class Bio::PAML::Codeml::Report

Description

Run PAML codeml and get the results from the output file. The Codeml::Report object is returned by Bio::PAML::Codeml#query. For example

codeml = Bio::PAML::Codeml.new('codeml', :runmode => 0, 
    :RateAncestor => 1, :alpha => 0.5, :fix_alpha => 0)
result = codeml.query(alignment, tree)

where alignment and tree are Bioruby objects. This class assumes we have a buffer containing the output of codeml.

References

Phylogenetic Analysis by Maximum Likelihood (PAML) is a package of programs for phylogenetic analyses of DNA or protein sequences using maximum likelihood. It is maintained and distributed for academic use free of charge by Ziheng Yang. Suggestion citation

Yang, Z. 1997
PAML: a program package for phylogenetic analysis by maximum likelihood 
CABIOS 13:555-556

abacus.gene.ucl.ac.uk/software/paml.html

Examples

Invoke Bioruby's PAML codeml parser, after having read the contents of the codeml result file into buf (for example using File.read)

>> c = Bio::PAML::Codeml::Report.new(buf)

Do we have two models?

>> c.models.size
=> 2
>> c.models[0].name
=> "M0"
>> c.models[1].name
=> "M3"

Check the general information

>> c.num_sequences
=> 6
>> c.num_codons
=> 134
>> c.descr
=> "M0-3"

Test whether the second model M3 is significant over M0

>> c.significant
=> true

Now fetch the results of the first model M0, and check its values

>> m0 = c.models[0]
>> m0.tree_length
=> 1.90227
>> m0.lnL
=> -1125.800375
>> m0.omega
=> 0.58589
>> m0.dN_dS
=> 0.58589
>> m0.kappa
=> 2.14311
>> m0.alpha
=> nil

We also have a tree (as a string)

>> m0.tree
=> "((((PITG_23265T0: 0.000004, PITG_23253T0: 0.400074): 0.000004, PITG_23257T0: 0.952614): 0.000004, PITG_23264T0: 0.445507): 0.000004, PITG_23267T0: 0.011814, PITG_23293T0: 0.092242);"

Check the M3 and its specific values

>> m3 = c.models[1]
>> m3.lnL
=> -1070.964046
>> m3.classes.size
=> 3
>> m3.classes[0]
=> {:w=>0.00928, :p=>0.56413}

And the tree

>> m3.tree
=> "((((PITG_23265T0: 0.000004, PITG_23253T0: 0.762597): 0.000004, PITG_23257T0: 2.721710): 0.000004, PITG_23264T0: 0.924326): 0.014562, PITG_23267T0: 0.000004, PITG_23293T0: 0.237433);"

Next take the overall posterior analysis

>> c.nb_sites.size
=> 44
>> c.nb_sites[0].to_a
=> [17, "I", 0.988, 3.293]

or by field

>> codon = c.nb_sites[0]
>> codon.position
=> 17
>> codon.probability
=> 0.988
>> codon.dN_dS
=> 3.293

with aliases

>> codon.p
=> 0.988
>> codon.w
=> 3.293

Now we generate special string 'graph' for positive selection. The following returns a string the length of the input alignment and shows the locations of positive selection:

>> c.nb_sites.graph[0..32]
=> "                **    *       * *"

And with dN/dS (high values are still an asterisk *)

>> c.nb_sites.graph_omega[0..32]
=> "                3*    6       6 2"

We also provide the raw buffers to adhere to the principle of unexpected use. Test the raw buffers for content:

>> c.header.to_s =~ /seed/
=> 1
>> m0.to_s =~ /one-ratio/
=> 3
>> m3.to_s =~ /discrete/
=> 3
>> c.footer.to_s =~ /Bayes/
=> 16

Finally we do a test on an M7+M8 run. Again, after loading the results file into buf

Invoke Bioruby's PAML codeml parser

>> c = Bio::PAML::Codeml::Report.new(buf78)

Do we have two models?

