Bio.Phylo.TreeConstruction模块

树构建的类和方法。

class Bio.Phylo.TreeConstruction.DistanceMatrix(names, matrix=None)

基类：_Matrix

可用于基于距离的树算法的距离矩阵类。

无论用户提供什么，所有对角线元素都将为零。

__init__(names, matrix=None)

初始化课程。

__setitem__(item, value)

将Matrix的项目设置为值。

format_phylip(handle)

将Phylip格式的数据写入给定的类似文件的对象或处理。

输出流是与Phylip程序一起使用的输入距离矩阵格式（例如“neighbor”）。请参阅：http://evolution.genetics.washington.edu/phylip/doc/neighbor.html

参数:

手柄文件或类似文件的目标

可写文本模式文件处理或支持“写”方法的其他对象，例如StringIO或sys. stdout。

__firstlineno__ = 314

__static_attributes__ = ()

class Bio.Phylo.TreeConstruction.DistanceCalculator(model='identity', skip_letters=None)

基类：object

根据DNA或蛋白质序列比对计算距离矩阵。

此类根据DNA或蛋白质序列的多序列比对以及替代模型的给定名称来计算距离矩阵。

目前仅使用评分矩阵。

参数:

模型str

用于计算距离的模型矩阵的名称。属性 dna_models 包含DNA序列的可用模型名称， protein_models for protein蛋白质sequences序列.

示例

加载小型PHYLIP对齐，从中计算距离：：

>>> from Bio.Phylo.TreeConstruction import DistanceCalculator
>>> from Bio import AlignIO
>>> aln = AlignIO.read(open('TreeConstruction/msa.phy'), 'phylip')
>>> print(aln)
Alignment with 5 rows and 13 columns
AACGTGGCCACAT Alpha
AAGGTCGCCACAC Beta
CAGTTCGCCACAA Gamma
GAGATTTCCGCCT Delta
GAGATCTCCGCCC Epsilon

具有“身份”模型的DNA计算器：：

>>> calculator = DistanceCalculator('identity')
>>> dm = calculator.get_distance(aln)
>>> print(dm)
  Alpha   0.000000
  Beta    0.230769    0.000000
  Gamma   0.384615    0.230769    0.000000
  Delta   0.538462    0.538462    0.538462    0.000000
  Epsilon 0.615385    0.384615    0.461538    0.153846    0.000000
      Alpha   Beta    Gamma   Delta   Epsilon

带有“blosum 62”模型的蛋白质计算器：：

>>> calculator = DistanceCalculator('blosum62')
>>> dm = calculator.get_distance(aln)
>>> print(dm)
Alpha   0.000000
Beta    0.369048    0.000000
Gamma   0.493976    0.250000    0.000000
Delta   0.585366    0.547619    0.566265    0.000000
Epsilon 0.700000    0.355556    0.488889    0.222222    0.000000
    Alpha   Beta    Gamma   Delta   Epsilon

相同的计算，使用新的对齐对象：

>>> from Bio.Phylo.TreeConstruction import DistanceCalculator
>>> from Bio import Align
>>> aln = Align.read('TreeConstruction/msa.phy', 'phylip')
>>> print(aln)
Alpha             0 AACGTGGCCACAT 13
Beta              0 AAGGTCGCCACAC 13
Gamma             0 CAGTTCGCCACAA 13
Delta             0 GAGATTTCCGCCT 13
Epsilon           0 GAGATCTCCGCCC 13

具有“身份”模型的DNA计算器：：

>>> calculator = DistanceCalculator('identity')
>>> dm = calculator.get_distance(aln)
>>> print(dm)
Alpha   0.000000
Beta    0.230769    0.000000
Gamma   0.384615    0.230769    0.000000
Delta   0.538462    0.538462    0.538462    0.000000
Epsilon 0.615385    0.384615    0.461538    0.153846    0.000000
    Alpha   Beta    Gamma   Delta   Epsilon

