Dataset Information

DFI: gene feature discovery in RNA-seq experiments from multiple sources.

ABSTRACT: Differential expression detection for RNA-seq experiments is often biased by normalization algorithms due to their sensitivity to parametric assumptions on the gene count distributions, extreme values of gene expression, gene length and total number of sequence reads.To overcome limitations of current methodologies, we developed Differential Feature Index (DFI), a non-parametric method for characterizing distinctive gene features across any number of diverse RNA-seq experiments without inter-sample normalization. Validated with qRT-PCR datasets, DFI accurately detected differentially expressed genes regardless of expression levels and consistent with tissue selective expression. Accuracy of DFI was very similar to the currently accepted methods: EdgeR, DESeq and Cuffdiff.In this study, we demonstrated that DFI can efficiently handle multiple groups of data simultaneously, and identify differential gene features for RNA-Seq experiments from different laboratories, tissue types, and cell origins, and is robust to extreme values of gene expression, size of the datasets and gene length.

SUBMITTER: Ozer HG

PROVIDER: S-EPMC3535702 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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DFI: gene feature discovery in RNA-seq experiments from multiple sources.

Ozer Hatice Gulcin HG Parvin Jeffrey D JD Huang Kun K

BMC genomics 20121217

<h4>Background</h4>Differential expression detection for RNA-seq experiments is often biased by normalization algorithms due to their sensitivity to parametric assumptions on the gene count distributions, extreme values of gene expression, gene length and total number of sequence reads.<h4>Results</h4>To overcome limitations of current methodologies, we developed Differential Feature Index (DFI), a non-parametric method for characterizing distinctive gene features across any number of diverse RN ...[more]

PMID: 23281963

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Project description:RNA-Seq has enabled high-throughput gene expression profiling to provide insight into the functional link between genotype and phenotype. Low quantities of starting RNA can be a severe hindrance for studies that aim to utilize RNA-Seq. To mitigate this bottleneck, whole transcriptome amplification (WTA) technologies have been developed to generate sufficient sequencing targets from minute amounts of RNA. Successful WTA requires accurate replication of transcript abundance without the loss or distortion of specific mRNAs. Here, we test the efficacy of NuGEN's Ovation RNA-Seq V2 system, which uses linear isothermal amplification with a unique chimeric primer for amplification, using white adipose tissue from standard laboratory rats (Rattus norvegicus). Our goal was to investigate potential biological artifacts introduced through WTA approaches by establishing comparisons between matched raw and amplified RNA libraries derived from biological replicates.We found that 93% of expressed genes were identical between all unamplified versus matched amplified comparisons, also finding that gene density is similar across all comparisons. Our sequencing experiment and downstream bioinformatic analyses using the Tuxedo analysis pipeline resulted in the assembly of 25,543 high-quality transcripts. Libraries constructed from raw RNA and WTA samples averaged 15,298 and 15,253 expressed genes, respectively. Although significant differentially expressed genes (P?<?0.05) were identified in all matched samples, each of these represents less than 0.15% of all shared genes for each comparison.Transcriptome amplification is efficient at maintaining relative transcript frequencies with no significant bias when using this NuGEN linear isothermal amplification kit under ideal laboratory conditions as presented in this study. This methodology has broad applications, from clinical and diagnostic, to field-based studies when sample acquisition, or sample preservation, methods prove challenging.

Dataset Information

DFI: gene feature discovery in RNA-seq experiments from multiple sources.

Publications

DFI: gene feature discovery in RNA-seq experiments from multiple sources.

Similar Datasets

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