To introduce SqueezeCall, a novel Squeezeformer-based model for accurate nanopore basecalling.
To evaluate SqueezeCall's performance in resisting noise and improving basecalling accuracy.
To compare SqueezeCall with existing recurrent neural network (RNN) and Transformer-based models.

Main Methods:

SqueezeCall utilizes convolution layers for signal down-sampling and local dependency modeling.
A Squeezeformer network is employed to capture global context in the sequencing data.
A connectionist temporal classification (CTC) decoder with beam search generates DNA sequences.

Main Results:

SqueezeCall demonstrated improved basecalling accuracy and noise resistance.
Training SqueezeCall with three types of loss collectively contributed to enhanced accuracy.
Experiments across multiple species confirmed the superiority of SqueezeCall over RNN and Transformer models.

Conclusions:

SqueezeCall represents a significant advancement in nanopore basecalling technology.
The Squeezeformer architecture shows great potential for improving genomic data analysis.
This model offers a robust solution for accurate DNA sequence generation from nanopore data.