带有“blosum 62”模型的蛋白质计算器：：

>>> calculator = DistanceCalculator('blosum62')
>>> dm = calculator.get_distance(aln)
>>> print(dm)
Alpha   0.000000
Beta    0.369048    0.000000
Gamma   0.493976    0.250000    0.000000
Delta   0.585366    0.547619    0.566265    0.000000
Epsilon 0.700000    0.355556    0.488889    0.222222    0.000000
    Alpha   Beta    Gamma   Delta   Epsilon

dna_models = ['benner22', 'benner6', 'benner74', 'blastn', 'dayhoff', 'feng', 'genetic', 'gonnet1992', 'hoxd70', 'johnson', 'jones', 'levin', 'mclachlan', 'mdm78', 'megablast', 'blastn', 'rao', 'risler', 'schneider', 'str', 'trans']

protein_models = ['blastp', 'blosum45', 'blosum50', 'blosum62', 'blosum80', 'blosum90', 'pam250', 'pam30', 'pam70']

models = ['identity', 'benner22', 'benner6', 'benner74', 'blastn', 'dayhoff', 'feng', 'genetic', 'gonnet1992', 'hoxd70', 'johnson', 'jones', 'levin', 'mclachlan', 'mdm78', 'megablast', 'blastn', 'rao', 'risler', 'schneider', 'str', 'trans', 'blastp', 'blosum45', 'blosum50', 'blosum62', 'blosum80', 'blosum90', 'pam250', 'pam30', 'pam70']

__init__(model='identity', skip_letters=None)

使用距离模型初始化。

get_distance(msa)

返回对齐或MultipleSeqConnection对象的DistanceMatrix。

参数:

MSA对齐或MultipleSeqAlignment对象，表示

DNA或蛋白质多序列比对。

__firstlineno__ = 364

__static_attributes__ = ('scoring_matrix', 'skip_letters')

class Bio.Phylo.TreeConstruction.TreeConstructor

基类：object

所有树构造函数的Base Class。

build_tree(msa)

调用者从对齐或MultipleSeqEqualition对象构建树。

这应该在子类中实现。

__firstlineno__ = 577

__static_attributes__ = ()

class Bio.Phylo.TreeConstruction.DistanceTreeConstructor(distance_calculator=None, method='nj')

基类：TreeConstructor

基于距离的树构造器。

参数:

方法str

距离树构建方法，“nj”（默认）或“upgma”。

distance_calculatorDistanceCalculator

用于多序列比对的距离矩阵计算器。如果 build_tree 将被称为。

示例

加载一个小型PHYLIP对齐来计算距离，然后构建upgma树：：

>>> from Bio.Phylo.TreeConstruction import DistanceTreeConstructor
>>> from Bio.Phylo.TreeConstruction import DistanceCalculator
>>> from Bio import AlignIO
>>> aln = AlignIO.read(open('TreeConstruction/msa.phy'), 'phylip')
>>> constructor = DistanceTreeConstructor()
>>> calculator = DistanceCalculator('identity')
>>> dm = calculator.get_distance(aln)
>>> upgmatree = constructor.upgma(dm)
>>> print(upgmatree)
Tree(rooted=True)
    Clade(branch_length=0, name='Inner4')
        Clade(branch_length=0.18749999999999994, name='Inner1')
            Clade(branch_length=0.07692307692307693, name='Epsilon')
            Clade(branch_length=0.07692307692307693, name='Delta')
        Clade(branch_length=0.11057692307692304, name='Inner3')
            Clade(branch_length=0.038461538461538464, name='Inner2')
                Clade(branch_length=0.11538461538461536, name='Gamma')
                Clade(branch_length=0.11538461538461536, name='Beta')
            Clade(branch_length=0.15384615384615383, name='Alpha')

建造新泽西树：：

>>> njtree = constructor.nj(dm)
>>> print(njtree)
Tree(rooted=False)
    Clade(branch_length=0, name='Inner3')
        Clade(branch_length=0.18269230769230765, name='Alpha')
        Clade(branch_length=0.04807692307692307, name='Beta')
        Clade(branch_length=0.04807692307692307, name='Inner2')
            Clade(branch_length=0.27884615384615385, name='Inner1')
                Clade(branch_length=0.051282051282051266, name='Epsilon')
                Clade(branch_length=0.10256410256410259, name='Delta')
            Clade(branch_length=0.14423076923076922, name='Gamma')

