Gene expression profiling of human breast tissue samples using SAGE-Seq.
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.
Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02115, USA.
Zhenhua Jeremy
ZJ
Wu
Clifford A
CA
Meyer
Sibgat
S
Choudhury
Michail
M
Shipitsin
Reo
R
Maruyama
Marina
M
Bessarabova
Tatiana
T
Nikolskaya
Saraswati
S
Sukumar
Armin
A
Schwartzman
Jun S
JS
Liu
Kornelia
K
Polyak
X Shirley
XS
Liu
D000704:Analysis of Variance; D001483:Base Sequence; D001499:Bayes Theorem; D001940:Breast / Q000166:cytology*; D001943:Breast Neoplasms / Q000378:metabolism*; D004847:Epithelial Cells / Q000378:metabolism*; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods*; D015723:Gene Library; D006801:Humans; D008969:Molecular Sequence Data; D012680:Sensitivity and Specificity; D017422:Sequence Analysis, DNA / Q000379:methods*