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 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.
Highlights
RNA-Seq is a revolutionary high-throughput process that allows researchers to observe the genetic makeup of a particular sample (Wang et al, 2009; Garber et al, 2011; Ozsolak and Milos, 2011) and can assist in determination of regulatory mechanisms and transcription unit prediction (Chou et al, 2015; Chen et al, 2017)
To address issue of mapping uncertainty, we present the machine learning-based tool GeneQC (Figure 1), which uses extracted multi-level features combined with novel applications of regularized regression and mixture model fitting approaches to quantify the mapping uncertainty issue (McDermaid et al, 2018b)
GeneQC takes as inputs three pieces of information that are found in most RNA-Seq analysis pipelines: (1) the read mapping result SAM file; (2) the fasta reference genome corresponding to the to-be-analyzed species; and (3) the species-specific annotation general feature format file (Figure 1B)
Summary
RNA-Seq is a revolutionary high-throughput process that allows researchers to observe the genetic makeup of a particular sample (Wang et al, 2009; Garber et al, 2011; Ozsolak and Milos, 2011) and can assist in determination of regulatory mechanisms and transcription unit prediction (Chou et al, 2015; Chen et al, 2017). The nature of DNA—long strands of millions of base-pairs created by a reordering of the four nucleotides—makes it inevitable that some similarities and duplications will occur throughout the genome This can lead to ambiguity during read mapping, with specific reads being aligned to multiple locations across the reference genome with the same alignment scores (Li et al, 2009; Oshlack et al, 2010; Swan, 2013; Trapnell et al, 2013; Baruzzo et al, 2017)
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