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Analyzing Data ​

Selecting a Nucleotide Model ​

Most methods will perform a global MG94xREV fit to optimize branch length and nucleotide substitution parameters before proceeding to hypothesis testing. Several methods (FEL, FUBAR, and MEME) additionally pre-fit a GTR nucleotide model to the data, using the estimated parameters as starting values for the global MG94xREV fit, as a computational speed-up. Resulting branch length and nucleotide substitution parameters are subsequently used as initial parameter values during model fitting for hypothesis testing.

Handling Ambiguities ​

When analyzing sequence data, ambiguous nucleotides (e.g., N, R, Y) can affect the accuracy of substitution rate calculations. This section explains the different strategies for handling ambiguities in your data. For more details, see the MBE paper.

Methods for Handling Ambiguities ​

Averaged (Default) ​

In the Averaged method, all possible resolutions of an ambiguous character contribute, in a weighted fashion, to the computation of Expected Nonsynonymous (EN), Expected Synonymous (ES), Observed Nonsynonymous (NN), and Observed Synonymous (NS) substitutions (see the methods paper for details).

  • Weights: Each possible resolution is weighted according to the relative frequency of that codon in the entire dataset.
  • Exclusion of Non-Informative Characters: Characters that provide no information (e.g., all gaps or all missing data) are excluded to avoid artificially inflating dN and dS estimates.

Resolved ​

In the Resolved method, only the most likely resolution for the given site is used in the computations.

  • Determining the Most Likely Resolution: Based solely on the data at that specific site, the most frequent codon is selected.
  • Ties: If multiple codons are equally frequent, one is chosen at random.

Skip ​

In the Skip method, any site containing an ambiguous character is entirely excluded from the computation of substitution rates.

  • Effect: This approach ensures that only fully unambiguous sites contribute to the analysis.

GapMM ​

The GapMM method treats gaps (-) in a specific way when matching sequences.

  • Gap Handling: A gap matched with any character other than another gap is considered as matching an N (which represents any nucleotide).
  • Purpose: This method acknowledges that gaps can represent unknown nucleotides and adjusts the calculations to account for this uncertainty.

Example ​

Consider the following site in your sequence alignment:

ACA ACG ACG ACR

Here, ACR is ambiguous because R can be either A or G, representing both ACA and ACG.

Using the Resolved Method ​

  • Most Frequent Codon: ACG appears most frequently at this site.
  • Resolution: The ambiguous codon ACR is resolved to ACG.
  • Calculation: Only ACG is used for EN, ES, NN, and NS computations at this site.

Using the Averaged Method ​

  • Possible Resolutions: ACR can be ACA or ACG.
  • Weight Factors:
    • Calculate Frequencies: Determine f(ACA) and f(ACG), the frequencies of ACA and ACG in the entire dataset.
    • Weights:
      • Weight for ACA = f(ACA) / (f(ACA) + f(ACG))
      • Weight for ACG = f(ACG) / (f(ACA) + f(ACG))
  • Calculation: Both ACA and ACG contribute to the computations, each weighted appropriately.

Using the Skip Method ​

  • Action: The entire site is excluded from all computations because it contains an ambiguous codon (ACR).

Using the GapMM Method ​

  • Not Applicable in This Example: Since there are no gaps in this site, the GapMM method does not alter the computations here.
  • General Behavior: If gaps were present, they would be treated as Ns when paired with any character other than another gap.

Choosing Significance Levels ​

For more details see MBE paper.