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Related Concept Videos

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Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
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Related Experiment Video

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Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
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Comparative evaluation of isoform-level gene expression estimation algorithms for RNA-seq and exon-array platforms.

Matthew Dapas, Manoj Kandpal1, Yingtao Bi2

  • 1Department of Veterinary Surgery & Radiology, College of Veterinary & Animal Sciences, GBPUAT, Pantnagar - 263 145, Uttarakhand, India.

Briefings in Bioinformatics
|March 6, 2016
PubMed
Summary

Comparing RNA sequencing (RNA-seq) and exon-array data for glioblastoma, this study found that both platforms can accurately assess isoform-level expression changes, especially when using fold change normalization. RNA-seq and exon-arrays provide comparable results for isoform analysis.

Keywords:
Exon-arrayRNA-seqalternative splicingcross-platform integrationgene expressionisoform-level expression

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Area of Science:

  • Genomics
  • Transcriptomics
  • Bioinformatics

Background:

  • Most multi-exon genes produce diverse functional products, necessitating isoform-level expression analysis.
  • Previous studies confirmed gene-level correlation between RNA sequencing (RNA-seq) and microarrays, but isoform-level concordance remained unexamined.

Purpose of the Study:

  • To compare isoform- and gene-level expression estimates between RNA-seq and exon-array platforms using various analysis pipelines.
  • To evaluate the concordance of expression data across platforms at the isoform level.

Main Methods:

  • Transcript abundance estimation was performed on raw RNA-seq and exon-array expression profiles from The Cancer Genome Atlas (TCGA) glioblastoma multiforme samples.
  • Multiple analysis pipelines were employed to compare isoform- and gene-level expression estimates.
  • Results were validated using reverse transcription-quantitative polymerase chain reaction (RT-qPCR).

Main Results:

  • Fold change estimates showed better concordance between RNA-seq/exon-array and RT-qPCR than raw abundance estimates, highlighting the importance of fold change normalization.
  • The eXpress and Multi-Mapping Bayesian Gene eXpression (MMBGX) programs demonstrated superior performance for RNA-seq and exon-array platforms, respectively, in deriving isoform-level fold changes.
  • While eXpress showed high overall correlation with RT-qPCR and MMBGX, RSEM correlated better with MMBGX for highly variable transcripts. Different programs showed varying strengths in discriminating transcript expression levels.

Conclusions:

  • Exon arrays provide results comparable to RNA-seq for evaluating isoform-level expression changes.
  • Isoform-level expression analysis is crucial as gene-level estimates can mask significant changes.
  • Fold change normalization is a key step for integrating expression data across different platforms.