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A package for accessing bigWig files using libBigWig

Project Description
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# pyBigWig
A python extension, written in C, for quick access to bigBed files and access to and creation of bigWig files. This extension uses [libBigWig]( for local and remote file access.

Table of Contents

* [Installation](#installation)
* [Usage](#usage)
* [Load the extension](#load-the-extension)
* [Open a bigWig or bigBed file](#open-a-bigwig-or-bigbed-file)
* [Determining the file type](#determining-the-file-type)
* [Access the list of chromosomes and their lengths](#access-the-list-of-chromosomes-and-their-lengths)
* [Print the header](#print-the-header)
* [Compute summary information on a range](#compute-summary-information-on-a-range)
* [A note on statistics and zoom levels](#a-note-on-statistics-and-zoom-levels)
* [Retrieve values for individual bases in a range](#retrieve-values-for-individual-bases-in-a-range)
* [Retrieve all intervals in a range](#retrieve-all-intervals-in-a-range)
* [Retrieving bigBed entries](#retrieving-bigbed-entries)
* [Add a header to a bigWig file](#add-a-header-to-a-bigwig-file)
* [Adding entries to a bigWig file](#adding-entries-to-a-bigwig-file)
* [Close a bigWig or bigBed file](#close-a-bigwig-or-bigbed-file)
* [Numpy](#numpy)
* [Remote file access](#remote-file-access)
* [A note on coordinates](#a-note-on-coordinates)
* [Galaxy](#galaxy)

# Installation
You can install this extension directly from github with:

pip install pyBigWig

or with conda

conda install pybigwig -c bioconda

Note that libcurl (and the `curl-config` command) are required for installation. This is typically already installed on many Linux and OSX systems (if you install with conda then this will happen automatically).

# Usage
Basic usage is as follows:

## Load the extension

>>> import pyBigWig

## Open a bigWig or bigBed file

This will work if your working directory is the pyBigWig source code directory.

>>> bw ="test/")

Note that if the file doesn't exist you'll see an error message and `None` will be returned. Be default, all files are opened for reading and not writing. You can alter this by passing a mode containing `w`:

>>> bw ="test/", "w")

Note that a file opened for writing can't be queried for its intervals or statistics, it can *only* be written to. If you open a file for writing then you will next need to add a header (see the section on this below).

Local and remote bigBed read access is also supported:

>>> bb ="")

While you can specify a mode for bigBed files, it is ignored. The object returned by `` is the same regardless of whether you're opening a bigWig or bigBed file.

## Determining the file type

Since bigWig and bigBed files can both be opened, it may be necessary to determine whether a given `bigWigFile` object points to a bigWig or bigBed file. To that end, one can use the `isBigWig()` and `isBigBed()` functions:

>>> bw ="test/")
>>> bw.isBigWig()
>>> bw.isBigBed()

## Access the list of chromosomes and their lengths

`bigWigFile` objects contain a dictionary holding the chromosome lengths, which can be accessed with the `chroms()` accessor.

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

You can also directly query a particular chromosome.

>>> bw.chroms("1")

The lengths are stored a the "long" integer type, which is why there's an `L` suffix. If you specify a non-existant chromosome then nothing is output.

>>> bw.chroms("c")

## Print the header

It's sometimes useful to print a bigWig's header. This is presented here as a python dictionary containing: the version (typically `4`), the number of zoom levels (`nLevels`), the number of bases described (`nBasesCovered`), the minimum value (`minVal`), the maximum value (`maxVal`), the sum of all values (`sumData`), and the sum of all squared values (`sumSquared`). The last two of these are needed for determining the mean and standard deviation.

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

Note that this is also possible for bigBed files and the same dictionary keys will be present. Entries such as `maxVal`, `sumData`, `minVal`, and `sumSquared` are then largely not meaningful.

## Compute summary information on a range

bigWig files are used to store values associated with positions and ranges of them. Typically we want to quickly access the average value over a range, which is very simple:

>>> bw.stats("1", 0, 3)

Suppose instead of the mean value, we instead wanted the maximum value:

>>> bw.stats("1", 0, 3, type="max")

Other options are "min" (the minimum value), "coverage" (the fraction of bases covered), and "std" (the standard deviation of the values).

