computational analysis of transcript identification using genbank
Post on 21-Dec-2015
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Computational Analysis of Transcript Identification Using
GenBank
Differentiation of hematopoietic cellsPluripotent stem cell
Myeloid Lymphoid
Erythrocyte PlateletMonocyteNeutrophil Eosinophil Basophil B cell T cell
Pluripotent stem cellMyeloid LymphoidMyeloid Lymphoid
Genome-wide gene expression
number of expressed genes level of expression
100
< 5 mRNA / cell
5--50 mRNA / cell
>500 mRNA / cell
9,000
900
SAGE (Serial Analysis of Gene Expression)
isolate SAGE tags
link tags together& sequencing
AAAAAAAAA
AAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAA
AAAAAAAAAA
AAAAAAAAAAA
AAAAAAA
AAAAAAAA
gene identification
mRNA/cDNA
SAGE & GLGI Overview
SPGI
SAGE
identify most of expressed genes
quantitative analysis of expressed genes by collecting tags
GLGI
Gene identification
GenBank
collect cDNA clones
mRNA
extend tags into longer 3' cDNAs
multi-match
single-match
no match
matchmatch
SAGE tags match to many genes(Tags from Hashimoto S, et al. Blood 94:837, 1999)
Tags matched gene numbers Matched genes (only show up to 10)
CCTGTAATCC 405 Hs.267557,Hs.240615,Hs.231705,Hs.283045,Hs.236713,Hs.232277,Hs.181553,Hs.262716,Hs.181392,Hs.220696GTGAAACCCC 305 Hs.282868,Hs.170225,Hs.184220,Hs.194021,Hs.231625,Hs.171830,Hs.270571,Hs.270572,Hs.272193,Hs.283921CCACTGCACT 174 Hs.118778,Hs.256868,Hs.96023,Hs.31575,Hs.47517,Hs.200451,Hs.271222,Hs.253240,Hs.270018,Hs.270415ACTTTTTCAA 44 Hs.16426,Hs.10669,Hs.75155,Hs.28166,Hs.13975,Hs.79136,Hs.111334,Hs.133430,Hs.79356,Hs.239100TTGGGGTTTC 9 Hs.231375,Hs.273127,Hs.275603,Hs.175173,Hs.276612,Hs.224773,Hs.62954,Hs.182771,Hs.276326TGCACGTTTT 8 Hs.199160,Hs.279943,Hs.36927,Hs.5338,Hs.169793,Hs.83450,Hs.173902,Hs.183506TGTGTTGAGA 5 Hs.284136,Hs.275865,Hs.275221,Hs.274466,Hs.181165CCCGTCCGGA 5 Hs.276353,Hs.277498,Hs.277573,Hs.276350,Hs.180842TTGGTCCTCT 4 Hs.12328,Hs.108124,Hs.9739,Hs.112845CTGACCTGTG 3 Hs.277477,Hs.181244,Hs.77961TACCTGCAGA 3 Hs.100000,Hs.256957,Hs.253884AGGCTACGGA 3 Hs.119122,Hs.211582,Hs.183297GGGCTGGGGT 3 Hs.183698,Hs.118757,Hs.90436CCCTGGGTTC 2 Hs.52891,Hs.111334CACAAACGGT 2 Hs.2043,Hs.195453GTGAAGGCAG 2 Hs.4221,Hs.77039GGGCATCTCT 2 Hs.75061,Hs.76807ATGGCTGGTA 2 Hs.254246,Hs.182426CGCCGCCGGC 2 Hs.182825,Hs.132753AGGGCTTCCA 2 Hs.29797,Hs.276544TTGGTGAAGG 2 Hs.278674,Hs.75968GTGGCCACGG 1 Hs.112405GTTCACATTA 1 Hs.84298TGGTGTTGAG 1 Hs.275865CCCATCGTCC 1 Hs.151604GTTGTGGTTA 1 Hs.75415TTGTAATCGT 1 Hs.125078CCCACAACCT 1 Hs.252136GAGGGAGTTT 1 Hs.76064CCAGAACAGA 1 Hs.111222
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
3' end
3' end5' end
SAGE tag
3' longer sequence
Generation of Longer 3'cDNA for Gene Identification (GLGI)
TAAAAAAAAAAACTCGCCGGCGAANNNNNNNNNNATTTTTTTTTTTGAGCGGCCGCTT
10 bases
hundred bases
TAAAAAAAAAAACTCGCCGGCGAANNNNNNNNNN
NNNNNNNNNN
NNNNNNNNNN
NNNNNNNNNN
NNNNNNNNNN
Sense extension
antisense extension TGAGCGGCCGCTT
nnnnnnnnnn
nnnnnnnnnn
nnnnnnnnnn
nnnnnnnnnn
nnnnnnnnnn
nnnnnnnnnn
SAGE tag
TAAAAAAAAAAACTCGCCGGCGAA TGAGCGGCCGCTT
TAAAAAAAAAAACTCGCCGGCGAA TGAGCGGCCGCTT
TAAAAAAAAAAACTCGCCGGCGAA TGAGCGGCCGCTT
TAAAAAAAAAAACTCGCCGGCGAA TGAGCGGCCGCTT
UniGene Human 3’ Part Length Distribution
Number of Tags which Move for k to k+25
Unique Tags among 878,938 EST Derived Tags
Unique Tags among 32,851 Gene Derived Tags
Idealized Construction
Random Model
Ideal Case Tag Count Progression
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 LeeJanet D. RowleySan Ming Wang
Terry ClarkAndrew HuntworkJosef JurekL. Ridgway Scott