批量从bam文件获取指定位置的碱基

批量从bam文件获取指定位置(RS.xlsx)的碱基后输出到一个excel，按照编号进行排序

#!/user/bin/python3
#coding:utf-8
 
import pandas as pd
import pysam
import os,glob
 
 
hetRatioCutoff=0.35
primaryRatioCutoff=0.90
baseRefL=['A', 'C', 'T', 'G']
sampleGT={}
 
 
def getGT(bam, chr_, pos_):
	bamfile = pysam.AlignmentFile(bam, "rb")
	depth={}
	totalDepth=0
	for pileupcolumn in bamfile.pileup(chr_, pos_-1, pos_+1):
		if pileupcolumn.reference_pos+1
			continue
		elif pileupcolumn.reference_pos+1>pos_:
			break
		for pileupread in pileupcolumn.pileups:
			if not pileupread.is_del and not pileupread.is_refskip:
				base=pileupread.alignment.query_sequence[pileupread.query_position].upper()
				depth[base]=1+depth[base] if base in depth else 1
				totalDepth+=1
	
	if totalDepth<=10: return 'NA'	  #低于10x时为No Call
	depth=dict(sorted(depth.items(), key=lambda item:item[1], reverse=True))
	baseL=list(depth.keys())
	faL=[float(depth[i])/totalDepth for i in depth]
	if len(baseL)==1:
		return baseL[0]+baseL[0]
	else:		
		if max(faL)>=primaryRatioCutoff:
			return baseL[faL.index(max(faL))]+baseL[faL.index(max(faL))]
		elif max(faL)>= hetRatioCutoff and max(faL) <=primaryRatioCutoff:
			faL_sort = sorted(faL,reverse=True)
			primary=faL_sort[0]+faL_sort[1]
			if primary>= primaryRatioCutoff:
				return baseL[faL.index(faL_sort[1])]+baseL[faL.index(faL_sort[0])]  # 获取深度最大和第二大的碱基
			else:
				return 'NA'
		else:
			return 'NA'
 
 
# read position
df = pd.read_excel('RS.xlsx',header = 0,encoding = 'utf-8')
df2 = []
def output(bamname):	
	barcode  = os.path.splitext(bamname)[0]
	outfilename	= barcode +'.xlsx'
	df1=df.to_dict(orient='records')
	for i in range(len(df1)):	
		chr_ = df.iloc[i]['chr']
		pos_ = int(df.iloc[i]['pos'])
		rs = df.iloc[i]['rs']
		sampleGT[rs]=getGT(bamname, chr_, pos_)
		gt = 'gt_'+ barcode
		genetype = 'genetype_'+ barcode
		df1[i][gt] = sampleGT[rs]
		gt_list =  list(df1[i][gt])
		if len(set(gt_list))== 1:	
			if ''.join(set(gt_list)) == df.iloc[i]['ref']:
				df1[i][genetype]= "野生型" 
			else:
				df1[i][genetype] = "纯合子" 
		else:
			df1[i][genetype] = "杂合子"
	
 
	outDf=pd.DataFrame(df1)
	tempdf = outDf.loc[:,['rs',gt,genetype]]
	df2.append(tempdf)
			
	#outDf.to_excel(outfilename, index=False, columns=['rs','chr','pos','ref','alt',gt,genetype], encoding='utf-8')
 
# 批量读取bam文件的前缀
bamlist = glob.glob('*.bam')
for bam in bamlist:
	output(bam)
	
df3 = pd.concat(df2,axis=1, join='inner',sort = False) #concat合并可以是多个表作为一个list,merge合并是两个表
df3 = df3.drop_duplicates().T.drop_duplicates().T  # 去掉重复列'rs'
outDf_result = pd.merge(df,df3,how='left',on='rs')
outDf_result.to_excel('output.xlsx', index=False, encoding='utf-8')

以上办法是用pysam包处理，对于indel，质量不好等不一定准确，最好的还是用专业的软件处理，bcftools 和gatk。bacftools call snp后输出vcf文件后，再使用bacftools query -f命令处理vcf文件。

此处的test文件就是指定的位置，获取多个bam文件中这些位置的碱基

bcftools mpileup -R test -f /media/gsadmin/vd2/tmp/database/GATK/library/hg19/ucsc.hg19.fasta -q 10 -Q 10 --ff UNMAP -Ou *.bam | bcftools call -m >variants.vcf 

bcftools call  -mv表示call为变异的位置碱基，如果不加v，就可以call出未变异的。