It's often the case that we would instead like to compute values of some number of evenly spaced bins in a given interval, which is also simple:

>>> bw.stats("1",99, 200, type="max", nBins=2)
[1.399999976158142, 1.5]

`nBins` defaults to 1, just as `type` defaults to `mean`.

If the start and end positions are omitted then the entire chromosome is used:

>>> bw.stats("1")

### A note on statistics and zoom levels

> A note to the lay reader: This section is rather technical and included only for the sake of completeness. The summary is that if your needs require exact mean/max/etc. summary values for an interval or intervals and that a small trade-off in speed is acceptable, that you should use the `exact=True` option in the `stats()` function.

By default, there are some unintuitive aspects to computing statistics on ranges in a bigWig file. The bigWig format was originally created in the context of genome browsers. There, computing exact summary statistics for a given interval is less important than quickly being able to compute an approximate statistic (after all, browsers need to be able to quickly display a number of contiguous intervals and support scrolling/zooming). Because of this, bigWig files contain not only interval-value associations, but also `sum of values`/`sum of squared values`/`minimum value`/`maximum value`/`number of bases covered` for equally sized bins of various sizes. These different sizes are referred to as "zoom levels". The smallest zoom level has bins that are 16 times the mean interval size in the file and each subsequent zoom level has bins 4 times larger than the previous. This methodology is used in Kent's tools and, therefore, likely used in almost every currently existing bigWig file.

When a bigWig file is queried for a summary statistic, the size of the interval is used to determine whether to use a zoom level and, if so, which one. The optimal zoom level is that which has the largest bins no more than half the width of the desired interval. If no such zoom level exists, the original intervals are instead used for the calculation.

For the sake of consistency with other tools, pyBigWig adopts this same methodology. However, since this is (A) unintuitive and (B) undesirable in some applications, pyBigWig enables computation of exact summary statistics regardless of the interval size (i.e., it allows ignoring the zoom levels). This was originally proposed [here]( and an example is below:

>>> import pyBigWig
>>> from numpy import mean
>>> bw ="")
>>> bw.stats('chr1', 89294, 91629)
>>> mean(bw.values('chr1', 89294, 91629))
>>> bw.stats('chr1', 89294, 91629, exact=True)
Additionally, `values()` can directly output a numpy vector:

>>> bw ="

## Retrieve values for individual bases in a range

While the `stats()` method **can** be used to retrieve the original values for each base (e.g., by setting `nBins` to the number of bases), it's preferable to instead use the `values()` accessor.

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

The list produced will always contain one value for every base in the range specified. If a particular base has no associated value in the bigWig file then the returned value will be `nan`.

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

## Retrieve all intervals in a range

Sometimes it's convenient to retrieve all entries overlapping some range. This can be done with the `intervals()` function:

>>> bw.intervals("1", 0, 3)
((0, 1, 0.10000000149011612), (1, 2, 0.20000000298023224), (2, 3, 0.30000001192092896))

What's returned is a list of tuples containing: the start position, end end position, and the value. Thus, the example above has values of `0.1`, `0.2`, and `0.3` at positions `0`, `1`, and `2`, respectively.

If the start and end position are omitted then all intervals on the chromosome specified are returned:

>>> bw.intervals("1")
((0, 1, 0.10000000149011612), (1, 2, 0.20000000298023224), (2, 3, 0.30000001192092896), (100, 150, 1.399999976158142), (150, 151, 1.5))

## Retrieving bigBed entries

As opposed to bigWig files, bigBed files hold entries, which are intervals with an associated string. You can access these entries using the `entries()` function:

>>> bb ="")
>>> 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')]

The output is a list of entry tuples. The tuple elements are the `start` and `end` position of each entry, followed by its associated `string`. The string is returned exactly as it's held in the bigBed file, so parsing it is left to you. To determine what the various fields are in these string, consult the SQL 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."

Note that the first three entries in the SQL string are not part of the string.

