Bio::SAGE::Comparison README
#
# Copyright (c) 2004 Scott Zuyderduyn <scottz@bccrc.ca>.
# All rights reserved. This program is free software; you
# can redistribute it and/or modify it under the same
# terms as Perl itself.
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# $Id: README,v 1.1.1.1 2004/05/25 01:34:52 scottz Exp $
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BACKGROUND
Serial analysis of gene expression (SAGE) is a molecular
technique for generating a near-global snapshot of a
cell populations transcriptome. Briefly, the technique
extracts short sequences at defined positions of
transcribed mRNA. These short sequences are then paired
to form ditags. The ditags are concatamerized to form
long sequences that are then cloned. The cloned DNA is
then sequenced. Bioinformatic techniques are then
employed to determine the original short tag sequences,
and to derive their progenitor mRNA. The number of times
a particular tag is observed can be used to quantitate
the amount of a particular transcript. The original
technique was described by Velculescu et al. (1995) and
utilized an ~14bp sequence tag. A modified protocol
was introduced by Saha et al. (2002) that produced ~21bp
tags.
PURPOSE
This module facilitates the comparison of SAGE libraries.
Specifically:
1. Calculations for determining the statistical
significance of expression differences.
2. Dynamically convert longer-tag libraries to
a shorter type for comparison (e.g. comparing
a LongSAGE vs. a regular SAGE library).
Both regular SAGE (14mer tag) and LongSAGE (21mer tag)
are supported by this module.
Statistical significance in library comparisons is
calculated using the method described by Audic and
Claverie (1997). Code was generated by directly
porting the authors' original C source.
REFERENCES
Velculescu V, Zhang L, Vogelstein B, Kinzler KW. (1995)
Serial analysis of gene expression. Science. 270:484-487.
Saha S, Sparks AB, Rago C, Akmaev V, Wang CJ, Vogelstein B,
Kinzler KW, Velculescu V. (2002) Using the transcriptome
to annotate the genome. Nat. Biotechnol. 20:508-512.
Audic S, Claverie JM. (1997) The significance of digital
gene expression profiles. Genome Res. 7:986-995.