结核分枝杆菌血清型分析代码

结核分枝杆菌血清型分析常常需要基于分子生物学方法进行，因此需要一些相应的代码来完成分析。以下是一些常用的代码示例：

1. 16S rRNA基因序列分析：

from Bio import SeqIO
from Bio.Seq import Seq
from Bio.Alphabet import generic_dna
import pandas as pd

# 读取fasta文件
records = SeqIO.parse("mycobacterium.fasta", "fasta")

# 将16S rRNA序列扩增
for record in records:
    if "16S rRNA" in record.description:
        seq = Seq(str(record.seq), generic_dna)
        start = seq.find("AGAGTTTGATCCTGGCTCAG") + 1
        end = seq.find("TTATTGCTTCCCCCGGA") + 15
        seq = seq[start:end]
        print(record.id, seq)

# 将16S rRNA序列保存到csv文件中
df = pd.DataFrame(columns=["id", "16S rRNA"])
for record in records:
    if "16S rRNA" in record.description:
        seq = Seq(str(record.seq), generic_dna)
        start = seq.find("AGAGTTTGATCCTGGCTCAG") + 1
        end = seq.find("TTATTGCTTCCCCCGGA") + 15
        seq = seq[start:end]
        df = df.append({"id": record.id, "16S rRNA": seq}, ignore_index=True)
df.to_csv("16S rRNA.csv", index=False)

2. IS6110扩增及限制性片段长度多态性分析 (RFLP)：

from Bio import SeqIO
from Bio.Seq import Seq
from Bio.Alphabet import generic_dna
import pandas as pd

# 读取fasta文件
records = SeqIO.parse("mycobacterium.fasta", "fasta")

# 将IS6110序列扩增，并进行限制性酶切
for record in records:
    if "IS6110" in record.description:
        seq = Seq(str(record.seq), generic_dna)
        start = seq.find("GGGTGCGGTGCTGGATCACCTCCT") + 1
        end = seq.find("CGAGGGCACGTCGTTTGGGTGAGGTG") + 24
        seq = seq[start:end]
        print(record.id, seq)
        # 进行限制性酶切
        from Bio.Restriction import EcoRI
        fragments = EcoRI.catalyze(seq)
        print(fragments)

# 将IS6110序列及其限制性酶切片段保存到csv文件中
df = pd.DataFrame(columns=["id", "IS6110", "Fragments"])
for record in records:
    if "IS6110" in record.description:
        seq = Seq(str(record.seq), generic_dna)
        start = seq.find("GGGTGCGGTGCTGGATCACCTCCT") + 1
        end = seq.find("CGAGGGCACGTCGTTTGGGTGAGGTG") + 24
        seq = seq[start:end]
        fragments = EcoRI.catalyze(seq)
        df = df.append({"id": record.id, "IS6110": seq, "Fragments": fragments}, ignore_index=True)
df.to_csv("IS6110.csv", index=False)

3. 多重位点变异分析 (MLVA)：

import pandas as pd
import numpy as np

# 读取csv文件
df = pd.read_csv("MLVA.csv")

# 计算每个位点的变异
df["Locus 1"] = df["Locus 1"].apply(lambda x: len(x))
df["Locus 2"] = df["Locus 2"].apply(lambda x: len(x))
df["Locus 3"] = df["Locus 3"].apply(lambda x: len(x))
df["Locus 4"] = df["Locus 4"].apply(lambda x: len(x))
df["Locus 5"] = df["Locus 5"].apply(lambda x: len(x))
df["Locus 6"] = df["Locus 6"].apply(lambda x: len(x))

# 计算每个样本的MLVA类型及其频率
df["MLVA Type"] = df.apply(lambda x: "".join([str(x[i]) for i in range(1, 7)]), axis=1)
counts = df["MLVA Type"].value_counts().to_dict()
df["Frequency"] = df["MLVA Type"].apply(lambda x: counts[x])

# 计算每个位点的Polymorphic Information Content (PIC)
for i in range(1, 7):
    p = df["Locus " + str(i)].value_counts(normalize=True).to_dict()
    df["PIC " + str(i)] = df["Locus " + str(i)].apply(lambda x: 1 - sum([p[xi]**2 for xi in set(df["Locus " + str(i)])]))

# 保存结果到csv文件中
df.to_csv("MLVA Analysis.csv", index=False)

这些代码只是基于Python语言的示例，实际应用中可能需要根据实际情况进行修改。同时，还需要注意数据的来源和质量，以确保分析结果的准确性和可靠性。