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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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Updated: Mar 7, 2026

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Sequence-specific bias correction for RNA-seq data using recurrent neural networks.

Yao-Zhong Zhang¹, Rui Yamaguchi¹, Seiya Imoto¹

¹The Institute of Medical Science, The University of Tokyo, Shirokanedai 4-6-1, Minato-ku, Tokyo, 108-8639, Japan.

|February 16, 2017

Summary

This summary is machine-generated.

Recurrent Neural Networks (RNNs) offer a novel method for RNA-seq data analysis by correcting sequence-specific biases. This deep learning approach models nucleotide sequences effectively without needing predefined structures, improving gene abundance estimation.

Keywords:

Gene expression analysis RNA-seq Recurrent neural network Sequence-specific bias

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Last Updated: Mar 7, 2026

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

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Published on: August 3, 2018

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Area of Science:

Bioinformatics
Machine Learning
Computational Biology

Background:

Deep learning is increasingly applied to bioinformatics challenges.
Biological data often benefits from automatic feature representation when prior knowledge is limited.
RNA sequencing (RNA-seq) data analysis faces challenges with sequence-specific biases.

Purpose of the Study:

To address sequence-specific bias correction in RNA-seq data.
To apply Recurrent Neural Networks (RNNs) for modeling nucleotide sequences.
To improve gene abundance estimation using bias-corrected RNA-seq data.

Main Methods:

Utilized Recurrent Neural Networks (RNNs) to model nucleotide sequences without predefined structures.
Calculated sequence-specific bias based on RNN-estimated sequence probabilities.
Explored two popular RNN recurrent units for bias correction.

Main Results:

RNN-based approaches provide a flexible method for nucleotide sequence modeling.
Training RNN models for nucleotide sequences is efficient.
RNN-based bias correction methods perform comparably to state-of-the-art methods on MAQC-III data.

Conclusions:

Recurrent Neural Networks (RNNs) offer a flexible alternative for calculating sequence-specific bias.
This method avoids the need for explicit, predetermined sequence structures.
RNNs present a powerful tool for enhancing RNA-seq data analysis.