Download Big Biology meets Obvious

Using the AT Grid for
Genomics Research at
the University of Florida
big biology meets obvious opportunity
Interdisciplinary Center for
Biotechnology Research
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Established at the University of Florida in 1987
by the Florida Legislature
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centralized organization of biomedical core
facilities
supporting biotechnology-based research
Organized under the office of the Vice President
of Research
Genomics group founded in 1998 to begin
providing large-scale DNA sequencing services
ICBR Genomics Group
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Bill Farmerie
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Mick Popp, David Moraga, Sharon Norton, Li Zhang
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Gene Expression Core
Li Liu, Fahong Yu, Brian Dill
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Scientific Director ICBR Genomics Group
Bioinformatics Core
Ernie Almira, Regina Shaw, Neda Panayotova, Kevin
Holland, Patrick Thimote, Tina Langaee, Stephen Marsh
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Genomics Core
What we do
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Large-scale DNA sequencing projects
Microarray gene expression analysis
Bioinformatics Resource
Faculty research programs
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On campus
Satellite research facilities
Other SUS Universities
Biotech industry
Exploring Genome
Space
Biology moves from a data-poor to
a data-rich science
The Genome Covers
The Human Genome Project
 HGP drives
innovation
 2 major technological benefits
 stimulated
development of high
throughput methods
 computational tools for data mining
and visualization of biological
information
Genbank August 15 2004
37,343,937 loci
41,808,045,653 bases
37,343,937 reported
sequences
Growth of DNA
Sequence
Output
What is this curious
relationship between
genes and computation?
It is all about information management
Bioinformatics
the intersection of biology and information sciences
Informational
Macromolecules
Living things store, search, and
selectively retrieve biological
information
DNA the structure
A:T
G:C
DNA as a linear array
we store lots of information using
simple linear codes
CTGGGTTCTGTTCGGGATCCCAGTCACAGGGACAATGGCGCATTCATATGTCACTTCCTTTACCTGCCTGGAG
GAGGTGTGGCCACAGACTCTGGTGGCTGCGAACGGGGACTCTGACCCAGTCGACTTTATCGCCTTGACGAAGG
GTTGGTTAATCCGTGCATGTGAGCTCCTCAGGGTGGAATCCAGGAGGATCCACGAGGGTGAATTGGCGGCATT
CTTGTCTTACGCCATCGCCTACCCCCAAAACTTCCTGTCTGTGATTGACAGCTACAGCGTAGGATGCGGTCTG
TTGAACTTCTGCGCGGTGGCTCTGGCTCTCTGTGAACTGGGCTACAGGCCTGTGGGGGTGCGTTTGGACAGCG
GTGACCTCTGCAGCCTGTCGGTGGATGTCCGCCAGGTCTTCAGACGCTGCAGCGAGCATTTCTCCGTCCCTGC
CTTTGATTCGTTGATCATCGTCGGGACGAATAACATCTCAGAGAAAAGCTTGACGGAGCTCAGCCTGAAGGAG
AACCAGATTGACGTTGTCGGAGTCGGAACTCACCTGGTCACCTGTACGACTCAGCCGTCGCTGGGTTGCGTTT
ACAAGCTGGTGGAGGTGAGGGGGAGGCCCCGGATGAAGATCAGCGAGGATCCGGAAAAGAGCACCGTTCCCGG
GAGGAAGCAGGTGTACCGCCTGATGGACACTGATGCTCCTCCAGAACCTGGAGTCCCTCTGAGCTGCTTCCCT
CTGTGCTCCGATCGCTCCTCCGTCTCCGTCACCCCGGCGCAGGTTCACCGTCTGCGGCAGGAAGTCTTTGTTG
ATGGACAGGTCACAGCCCGTCTGTGCAGCGCCACAGAGACCAGAACGGAGGTCCAGACCGCTCTCAAGACCCT
CCACCCTCGACACCAGAGGCTGCAGGAGCCAGACTCGTACACGGTGATTCACATTCTGAAGAAAACAACATTG
GATCGCGCTTTTCCGCTCTCTTCCCTTAGTTTCCCCTCCGAACTCCGCCGCTGGGCCGGAGGACTGAACCGGC
CCCCGACGGTGTCCCAGCGGCGGTGCAATGTGGCCCGGGTCCGGGAGGAGTGCGTGACGCCAGAGCAGAATGG
TTCGGTGGACGGGGGCGCACACGCTTCTCGCCGCGGCCGCTCCCCGCGGCCCACGGAACCGCGGGATCGGAGC
TGTTTTGTGCCGCCTGAAGGACTCGAAGGGGGACGGATAAATGCTGGATCCCCGAGTCCAGATCTGACCGTCT
GCATTCCGCTGGTGAGCTGCCAGACGCATCTGGAAACGAGCGCCGACAGAAGCAGCTCCGGACCATGTCGCCG
TCCGCGCACACAGGTCGCGTGTAAAGGGGACTTGGTCAGATCATCTTGCACCGGAACCAGGTCTCCCCTGGAG
ATGGGGACGGTCATGACCGTCTTCTACCAGAAGAAGTCCCAGCGGCCGGAGAGGAGAACCTTCCAGATCAAGC
CTGACACGCGGCTCCTCGTGTGGAGCCGAAACCCCGACAAAAGCGAAGGAGAGAGTGAGTATGAGCAGGCGGG
CCGTGCCGGGACCGGGCCCACGCCGCCCAGAACCTCATGTTCCTGGTGTTCCAGCACCGACCGGCCAGTTCTG
GCTCAGCTCCACACAACATCTGACAAACCCTCGTGGTTCCTGGTGGTCGACCACACGGCTGGTGAGGCGGCCT
CAGGTAGCTCAGGTAGCTCAGGTTAGCGTAAAGGGAGTTTTAAGCATCACCTGGTGACGGGGCAGGTGAGCTC
CAGCCACTCAGCAGTGCACGGCCGTGCACATACACACACACCTCTGTGTCGAGGTTACAGGTGGGGCCAAAGC
CCAACACCTTCAATGGCCCTCAGAGCTTTGAGGTTTTGAGGAATTGAGCCTTTAATCAGAAAA
another simple linear code is the
basis of life
Biological Information
