Python一阶马尔科夫链生成随机DNA序列实现示例

import numpy
import random
import seaborn as sns
import matplotlib.pyplot as plt

# 状态空间
states = ["A","G","C","T","E"]

# 可能的事件序列
transitionName = [["AA","AG","AC","AT","AE"],
                  ["GA","GG","GC","GT","GE"],
                  ["CA","CG","CC","CT","CE"],
                  ["TA","TG","TC","TT","TE"],]

# 概率矩阵（转移矩阵）
transitionMatrix = [[0.25,0.20,0.20,0.20,0.15],
                    [0.20,0.25,0.20,0.20,0.15],
                    [0.20,0.20,0.25,0.20,0.15],
                    [0.20,0.20,0.20,0.25,0.15]]

def RandomDNAs(Num):
    max_len = 0
    i = 0
    Seq = [] #创建列表(Seq)用于添加碱基，以组成DNA序列
    Len = [] #创建列表(Len)用于记录每条生成序列的长度
    while i != Num:
        Base = ["A","G","C","T"]
        START = random.choice(Base) #随机从碱基中选择一个作为序列的起始碱基
        Seq.append(START) #将起始碱基添加至Seq中
        while START != "E":
            if START == "A":
                change = numpy.random.choice(transitionName[0],p=transitionMatrix[0])
                #以transitionMatrix矩阵第一行的概率分布随机抽取transitionName第一行包含的事件
                if change == "AA":
                    START = "A" #如果转移状态是AA(即A碱基接下来的碱基是A，则将起始碱基设为A)
                elif change == "AG":
                    START = "G"
                elif change == "AC":
                    START = "C"
                elif change == "AT":
                    START = "T"
                elif change == "AE":
                    START = "E"
            elif START == "G":
                change = numpy.random.choice(transitionName[1],p=transitionMatrix[1])
                if change == "GA":
                    START = "A"
                elif change == "GG":
                    START = "G"
                elif change == "GC":
                    START = "C"
                elif change == "GT":
                    START = "T"
                elif change == "GE":
                    START = "E"
            elif START == "C":
                change = numpy.random.choice(transitionName[2],p=transitionMatrix[2])
                if change == "CA":
                    START = "A"
                elif change == "CG":
                    START = "G"
                elif change == "CC":
                    START = "C"
                elif change == "CT":
                    START = "T"
                elif change == "CE":
                    START = "E"
            elif START == "T":
                change = numpy.random.choice(transitionName[3],p=transitionMatrix[3])
                if change == "TA":
                    START = "A"
                elif change == "TG":
                    START = "G"
                elif change == "TC":
                    START = "C"
                elif change == "TT":
                    START = "T"
                elif change == "TE":
                    START = "E"
            if START != "E":
                Seq.append(START) #如果状态转移后不为End(E)，则将转移后的碱基加到Seq序列中
        i += 1
        Len.append(len(Seq))
        if len(Seq) > max_len:
            max_len = len(Seq)
        #print(''.join(Seq))
        Seq.clear()
    plt.hist(numpy.array(Len), bins=max_len, edgecolor="white")
    # 显示横轴标签
    plt.xlabel("DNA Sequence Length")
    # 显示纵轴标签
    plt.ylabel("Frequency")
    # 显示图标题
    plt.title("Histogram of frequency distribution of DNA sequence length")
    plt.show()
    print("DNA序列的最大长度为:",max_len)
    print("DNA序列长度的众数为:",max(Len, key=Len.count))

%matplotlib notebook #若未使用Jupyter Notebook，此句不需要
RandomDNAs(1000) #1000表示随机生成1000条序列