Difference between revisions of "DNA Melting Report Requirements"

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* The name of the submitted file must consist of the last name of each group member separated by underscores: <LastName1>_<LastName2>_<LastName2>.pdf
 
* The name of the submitted file must consist of the last name of each group member separated by underscores: <LastName1>_<LastName2>_<LastName2>.pdf
 
* Include computer code in an appendix at the end of the file. Do not submit code separately.
 
* Include computer code in an appendix at the end of the file. Do not submit code separately.
 +
* All plots must be presented properly, including a descriptive title, axis labels, and legend.
 
* Begin the report with a cover page the lists the full names of all group members, your assigned DNA sample number, the type of investigation (length/ionic strength/complementarity), and a haiku about DNA melting curves.
 
* Begin the report with a cover page the lists the full names of all group members, your assigned DNA sample number, the type of investigation (length/ionic strength/complementarity), and a haiku about DNA melting curves.
 +
  
 
''Failure to follow the format guidelines will result in ridiculously large grade penalties''
 
''Failure to follow the format guidelines will result in ridiculously large grade penalties''
  
 
==Report outline==
 
==Report outline==
−
# Results
+
#Abstract:  
−
#;Samples run:List all of the samples you characterized (length/match/ionic strength)
+
**In one paragraph of less than six sentences, summarize the investigation you undertook and key results.
−
#;Data plots:All plots should be complete with title, axis labels, and legend. Plot both your experimental data and the best fit curves from the DNA melting mode. ''Plots in this section should include only data that was created by your group's own hands in the lab.'' Analysis of other people's datasets belongs in a different section (see below).
+
#;Raw data
−
##Single set of axes with plots of dsDNA concentration versus temperature for ALL raw data from all "known" samples that you ran.
+
**Plot all of your group's raw data, fluorescence vs. temperature, on the smallest number of axes that clearly convey the dataset. Include only data datasets generated by your own group.
−
##Single set of axes with plots of &Delta;dsDNA concentration/&Delta;temperature vs temperature for same.
+
** On similary-grouped sets of axes, plot &Delta;dsDNA fraction/&Delta;temperature.
−
##Similar figure, single axes, showing results for unknown sample, possibly including other samples run for comparison.
+
#;Model parameters:Develop a model for the melting experiment and use nonlinear regression to determine best-fit parameters.  
−
#;Model Parameter Determination: For each sample type, show a comparison of your data to a modeled sigmoidal curve from the thermodynamic model, and compare each to a simulated result obtained from DINAmelt or another melting curve simulator.
+
**Use the smallest possible number of plots to compare the model with best-fit parameters to your data and a simulated result obtained from DINAmelt or another melting curve simulator.
−
##These plots should be in the "ideal" format. For each sample type:
+
**Include a table of estimated thermodynamic parameters, &Delta;H, &Delta;S, and T<sub>m</sub>. Use multiple methods to find T<sub>m</sub>.
−
###Fit a model to your as-observed melting curve data by applying corrections to the ideal curve output by DnaFraction.m. For temperature input to DnaFraction.m, use a predicted sample temperature based on the measured heating block temperature.
+
#;Unknown sample determination:
−
###Now use the parameters found in that fit and the inverse of your corrections to scale the data to make it look like the ideal model, plotted vs the predicted sample temperature.
+
**Plot results for unknown sample, including other samples for comparison.
−
###Average the scaled curves for all successful runs of that sample type.
+
**Identify your unknown sample and state your level of confidence in the answer.
−
###Compare each of those averaged, scaled curves to the ideal sigmoidal curve for that sample type obtained using the &Delta;S and &Delta;H you obtained by fitting your model.
+
**Use the smallest possible number of plots to compare the unknown sample to the corresponding known sample.
−
###In the same figure, show the melting curve predicted by simulation with DINAMelt of equivalent.
+
#;Comparative data analysis
−
##Also, in a separate figure include an example or two of plots in the "as-observed" format, showing that your model fit well to the as-observed data, and showing your initial guess(es).
+
**Compare your data to results from other groups.
−
#;Unknown determination: Finally, include averaged, corrected data and a modeled response for your unknown sample either on the above dsDNA and &Delta;dsDNA plots, or in separate plots.
+
#Analysis
−
#;Include a table of estimated thermodynamic parameters for each sample. Include estimated &Delta;H, &Delta;S, and T<sub>m</sub> values (by multiple methods)
+
**Use bullet points to explain your data analysis methodology.
−
#;Show comparative data analysis and plots:Plots of any data you analyzed that came from other groups.
+
#;Discussion: Compare your results to theoretical models and/or other group's datasets.
−
#;Give a data analysis overview using an informative '''Bullet point''' summary of your data analysis methodology. Teach us what you did.
+
−
#;Discuss your results: Compare your results to theoretical models and/or other group's datasets. Be concise, but express yourself clearly.
+
 
