Bias detection and correction in RNA-Sequencing data

Abstract

BackgroundHigh throughput sequencing technology provides us unprecedented opportunities to study transcriptome dynamics. Compared to microarray-based gene expression profiling, RNA-Seq has many advantages, such as high resolution, low background, and ability to identify novel transcripts. Moreover, for genes with multiple isoforms, expression of each isoform may be estimated from RNA-Seq data. Despite these advantages, recent work revealed that base level read counts from RNA-Seq data may not be randomly distributed and can be affected by local nucleotide composition. It was not clear though how the base level read count bias may affect gene level expression estimates.ResultsIn this paper, by using five published RNA-Seq data sets from different biological sources and with different data preprocessing schemes, we showed that commonly used estimates of gene expression levels from RNA-Seq data, such as reads per kilobase of gene length per million reads (RPKM), are biased in terms of gene length, GC content and dinucleotide frequencies. We directly examined the biases at the gene-level, and proposed a simple generalized-additive-model based approach to correct different sources of biases simultaneously. Compared to previously proposed base level correction methods, our method reduces bias in gene-level expression estimates more effectively.ConclusionsOur method identifies and corrects different sources of biases in gene-level expression measures from RNA-Seq data, and provides more accurate estimates of gene expression levels from RNA-Seq. This method should prove useful in meta-analysis of gene expression levels using different platforms or experimental protocols.

Development in Rice Genome Research Based on Accurate Genome Sequence

“…Recently, there have been several investigations [13][14][15] into the biases that affect the accuracy with which RNA-Seq represents the absolute abundance of a given transcript as measured by high precision approaches such as Taqman RT-PCR [16]. It has been shown that these abundance measures are prone to biases correlated with the nucleotide composition [14,17] and length of the transcript [1,18].…”

“…It has been shown that these abundance measures are prone to biases correlated with the nucleotide composition [14,17] and length of the transcript [1,18]. Several within and between sample correction and normalisation procedures have recently been developed to address these biases either as nucleotide composition effects [17] or various combinations of nucleotide, length or library preparation biases [14,15].…”

Computational Methods for Quality Check, Preprocessing and Normalization of RNA-Seq Data for Systems Biology and Analysis

“…Thus, it is likely to have a higher level of expression rather than shorter transcripts due to this technical problem and not due to a real activation or inactivation of the transcription (Zheng et al 2011;Oshlack and Wakefield 2009). …”

A structured sparse regression method for estimating isoform expression level from multi-sample RNA-seq data

“…This model assumes that the reads are distributed uniformly across the genome, and hence reads are sampled independently and uniformly from every possible nucleotide in the sample (Vardhanabhuti et al, 2013). However, due to a number of biases, such as the 5'-and 3'-end biases, priming or GC bias, and so on, the uniform assumption of read distribution is untenable, and moreover, these various biases cause the nonuniform read distribution to be illogical, which causes the non-uniformity read distribution (Hansen et al, 2010;Zheng et al, 2011). This leads to difficulties in estimating isoform express levels.…”