相同的示例，使用新的对齐类：：

>>> from Bio.Phylo.TreeConstruction import DistanceTreeConstructor
>>> from Bio.Phylo.TreeConstruction import DistanceCalculator
>>> from Bio import Align
>>> aln = Align.read(open('TreeConstruction/msa.phy'), 'phylip')
>>> constructor = DistanceTreeConstructor()
>>> calculator = DistanceCalculator('identity')
>>> dm = calculator.get_distance(aln)
>>> upgmatree = constructor.upgma(dm)
>>> print(upgmatree)
Tree(rooted=True)
    Clade(branch_length=0, name='Inner4')
        Clade(branch_length=0.18749999999999994, name='Inner1')
            Clade(branch_length=0.07692307692307693, name='Epsilon')
            Clade(branch_length=0.07692307692307693, name='Delta')
        Clade(branch_length=0.11057692307692304, name='Inner3')
            Clade(branch_length=0.038461538461538464, name='Inner2')
                Clade(branch_length=0.11538461538461536, name='Gamma')
                Clade(branch_length=0.11538461538461536, name='Beta')
            Clade(branch_length=0.15384615384615383, name='Alpha')

建造新泽西树：：

>>> njtree = constructor.nj(dm)
>>> print(njtree)
Tree(rooted=False)
    Clade(branch_length=0, name='Inner3')
        Clade(branch_length=0.18269230769230765, name='Alpha')
        Clade(branch_length=0.04807692307692307, name='Beta')
        Clade(branch_length=0.04807692307692307, name='Inner2')
            Clade(branch_length=0.27884615384615385, name='Inner1')
                Clade(branch_length=0.051282051282051266, name='Epsilon')
                Clade(branch_length=0.10256410256410259, name='Delta')
            Clade(branch_length=0.14423076923076922, name='Gamma')

methods = ['nj', 'upgma']

__init__(distance_calculator=None, method='nj')

初始化课程。

build_tree(msa)

构建并返回树、邻居连接或UPGMA。

upgma(distance_matrix)

构建并返回UPGMA树。

构造并返回具有算术平均值的未加权对群方法（UPGMA）树。

参数:

distance_matrixDistanceMatrix

用于树构建的距离矩阵。

nj(distance_matrix)

构建并返回邻居连接树。

参数:

distance_matrixDistanceMatrix

用于树构建的距离矩阵。

__annotations__ = {}

__firstlineno__ = 588

__static_attributes__ = ('distance_calculator', 'method')

class Bio.Phylo.TreeConstruction.Scorer

基类：object

所有树评分方法的基本类别。

get_score(tree, alignment)

调用者获取给定对齐的树的分数。

这应该在子类中实现。

__firstlineno__ = 883

__static_attributes__ = ()

class Bio.Phylo.TreeConstruction.TreeSearcher

基类：object

所有树搜索方法的Base Class。

search(starting_tree, alignment)

呼叫者以搜索具有开始树的最佳树。

这应该在子类中实现。

__firstlineno__ = 894

__static_attributes__ = ()

class Bio.Phylo.TreeConstruction.NNITreeSearcher(scorer)

基类：TreeSearcher

使用最近邻立交桥（NNI）算法进行树搜索。

参数:

得分手ParsimonyScorer

简约评分器，用于计算NNI算法期间不同树的简约评分。

__init__(scorer)

初始化课程。

search(starting_tree, alignment)

实现TreeSearcher.search方法。

参数:

starting_tree树

NNI方法的开始树。

对准对齐或MultipleSeq对齐对象

多序列比对用于计算不同NNI树的简约得分。

__annotations__ = {}

__firstlineno__ = 905

__static_attributes__ = ('scorer',)

class Bio.Phylo.TreeConstruction.ParsimonyScorer(matrix=None)

基类：Scorer

具有得分矩阵的节俭得分手。

这是Fitch算法和Sankoff算法的组合。有关用法，请参阅ParsimonyTreeConstructor。

参数:

矩阵_Matrix

用于节俭分数计算的评分矩阵。

__init__(matrix=None)

初始化课程。

get_score(tree, alignment)

使用惠誉算法计算节俭分数。

使用Fitch算法（没有惩罚矩阵）或Sankoff算法（有矩阵）计算并返回给定树和MSA的简约得分。

__annotations__ = {}

__firstlineno__ = 1056

__static_attributes__ = ('matrix',)

class Bio.Phylo.TreeConstruction.ParsimonyTreeConstructor(searcher, starting_tree=None)

基类：TreeConstructor

节俭树构造函数。

参数:

