A New Machine Learning-Based Framework for Mapping Uncertainty Analysis in RNA-Seq Read Alignment and Gene Expression Estimation

Frontiers in Genetics
Adam McDermaidQin Ma

Abstract

One of the main benefits of using modern RNA-Sequencing (RNA-Seq) technology is the more accurate gene expression estimations compared with previous generations of expression data, such as the microarray. However, numerous issues can result in the possibility that an RNA-Seq read can be mapped to multiple locations on the reference genome with the same alignment scores, which occurs in plant, animal, and metagenome samples. Such a read is so-called a multiple-mapping read (MMR). The impact of these MMRs is reflected in gene expression estimation and all downstream analyses, including differential gene expression, functional enrichment, etc. Current analysis pipelines lack the tools to effectively test the reliability of gene expression estimations, thus are incapable of ensuring the validity of all downstream analyses. Our investigation into 95 RNA-Seq datasets from seven plant and animal species (totaling 1,951 GB) indicates an average of roughly 22% of all reads are MMRs. Here we present a machine learning-based tool called GeneQC (Gene expression Quality Control), which can accurately estimate the reliability of each gene's expression level derived from an RNA-Seq dataset. The underlying algorithm is designed based on extr...Continue Reading

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Citations

Nov 19, 2019·Frontiers in Microbiology·Qin MaSenthil Subramanian
Jul 26, 2019·Nature Reviews. Genetics·Rory StarkJames Hadfield
Jul 9, 2020·Computational and Structural Biotechnology Journal·Gabrielle Deschamps-FrancoeurMichelle S Scott
Aug 21, 2020·Trends in Biotechnology·Anjun MaQin Ma
Aug 10, 2021·Journal of Biotechnology·Julian C KrappingerJulia Feichtinger
Jan 14, 2022·Scientific Reports·Nikita PotemkinJoanna M Williams

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Methods Mentioned

BETA
RNA-Seq
feature extraction
real

Software Mentioned

HISAT2
R package
MMR
FastQC
GeneQC
Perl

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