Gene expression profiling of human breast tissue samples using SAGE-Seq

Abstract

We present a powerful application of ultra high-throughput sequencing, SAGE-Seq, for the accurate quantification of normal and neoplastic mammary epithelial cell transcriptomes. We develop data analysis pipelines that allow the mapping of sense and antisense strands of mitochondrial and RefSeq genes, the normalization between libraries, and the identification of differentially expressed genes. We find that the diversity of cancer transcriptomes is significantly higher than that of normal cells. Our analysis indicates that transcript discovery plateaus at 10 million reads/sample, and suggests a minimum desired sequencing depth around five million reads. Comparison of SAGE-Seq and traditional SAGE on normal and cancerous breast tissues reveals higher sensitivity of SAGE-Seq to detect less-abundant genes, including those encoding for known breast cancer-related transcription factors and G protein-coupled receptors (GPCRs). SAGE-Seq is able to identify genes and pathways abnormally activated in breast cancer that traditional SAGE failed to call. SAGE-Seq is a powerful method for the identification of biomarkers and therapeutic targets in human disease.

Patterns of regulatory activity across diverse human cell types predict tissue identity, transcription factor binding, and long-range interactions

“…Gene expression profiling has emerged as a powerful tool to classify tumors (Wu et al 2010). The added resolution of regulatory information may provide a more robust way to classify cell types.…”

PAT-seq: a method to study the integration of 3′-UTR dynamics with gene expression in the eukaryotic transcriptome

“…Most of these have been selectively interrogated by high-throughput sequencing technologies to capture snapshots of systemwide control of gene expression. The convenient "hook" provided by the poly(A) tail on the vast majority of mRNA has also given rise to digital gene expression approaches that use 3 ′ focused sequencing based around SAGE (Velculescu et al 1995) as a means to quantify the composition of the transcriptome (Ruzanov and Riddle 2010;Wu et al 2010;Hong et al 2011). Such approaches provide inexpensive and relatively simple tools to monitor eukaryotic gene expression.…”

Digital gene expression for non-model organisms

“…This compares favorably with analogous results from two mouse skin RNA-seq libraries, in which 60% of the 36-bp pass-filter reads aligned uniquely to the mouse transcriptome (Table 1). In contrast, MmeI, a Type IIs restriction endonuclease commonly used in tag-based cDNA sequencing protocols (Asmann et al 2009;Wu et al 2010), generates a 21-bp tag that results in a smaller proportion of uniquely mapped tags-78% of a simulated MmeI-tagged data set mapped uniquely to the mouse transcriptome compared to 86% with EDGE-and that translates to 3% reduction in genes detected. Among the EDGE tags, 78% and 8% mapped uniquely to the sense and antisense strands of mouse transcripts, respectively (Table 1), and 6% mapped to multiple genomic locations or to introns and unannotated regions of the genome (Table 1).…”