最近在学习 Bioinformatics with python cookbook 这本书第六章 Phylogenetics 的内容，了解到python中与系统发育相关的两个模块 Dendropy和 ete3 (A Python framework for the analysis and visualization of trees)，浏览ete3的文档的时候发现了很多非常漂亮的图片，第一感觉是和R语言里的ggtree功能很相似，所以觉得还是有必要学习一下。以下内容记录自己重复ete3文档中漂亮图片的过程。(题外话：个人感觉python绘图系统的默认配色比R语言的配色漂亮一点)

第一步 安装

自己 windows 的电脑按住了Anaconda3,直接在DOS命令行下使用easy_install即可安装相应的python模块.(正常应该使用pip install安装也是可以的，但是自己尝试的时候遇到了报错，没有搞清楚是什么原因)

easy_install ete3

第一个简单的小例子

读入树文件，查看，然后保存为pdf文件

from ete3 import Tree

t = Tree("../../Desktop/Malus.output.fasta.treefile")

t.show()

运行完 t.show() 会跳出来一个ETE Tree Browser

25.PNG

有点像figtree

未完待续......

更新

将读入的树文件写入到新文件中

from ete3 import Tree

t = Tree("(A:1,(B:1,(E:1,D:1)Internal_1:0.5)Internal_2:0.5)Root;")

t.write() #输出到屏幕

t.write(outfile="new_tree.nex") #写入到文件中

文档的内容有些枯燥，还是先从重复美图开始吧

t.show()函数运行后会跳出来ETE Tree Browser窗口，将树显示到桌面上

t.render()函数可以将树输出到图片里，可以生成png,pdf,svg格式

一个简单的小例子

from ete3 import Tree, TreeStyle

t = Tree()

t.render("mytree.png",w=183,units="mm")

mytree.png

第二个简单的小例子

from ete3 import Tree

from ete3 import TreeStyle

t = Tree()

t.populate(10)

ts.show_leaf_name = True

ts.mode = "c"

ts.arc_start = -180

ts.arc_span = 180

t.show(tree_style=ts)

t.render("tree.png",tree_style=ts)

tree.png

3、第三个简单的小例子

from ete3 import Tree

t = Tree("((((a1,a2),a3), ((b1,b2),(b3,b4))), ((c1,c2),c3));")

t.render("46.png")

46.png

from ete3 import Tree

from ete3 import NodeStyle

t = Tree("((((a1,a2),a3), ((b1,b2),(b3,b4))), ((c1,c2),c3));")

n1 = t.get_common_ancestor("a1","a2","a3")

nst1 = NodeStyle()

nst1["bgcolor"] = "LightSteelBlue"

n1.set_style(nst1)

t.render("47.png")

47.png

from ete3 import Tree

from ete3 import NodeStyle

from ete3 import AttrFace

from ete3 import faces

from ete3 import TreeStyle

t = Tree("((((a1,a2),a3), ((b1,b2),(b3,b4))), ((c1,c2),c3));")

n1 = t.get_common_ancestor("a1","a2","a3")

nst1 = NodeStyle()

nst1["bgcolor"] = "LightSteelBlue"

n1.set_style(nst1)

n2 = t.get_common_ancestor("b1","b2","b3","b4")

nst2 = NodeStyle()

nst2["bgcolor"] = "DarkSeaGreen"

n2.set_style(nst2)

def lauout(node):

if node.is_leaf():

N = AttrFace("name",fsize=30)

faces.add_face_to_node(N,node,0,position="aligned")

ts = TreeStyle()

ts.layout_fn = layout

ts.show_leaf_name = False

ts.render(tree_style = ts,file_name = "48.png")

48.png

rom ete3 import Tree

from ete3 import NodeStyle

from ete3 import AttrFace

from ete3 import faces

from ete3 import TreeStyle

t = Tree("((((a1,a2),a3), ((b1,b2),(b3,b4))), ((c1,c2),c3));")

for n in t.traverse():

n.dist = 2

n1 = t.get_common_ancestor("a1","a2","a3")

nst1 = NodeStyle()

nst1["bgcolor"] = "LightSteelBlue"

n1.set_style(nst1)

n2 = t.get_common_ancestor("b1","b2","b3","b4")

nst2 = NodeStyle()

nst2["bgcolor"] = "Moccasin"

n2.set_style(nst2)

n2 = t.get_common_ancestor("c1","c2","c3")

nst3 = NodeStyle()

nst3["bgcolor"] = "DarkSeaGreen"

n2.set_style(nst3)

ts = TreeStyle()

ts.mode = "c"

t.render(tree_style=ts,file_name="49.png",w=1000,h=1000,dpi=300)

