pyBigWig是用C编写的调用libBigWig库的python扩展，可以快速访问和处理bigBed和bigWig文件。

# 依赖

• libcurl
• zlib

# 安装

pip install pyBigWig

# 调用

import pyBigWig

# 读取文件

#本地文件
bw = pyBigWig.open("test/test.bw")
#远程文件
bb = pyBigWig.open("https://www.encodeproject.org/files/ENCFF001JBR/@@download/ENCFF001JBR.bigBed")
#写入文件
bw = pyBigWig.open("test/output.bw", "w")

# 查看文件类型

>>> bw = pyBigWig.open("test/test.bw")
>>> bw.isBigWig()
True
>>> bw.isBigBed()
False

# 查看染色体和长度

>>> bw.chroms()
dict_proxy({'1': 195471971L, '10': 130694993L})
>>> bw.chroms("1")
195471971L

# 查看header

>>> bw.header()
{'maxVal': 2L, 'sumData': 272L, 'minVal': 0L, 'version': 4L, 'sumSquared': 500L, 'nLevels': 1L, 'nBasesCovered': 154L}

# 查看一段位置的总结

min，coverage，std

#输出范围的均值
>>> bw.stats("1", 0, 3)
[0.2000000054637591]
>>> bw.stats("1", 0, 3, type="max")
[0.30000001192092896]
>>> bw.stats("1",99, 200, type="max", nBins=2)
[1.399999976158142, 1.5]
#未设置起始终止位置，输出整条染色体信息
>>> bw.stats("1")
[1.3351851569281683]

# 一段位置内单碱基的值

使用stats() nBins设置为位置范围内碱基总数也可以实现此功能。

>>> bw.values("1", 0, 3)
[0.10000000149011612, 0.20000000298023224, 0.30000001192092896]
>>> bw.values("1", 0, 4)
[0.10000000149011612, 0.20000000298023224, 0.30000001192092896, nan]

# Retrieve all intervals in a range

>>> bw.intervals("1", 0, 3)
((0, 1, 0.10000000149011612), (1, 2, 0.20000000298023224), (2, 3, 0.30000001192092896))
#返回值组成为起始位置，终止位置，值

# Retrieving bigBed entries

>>> bb = pyBigWig.open("https://www.encodeproject.org/files/ENCFF001JBR/@@download/ENCFF001JBR.bigBed")
>>> bb.entries('chr1', 10000000, 10020000)
[(10009333, 10009640, '61035\t130\t-\t0.026\t0.42\t404'), (10014007, 10014289, '61047\t136\t-\t0.029\t0.42\t404'), (10014373, 10024307, '61048\t630\t-\t5.420\t0.00\t2672399')]
#返回起始位置，终止位置，其他信息组成的string

>>> bb.SQL()
table RnaElements
"BED6 + 3 scores for RNA Elements data"
    (
    string chrom;      "Reference sequence chromosome or scaffold"
    uint   chromStart; "Start position in chromosome"
    uint   chromEnd;   "End position in chromosome"
    string name;       "Name of item"
    uint   score;      "Normalized score from 0-1000"
    char[1] strand;    "+ or - or . for unknown"
    float level;       "Expression level such as RPKM or FPKM. Set to -1 for no data."
    float signif;      "Statistical significance such as IDR. Set to -1 for no data."
    uint score2;       "Additional measurement/count e.g. number of reads. Set to 0 for no data."
    )

# Add a header to a bigWig file

>>> bw.addHeader([("chr1", 1000000), ("chr2", 1500000)])
>>> bw.addHeader([("chr1", 1000000), ("chr2", 1500000)], maxZooms=0)

#Adding entries to a bigWig file

>>> bw.addEntries(["chr1", "chr1", "chr1"], [0, 100, 125], ends=[5, 120, 126], values=[0.0, 1.0, 200.0])
>>> bw.addEntries("chr1", [500, 600, 635], values=[-2.0, 150.0, 25.0], span=20)
>>> bw.addEntries("chr1", 900, values=[-5.0, -20.0, 25.0], span=20, step=30)
>>> bw.addEntries(["chr1", "chr1", "chr1"], [100, 0, 125], ends=[120, 5, 126], values=[0.0, 1.0, 200.0], validate=False)

# Close a bigWig or bigBed file

bw.close()

# pyBigWig支持Numpy数据类型输入

• 检查是否当前版本pyBigWig是否支持Numpy数据类型输入

>>> import pyBigWig
>>> pyBigWig.numpy
1
# 1表示支持

>>> import pyBigWig
>>> import numpy
>>> bw = pyBigWig.open("/tmp/delete.bw", "w")
>>> bw.addHeader([("1", 1000)], maxZooms=0)
>>> chroms = np.array(["1"] * 10)
>>> starts = np.array([0, 10, 20, 30, 40, 50, 60, 70, 80, 90], dtype=np.int64)
>>> ends = np.array([5, 15, 25, 35, 45, 55, 65, 75, 85, 95], dtype=np.int64)
>>> values0 = np.array(np.random.random_sample(10), dtype=np.float64)
>>> bw.addEntries(chroms, starts, ends=ends, values=values0)
>>> bw.close()

• values()直接返回numpy 对象

>>> bw = bw.open("/tmp/delete.bw")
>>> bw.values('1', 0, 10, numpy=True)
[ 0.74336642  0.74336642  0.74336642  0.74336642  0.74336642         nan
     nan         nan         nan         nan]
>>> type(bw.values('1', 0, 10, numpy=True))
<type 'numpy.ndarray'>

#原文

Github pyBigWig