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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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Related Experiment Video

Updated: Jul 24, 2025

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

A deep-learning-based RNA-seq germline variant caller.

Daniel E Cook¹, Aarti Venkat², Dennis Yelizarov¹

¹Google LLC., Mountain View, CA 94043, USA.

Bioinformatics Advances

|July 7, 2023

Summary

This summary is machine-generated.

DeepVariant, a deep learning tool, now accurately calls genetic variants from RNA sequencing data. This enhanced model overcomes common RNA sequencing errors, outperforming existing variant callers.

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Last Updated: Jul 24, 2025

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

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Targeted DNA Methylation Analysis by Next-generation Sequencing

Targeted DNA Methylation Analysis by Next-generation Sequencing

Published on: February 24, 2015

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

RNA sequencing (RNA-seq) is versatile for gene expression, QTL discovery, and fusion event identification.
RNA-seq data presents unique error sources like variable transcript abundance, complicating germline variant detection.
Existing variant callers struggle with RNA-seq data complexities.

Purpose of the Study:

To adapt DeepVariant, a deep learning variant caller, for accurate variant calling from RNA-seq data.
To develop a model that learns and mitigates RNA-seq specific error sources.
To evaluate the performance of the enhanced DeepVariant model against established methods.

Main Methods:

Extension of the DeepVariant deep learning framework to process RNA-seq data.
Training the model to recognize and correct errors inherent in RNA sequencing.
Comparative analysis with existing variant callers like Platypus and GATK.

Main Results:

The DeepVariant RNA-seq model achieves high accuracy in variant calls from RNA sequencing data.
The model demonstrates superior performance compared to Platypus and GATK.
Analysis identifies factors influencing accuracy and the model's ability to handle RNA editing events.

Conclusions:

DeepVariant can be effectively extended for accurate variant calling in RNA sequencing.
The developed model offers improved accuracy and robustness over current RNA-seq variant callers.
Further thresholding strategies can optimize the model for production pipelines.