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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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Machine learning on alignment features for parent-of-origin classification of simulated hybrid RNA-seq.

Jason R Miller^1,2,3, Donald A Adjeroh⁴

¹Department of Computer Science, Mathematics, Engineering, Shepherd University, Shepherdstown, WV, USA. jmill02@shepherd.edu.

BMC Bioinformatics

|March 13, 2024

Summary

Machine learning improves parent-of-origin classification for RNA-seq reads in interspecies hybrids. This enhances allele-specific gene expression (ASE) detection accuracy by correcting alignment errors.

Keywords:

Allele-specific expression Machine learning RNA-seq Sequence alignment

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Parent-of-origin allele-specific gene expression (ASE) is detectable in interspecies hybrids via RNA sequencing (RNA-seq).
Differential expression analysis (DEA) relies on accurate alignment of RNA-seq reads to parental references, but current aligners often misassign reads.
This misassignment hinders accurate ASE detection in hybrid organisms.

Purpose of the Study:

To develop and evaluate a machine learning approach to improve the accuracy of assigning RNA-seq reads to their correct parental reference.
To enhance the reliability of allele-specific gene expression analysis in interspecies hybrids.

Main Methods:

Utilized public RNA-seq data from known hybridizing species.
Assessed the performance of existing software packages in assigning reads to parental references.
Developed a novel machine learning post-processing method using a random forest classifier trained on alignment features.
Evaluated the post-processor's accuracy in assigning reads to the correct parent-of-origin on simulated hybrid datasets.

Main Results:

Standard RNA-seq alignment software frequently favored the incorrect species reference, leading to misclassification.
The developed machine learning post-processor significantly improved the accuracy of assigning RNA-seq read pairs to the correct parent-of-origin compared to aligners alone.
The post-processor demonstrated higher accuracy on simulated hybrid datasets.

Conclusions:

Machine learning offers a powerful approach to enhance the accuracy of alignment-based methods for parent-of-origin classification in RNA-seq data.
This method holds promise for improving allele-specific gene expression detection in interspecies hybrids.
This represents a novel application of machine learning to the problem of ASE detection in hybrid systems.