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ABSSeq: a new RNA-Seq analysis method based on modelling absolute expression differences.

ABSTRACT: The recent advances in next generation sequencing technology have made the sequencing of RNA (i.e., RNA-Seq) an extemely popular approach for gene expression analysis. Identification of significant differential expression represents a crucial initial step in these analyses, on which most subsequent inferences of biological functions are built. Yet, for identification of these subsequently analysed genes, most studies use an additional minimal threshold of differential expression that is not captured by the applied statistical procedures.Here we introduce a new analysis approach, ABSSeq, which uses a negative binomal distribution to model absolute expression differences between conditions, taking into account variations across genes and samples as well as magnitude of differences. In comparison to alternative methods, ABSSeq shows higher performance on controling type I error rate and at least a similar ability to correctly identify differentially expressed genes.ABSSeq specifically considers the overall magnitude of expression differences, which enhances the power in detecting truly differentially expressed genes by reducing false positives at both very low and high expression level. In addition, ABSSeq offers to calculate shrinkage of fold change to facilitate gene ranking and effective outlier detection.

SUBMITTER: Yang W

PROVIDER: S-EPMC4973090 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature

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ABSSeq: a new RNA-Seq analysis method based on modelling absolute expression differences.

Yang Wentao W Rosenstiel Philip C PC Schulenburg Hinrich H

BMC genomics 20160804

<h4>Background</h4>The recent advances in next generation sequencing technology have made the sequencing of RNA (i.e., RNA-Seq) an extemely popular approach for gene expression analysis. Identification of significant differential expression represents a crucial initial step in these analyses, on which most subsequent inferences of biological functions are built. Yet, for identification of these subsequently analysed genes, most studies use an additional minimal threshold of differential expressi ...[more]

PMID: 27488180

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Project description:When the reads obtained from high-throughput RNA sequencing are mapped against a reference database, a significant proportion of them - known as multireads - can map to more than one reference sequence. These multireads originate from gene duplications, repetitive regions or overlapping genes. Removing the multireads from the mapping results, in RNA-Seq analyses, causes an underestimation of the read counts, while estimating the real read count can lead to false positives during the detection of differentially expressed sequences.We present an innovative approach to deal with multireads and evaluate differential expression events, entirely based on fuzzy set theory. Since multireads cause uncertainty in the estimation of read counts during gene expression computation, they can also influence the reliability of differential expression analysis results, by producing false positives. Our method manages the uncertainty in gene expression estimation by defining the fuzzy read counts and evaluates the possibility of a gene to be differentially expressed with three fuzzy concepts: over-expression, same-expression and under-expression. The output of the method is a list of differentially expressed genes enriched with information about the uncertainty of the results due to the multiread presence. We have tested the method on RNA-Seq data designed for case-control studies and we have compared the obtained results with other existing tools for read count estimation and differential expression analysis.The management of multireads with the use of fuzzy sets allows to obtain a list of differential expression events which takes in account the uncertainty in the results caused by the presence of multireads. Such additional information can be used by the biologists when they have to select the most relevant differential expression events to validate with laboratory assays. Our method can be used to compute reliable differential expression events and to highlight possible false positives in the lists of differentially expressed genes computed with other tools.

Project description:BackgroundUse of next-generation sequencing technologies to transcriptomics (RNA-seq) for gene expression profiling has found widespread application in studying different biological conditions including cancers. However, RNA-seq experiments are still small sample size experiments due to the cost. Recently, an increased focus has been on meta-analysis methods for integrated differential expression analysis for exploration of potential biomarkers. In this study, we propose a p-value combination method for meta-analysis of multiple independent but related RNA-seq studies that accounts for sample size of a study and direction of expression of genes in individual studies.ResultsThe proposed method generalizes the inverse-normal method without an increase in statistical or computational complexity and does not pre- or post-hoc filter genes that have conflicting direction of expression in different studies. Thus, the proposed method, as compared to the inverse-normal, has better potential for the discovery of differentially expressed genes (DEGs) with potentially conflicting differential signals from multiple studies related to disease. We demonstrated the use of the proposed method in detection of biologically relevant DEGs in glioblastoma (GBM), the most aggressive brain cancer. Our approach notably enabled the identification of over-expressed tumour suppressor gene RAD51 in GBM compared to healthy controls, which has recently been shown to be a target for inhibition to enhance radiosensitivity of GBM cells during treatment. Pathway analysis identified multiple aberrant GBM related pathways as well as novel regulators such as TCF7L2 and MAPT as important upstream regulators in GBM.ConclusionsThe proposed meta-analysis method generalizes the existing inverse-normal method by providing a way to establish differential expression status for genes with conflicting direction of expression in individual RNA-seq studies. Hence, leading to further exploration of them as potential biomarkers for the disease.

Dataset Information

ABSSeq: a new RNA-Seq analysis method based on modelling absolute expression differences.

Publications

ABSSeq: a new RNA-Seq analysis method based on modelling absolute expression differences.

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