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

Alexei V. Evsikov,Caralina Marín de Evsikova,Isaac D. Raplee

doi:10.3390/jpm9020018

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.

Highlights

After next-generation sequencing (NGS) technology was introduced in 2005, development of many high-throughput bioinformatics tools ensued, such as Bowtie, Tophat, Cufflinks, and CuffDiff “TuxedoSuite” [1]
RNA sequencing (RNA-seq) reads need to be assessed for quality and aligned to a reference genome. This underscores the essential importance to investigate the impacts of bioinformatics tools for sequence alignment and differential expression on accurate results and interpretation of transcriptome studies collected from FFPE specimens, which was the goal of our study
This paper serves to point out the pragmatic shortcomings of universally applying a rigid “standard operating procedure” set of bioinformatics tools to RNA-seq data, by demonstrating the impacts and limitations of biological conditions, especially in sample processing and choice of programs

Summary

Introduction

After next-generation sequencing (NGS) technology was introduced in 2005, development of many high-throughput bioinformatics tools ensued, such as Bowtie, Tophat, Cufflinks, and CuffDiff “Tuxedo. One of the most rapidly adopted NGS applications, RNA sequencing (RNA-seq), was introduced in 2008 and captures the transcriptome from cells or tissue samples. Bioinformatics tools have been developed in order to ease read mapping, splice junction, novel gene structure, and differential expression analysis of RNA-seq output. Sequence reads generated by RNA-seq can be assessed for single nucleotide polymorphisms (SNPs), splice variants, fusion genes, and individual transcript abundance in samples for differential expression analysis.

Methods

Results

Discussion

Conclusion