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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....

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Related Experiment Video

Updated: Dec 28, 2025

Sequencing of mRNA from Whole Blood using Nanopore Sequencing

Sequencing of mRNA from Whole Blood using Nanopore Sequencing

Published on: June 3, 2019

Causalcall: Nanopore Basecalling Using a Temporal Convolutional Network.

Jingwen Zeng¹, Hongmin Cai¹, Hong Peng¹

¹School of Computer Science and Engineering, South China University of Technology, Guangzhou, China.

Frontiers in Genetics

|February 11, 2020

Summary

This summary is machine-generated.

Causalcall, a new deep learning model, improves nanopore sequencing basecalling accuracy and speed. This temporal convolutional network (TCN) method enhances DNA/RNA sequence identification for genomics.

Keywords:

assembly basecalling deep neural network nanopore sequencing performance comparison temporal convolution

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Last Updated: Dec 28, 2025

Sequencing of mRNA from Whole Blood using Nanopore Sequencing

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Published on: June 3, 2019

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Nanopore DNA Sequencing for Metagenomic Soil Analysis

Published on: December 14, 2017

Validating Whole Genome Nanopore Sequencing, using Usutu Virus as an Example

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Published on: March 11, 2020

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Nanopore sequencing offers long reads and high speed, crucial for genomic analyses.
Accurate basecalling translates electrical signals from nanopore passage into DNA/RNA sequences.
Current basecalling methods face challenges with noise, molecular variations, and speed.

Purpose of the Study:

To develop a novel deep learning model for accurate and rapid nanopore basecalling.
To address limitations in existing basecalling algorithms for improved genomic data analysis.

Main Methods:

Proposed Causalcall, an end-to-end deep learning model utilizing a temporal convolutional network (TCN).
Employed a connectionist temporal classification decoder for direct base sequence identification from time-series current data.
Compared TCN-based Causalcall against recurrent neural network (RNN)-based models.

Main Results:

Causalcall demonstrated improved accuracy and speed in basecalling across multiple species compared to RNN models.
The TCN architecture facilitated faster basecalling through efficient matrix computations.
Causalcall proved effective in reference-based genome assembly tasks.

Conclusions:

Causalcall offers a promising TCN-based approach for enhancing nanopore sequencing basecalling.
The model's speed and accuracy benefit downstream applications like genome assembly and variant detection.
Causalcall represents a significant advancement in computational genomics tools.