49.png

第4个小例子

from ete3 import Tree

from ete3 import TreeStyle

from ete3 import faces

from ete3 import AttrFace

from ete3 import PieChartFace

from ete3 import COLOR_SCHEMES

from random import sample

from random import randint

t = Tree("((((a1,a2),a3), ((b1,b2),(b3,b4))), ((c1,c2),c3));")

ts = TreeStyle()

def layout(node):

if node.is_leaf():

N = AttrFace("name",fsize=20)

faces.add_face_to_node(N,node,column=0,position="branch-right")

pieF = PieChartFace([10,20,60,10],colors=COLOR_SCHEMES[sample(schema_names,1)[0]],width=40,height=40)

faces.add_face_to_node(pieF,node,column=0,position="aligned")

else:

node.img_style["size"] = randint(3,6)

node.img_style["shape"] = "square"

node.img_style["fgcolor" ] = "green"

ts.layout_fn = layout

ts.show_leaf_name = False

ts.show_scale = False

t.render(tree_style=ts,file_name = "50.png",w=500,h=500)

50.png

第五个小例子

from ete3 import Tree

from ete3 import TreeStyle

from ete3 import faces

from ete3 import TextFace

from ete3 import AttrFace

from ete3 import CircleFace

from random import randint

t = Tree("((((a1,a2),a3), ((b1,b2),(b3,b4))), ((c1,c2),c3));")

def layout(node):

if node.is_leaf():

N = AttrFace("name",fsize=20)

faces.add_face_to_node(N,node,column=0,position="branch-right")

node.img_style["size"] = 0

else:

node.img_style['size'] = randint(5,8)

node.img_style["shape"] = "square"

node.img_style["fgcolor"] = "green"

bubble_face = CircleFace(randint(5,10),'steelblue','sphere')

bubble_face.opacity = 0.6

faces.add_face_to_node(bubble_face,node,column=0,position="float-behind")

faces.add_face_to_node(AttrFace("dist",fsize=7,fgcolor="red"),node,column=0,position="branch-top")

if node.up and not node.up.up:

node.img_style['vt_line_width'] = 3

node.img_style['hz_line_width'] = 4

ts = TreeStyle()

ts.lsyout _fn = layout

ts.show_leaf_name = False

ts.show_scale = False

ts.mode = 'c'

ts.arc_start = 270

ts.arc_span = 185

t.show(tree_style=ts)

t.render(tree_style=ts,w=800,file_name="51.png")

51.png

更新 Dendropy 模块的内容

比对格式之间的转化，比较常用的比如从fasta格式转换成newick格式，或者newick转换成nexus格式，自己之前遇到此类问题一直使用的是在线工具 http://sing.ei.uvigo.es/ALTER/ 。今天浏览dendropy文档时发现这个模块也可以实现格式转换，多了一种选择，简单记录。(具体都可以转换那些格式自己还不是很清楚，自己目前知道的是fasta,newick,nexus,phylip)使用到的示例文件

https://pan.baidu.com/s/1chchsxMjP2fM-ghKaOaArQ

import dendropy

ccsA = dendropy.DnaCharacterMatrix.get(path = "ccsA_KaKs_pra.fas", schema = "fasta")

ccsA.write(path = "ccsA.phy",schema = "phylip")

ccsA.write(path = "ccsA.newick", schema = "newick")

ccsA.write(path = "ccsA.nexus", schema = "nexus")

使用mega利用上一步的比对文件建一棵树，导出为newick格式，然后利用dendropy模块转化为nexus格式(converting a single tree from Newick schema to nexus)

import dendropy

ccsA = dendropy.Tree.get(path = "ccsA.newick",schema = "newick")

ccsA.write(path="ccsA.nex",schema = "nexus")

查看树(viewing and displaying trees)

两种方式

print_plot()可以查看拓扑结构

as_string()可以查看文本形式的树

import dendropy

t = dendropy.Tree.get(path = "ccsA.newick",schema = "newick")

t.print_plot()

print(t.as_string(schema="newick"))

print(t.as_string(schema="nexus"))

自genbank数据库下载fasta格式的数据(这部分是重复Bioinformatics with python cookbook 这本书第六章 Phylogenetics 的内容第一步：下载诶博拉病毒的基因组数据，之前尝试了好多次一直没有看懂书中的代码，尝试原封不动的重复一直遇到错误，今天浏览dendropy的文档的过程中找到了一直遇到报错的原因：dendropy的部分代码已经更新，书中提到的部分代码已经不再使用)

先重复文档中的两个小例子

import dendropy

from dendropy.interop import genbank

gb_dna = genbank.GenBankDna(ids = ['EU105474','EU105475'])

