Transcript - Cal State LA - Instructional Web Server

By Angela Brooks and David Chapman
Mentor: Dr. Garry Larson
Molecular Medicine, City Of Hope
Southern California Bioinformatics Institute 2004
Outline
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Objective
Biological Background
Programming Project
Database Design
SIBXP Screen Shots
Future Tasks
Acknowledgements
SIBXP – SIB eXPeriment


A relational database that contains all important
components of PCR experiments performed in
the lab
SIB is an existing database that contains the
data obtained from the PCR experiments
PCR
Experiments
SIBXP Database
Experimental
Outcomes
SIB Database
What is the goal of the study?

Alleles – alternative forms of a gene


Alternative forms of a region of DNA
Ultimate goal:

Identify alleles that contribute to cancer risk


Breast
Prostate
http://www.el-minjas.com/alleles.gif
Affected Sibling Pairs (ASP)
Brothers with
prostate cancer
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
To help search for disease causing alleles, the
study examines siblings that have the same type
of cancer.
If there is evidence of linkage, the sibling pairs
will share alleles more often then by chance
alone.
Why SIBXP?
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Important to keep track of all experiments
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
Knowledge of what has been analyzed and
what has yet to be analyzed
There are many associated components to
an experiment that you need to keep track
of
Polymerase Chain
Reaction(PCR) – Detecting
genetic markers

PCR creates multiple copies which
amplifies a specific region in a DNA
template


The amplification makes it easier to detect the
region of DNA
PCR experiments are used to detect
genetic markers

Genetic markers help to detect the alleles that
can contribute to cancer risk
Genetic Markers

What’s
Look for genetic markers that Polymorphism?
are close
tothat?
A difference in DNA sequence
locus of interest
among individuals.

Microsatellites/minisatellites
Length polymorphism. Short repeated sequences
of DNA
 Usually found in non-protein coding regions of
DNA
 i.e. CACACACACACACACACA

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SNPs – Single Nucleotide Polymorphism
A single nucleotide base, at a specific location, that
shows variation in the population
 i.e. AT CA
AT CA
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PCR Experiment Tracking

In order to track PCR experiments that
detect the genetic markers you need to
know:
What marker you are detecting
 Ingredients that are added in the PCR
reaction – cocktail
 What human DNA template you are using
 Experimental Details

Programming Project
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
Create a software solution to enable
researchers to track PCR experiments.
Lab currently uses Excel spreadsheets to
track experiments.

Lab notes are referred to as “cooksheets”
What is a cooksheet?

