Abstract
BackgroundOverlapping transcription constitutes a common mechanism for regulating gene expression. A major limitation of the overlapping transcription assays is the lack of high throughput expression data.ResultsWe developed a new tool (IAOseq) that is based on reads distributions along the transcribed regions to identify the expression levels of overlapping genes from standard RNA-seq data. Compared with five commonly used quantification methods, IAOseq showed better performance in the estimation accuracy of overlapping transcription levels. For the same strand overlapping transcription, currently existing high-throughput methods are rarely available to distinguish which strand was present in the original mRNA template. The IAOseq results showed that the commonly used methods gave an average of 1.6 fold overestimation of the expression levels of same strand overlapping genes.ConclusionsThis work provides a useful tool for mining overlapping transcription levels from standard RNA-seq libraries. IAOseq could be used to help us understand the complex regulatory mechanism mediated by overlapping transcripts. IAOseq is freely available at http://lifecenter.sgst.cn/main/en/IAO_seq.jsp.
Highlights
Overlapping transcription constitutes a common mechanism for regulating gene expression
Multifunctional usage of the same genomic space leads to identical cDNA sequences produced from the same or opposite strands of DNA
When dealing with reads mapped within exon challenge must be overcome to the inference without splicing information; besides, for those reads mapped within overlapping regions of same strand overlapping genes, even strand specific RNA-seq methods could not distinguish which strand was present in the original mRNA template
Summary
There are two principal types of overlapping transcripts: the same strand overlapping type in which the genes involved are transcribed from the same strand, and the different strand overlapping type in which at least two genes are transcribed from different strands [3]. When dealing with transcription signals mapped to the overlapping regions of the same strand overlapping gene pairs, most commonly used highthroughput methods for measuring gene expression, i.e. To test the significance of performance difference between IAOseq and the five commonly used quantification methods, we used Wilcoxon rank test for the median difference and Ansari-Bradley two-sample test for the variance difference of LEARatios. It is reasonably that little difference was observed between the expression levels deduced from nonST data and Figure 3 Performances on same strand overlapping genes. IAOseq results showed that the expression levels of same strand overlapping genes were much lower than average abundance over the values estimated by the four methods (Cufflinks, RSEM, eXpress and Bitseq) (Figure 3A, Wilcoxon test, W = 29579, p-value = 5e-07). For those overlapping genes which are simulated with no expression estimates, IAOseq show much better performance, more than 72% genes are estimated with low level, whereas overestimation is pronounced using other five methods (Additional file 1: Figure S6B)
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