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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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Exact Bayesian inference for the detection of graft-mobile transcripts from sequencing data.

Melissa Tomkins¹, Franziska Hoerbst¹, Saurabh Gupta²

¹Computational and Systems Biology, John Innes Centre, Norwich Research Park, Norwich NR47UH, UK.

Journal of the Royal Society, Interface

|December 14, 2022

Summary

This study introduces a new Bayes factor method to accurately identify graft-mobile messenger RNAs (mRNAs) in plants by distinguishing single-nucleotide polymorphisms (SNPs) from sequencing errors using RNA-Seq data.

Keywords:

Bayesian inference RNA-Seq analysis grafting long-distance transport mobile mRNA sequencing errors

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Area of Science:

Plant molecular biology
Genetics
Bioinformatics

Background:

Long-distance transport of messenger RNAs (mRNAs) is crucial for plant development.
RNA sequencing (RNA-Seq) on grafted plants is a common method to study mobile mRNAs.
Accurate assignment of sequenced mRNAs to their genetic origin, often via single-nucleotide polymorphisms (SNPs), is challenging due to sequencing errors.

Purpose of the Study:

To develop a robust computational framework for identifying graft-mobile transcripts.
To accurately distinguish true single-nucleotide polymorphisms (SNPs) from RNA sequencing errors.
To provide guidelines for experimental design in graft-mobile mRNA detection.

Main Methods:

Analytical computation of Bayes factors using RNA-Seq data across all SNPs within an mRNA.
Performance evaluation through simulations to assess the accuracy of the proposed framework.
Comparison with existing detection methods, considering variability in read depth, error rates, and multiple SNPs per transcript.

Main Results:

Bayes factors accurately identify graft-mobile transcripts.
The proposed framework effectively distinguishes SNPs from sequencing errors.
Ignoring variability in read depth, error rates, and multiple SNPs can lead to misclassification.

Conclusions:

The Bayes factor approach offers a reliable method for detecting graft-mobile mRNAs.
Accurate identification requires considering multiple factors beyond single SNPs.
The study provides essential criteria for successful experimental design in this field.