BISC 219/F10: Assignment Help- Data Analysis 1
LAB 2 Assignment: Autosomal vs. X-Linked
Independent Assortment vs. Linked Data Analysis (25 pts)
Due at the beginning of Lab 4.
You scored your initial crosses of N2 (WT) males with three different mutant hermaphrodites of strains MB1, MB2, and MB3. By now, you should know whether or not the Dpy and Unc mutations observed in each strain are 1) linked (meaning they are on the same autosomal chromosome or linkage group), or 2) are on two different autosomes (autosomal unlinked), or 3) are on two different chromosomes- one of them on the X and the other on an autosome. In case 3), the two mutations are unlinked because they are on different chromosomes, but one is referred to as X-linked because the mutation is in a gene on the X chromosome).
You should have sufficient data to answer our first experimental question, "Are the genes responsible for the dumpy and uncoordinated phenotype observed in MB1, MB2, and MB3 strains of C. elegans on autosomes or X-linked and, if both are on autosomes, are those genes inherited independently or are they on the same linkage group?" Your assignment will be to show how your data answers all parts of that question. You will write in the form of a Results section in a scientific paper.
Scientific writing uses data from experimentation to answer such questions as ours. You did the experiment (setting up and scoring your first set of crosses) and you figured out the answer from comparing your expectations (which are based on a hundred years of other genetic investigators combined wisdom) to your results. Now you need to present your evidence and your reasoning to an audience that doesn't know much about worms or genetics. In science, this part is called data analysis or results. There are two equally important parts to a data analysis: figures (graphs, drawings, or photos) and/or tables, AND a thorough explanatory narrative that begins with the experiment goal(s) and a brief summary of the methods---"In order to find out_________, we did__________."
If you think your data indicates that both mutations in a particular strain are autosomal, what is the evidence for that conclusion? Yes, evidence could be finding and citing a reliable, peer-reviewed published source (if others have worked on these strains before you), but the Results section, generally, limits itself to experimental evidence, preferably data from the authors’ experiments.
After the general introduction to your results section, it will be best to break down our multifaceted experimental question into parts and discuss separately the evidence for each part. For example, 1) Is there data from your observations of the F1 progeny that allows the conclusion that the mutations in two of the strains are not sex linked? 2) Is there data from your scoring of the F2 progeny from your crosses that allows the conclusion that the mutations in one of the strains are autosomal and linked? 3) Is there data from your scoring of the F2 progeny from your crosses that allows the conclusion that the mutations in one of the strains are on different autosomes (unlinked)? How you came to your conclusions will not be as clear to your reader if you give the scoring results in text form only, rather, it will be helpful to include tables or figures (photo, graph, or drawing) as visual aids to help your reader (who, you remember, doesn’t necessarily know Mendel's carefully worked out progeny ratios). Therefore, you will need to both show AND explain (figures/tables and text), not only how your results compare or don't compare to expected ratios, but also include what those expected ratios are.
Since you must write as though the reader and evaluator of this data analysis is NOT your lab instructor and is NOT another student in this class who has access to this wiki or knows much about C. elegans or genetics, you will have to include the essential information on which your investigation and the data interpretation is based. You can distill the basics from the introductory material provided in this wiki, your textbook or from your lecture notes, but be careful not to plagiarize and not to include too much general information. This data analysis/results section must be written completely in your own words and it should include only what is necessary to follow your data analysis from question to conclusion.
There are many ways to write a good Results analysis. The most important thing is to be clear about which observations/data serve as evidence for the autosomal or sex/linked question and which crosses and scoring answers the linked or independent assortment question that we also addressed. If the genes were sorted independently, the F2 ratios would be close to: 9/16 wild type (+/+;+/+); 3/16 Dpy(d/d;+/+); 3/16 Unc(+/+;u/u); 1/16 Dpy Unc (d/d;u/u)(9:3:3:1 ratio). If the genes were closely linked, your ratios would be very different from the 9:3:3:1 ratio. Don't fail to include conclusions! You started out the results section with an experimental question or goals. If your data allow you to answer all parts of the experimental question, then your conclusions should NOT be saved for the discussion, but should be part of the results.
To be useful as evidence for a conclusion, your observed ratios should include a more objective evaluation than a subjective assessment of “close or not close” to expected values for independent assortment. It is common to perform some objective "goodness of fit" analysis, such as a Chi square, to see if the deviation your data shows from the expected ratios is likely to be due to chance alone or if the deviation is because your genes are not sorting independently because of linkage. However, Chi square tests for linkage are performed from a test cross (1:1:1:1) rather than ratios from the dihybrid cross (9:3:3:1 ratio) we did, so we can't use that statistical tool this time.
Some suggestions for tables or figures to adequately, visually support your results narrative:
- The diagram of each of the three crosses, including the identity of the strains
- A table of processed data showing your observed phenotype scores for the F2 of each of the autosomal strains compared to the expected scores for independent assortment.
- Whatever else you feel is appropriate or useful for your audience. DO NOT include tables of raw, unprocessed data.
To get a feel for how a data analysis is written as a Results section in a scientific paper, take a look at the results section in a variety of published science journals, such as Cell or Genetics. The Wellesley library has electronic subscriptions to many of the journals that model this concept well. Also refer to the “How to Write a Scientific Paper” section in the Resources section of this wiki. There you will find valuable information on how to format figures/ tables with proper legends and the basics of how to write about data.
Grading Rubric
Data Analysis Rubric- Sex Linkage & Independent Assortment – 25 points
| At or Above Standard | Below Standard | Possible Points |
Points
Earned | |
|---|---|---|---|---|
| Table(s) &/or Figure(s) |
Table(s) and/or Figure(s) well designed to illustrate conclusions. Included all crucial information that allows the figure or table to make the main points visually and to “stand alone”: novice reader does not need to read the narrative to see the data’s meaning. All data adequately identified, correct units included, labeling appropriate. | Figure(s) or table(s) not well designed to illustrate main points or missing essential information needed for understanding. | 7.5 | __/7.5 |
| Legends | Figure legend is below figure & includes a number. Table legend is above table and includes sequential numbers independent of figure numbers. All legends include all essential information and no unnecessary detail about how data included was generated. Figure or table title gives the main point of the figure or table. Body of legend does not summarize main conclusions or include other material appropriate for the narrative data analysis. All data adequately identified and parameters defined. | Missing figure or table#, title, or legend. Legend (or title) is in wrong place or does not include appropriate numbering. Missing information about how data was generated. Missing part or all of key to symbols/ colors or other ambiguous information. Missing part or all crucial information that helps the figure or table to “stand alone”. Legend includes unimportant detail or includes a summary of the findings that is more appropriate for the narrative portion of the data analysis. | 2.5 | __/2.5 |
| Data Analysis | Narrative begins with an appropriately concise description of both experimental goals and experimental design. Narrative includes key findings, describes the data accurately, concisely and clearly, & includes only relevant information. Data analysis leads incrementally & clearly from data to appropriate conclusions to experimental question. Specific figure and table numbers for data that supports conclusions are cited in the narrative. | Narrative doesn't begin with an appropriately concise description of the experimental goals and experimental design. Narrative omits key findings, describes the data inaccurately or unclearly, includes irrelevant information, or is repetitive. Narrative fails to give appropriate conclusions to the experimental questions or fails to show how the experimental data allow the conclusions. Specific figure and table numbers for data that support conclusions is not cited in the narrative. | 15 | __/15 |
| Total | 25 | __/25 |
