Large-scale production of SAGE libraries from microdissected tissues, flow-sorted cells, and cell lines
Author(s) -
Jaswinder Khattra,
Allen Delaney,
Yongjun Zhao,
Asim Siddiqui,
Jennifer Asano,
Helen McDonald,
Pawan Pandoh,
Noreen Dhalla,
AnnaLiisa Prabhu,
Kevin Ma,
Stephanie J. Lee,
Adrian Ally,
Angela Tam,
Danne Sa,
Sean A Rogers,
David L. Charest,
Jeff Stott,
Scott Zuyderduyn,
Richard Varhol,
Connie J. Eaves,
Steven J.M. Jones,
Robert A. Holt,
Martin Hirst,
Pamela A. Hoodless,
Marco A. Marra
Publication year - 2006
Publication title -
genome research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 9.556
H-Index - 297
eISSN - 1549-5469
pISSN - 1088-9051
DOI - 10.1101/gr.5488207
Subject(s) - biology , serial analysis of gene expression , sage , computational biology , gene expression profiling , genetics , gene , gene expression , physics , nuclear physics
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.
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