搜索器TreeSearcher

树搜索最好的节俭树。

starting_tree树

开始树提供给客户。

示例

我们将加载对齐，然后加载已经从中计算出的各种树：：

>>> from Bio import AlignIO, Phylo
>>> aln = AlignIO.read(open('TreeConstruction/msa.phy'), 'phylip')
>>> print(aln)
Alignment with 5 rows and 13 columns
AACGTGGCCACAT Alpha
AAGGTCGCCACAC Beta
CAGTTCGCCACAA Gamma
GAGATTTCCGCCT Delta
GAGATCTCCGCCC Epsilon

加载开始树：：

>>> starting_tree = Phylo.read('TreeConstruction/nj.tre', 'newick')
>>> print(starting_tree)
Tree(rooted=False, weight=1.0)
    Clade(branch_length=0.0, name='Inner3')
        Clade(branch_length=0.01421, name='Inner2')
            Clade(branch_length=0.23927, name='Inner1')
                Clade(branch_length=0.08531, name='Epsilon')
                Clade(branch_length=0.13691, name='Delta')
            Clade(branch_length=0.2923, name='Alpha')
        Clade(branch_length=0.07477, name='Beta')
        Clade(branch_length=0.17523, name='Gamma')

从开始树构建节俭树：：

>>> scorer = Phylo.TreeConstruction.ParsimonyScorer()
>>> searcher = Phylo.TreeConstruction.NNITreeSearcher(scorer)
>>> constructor = Phylo.TreeConstruction.ParsimonyTreeConstructor(searcher, starting_tree)
>>> pars_tree = constructor.build_tree(aln)
>>> print(pars_tree)
Tree(rooted=True, weight=1.0)
    Clade(branch_length=0.0)
        Clade(branch_length=0.19732999999999998, name='Inner1')
            Clade(branch_length=0.13691, name='Delta')
            Clade(branch_length=0.08531, name='Epsilon')
        Clade(branch_length=0.04194000000000003, name='Inner2')
            Clade(branch_length=0.01421, name='Inner3')
                Clade(branch_length=0.17523, name='Gamma')
                Clade(branch_length=0.07477, name='Beta')
            Clade(branch_length=0.2923, name='Alpha')

相同的示例，使用新的对齐类：：

>>> from Bio import Align, Phylo
>>> alignment = Align.read(open('TreeConstruction/msa.phy'), 'phylip')
>>> print(alignment)
Alpha             0 AACGTGGCCACAT 13
Beta              0 AAGGTCGCCACAC 13
Gamma             0 CAGTTCGCCACAA 13
Delta             0 GAGATTTCCGCCT 13
Epsilon           0 GAGATCTCCGCCC 13

加载开始树：：

>>> starting_tree = Phylo.read('TreeConstruction/nj.tre', 'newick')
>>> print(starting_tree)
Tree(rooted=False, weight=1.0)
    Clade(branch_length=0.0, name='Inner3')
        Clade(branch_length=0.01421, name='Inner2')
            Clade(branch_length=0.23927, name='Inner1')
                Clade(branch_length=0.08531, name='Epsilon')
                Clade(branch_length=0.13691, name='Delta')
            Clade(branch_length=0.2923, name='Alpha')
        Clade(branch_length=0.07477, name='Beta')
        Clade(branch_length=0.17523, name='Gamma')

从开始树构建节俭树：：

>>> scorer = Phylo.TreeConstruction.ParsimonyScorer()
>>> searcher = Phylo.TreeConstruction.NNITreeSearcher(scorer)
>>> constructor = Phylo.TreeConstruction.ParsimonyTreeConstructor(searcher, starting_tree)
>>> pars_tree = constructor.build_tree(alignment)
>>> print(pars_tree)
Tree(rooted=True, weight=1.0)
    Clade(branch_length=0.0)
        Clade(branch_length=0.19732999999999998, name='Inner1')
            Clade(branch_length=0.13691, name='Delta')
            Clade(branch_length=0.08531, name='Epsilon')
        Clade(branch_length=0.04194000000000003, name='Inner2')
            Clade(branch_length=0.01421, name='Inner3')
                Clade(branch_length=0.17523, name='Gamma')
                Clade(branch_length=0.07477, name='Beta')
            Clade(branch_length=0.2923, name='Alpha')

__annotations__ = {}

__firstlineno__ = 1164

__init__(searcher, starting_tree=None)

初始化课程。

__static_attributes__ = ('searcher', 'starting_tree')

build_tree(alignment)

建造这棵树。

参数:

对准MultipleSeqAlignment

多序列比对以计算节俭树。