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

Front Genet 2018 14;9:313. Epub 2018 Aug 14.

Bioinformatics and Mathematical Biosciences Lab, Department of Agronomy, Horticulture, and Plant Science, South Dakota State University, Brookings, SD, United States.

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 ( expression uality ontrol), 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 extracted genomic and transcriptomic features, which are then combined using elastic-net regularization and mixture model fitting to provide a clearer picture of mapping uncertainty for each gene. GeneQC allows researchers to determine reliable expression estimations and conduct further analysis on the gene expression that is of sufficient quality. This tool also enables researchers to investigate continued re-alignment methods to determine more accurate gene expression estimates for those with low reliability. Application of GeneQC reveals high level of mapping uncertainty in plant samples and limited, severe mapping uncertainty in animal samples. GeneQC is freely available at http://bmbl.sdstate.edu/GeneQC/home.html.

Download full-text PDF

Source
http://dx.doi.org/10.3389/fgene.2018.00313DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6102479PMC
August 2018

Publication Analysis

Top Keywords

gene expression
28
mapping uncertainty
16
expression estimations
12
expression
11
machine learning-based
8
expression estimation
8
gene
8
accurate gene
8
plant animal
8
rna-seq read
8
downstream analyses
8
rna-seq
5
investigation rna-seq
4
incapable ensuring
4
ensuring validity
4
tool enables
4
validity downstream
4
analyses investigation
4
quality tool
4
rna-seq datasets
4

Similar Publications