information
storage
selective
information
retrieval
function
From Sequence to Function
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The genomic sequence identifies the 'parts'
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next trick is understanding gene
function
Post genomic era = functional genomics
 Critical concept: genes of similar sequence
may have similar functions
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 Inferring
function for a new gene begins
with searching for it’s nearest neighbor (or
homolog) of known function
BLAST
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Most common starting point for gene identification
Input: nucleotide or amino acid sequence
Similarity search of sequence repository (GenBank)
Output
Calculated scores (bit score and e-value)
 Text string (definition line), ID Reference Tag
 Sequence alignment
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Advantages
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Fast algorithm, very good at finding close homologs
Disadvantages
Only finds genes existing in the search database
 Not good at finding distant relatives
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Alternatives to BLAST
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HMMER developed by Sean Eddy
Uses Hidden Markov Models
Searches unknown protein query sequence against a
database of protein family models
Statistical models constructed from alignment of
conserved protein regions (Pfam)
 7677 families in Pfam release 16.0
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Advantages
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Superior to BLAST for discovering more distant homology
relations
Disadvantages
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More computationally intensive than BLAST
This could be a super computer
AT Grid: http://at.ufl.edu/grid/
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Office of Academic
Technology
Fedro Zazueta Director
Mike Kutyna Project
Manager
Links 500 desktop PCs
using United Devices
GridMP 4.2 software
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HMMER
BLAST
Blast Query
HMMER
Query
Query 1
Genome
DB
segment
Query 1
Genome
DB
segment
Query 1
Results
Genome
DB
segment
Query 1
Genome
Database
Pfam DB
Genome segments
Query 1
Query 1
Query 1
Genome
DB
segment
Genome
Query 1
segment
Genome
segment
Genome
segment
Genome
segment
Genome
UD NCBI Blast Implementation
UD
HMMER implementation
Sample HMMER Output
Query: 7.UF_CU.3.CB366 804 1 630 nseq=40; translated
Scores for sequence family classification (score includes all domains):
Model Description
Score E-value N
-------- --------------- ------- --KH_1 KH domain
127.6 2.3e-34 2
KH_2 KH domain
3.0
0.88 2
Parsed for domains:
Model Domain seq-f seq-t hmm-f hmm-t
score E-value
-------- ------- ----- ----- ----- --------- ------KH_2
1/2
36 93 .. 1 78 [] 3.4
0.8
KH_1
1/2
36 98 .. 1 74 [] 66.9 4.4e-16
KH_2
2/2 118 160 .. 1 78 [] 0.1
1.8
KH_1
2/2 118 180 .. 1 74 [] 68.3 1.6e-16
Alignments of top-scoring domains:
KH_2: domain 1 of 2, from 36 to 93: score 3.4, E = 0.8
*->avivvirtsrpGivIGKgGsnIkklgkelrklltgkkvqieviEySd
i + ++ G +IG+gGs I+ l+++ + ++i E
7.UF_CU.3. 36 SDIMMVESANVGKIIGRGGSKIRDLEQDSNAR-----IKISRDE--- 74
eeFgkkVfLeLwVKVkknWvknpellaqLga<-*
+++
v+ ++ ++ a
7.UF_CU.3. 75 ---DENGMKS---------VEISGTDEEIDA
93
KH_1: domain 1 of 2, from 36 to 98: score 66.9, E = 4.4e-16
*->terilippskvgriIGkgGstIkeIreetGakIdipddgseskplpe
+ + + + vg+iIG+gGs+I+ ++++++a+I+i++d+
7.UF_CU.3. 36 SDIMMVESANVGKIIGRGGSKIRDLEQDSNARIKISRDE-------- 74
dplngsdertvtIsGtpeavekAkkli<-*
+ +++ + v+IsGt e++++Ak++i
7.UF_CU.3. 75 -D--ENGMKSVEISGTDEEIDAAKRMI
98
Good news -- Bad news
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AT Grid compresses time for HMMER searches ~100X
Accepts batch queries as input
Query sequences must be pre-computed protein
translation
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Requires additional step & CDS prediction
Output is flat text
Developed Perl-based parser
 Tabbed output as input to relational DB
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Integrating gene annotation data from as many
applications as possible
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Facilitates comparison of results for the same query