#如果序列号之间是连续的，还可以换一种写法

gb_dna = genbank.GenBankDna(id_range=(74,75),prefix="EU1054")

for gb in gb_dna:

print(gb)

char_mat = gb_dna.generate_char_matrix()

#输出到屏幕

print(char_mat.as_string("fasta"))

#写到文件里

fw = open("dendropy_practice_1.fasta","w")

char_mat.write_to_stream(fw,'fasta')

fw.close()

import dendropy

from dendropy.interop import genbank

def get_other_ebolavirus_sources():

yield 'BDBV', genbank.GenBankDna(id_range=(3,6),prefix='KC54539')

yield 'BDBV', genbank.GenBankDna(ids=['FJ217161'])

yield 'RESTV', genbank.GenBankDna(ids=['AB050936','JX477165','JX477166','FJ621583','FJ621584','FJ621585'])

yield 'SUDV', genbank.GenBankDna(ids=['KC242783','AY729654','EU338380','JN638998','FJ968794','KC589025','JN638998'])

yield 'SUDV', genbank.GenBankDna(id_range=(89,92),prefix='KC5453')

yield 'TAFV', genbank.GenBankDna(ids=['FJ217162'])

#原书中需要更新的代码

#这部分代码自己也不是太明白，反正目的是将序列的名字改成自己想要的格式

def gb_to_taxon(gb,taxon_namespace):

label = species + "_" + gb.accession

taxon = taxon_namespace.require_taxon(label=label)

return taxon

taxon_namespace = dendropy.TaxonNamespace()

other = open('other.fasta','w')

for species, recs in get_other_ebolavirus_sources():

char_mat = recs.generate_char_matrix(taxon_namespace = taxon_namespace,gb_to_taxon_fn = gb_to_taxon)

print(char_mat.as_string("fasta"))

char_mat.write_to_stream(other,'fasta')

other.close()

下载所有序列用到的完整代码(小插曲：第一次试运行遇到了报错，仔细检查才发现把序列号中的数字0错看成了字母O)

import dendropy

from dendropy.interop import genbank

def get_other_ebolavirus_sources():

yield 'BDBV', genbank.GenBankDna(id_range=(3,6),prefix='KC54539')

yield 'BDBV', genbank.GenBankDna(ids=['FJ217161'])

yield 'RESTV', genbank.GenBankDna(ids=['AB050936','JX477165','JX477166','FJ621583','FJ621584','FJ621585'])

yield 'SUDV', genbank.GenBankDna(ids=['KC242783','AY729654','EU338380','JN638998','FJ968794','KC589025','JN638998'])

yield 'SUDV', genbank.GenBankDna(id_range=(89,92),prefix='KC5453')

yield 'TAFV', genbank.GenBankDna(ids=['FJ217162'])

def get_ebov_2014_sources():

yield 'EBOV_2014', genbank.GenBankDna(id_range=(233036,233118),prefix="KM")

yield 'EBOV_2014', genbank.GenBankDna(id_range=(34549,34563),prefix='KM0')

def get_other_ebov_sources():

yield 'EBOV_1976', genbank.GenBankDna(ids=['AF272001','KC242801'])

yield 'EBOV_1995', genbank.GenBankDna(ids=['KC242796','KC242799'])

yield 'EBOV_2007', genbank.GenBankDna(id_range=(84,90),prefix='KC2427')

#原书中需要更新的代码

#这部分代码自己也不是太明白，反正目的是将序列的名字改成自己想要的格式

def gb_to_taxon(gb,taxon_namespace):

label = species + "_" + gb.accession

taxon = taxon_namespace.require_taxon(label=label)

return taxon

taxon_namespace = dendropy.TaxonNamespace()

sampled = open('sample.fasta','w')

for species, recs in get_other_ebolavirus_sources():

char_mat = recs.generate_char_matrix(taxon_namespace = taxon_namespace,gb_to_taxon_fn = gb_to_taxon)

char_mat.write_to_stream(sampled,'fasta')

def gb_to_taxon1(gb,taxon_namespace):

label = "EBOV_2014_" + gb.accession

taxon = taxon_namespace.require_taxon(label=label)

return taxon

for species, recs in get_ebov_2014_sources():

char_mat = recs.generate_char_matrix(taxon_namespace = taxon_namespace,gb_to_taxon_fn = gb_to_taxon1)

char_mat.write_to_stream(sampled,'fasta')

for species, rec in get_other_ebov_sources():

char_mat = recs.generate_char_matrix(taxon_namespace = taxon_namespace,gb_to_taxon_fn = gb_to_taxon1)

char_mat.write_to_stream(sampled,'fasta')

sampled.close()

接下来可以重复比对和建树了