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An Approach for Assessing RNA-seq Quantification Algorithms in Replication Studies.

ABSTRACT: One way to gain a more comprehensive picture of the complex function of a cell is to study the transcriptome. A promising technology for studying the transcriptome is RNA sequencing, an application of which is to quantify elements in the transcriptome and to link quantitative observations to biology. Although numerous quantification algorithms are publicly available, no method of systematically assessing these algorithms has been developed. To meet the need for such an assessment, we present an approach that includes (1) simulated and real datasets, (2) three alignment strategies, and (3) six quantification algorithms. Examining the normalized root-mean-square error, the percentage error of the coefficient of variation, and the distribution of the coefficient of variation, we found that quantification algorithms with the input of sequence alignment reported in the transcriptomic coordinate usually performed better in terms of the multiple metrics proposed in this study.

SUBMITTER: Wu PY

PROVIDER: S-EPMC4981182 | biostudies-literature | 2013 Nov

REPOSITORIES: biostudies-literature

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An Approach for Assessing RNA-seq Quantification Algorithms in Replication Studies.

Wu Po-Yen PY Phan John H JH Wang May D MD

IEEE International Workshop on Genomic Signal Processing and Statistics : [proceedings]. IEEE International Workshop on Genomic Signal Processing and Statistics 20131101

One way to gain a more comprehensive picture of the complex function of a cell is to study the transcriptome. A promising technology for studying the transcriptome is RNA sequencing, an application of which is to quantify elements in the transcriptome and to link quantitative observations to biology. Although numerous quantification algorithms are publicly available, no method of systematically assessing these algorithms has been developed. To meet the need for such an assessment, we present an ...[more]

PMID: 27525167

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Project description:In recent years, RNA-seq is emerging as a powerful technology in estimation of gene and/or transcript expression, and RPKM (Reads Per Kilobase per Million reads) is widely used to represent the relative abundance of mRNAs for a gene. In general, the methods for gene quantification can be largely divided into two categories: transcript-based approach and 'union exon'-based approach. Transcript-based approach is intrinsically more difficult because different isoforms of the gene typically have a high proportion of genomic overlap. On the other hand, 'union exon'-based approach method is much simpler and thus widely used in RNA-seq gene quantification. Biologically, a gene is expressed in one or more transcript isoforms. Therefore, transcript-based approach is logistically more meaningful than 'union exon'-based approach. Despite the fact that gene quantification is a fundamental task in most RNA-seq studies, however, it remains unclear whether 'union exon'-based approach for RNA-seq gene quantification is a good practice or not. In this paper, we carried out a side-by-side comparison of 'union exon'-based approach and transcript-based method in RNA-seq gene quantification. It was found that the gene expression levels are significantly underestimated by 'union exon'-based approach, and the average of RPKM from 'union exons'-based method is less than 50% of the mean expression obtained from transcript-based approach. The difference between the two approaches is primarily affected by the number of transcripts in a gene. We performed differential analysis at both gene and transcript levels, respectively, and found more insights, such as isoform switches, are gained from isoform differential analysis. The accuracy of isoform quantification would improve if the read coverage pattern and exon-exon spanning reads are taken into account and incorporated into EM (Expectation Maximization) algorithm. Our investigation discourages the use of 'union exons'-based approach in gene quantification despite its simplicity.

Dataset Information

An Approach for Assessing RNA-seq Quantification Algorithms in Replication Studies.

Publications

An Approach for Assessing RNA-seq Quantification Algorithms in Replication Studies.

Similar Datasets

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