Dataset Information

CEDER: accurate detection of differentially expressed genes by combining significance of exons using RNA-Seq.

ABSTRACT: RNA-Seq is widely used in transcriptome studies, and the detection of differentially expressed genes (DEGs) between two classes of individuals, e.g., cases versus controls, using RNA-Seq is of fundamental importance. Many statistical methods for DEG detection based on RNA-Seq data have been developed and most of them are based on the read counts mapped to individual genes. On the other hand, genes are composed of exons and the distribution of reads for the different exons can be heterogeneous. We hypothesize that the detection accuracy of differentially expressed genes can be increased by analyzing individual exons within a gene and then combining the results of the exons. We therefore developed a novel program, termed CEDER, to accurately detect DEGs by combining the significance of the exons. CEDER first tests for differentially expressed exons yielding a p-value for each, and then gives a score indicating the potential for a gene to be differentially expressed by integrating the p-values of the exons in the gene. We showed that CEDER can significantly increase the accuracy of existing methods for detecting DEGs on two benchmark RNA-Seq data sets and simulated datasets.

SUBMITTER: Wan L

PROVIDER: S-EPMC3488134 | biostudies-literature | 2012 Sep-Oct

REPOSITORIES: biostudies-literature

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Publications

CEDER: accurate detection of differentially expressed genes by combining significance of exons using RNA-Seq.

Wan Lin L Sun Fengzhu F

IEEE/ACM transactions on computational biology and bioinformatics 20120901 5

RNA-Seq is widely used in transcriptome studies, and the detection of differentially expressed genes (DEGs) between two classes of individuals, e.g., cases versus controls, using RNA-Seq is of fundamental importance. Many statistical methods for DEG detection based on RNA-Seq data have been developed and most of them are based on the read counts mapped to individual genes. On the other hand, genes are composed of exons and the distribution of reads for the different exons can be heterogeneous. W ...[more]

PMID: 22641709

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Project description:BackgroundFor plant species with unsequenced genomes, cDNA contigs created by de novo assembly of RNA-Seq reads are used as reference sequences for comparative analysis of RNA-Seq datasets and the detection of differentially expressed genes (DEGs). Redundancies in such contigs are evident in previous RNA-Seq studies, and such redundancies can lead to difficulties in subsequent analysis. Nevertheless, the effects of removing redundancy from contig assemblies on comparative RNA-Seq analysis have not been evaluated.ResultsHere we describe a method for removing redundancy from raw contigs that were primarily created by de novo assembly of Arabidopsis thaliana RNA-Seq reads. Specifically, the contigs with the highest bit scores were selected from raw contigs by a homology search against the gene dataset in the TAIR10 database. The two existing methods for removal of redundancy based on contig length or clustering analysis used to eliminate redundancies from raw contigs. Contig number was reduced most effectively with the method based on homology search. In a comparative analysis of RNA-Seq datasets, DEGs detected in contigs that underwent redundancy removal via the homology search method showed the highest identity to the DEGs detected when the TAIR10 gene dataset was used as an exact reference. Redundancy in raw contigs could also be removed by a homology search against integrated protein datasets from several plant species other than A. thaliana. DEGs detected using contigs that underwent such redundancy-removed also showed high homology to DEGs detected using the TAIR10 gene dataset.ConclusionHere we describe a method for removing redundant contigs within raw contigs; this method involves a homology search against a gene or protein database. In principal, this method can be used with unsequenced plant genomes that lack a well-developed gene database. Redundant contigs were not removed adequately via either of two existing methods, but our method allowed for removal of all redundant contigs. To our knowledge, this is the first reported improvement in accurate detection of DEGs via comparative RNA-Seq analysis that involved preparation of a non-redundant reference sequence. This method could be used to rapidly and cost-effectively detect useful genes in unsequenced plants.

Dataset Information

CEDER: accurate detection of differentially expressed genes by combining significance of exons using RNA-Seq.

Publications

CEDER: accurate detection of differentially expressed genes by combining significance of exons using RNA-Seq.

Similar Datasets

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