BISC 219: Assignment Help- Data Analysis 1
Autosomal vs. X-Linked
Independent Assortment vs. Linked Data Analysis (15pt)
You have scored your initial crosses of N2 (WT) males with three different mutant hermaphrodites and should have come to some decision about whether or not the mutations observed are on the same autosomal chromosome (or linkage group:linked) or are on two different autosomes (autosomal unliked) or are on two different chromosomes, one of them on the X (unlinked between them but called X-linked).
Since you can now answer our experimental question, you are ready to practice writing your answer in the form of a Results section in a scientific paper. Scientific writing uses data from experimentation to answer such questions. You did the experiment (setting up and scoring your first set of crosses) and you know the answer, so now you need to figure out how to present this evidence to an audience that doesn't know much about worms or genetics. In science, this part is called data analysis, and the outcome of your data analysis is written up as figures (graphs, drawings, or photos) and/or tables, with an explanatory narrative. It's called the Results section.
If you think that the mutation(s) is/are autosomal, what is the evidence for that conclusion? Yes, evidence could be finding and citing a reliable, peer-reviewed published source that says so, but the Results section, generally, limits itself to experimental evidence, preferably data from the authors’ experiments. 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? 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 linked?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)? Will it be effective to write about those observations and to give the scoring results in text form only, or would it be helpful to add a table or figure (photo, graph, or drawing) as a visual aid to help your reader (who, remember, doesn’t know a male from a hermaphrodite worm) understand the evidence?
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, you will have to start your data analysis (Results) by explaining the basics of your experiment and its goals, including crucial information about C. elegans and about classical genetics rules of inheritance. You can distill the essentials from the introductory material provided in this wiki, but be careful not to plagiarize and not to include too much general information. Your submitted data analysis must be written completely in your own words and it should include only essential, briefly described background information.
It is often easier to consider each part of the experimental goals you have addressed separately, but there are many ways to write a good Results analysis. Just don’t forget to be clear about which observations 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.
To be useful as evidence for a conclusion, your observed ratios would need a more objective evaluation than a subjective assessment of “close or not close” to expected values for independent assortment. You would need 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 it is because your genes are more likely not sorting independently because they are closely linked. However, chi square tests for linkage are performed comparing to expected ratios from a test cross (1:1:1:1) rather than ratios from a dihybrid cross (9:3:3:1 ratio), so we can't use a statistical tool this time.
Within your results section make sure you include:
- The diagram of all three crosses identifying which strain is which
- A table with your observed phenotype scores for the F2 of each of the autosomal strains
- A comparison to the expected unlinked phenotypic ratio for the two autosomal strains (also in table form)
- A conclusion on which strain has produced progeny in ratios further from that expected ratio
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 the results of statistical analysis tools such as Chi Square.
Grading Rubric
Data Analysis Rubric- Sex Linkage & Independent Assortment – 15 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. Missing part or 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. | 5 | __/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 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 a summary of the findings more appropriate for the narrative portion of the data analysis. | 2.5 | __/2.5 |
| Data Analysis | Narrative began with an appropriately concise description of both experimental goals and experimental design. Narrative included key findings, described the data accurately, concisely and clearly, & included only relevant information. Data analysis led incrementally & clearly from data to appropriate conclusions to both experimental questions. Specific figure and table numbers for data that supported conclusions were cited in the narrative. | Narrative didn’t begin with an appropriately concise description of the experimental goals and experimental design. Narrative omitted key findings, described the data inaccurately or unclearly, included irrelevant information, or was repetitive. Narrative failed to give appropriate conclusions to the experimental questions or failed to show how the experimental data allowed the conclusions. Specific figure and table numbers for data that supported conclusions was not cited in the narrative. | 7.5 | __/7.5 |
| Total | 15 | __/15 |
