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Aligning the Aligners: Comparison of RNA Sequencing Data Alignment and Gene Expression Quantification Tools for Clinical Breast Cancer Research.

Authors
  • Raplee, Isaac D1
  • Evsikov, Alexei V2
  • Marín de Evsikova, Caralina3, 4
  • 1 Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL 33612, USA. [email protected]
  • 2 Epigenetics & Functional Genomics Laboratory, Department of Research and Development, Bay Pines Veteran Administration Healthcare System, Bay Pines, FL 33744, USA. [email protected]
  • 3 Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL 33612, USA. [email protected]
  • 4 Epigenetics & Functional Genomics Laboratory, Department of Research and Development, Bay Pines Veteran Administration Healthcare System, Bay Pines, FL 33744, USA. [email protected]
Type
Published Article
Journal
Journal of personalized medicine
Publication Date
Apr 03, 2019
Volume
9
Issue
2
Identifiers
DOI: 10.3390/jpm9020018
PMID: 30987214
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

The rapid expansion of transcriptomics and affordability of next-generation sequencing (NGS) technologies generate rocketing amounts of gene expression data across biology and medicine, including cancer research. Concomitantly, many bioinformatics tools were developed to streamline gene expression and quantification. We tested the concordance of NGS RNA sequencing (RNA-seq) analysis outcomes between two predominant programs for read alignment, HISAT2, and STAR, and two most popular programs for quantifying gene expression in NGS experiments, edgeR and DESeq2, using RNA-seq data from breast cancer progression series, which include histologically confirmed normal, early neoplasia, ductal carcinoma in situ and infiltrating ductal carcinoma samples microdissected from formalin fixed, paraffin embedded (FFPE) breast tissue blocks. We identified significant differences in aligners' performance: HISAT2 was prone to misalign reads to retrogene genomic loci, STAR generated more precise alignments, especially for early neoplasia samples. edgeR and DESeq2 produced similar lists of differentially expressed genes, with edgeR producing more conservative, though shorter, lists of genes. Gene Ontology (GO) enrichment analysis revealed no skewness in significant GO terms identified among differentially expressed genes by edgeR versus DESeq2. As transcriptomics of FFPE samples becomes a vanguard of precision medicine, choice of bioinformatics tools becomes critical for clinical research. Our results indicate that STAR and edgeR are well-suited tools for differential gene expression analysis from FFPE samples.

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