Two Problems in Functional Genomics

Concepts:

1. Fundamentals of molecular genetics.

• Information flow in a cell.
• Exploitable properties of nucleic acids.
• Molecular basis of polymorphism.
• Linkage analysis with single-nucleotide polymorphisms.

2. Introduction to molecular medicine.

• Mapping disease genes.
• Medical applications of functional genomics.
• Ethical issues for bioinformaticists.

3. Techniques and software for analysis of gene expression.

• Oligonucleotide primer design.
• Microarray technology.
• Software for curation and analysis of gene expression data.

Background:

The focus of functional genomics is on the tiny fraction of genomic DNA responsible for production of proteins. The concept of cellular information flow (also known as the Central Dogma of Molecular Biology) can be simply stated: Genetic information is stored in DNA, transcribed into messenger RNA, and translated into protein which accounts for the phenotype of the organism.

Significant effort has gone into mapping expressed sequences (Genome User Guide Ch. 2), correlating these mapped sites with genetic loci (Genome User Guide Ch. 4), and tracking down expressed sequences responsible for disease phenotypes (Genome User Guide Ch. 3, see also SNP catalog and mouse SNP mapping). Single nucleotide polymorphisms (SNPs) have been essential to these efforts, in addition to revealing much about evolutionary history and process (SNP mapping consortium, also see SNPs as Windows on Evolution).

While the processes of transcription and translation are both required for complete expression of a gene, transcription has been technically easier to evaluate and so has been used as a proxy for gene expression. Two powerful experimental techniques have been used to analyze the transcriptome: serial analysis of transcription and microarray analysis using oligonucleotides or cDNAs.

A number of software packages are available for analyzing microarray data including CAGED, Retriever / CompareTable, and Cluster / TreeView. Software for SAGE analysis is available online through the Cancer Genome Anatomy Project.

Expression profiling has recently been applied to classification of tumors in an effort to increase understanding of the molecular basis of specific cancers and to identify tumor signatures that can be used to improve the specificity of treatments (see for example Classifying Breast Cancer Models and DNA Arrays Reveal Cancer in Its Many Forms.

Analysis of the transcriptome thus represents the second major phase of any genome project. Expressed genes reveal patterns of evolution, show how genome structure determines organismal development and function, and provide insight into disease processes. Many interesting bioinformatics problems are presented by the need to archive massive amounts of expression data in some kind of standard format, and the need for software to extract biologically meaningful information. The task has just begun.

Pick a problem:

How Do You Map A Cancer Gene?

Cancer phenotypes are complex traits. Unlike simple Mendelian disease genes, multiple genetic loci contribute to oncogenesis. For example, Robert Weinberg's group at the Whitehead Institute in Cambridge, MA showed that in the culture dish, normal cells could be transformed into cancer cells by three genetic alterations. Weinberg's group first mutated the gene for telomerase (which maintains the caps on chromosomes), then mutated the ras gene, and finally inhibited the p53 gene. Only after all three changes did the cells become cancerous. So, a cancer gene is really a collection of cancer genes acting (or failing to act) together to transform the behavior of the cell.

For this problem, choose a cancer that interests the members of your group. Having chosen a cancer, develop an approach to mapping it using publically available bioinformatics tools and databases. You may want to make a short list of candidate cancers and do a quick survey to see which one would be easiest to work on. Hint: Check out MedMiner for a quick way to access everything published on a particular cancer. There is a nice tutorial. Click here for a copy of an article describing this text-mining tool.

Which Genes Make Us Human?

The sequence of the human genome provides a new tool with which to investigate human origins. It has been known since 1975, through the work of Mary-Claire King and Allan Wilson, that the genomes of humans and chimpanzees differ by only 1.3%. This DNA sequence difference is unusually small for two species so different in anatomy and behavior (Pray, 2002). This puzzle has sparked intense interest in the chimpanzee genome, now scheduled to be completely sequenced. A comparative chimp-human clone map has recently been published (Fujiyama, 2002). SNP mappers have jumped into the question, reasoning that single nucleotide polymorphisms may hold the key (Lewis, 2002).

However, gene expression studies will be required for any real answer to the question, as predicted by King and Wilson. Researchers last year presented the first comparative gene expression studies in humans and other primates (Ape Genomics). A more comprehensive analysis, including some proteomic data, shows major differences in the pattern of brain gene expression between humans and chimps (Enard, 2002). Recently, a very exciting candidate gene has been identified that appears to be linked with language ability (see speech gene). In addition, the gene shows statistical evidence of strong selection during human evolution (Stephens, 2001).

The background above describes at least three different approaches to answering the question of what, genetically speaking, defines our human species. Based on publicly available bioinformatic tools and databases, can your group suggest a purely bioinformatic approach? Your group should outline the approach and provide a critical evaluation of the limitations. Hint: Start with the hyperlinked references provided above.

 
Getting started:

Planning an approach:

1. Evaluate the problem as a group. Then decide what general steps are needed to work out a solution to the problem and what preliminary information will be needed.

2. Identify bioinformatic methods and resources that can be combined to answer the question.

3. Divide up tasks so that each member of the group takes responsibility for an important part of the project.

The next steps:

1. Assemble and integrate the results of your individual investigations. Each person should explain what he or she did and what results were obtained.

2. Analyze the combined results. Have you put together a reasonable solution to the problem?

3. Decide how to present your solution in class.

Discussion and report summary (points = 20, due 10/23/2003):

1. For discussion on Thursday, October 23rd, each group will prepare a presentation [10 minutes maximum] of your findings, including a brief summary of methods, results, and conclusions.

a. For visual aids, you may include overheads of results or use a computer presentation, such as PowerPoint. Let me know what you plan, so I can make the appropriate arrangements.

b. Be prepared to field questions on your work.

2. Submit the following as a typed report due October 23, 2003. As a guideline, a finished report on the search should be about three pages of text [12 pt standard font] and no more than four pages of appended graphs, tables, images, etc.

a. Name of the problem and names of the members of your group.

b. A brief review of your problem, including an overview, how you approached the problem, information on software and databases you took advantage of, and why you believe your solution is a reasonable one. Include citations to published work or web pages and add figures as appropriate.

Grading will be on content, organization, spelling, & grammar.

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