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.

As one of the most widespread metabolic diseases, atherosclerosis affects nearly everyone as they age; arteries gradually narrow from plaque accumulation over time reducing oxygenated blood flow to central and periphery causing heart disease, stroke, kidney problems, and even pulmonary disease. Personalized medicine promises to bring treatments based on individual genome sequencing that precisely target the molecular pathways underlying atherosclerosis and its symptoms, but to date only a few genotypes have been identified. A promising alternative to this genetic approach is the identification of pathways altered in atherosclerosis by transcriptome analysis of atherosclerotic tissues to target specific aspects of disease. Transcriptomics is a potentially useful tool for both diagnostics and discovery science, exposing novel cellular and molecular mechanisms in clinical and translational models, and depending on experimental design to identify and test novel therapeutics. The cost and time required for transcriptome analysis has been greatly reduced by the development of next generation sequencing. The goal of this resource article is to provide background and a guide to appropriate technologies and downstream analyses in transcriptomics experiments generating ever-increasing amounts of gene expression data.

The rapid expansion of transcriptomics from increased affordability of next-generation sequencing (NGS) technologies generates rocketing amounts of gene expression data across biology and medicine, and notably in cancer research. Concomitantly, many bioinformatics tools were developed to streamline gene expression analysis and quantification. We tested the concordance of NGS RNA sequencing (RNA-seq) analysis outcomes between the two predominant programs for reads alignment, HISAT2 and STAR, and the two most popular programs for quantifying gene expression in NGS experiments, edgeR and DESeq2, using RNA-seq data from a series of breast cancer progression specimens, 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 in stage comparisons, with edgeR producing more conservative, though shorter, lists of genes. Albeit, Gene Ontology (GO) enrichment analysis revealed no skewness in significant GO categories identified among differentially expressed genes by edgeR vs DESeq2. As transcriptome analysis of archived FFPE samples becomes a vanguard of precision medicine, identification and fine-tuning 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.