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Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples
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Improving the Flexibility of RNA-Seq Data Analysis Pipelines.

John H Phan1, Po-Yen Wu2, May D Wang3

  • 1Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA, jhphan@gatech.edu.

IEEE International Workshop on Genomic Signal Processing and Statistics : [Proceedings]. IEEE International Workshop on Genomic Signal Processing and Statistics
|August 19, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a flexible RNA-Seq analysis method that merges transcriptome and genome mapping stages. This approach enhances novel isoform discovery and improves gene expression quantification accuracy.

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Area of Science:

  • Genomics
  • Bioinformatics

Background:

  • Accurate RNA-Seq quantification relies on comprehensive transcriptome knowledge.
  • Existing RNA-Seq pipelines often use a single mapping algorithm, limiting flexibility and potentially impacting quantification accuracy.
  • Novel isoform discovery is crucial due to incomplete genomic annotations.

Purpose of the Study:

  • To present a method for merging transcriptome and reference genome mapping stages in RNA-Seq analysis.
  • To enhance flexibility in selecting RNA-Seq data analysis pipelines.
  • To improve the accuracy of gene and isoform expression quantification.

Main Methods:

  • Developed a method to merge transcriptome and reference genome mapping stages.
  • Ensured compatibility with the standard SAM/BAM format for seamless integration.
  • Demonstrated the flexible pipeline's utility in novel isoform discovery and quantification validation.

Main Results:

  • The proposed method allows for flexible integration of different mapping algorithms.
  • Demonstrated improved novel isoform discovery capabilities.
  • Validated enhanced quantification performance using quantitative real-time PCR (qRT-PCR).

Conclusions:

  • Merging mapping stages increases flexibility in RNA-Seq data analysis pipelines.
  • This approach has the potential to improve the accuracy of gene and isoform quantification.
  • The method facilitates more robust novel isoform discovery.