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Integrative RNA-seq and microarray data analysis reveals GC content and gene length biases in the psoriasis
William R Swindell1, Xianying Xing2, John J Voorhees2
1Department of Dermatology, University of Michigan School of Medicine, Ann Arbor, Michigan wswindel@umich.edu.
Physiological Genomics
|May 22, 2014
Summary
Comparing RNA-sequencing (RNA-seq) and microarray technologies for gene expression profiling in psoriasis reveals complementary strengths. Gene length significantly impacts differential expression analysis, offering a richer understanding of the psoriatic transcriptome when integrating both methods.
Area of Science:
- Genomics
- Transcriptomics
- Dermatology
Background:
- Gene expression profiling advances psoriasis research, potentially leading to clinical applications.
- RNA-sequencing (RNA-seq) is a competitive technology for expression profiling, but its results may differ from microarray data.
- Understanding these differences is crucial for accurate interpretation of psoriasis transcriptomes.
Purpose of the Study:
- To compare gene expression findings from RNA-seq with those from multiple microarray studies in psoriasis.
- To identify factors influencing the correspondence between RNA-seq and microarray data.
- To highlight the complementary nature of these technologies for psoriasis research.
Main Methods:
- Comparative analysis of RNA-seq data against eight independent microarray datasets from psoriasis studies.
- Assessment of differentially expressed genes (DEGs) identified by each technology.
- Investigation of factors such as mRNA abundance, GC content, and gene length on DEG identification and correspondence.
Main Results:
- Both RNA-seq and microarrays identified similar numbers of DEGs, with unique genes found by each platform.
- mRNA abundance, GC content, and gene length influenced the concordance between platforms.
- Gene length bias, showing decreased expression in lesions, was the strongest and most consistent trend across all datasets and technologies.
Conclusions:
- RNA-seq and microarray technologies offer complementary views of the transcriptome.
- Integrative analysis of both RNA-seq and microarray data provides a more comprehensive understanding of psoriasis gene expression.
- Gene length is a critical factor influencing differential gene expression in psoriasis lesions, impacting comparisons between profiling methods.