Abstract
BackgroundThe availability of fast alignment-free algorithms has greatly reduced the computational burden of RNA-seq processing, especially for relatively poorly assembled genomes. Using these approaches, previous RNA-seq datasets could potentially be processed and integrated with newly sequenced libraries. Confounding factors in such integration include sequencing depth and methods of RNA extraction and selection. Different selection methods (typically, either polyA-selection or rRNA-depletion) omit different RNAs, resulting in different fractions of the transcriptome being sequenced. In particular, rRNA-depleted libraries sample a broader fraction of the transcriptome than polyA-selected libraries. This study aimed to develop a systematic means of accounting for library type that allows data from these two methods to be compared.ResultsThe method was developed by comparing two RNA-seq datasets from ovine macrophages, identical except for RNA selection method. Gene-level expression estimates were obtained using a two-part process centred on the high-speed transcript quantification tool Kallisto. Firstly, a set of reference transcripts was defined that constitute a standardised RNA space, with expression from both datasets quantified against it. Secondly, a simple ratio-based correction was applied to the rRNA-depleted estimates. The outcome is an almost perfect correlation between gene expression estimates, independent of library type and across the full range of levels of expression.ConclusionA combination of reference transcriptome filtering and a ratio-based correction can create equivalent expression profiles from both polyA-selected and rRNA-depleted libraries. This approach will allow meta-analysis and integration of existing RNA-seq data into transcriptional atlas projects.
Highlights
The availability of fast alignment-free algorithms has greatly reduced the computational burden of RNAseq processing, especially for relatively poorly assembled genomes
RNA sequencing (RNA-seq) libraries vary in how RNA is extracted from the cell line or tissue and in how RNAs are selected for sequencing
RNA selection method affects estimates of expression level In other species, including mice and humans [37], pigs [38] and horses [39], pure populations of macrophages can be generated by cultivating precursors in the macrophage growth factor, CSF1, with these cells undergoing a profound and rapid change in gene expression when exposed to bacterial lipopolysaccharide (LPS)
Summary
The availability of fast alignment-free algorithms has greatly reduced the computational burden of RNAseq processing, especially for relatively poorly assembled genomes Using these approaches, previous RNA-seq datasets could potentially be processed and integrated with newly sequenced libraries. As lower material costs help overcome limitations of breadth (diversity of samples sequenced) and depth (the ability to capture rare transcripts) in a given study, an increasing number of gene expression atlases – large-scale compendia of transcript abundance across a range of tissues and cell types – have been developed, including atlases for the rat [6], sheep [7], maize [8, 9], soybean [10], the string bean Phaesolus vulgaris [11], the pea Pisum sativum [12], and humans (as part of the ENCODE project) [13]. RNA-seq libraries vary in how RNA is extracted from the cell line or tissue (commonly using either phenol-chloroform or silica-gel column based methods [14]) and in how RNAs are selected for sequencing.
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