RT Journal
A1 Khattra, Jaswinder
A1 Delaney, Allen D.
A1 Zhao, Yongjun
A1 Siddiqui, Asim
A1 Asano, Jennifer
A1 McDonald, Helen
A1 Pandoh, Pawan
A1 Dhalla, Noreen
A1 Prabhu, Anna-liisa
A1 Ma, Kevin
A1 Lee, Stephanie
A1 Ally, Adrian
A1 Tam, Angela
A1 Sa, Danne
A1 Rogers, Sean
A1 Charest, David
A1 Stott, Jeff
A1 Zuyderduyn, Scott
A1 Varhol, Richard
A1 Eaves, Connie
A1 Jones, Steven
A1 Holt, Robert
A1 Hirst, Martin
A1 Hoodless, Pamela A.
A1 Marra, Marco A.
T1 Large-scale production of SAGE libraries from microdissected tissues, flow-sorted cells, and cell lines
JF Genome Research 
JO Genome Research 
YR 2007 
FD January 01 
VO 17 
IS 1 
SP 108 
OP 116 
DO 10.1101/gr.5488207 
UL http://genome.cshlp.org/content/17/1/108.abstract 
AB We describe the details of a serial analysis of gene expression (SAGE) library construction and analysis platform that has enabled the generation of >298 high-quality SAGE libraries and >30 million SAGE tags primarily from sub-microgram amounts of total RNA purified from samples acquired by microdissection. Several RNA isolation methods were used to handle the diversity of samples processed, and various measures were applied to minimize ditag PCR carryover contamination. Modifications in the SAGE protocol resulted in improved cloning and DNA sequencing efficiencies. Bioinformatic measures to automatically assess DNA sequencing results were implemented to analyze the integrity of ditag structure, linker or cross-species ditag contamination, and yield of high-quality tags per sequence read. Our analysis of singleton tag errors resulted in a method for correcting such errors to statistically determine tag accuracy. From the libraries generated, we produced an essentially complete mapping of reliable 21-base-pair tags to the mouse reference genome sequence for a meta-library of ∼5 million tags. Our analyses led us to reject the commonly held notion that duplicate ditags are artifacts. Rather than the usual practice of discarding such tags, we conclude that they should be retained to avoid introducing bias into the results and thereby maintain the quantitative nature of the data, which is a major theoretical advantage of SAGE as a tool for global transcriptional profiling.