Computational Analysis of Transcript Identification Using GenBank

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Computational Analysis of Transcript
Identification Using GenBank

• Slides by Terry Clark

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Differentiation of hematopoietic cells
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Genome-wide gene expression
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SAGE (Serial Analysis of Gene Expression)
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Figure 1 Schematic illustration of the SAGE
process
Jes Stollberg et al. Genome Res. 2000 10
1241-1248
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SAGE GLGI Overview
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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

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Random Model
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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 .

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SAGE tags match to many genes(Tags from
Hashimoto S, et al. Blood 94837, 1999)
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Tag Frequency Groups for 10-base Tag
SetContaining 878,938 Tags for UniGene Human
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Unique Tags among 878,938 EST Derived Tags
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Unique Tags among 32,851 Gene Derived Tags
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Converting tag into longer 3 sequence
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Generation of Longer 3'cDNA for Gene
Identification (GLGI)
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UniGene Human 3 Part Length Distribution
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Myeloid Tag Matches with UniGene Human SAGE Tag
Reference Database
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SAGE Tag Processing with GIST
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k-mer tree
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GIST Performance with Improved IO
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Conspirators
Terry Clark Andrew Huntwork Josef Jurek L.
Ridgway Scott
Sanggyu Lee Janet D. Rowley San Ming Wang