A bioinformatics simulation of a mutant workup from a model genetic organism Christopher J. Harendza...

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A bioinformatics simulation of a mutant workup from a model genetic organism Christopher J. Harendza – Montgomery County Community College

Transcript of A bioinformatics simulation of a mutant workup from a model genetic organism Christopher J. Harendza...

Page 1: A bioinformatics simulation of a mutant workup from a model genetic organism Christopher J. Harendza – Montgomery County Community College.

A bioinformatics simulation of a mutant workup from a model genetic organismChristopher J. Harendza – Montgomery County Community College

Page 2: A bioinformatics simulation of a mutant workup from a model genetic organism Christopher J. Harendza – Montgomery County Community College.

Importance Students are losing the appreciation for the

power of traditional “forward genetic” approaches and a situation is arising where most everything is mass “stare and compare” informatics and reverse genetics

While the new approaches are very powerful, full scale mutant screens have and should continue to be important

Page 3: A bioinformatics simulation of a mutant workup from a model genetic organism Christopher J. Harendza – Montgomery County Community College.

Example Nusslein Volhard, who did a full scale

saturation mutagenesis to identify developmentally important genes in Drosophila, is now doing similar work with Zebrafish

“Genetics (mutant analysis) is the window to the unknown”

-Christopher Harendza, not a famous person. Ha ha

Page 4: A bioinformatics simulation of a mutant workup from a model genetic organism Christopher J. Harendza – Montgomery County Community College.

Objective

Introduce students to the power of traditional genetic analysis of mutants and extrapolate this to bioinformatics tools on the web

Page 5: A bioinformatics simulation of a mutant workup from a model genetic organism Christopher J. Harendza – Montgomery County Community College.

Target Audience:

Students in a sophomore genetics class

Advanced freshman biology majors

Page 6: A bioinformatics simulation of a mutant workup from a model genetic organism Christopher J. Harendza – Montgomery County Community College.

Overview

Give students model data on a real mutant using, Drosophila, C. elegans, etc.

This data could parallel a wet lab, or series of wet labs, where students learn the techniques, but can then do concordant studies with informatics tools

Flow chart

Page 7: A bioinformatics simulation of a mutant workup from a model genetic organism Christopher J. Harendza – Montgomery County Community College.

Flow chart of the projectGive students a collection of mutants

Allow groups to choose a mutant of interest

Groups perform a series of crosses to establish linkage-use Virtual Fly to obtain real data

Students design appropriate 3 point cross to map the gene

Page 8: A bioinformatics simulation of a mutant workup from a model genetic organism Christopher J. Harendza – Montgomery County Community College.

Go over strategies to clone the gene (here is where corners would have to be cut)

e.g. positional cloning e.g. P-element cloning by complementation

etc.↓

Instructor provides the DNA sequence data

Students do bioinformatic analysis

Page 9: A bioinformatics simulation of a mutant workup from a model genetic organism Christopher J. Harendza – Montgomery County Community College.

Informatics phase

DNA SEQUENCE

Find homologs in fly cDNA library database

BLAST

Generate restriction enzyme map (do this backwards)

Page 10: A bioinformatics simulation of a mutant workup from a model genetic organism Christopher J. Harendza – Montgomery County Community College.

cDNA and application

The gene of interest would likely be eukaryotic and therefore possess introns

Therefore obtaining the cDNA is vital

Use the cDNA to identify open reading frames and translation tools to infer the amino acid sequence of the protein

Page 11: A bioinformatics simulation of a mutant workup from a model genetic organism Christopher J. Harendza – Montgomery County Community College.

Blast applications

BLAST

Find orthologs

Go to OMIM to find information on human ortholog

Do Clustal analysis to compare to known proteins

Page 12: A bioinformatics simulation of a mutant workup from a model genetic organism Christopher J. Harendza – Montgomery County Community College.

BLAST

Use the sequence to find orthologs in other organisms

Ask questions regarding conservation of function

Work up to human, if applicable, to find cognate genes

Page 13: A bioinformatics simulation of a mutant workup from a model genetic organism Christopher J. Harendza – Montgomery County Community College.

Clustal Analysis

Once orthologs are obtained, students could establish a database and compare related gene products

Discuss evolution of function

Page 14: A bioinformatics simulation of a mutant workup from a model genetic organism Christopher J. Harendza – Montgomery County Community College.

Protein analysis

Go to Protein Data Bank to find orthologs or a protein (s) in the same gene family

If someone has solved the structure of this or some related protein, structural analysis could be performed

Page 15: A bioinformatics simulation of a mutant workup from a model genetic organism Christopher J. Harendza – Montgomery County Community College.

OMIM Application

Human applications would be the ultimate “hook” to draw in the interest

Students could then analyze the orthologous human gene

At this point they would have access to a tremendous wealth of information

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Discussion of future applications Reverse genetic approaches in mouse

models (knock outs, knock ins)

Biotech applications

Gene therapy

Page 17: A bioinformatics simulation of a mutant workup from a model genetic organism Christopher J. Harendza – Montgomery County Community College.

Summary This activity will expose students to an

authentic research simulation while preserving the traditional discussion of genetics

Curriculum evolution

Page 18: A bioinformatics simulation of a mutant workup from a model genetic organism Christopher J. Harendza – Montgomery County Community College.

Wishes It would’ve been nice if others at the

workshop had interest in this project and I could stay for the last session; lack of interest makes me think it may not be such a good idea!

A trial run this term with my newfound tools will allow assessment of its efficacy