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.