Computational Analysis of Transcript Identification Using GenBank Slides by Terry Clark.

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Computational Analysis of Transcript Identification Using GenBank Slides by Terry Clark

Differentiation of hematopoietic cells

Genome-wide gene expression

SAGE (Serial Analysis of Gene Expression)

Jes Stollberg et al. Genome Res. 2000; 10: Figure 1 Schematic illustration of the SAGE process

SAGE & GLGI Overview

What is the chance of duplicate tags? We can assume we are drawing randomly from the set of all 4-letters sequences of the given tag length This is the same problem as having unique overlaps in the contig matching problem for shotgun sequencing

Random Model

Random model does not reflect biological process Genes evolve by duplication as well as point mutation Many motifs are repeated Function widgets at work? Result is a strong bias in observed biological sequences, not a uniform distribution as the simple model hopes. Here are some numbers ….

SAGE tags match to many genes (Tags from Hashimoto S, et al. Blood 94:837, 1999)

Tag Frequency Groups for 10-base Tag Set Containing 878,938 Tags for UniGene Human

Unique Tags among 878,938 EST Derived Tags

Unique Tags among 32,851 Gene Derived Tags

Converting tag into longer 3’ sequence

Generation of Longer 3'cDNA for Gene Identification (GLGI)

UniGene Human 3’ Part Length Distribution

Myeloid Tag Matches with UniGene Human SAGE Tag Reference Database

SAGE Tag Processing with GIST

k-mer tree

GIST Performance with Improved IO

Conspirators Sanggyu Lee Janet D. Rowley San Ming Wang Terry Clark Andrew Huntwork Josef Jurek L. Ridgway Scott