>> c.models.size
=> 2
>> c.models[0].name
=> "M7"
>> c.models[1].name
=> "M8"

Assert the results are significant

>> c.significant
=> true

Compared to M0/M3 there are some differences. The important ones are the parameters and the full Bayesian result available for M7/M8. This is the naive Bayesian result:

>> c.nb_sites.size
=> 10

And this is the full Bayesian result:

>> c.sites.size
=> 30
>> c.sites[0].to_a
=> [17, "I", 0.672, 2.847]
>> c.sites.graph[0..32]
=> "                **    *       * *"

Note the differences of omega with earlier M0-M3 naive Bayesian analysis:

>> c.sites.graph_omega[0..32]
=> "                24    3       3 2"

The locations are the same, but the omega differs.

Attributes

header[R]
models[R]

Public Class Methods

new(buf) click to toggle source

Parse codeml output file passed with buf, where buf contains the content of a codeml result file

# File lib/bio/appl/paml/codeml/report.rb, line 222
def initialize buf
  # split the main buffer into sections for each model, header and footer.
  sections = buf.split("\nModel ")
  model_num = sections.size-1
  raise ReportError,"Incorrect codeml data models=#{model_num}" if model_num > 2
  foot2 = sections[model_num].split("\nNaive ")
  if foot2.size == 2
    # We have a dual model
    sections[model_num] = foot2[0]
    @footer = 'Naive '+foot2[1]
    @models = []
    sections[1..-1].each do | model_buf |
      @models.push Model.new(model_buf)
    end
  else
    # A single model is run
    sections = buf.split("\nTREE #")
    model_num = sections.size-1
    raise ReportError,"Can not parse single model file" if model_num != 1
    @models = []
    @models.push sections[1]
    @footer = sections[1][/Time used/,1]
    @single = ReportSingle.new(buf)
  end
  @header = sections[0]
end

Public Instance Methods

descr() click to toggle source

Give a short description of the models, for example 'M0-3'

# File lib/bio/appl/paml/codeml/report.rb, line 250
def descr
  num = @models.size
  case num
    when 0 
      'No model'
    when 1 
      @models[0].name
    else 
      @models[0].name + '-' + @models[1].modelnum.to_s
  end
end
nb_sites() click to toggle source

Return a PositiveSites (naive empirical bayesian) object

# File lib/bio/appl/paml/codeml/report.rb, line 273
def nb_sites
  PositiveSites.new("Naive Empirical Bayes (NEB)",@footer,num_codons)
end
num_codons() click to toggle source

Return the number of condons in the codeml alignment

# File lib/bio/appl/paml/codeml/report.rb, line 263
def num_codons
  @header.scan(/seed used = \d+\n\s+\d+\s+\d+/).to_s.split[5].to_i/3
end
num_sequences() click to toggle source

Return the number of sequences in the codeml alignment

# File lib/bio/appl/paml/codeml/report.rb, line 268
def num_sequences
  @header.scan(/seed used = \d+\n\s+\d+\s+\d+/).to_s.split[4].to_i
end
significant() click to toggle source

If the number of models is two we can calculate whether the result is statistically significant, or not, at the 1% significance level. For example, for M7-8 the LRT statistic, or twice the log likelihood difference between the two compared models, may be compared against chi-square, with critical value 9.21 at the 1% significance level.

Here we support a few likely combinations, M0-3, M1-2 and M7-8, used most often in literature. For other combinations, or a different significance level, you'll have to calculate chi-square yourself.

Returns true or false. If no result is calculated this method raises an error

# File lib/bio/appl/paml/codeml/report.rb, line 294
def significant
  raise ReportError,"Wrong number of models #{@models.size}" if @models.size != 2
  lnL1 = @models[0].lnL
  model1 = @models[0].modelnum
  lnL2 = @models[1].lnL
  model2 = @models[1].modelnum
  case [model1, model2]
    when [0,3]
      2*(lnL2-lnL1) > 13.2767   # chi2: p=0.01, df=4
    when [1,2]
      2*(lnL2-lnL1) >  9.2103   # chi2: p=0.01, df=2
    when [7,8]
      2*(lnL2-lnL1) >  9.2103   # chi2: p=0.01, df=2
    else
      raise ReportError,"Significance calculation for #{descr} not supported"
  end
end
sites() click to toggle source

Return a PositiveSites Bayes Empirical Bayes (BEB) analysis

# File lib/bio/appl/paml/codeml/report.rb, line 278
def sites
  PositiveSites.new("Bayes Empirical Bayes (BEB)",@footer,num_codons)
end