#'''Sources of error: '''Provide a detailed discussion of error sources. Indicate whether each source causes a systematic or random distortion in the data. (The uncertainty from a random error decreases with additional experimental runs; systematic error does not.) Consider all possible sources of error including all aspects of your instrument, the oligo design, the dye used, the experimental methodology, and the analysis methodology.
 
#'''Sources of error: '''Provide a detailed discussion of error sources. Indicate whether each source causes a systematic or random distortion in the data. (The uncertainty from a random error decreases with additional experimental runs; systematic error does not.) Consider all possible sources of error including all aspects of your instrument, the oligo design, the dye used, the experimental methodology, and the analysis methodology.
 
# Instrument documentation
 
# Instrument documentation

Revision as of 01:07, 16 November 2012

20.309: Biological Instrumentation and Measurement

ImageBar 774.jpg


Format

  • One group member must submit a single PDF file no more than 20 MB to Stellar before the deadline.
  • The name of the submitted file must consist of the last name of each group member separated by underscores: <LastName1>_<LastName2>_<LastName2>.pdf
  • Include computer code in an appendix at the end of the file. Do not submit code separately.
  • All plots must be presented properly, including a descriptive title, axis labels, and legend.
  • Begin the report with a cover page the lists the full names of all group members, your assigned DNA sample number, the type of investigation (length/ionic strength/complementarity), and a haiku about DNA melting curves.


Failure to follow the format guidelines will result in ridiculously large grade penalties

Report outline

  1. Abstract:
    • In one paragraph of less than six sentences, summarize the investigation you undertook and key results.
  1. Raw data
    • Plot all of your group's raw data, fluorescence vs. temperature, on the smallest number of axes that clearly convey the dataset. Include only data datasets generated by your own group.
    • On similary-grouped sets of axes, plot ΔdsDNA fraction/Δtemperature.
  1. Model parameters
    Develop a model for the melting experiment and use nonlinear regression to determine best-fit parameters.
    • Use the smallest possible number of plots to compare the model with best-fit parameters to your data and a simulated result obtained from DINAmelt or another melting curve simulator.
    • Include a table of estimated thermodynamic parameters, ΔH, ΔS, and Tm. Use multiple methods to find Tm.
  1. Unknown sample determination
    • Plot results for unknown sample, including other samples for comparison.
    • Identify your unknown sample and state your level of confidence in the answer.
    • Use the smallest possible number of plots to compare the unknown sample to the corresponding known sample.
  1. Comparative data analysis
    • Compare your data to results from other groups.
  1. Analysis
    • Use bullet points to explain your data analysis methodology.
  1. Discussion
    Compare your results to theoretical models and/or other group's datasets.
  2. Sources of error: Provide a detailed discussion of error sources. Indicate whether each source causes a systematic or random distortion in the data. (The uncertainty from a random error decreases with additional experimental runs; systematic error does not.) Consider all possible sources of error including all aspects of your instrument, the oligo design, the dye used, the experimental methodology, and the analysis methodology.
  3. Instrument documentation
    Block diagram and schematics
    Include component values, relevant distances, and possibly a photograph or two. It is not necessary to document construction details, but do show your work in determining your component values, distances, etc.
    Signal to noise results
    Design evolution
    Give a bullet point summary of changes you made to your instrument design to address problems in the lab.

Lab manual sections

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