Mr. Rogers - AP Statistics Objectives


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Background for Bioinformatics Stats Investigation


How genetic information is stored: The information stored in DNA is remarkably similar to a low level computer language written with 4 letters: A, C, G, T. Each of these represents a different base arranged in a strand of DNA. A pair of complementary strands are joined with hydrogen bonds and form the famous double helix.

DNA Sequences: To sequence DNA, the bases are read from a single strand a few pieces at a time and then assembled into a lengthy string of As, T, G, and Cs. An entire set of these strings for a single organism is called a Genome.


The human genome contains 3,164.7 million of these letters as compare to 3.6 million letters in the King James Version of the Bible. The letters in the genome are arranged like the chapters in a book on 24 separate DNA molecules each contained in a different chromosome. Each chromosome contains numerous genes which are considered the basic functional units of heredity.

Genes vs. intergenetic regions: Although the human genome is thought to contain about 20,000 to 25,000 genes, the genes make up only about 2% of the human genome. The remaining 98% of DNA is called the intergenetic regions. It purpose is unknown, although, even a relatively simple analysis indicates that it contains some form of information--maybe useless, scrambled or abandoned information.



Genome punctuation: Quite simply there isn't any. There are no spaces, no punctuation marks, or capital letters to denote the beginnings and endings of words in a sequence. Try reading the paragraph at right for a comparison of how this would look in English.

Statistical analysis of genomes: Statistics is probably the single most important mathematical tool for understanding genetics. The investigation below will help show some of the many ways it can be used.

Stats Investigation: Statistical Analysis of Genome Data - time approx 2 class periods

Purpose: Determine if a statistical method can determine whether a sequence is randomly generated or contains actual information.


  1. Begin by comparing the two sequences shown in the link. One is a real genetic sequence, the other a randomly generated sequence. Can you tell the difference just by looking?
  2. Go the article about Genome mining, "Meaningful sequences" and read through it to the end. (You will have to click on the small arrow icon on the right side below the text.)
  3. Now go to "Analysing DNA" and follow the exercises using Geneboy all the way to the end of the article.
  4. Using Analyze composition, Singles feature of Geneboy obtain the distribution of frequencies for A, C, G, T bases for Genetic 1, for Random 1 and for Intergenetic 1. What type of distribution would be predicted using the law of large numbers for a random distribution of As, Cs, Gs, and Ts? What statistical tool could you use to confirm whether the 2 sets of data are or are not random ? Perform this analysis. Note, the question is not asking if the two distributions are different from each other.
  5. Plot 2 bar graphs using the distribution of frequencies for A, C, G, and T of Genetic 1. In the first scale the y-axis from 15 to 35%, the second from 0 to 300%. Repeat the process for Random 1.

Questions /Conclusions:

  1. Did the changing the scale of the above graphs alter the perception of their meaning and could it alter the meaning of a different set of graphs?
  2. Which is more likely to be universally accepted, conclusions based on graphs or conclusions based on statistical analysis? Why?
  3. If genes are only 2% of the sequence how would you find one and establish that it was one using statistics?



The articles referenced in the above articles as well as Geneboy come from The Dolan DNA Learning Center (DNALC) is the world's first science center devoted entirely to genetics education and is an operating unit of Cold Spring Harbor Laboratory, an important center for molecular genetics research.

For more information on bioinformatics--the discipline of decoding gene sequences got to DNALC's site on bioinformatics.