def plotRelativeAbundanceCorrelations(infiles, outfile):
    '''
    plot the correlation between the estimated 
    relative abundance of species and the true
    relative abundances - done on the shared set
    '''
    PipelineMetagenomeBenchmark.plotRelativeAbundanceCorrelations(infiles, outfile)
def plotRelativeAbundanceCorrelations(infiles, outfile):
    '''
    plot the correlation between the estimated 
    relative abundance of species and the true
    relative abundances - done on the shared set
    '''
    PipelineMetagenomeBenchmark.plotRelativeAbundanceCorrelations(
        infiles, outfile)
def buildTrueTaxonomicRelativeAbundances(infiles, outfile):
    '''
    get species level relative abundances for the simulateds
    data. This involes creating maps between different identifiers
    from the NCBI taxonomy. This is so that the results are comparable
    to species level analysis from metaphlan
    The gi_taxid_nucl is a huge table and therefore this function
    takes an age to run - can think of optimising this somehow
    '''
    to_cluster = True
    PipelineMetagenomeBenchmark.buildTrueTaxonomicRelativeAbundances(infiles, outfile)
def buildTrueTaxonomicRelativeAbundances(infiles, outfile):
    '''
    relative abundances for the simulateds at different levels of
    the taxonomy.
    This involes creating maps between different identifiers
    from the NCBI taxonomy. This is so that the results are comparable
    to species level analysis from metaphlan
    The gi_taxid_nucl is a huge table and therefore this function
    takes an age to run - can think of optimising this somehow
    '''
    to_cluster = True
    PipelineMetagenomeBenchmark.buildTrueTaxonomicRelativeAbundances(
        infiles, outfile)
def plotCoverageOverGenomes(infile, outfile):
    '''
    plot the percent coverage over each genome
    '''
    PipelineMetagenomeBenchmark.plotCoverageOverGenomes(infile, outfile)
def calculateFalsePositiveRate(infiles, outfile):
    '''
    calculate the false positive rate in taxonomic
    abundances
    '''
    PipelineMetagenomeBenchmark.calculateFalsePositiveRate(infiles, outfile)
def calculateFalsePositiveRate(infiles, outfile):
    '''
    calculate the false positive rate in taxonomic
    abundances
    '''
    PipelineMetagenomeBenchmark.calculateFalsePositiveRate(infiles, outfile)
def plotCoverageOverGenomes(infile, outfile):
    '''
    plot the percent coverage over each genome
    '''
    PipelineMetagenomeBenchmark.plotCoverageOverGenomes(infile, outfile)