Abstract

BackgroundRecently, rapid improvements in technology and decrease in sequencing costs have made RNA-Seq a widely used technique to quantify gene expression levels. Various normalization approaches have been proposed, owing to the importance of normalization in the analysis of RNA-Seq data. A comparison of recently proposed normalization methods is required to generate suitable guidelines for the selection of the most appropriate approach for future experiments.ResultsIn this paper, we compared eight non-abundance (RC, UQ, Med, TMM, DESeq, Q, RPKM, and ERPKM) and two abundance estimation normalization methods (RSEM and Sailfish). The experiments were based on real Illumina high-throughput RNA-Seq of 35- and 76-nucleotide sequences produced in the MAQC project and simulation reads. Reads were mapped with human genome obtained from UCSC Genome Browser Database. For precise evaluation, we investigated Spearman correlation between the normalization results from RNA-Seq and MAQC qRT-PCR values for 996 genes. Based on this work, we showed that out of the eight non-abundance estimation normalization methods, RC, UQ, Med, TMM, DESeq, and Q gave similar normalization results for all data sets. For RNA-Seq of a 35-nucleotide sequence, RPKM showed the highest correlation results, but for RNA-Seq of a 76-nucleotide sequence, least correlation was observed than the other methods. ERPKM did not improve results than RPKM. Between two abundance estimation normalization methods, for RNA-Seq of a 35-nucleotide sequence, higher correlation was obtained with Sailfish than that with RSEM, which was better than without using abundance estimation methods. However, for RNA-Seq of a 76-nucleotide sequence, the results achieved by RSEM were similar to without applying abundance estimation methods, and were much better than with Sailfish. Furthermore, we found that adding a poly-A tail increased alignment numbers, but did not improve normalization results.ConclusionSpearman correlation analysis revealed that RC, UQ, Med, TMM, DESeq, and Q did not noticeably improve gene expression normalization, regardless of read length. Other normalization methods were more efficient when alignment accuracy was low; Sailfish with RPKM gave the best normalization results. When alignment accuracy was high, RC was sufficient for gene expression calculation. And we suggest ignoring poly-A tail during differential gene expression analysis.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0778-7) contains supplementary material, which is available to authorized users.

Highlights

  • Rapid improvements in technology and decrease in sequencing costs have made RNA-Seq a widely used technique to quantify gene expression levels

  • The experiments were based on the real Illumina high-throughput RNA-Seq data used in the MicroArray Quality Control (MAQC) project on two RNA samples, brain tissue (HBR) and a mixture of tissue types (UHR), with read lengths of 35 and 76 nucleotides

  • We investigated the Spearman correlation coefficient between the normalization results of each method and the values of MAQC TaqMan quantitative reverse transcription polymerase chain reaction (qRT-PCR)

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Summary

Introduction

Rapid improvements in technology and decrease in sequencing costs have made RNA-Seq a widely used technique to quantify gene expression levels. Various normalization approaches have been proposed, owing to the importance of normalization in the analysis of RNA-Seq data. Rapid improvements in technology and decreased sequencing costs have made next-generation sequencing possible, facilitating millions of short sequence reads that have broad genomic research applications [3,4,5]. Using next-generation sequencing to study the RNA in a sample (i.e., RNA-Seq) allows for whole transcriptome shotgun sequencing, which is useful in analyses such as gene expression analysis [6, 7], single nucleotide variation discovery [8], and fusion gene detection [9, 10]. We focused on accurately quantifying gene expression levels using deep-sequencing methods. Even though various technologies have been proposed in recent years for RNA-Seq analysis, it is a technology that is still being actively developed

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