Details

Autor(en) / Beteiligte

• Khattra, Jaswinder
• Delaney, Allen D
• Zhao, Yongjun
• Siddiqui, Asim
• Asano, Jennifer
• McDonald, Helen
• Pandoh, Pawan
• Dhalla, Noreen
• Prabhu, Anna-Liisa
• Ma, Kevin
• Lee, Stephanie
• Ally, Adrian
• Tam, Angela
• Sa, Danne
• Rogers, Sean
• Charest, David
• Stott, Jeff
• Zuyderduyn, Scott
• Varhol, Richard
• Eaves, Connie
• Jones, Steven
• Holt, Robert
• Hirst, Martin
• Hoodless, Pamela A
• Marra, Marco A

Titel

Large-scale production of SAGE libraries from microdissected tissues, flow-sorted cells, and cell lines

Ist Teil von

• Genome Research, 2007-01, Vol.17 (1), p.108-116

Ort / Verlag

United States: Cold Spring Harbor Laboratory Press

Erscheinungsjahr

2007

Quelle

MEDLINE

Beschreibungen/Notizen

• 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 approximately 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.