A cooksheet is the term the lab technicians use
for the set of data they use to keep track of PCR
experiments.
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It is in essence a “recipe” of ingredients and
conditions for the experiment.
Cooksheets keep track of:
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DNA samples used
Primers
Reagents (such as TAQ polymerase and buffer)
Thermal cycler conditions
Gel Experiments
0.075
0.075
MS PCR E1Y97 Tray 10 07-26-04
D4S2917 HEX F PRIMER
0.075
D4S2917 R PRIMER
0.075
DUPLEX
COCKTAIL
#1
@
55
per
RXN
dNTPs
0.12 50
10X PCR BUFFER
0.6
30
TAQ GOLD POLY.
D4S2460 HEX F PRIMER
0.0750.073.75
D4S2460 R PRIMER
0.075
3.75
dH20
D4S2917 HEX F PRIMER
0.0754.913.75
D4S2917 R PRIMER
0.075
TOTALS
6.003.75
dNTPs
0.12
6
TAQ GOLD POLY.
0.07
3.5
COCKTAIL #2
@
55
per
RXN
dH20
4.91
245.5
TOTALS
6.00 0.6 300
10X PCR BUFFER
COCKTAIL #2 @ 55
per RXN
100
D4S1615 TET10XFPCR
PRIMER
BUFFER
0.6 0.09 60
D4S1615 TET F PRIMER
0.09
9
D4S1615 R PRIMER
D4S1615 R PRIMER
0.090.09 9
dNTPs
0.12
dNTPs
0.12 128
TAQ GOLD POLY.
0.08
dH20
5.020.08502
TAQ GOLD POLY.
TOTALS
6.00
600
dH20
5.02100
COCKTAIL #3 @ 55
per RXN
D4S2460 HEX F PRIMER
D4S2460 R PRIMER
Actual
Cooksheet
Stored as
Excel
Spreadsheet
TOTALS
10X PCR BUFFER
D4S2913 FAM F PRIMER
D4S2913 R PRIMER
COCKTAIL #3
@ 55
dNTPs
TAQ GOLD POLY.
dH20
TOTALS
10X PCR BUFFER
D4S2913 FAM F PRIMER
D4S2913 R PRIMER
COCKTAIL
dNTPs
TAQ GOLD POLY.
C1
dH20
C2
TOTALS
C3
COCKTAIL
0.6
0.075
0.075
0.12
0.06
5.07
6.00
6.00
per RXN
DNA PLATES
10A: 5ng/well 07-07-04
10A: 5ng/well 07-07-04
10B: 4ng/well 07-09-04
0.6
0.075
0.075
MACHINE
0.12
PTC-100
0.06
5.07MJ-A
6.00MJ-A
10A: 5ng/well 07-07-04
10B: 4ng/well 07-09-04
DNA PLATES
10A: 5ng/well 07-07-04
C1
60
7.5
7.5
12
6
507
600
MJ-B
MJ-B
3.75
3.75
HEX
Chr. 4 multi 1
D
COMPUTER REF: GS MS E1Y97 Tray 10 07-27-04
3.75
3.75
pooling
6
TET TRAY 10A D
Ch 4 m.p. 2
Chr. 4 multi 1
HEX
D4S2460 179-191 55
1.2
3.5
HEX
Chr. 4 multi 1
D4S2917 130-146 55
1.2
245.5
TRAY 10A,B
300
3
FAM
D
TET Ch 4 m.p.
Ch 4 m.p. 2
D4S1615 114-126 55
0.6
100
TRAY 10A,B
Ch
D4S2913 192-210 55
0.5
604 m.p. 3 FAM
9
9 CONDITIONS: PCR CONDITIONS:
PCR
12
95 C
10 MIN
HOLD
8
950C
10 MIN
HO
94 C
30 SEC
502
55
C
45 SEC
35 cycles
0
0
0
720C
0
30 SEC
720C
0
60 MIN
40C
HOLD
0
94 C
600
55 C
100
30 SEC
HOLD
60
72 C
7.5
7.5
720C
Suggested POOLING INFO FOR ABI RUN:
THE
12POOLING RATIO IS GOING TO BE:
6
40C 1.2
C1
C2
0.6
507
C3
0.5
600
total:
45 SEC
30 SEC
60 MIN
HO
HOLD
load 0.95
2.3
MACHINE
INFO FOR ABI
POOLED TO THE CIDRSuggested
MW COCKTAIL AT APOOLING
RATIO OF
dFORM
220 ul
THE POOLING
RATIO IS GOING TO BE
CIDR LITE.
9 ul
2.6ul of CIDR LITE
B.DEXTRAN
37
ul
PTC-100
266 ul total
C1
1.2
C2
0.6
Current System
Shortcomings
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The current system is unsatisfactory for the
following reasons:
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Time consuming to create new cooksheets
Errors can be easily introduced
Retrieving data is cumbersome
Data cannot be cross-referenced
Cooksheets are not dynamically linked to the existing
SIB database
Preferred or standard conditions for PCR cannot be
easily ascertained
Programming Solution
Use a relational database.
 Incorporate experimental data into
SIB DB
 SIB DB back end is administered with
Microsoft SQL Server
 Program front end with Active Server
Pages (web interface)
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Why use a relational
database?
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Ensures data consistency & integrity
Using a relational database will allow
users to cross-reference data sets
Cooksheet creation can be partially
automated
Entity Relationship Diagram for SIB XP
Consists of: 23 Tables & 148 Fields
Project Timeline
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Weeks 1-4
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Gather requirements
Analyze cooksheets
Produce specification documentation
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SIB XP Documentation
Created rapid prototype of interface in Power Point
Weeks 5-6
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Create database tables
Implement “front end” user interface in ASP
Future Tasks
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Continue implementation
Test and debug
Add additional features
Special Thanks To…
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City of Hope
Personnel
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Garry Larson
Yan Ding
Louis Geller
Dave Ko
Catheryn Lundberg
Guillermo Rivas
Bryan Pacada
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SoCalBSI Members
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Professors
Fellow Interns
NIH and NSF for
funding
Sources
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Database information:
http://support.microsoft.com
 http://his.osu.edu/help/database/terms.cfm
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