If you only need to know where entries are and not their associated values, you can save memory by additionally specifying `withString=False` in `entries()`:

>>> bb.entries('chr1', 10000000, 10020000, withString=False)
[(10009333, 10009640), (10014007, 10014289), (10014373, 10024307)]

## Add a header to a bigWig file

If you've opened a file for writing then you'll need to give it a header before you can add any entries. The header contains all of the chromosomes, **in order**, and their sizes. If your chromosome has two chromosomes, chr1 and chr2, of lengths 1 and 1.5 million bases, then the following would add an appropriate header:

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

bigWig headers are case-sensitive, so `chr1` and `Chr1` are different. Likewise, `1` and `chr1` are not the same, so you can't mix Ensembl and UCSC chromosome names. After adding a header, you can then add entries.

By default, up to 10 "zoom levels" are constructed for bigWig files. You can change this default number with the `maxZooms` optional argument. A common use of this is to create a bigWig file that simply holds intervals and no zoom levels:

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

## Adding entries to a bigWig file

Assuming you've opened a file for writing and added a header, you can then add entries. Note that the entries **must** be added in order, as bigWig files always contain ordered intervals. There are three formats that bigWig files can use internally to store entries. The most commonly observed format is identical to a [bedGraph]( file:

chr1 0 100 0.0
chr1 100 120 1.0
chr1 125 126 200.0

These entries would be added as follows:

>>> bw.addEntries(["chr1", "chr1", "chr1"], [0, 100, 125], ends=[5, 120, 126], values=[0.0, 1.0, 200.0])

Each entry occupies 12 bytes before compression.

The second format uses a fixed span, but a variable step size between entries. These can be represented in a [wiggle]( file as:

variableStep chrom=chr1 span=20
500 -2.0
600 150.0
635 25.0

The above entries describe (1-based) positions 501-520, 601-620 and 636-655. These would be added as follows:

>>> bw.addEntries("chr1", [500, 600, 635], values=[-2.0, 150.0, 25.0], span=20)

Each entry of this type occupies 8 bytes before compression.

The final format uses a fixed step and span for each entry, corresponding to the fixedStep [wiggle format](

fixedStep chrom=chr1 step=30 span=20

The above entries describe (1-based) bases 901-920, 931-950 and 961-980 and would be added as follows:

>>> bw.addEntries("chr1", 900, values=[-5.0, -20.0, 25.0], span=20, step=30)

Each entry of this type occupies 4 bytes.

Note that pyBigWig will try to prevent you from adding entries in an incorrect order. This, however, requires additional over-head. Should that not be acceptable, you can simply specify `validate=False` when adding entries:

>>> bw.addEntries(["chr1", "chr1", "chr1"], [100, 0, 125], ends=[120, 5, 126], values=[0.0, 1.0, 200.0], validate=False)

You're obviously then responsible for ensuring that you **do not** add entries out of order. The resulting files would otherwise largley not be usable.

## Close a bigWig or bigBed file

A file can be closed with a simple `bw.close()`, as is commonly done with other file types. For files opened for writing, closing a file writes any buffered entries to disk, constructs and writes the file index, and constructs zoom levels. Consequently, this can take a bit of time.

# Numpy

As of version 0.3.0, pyBigWig supports input of coordinates using numpy integers and vectors in some functions **if numpy was installed prior to installing pyBigWig**. To determine if pyBigWig was installed with numpy support by checking the `numpy` accessor:

>>> import pyBigWig
>>> pyBigWig.numpy

If `pyBigWig.numpy` is `1`, then pyBigWig was compiled with numpy support. This means that `addEntries()` can accept numpy coordinates:

>>> import pyBigWig
>>> import numpy
>>> bw ="/tmp/", "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()

Additionally, `values()` can directly output a numpy vector:

>>> bw ="/tmp/")
>>> 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'>

# Remote file access

If you do not have curl installed, pyBigWig will be installed without the ability to access remote files. You can determine if you will be able to access remote files with `pyBigWig.remote`. If that returns 1, then you can access remote files. If it returns 0 then you can't.

# A note on coordinates

Wiggle, bigWig, and bigBed files use 0-based half-open coordinates, which are also used by this extension. So to access the value for the first base on `chr1`, one would specify the starting position as `0` and the end position as `1`. Similarly, bases 100 to 115 would have a start of `99` and an end of `115`. This is simply for the sake of consistency with the underlying bigWig file and may change in the future.

# Galaxy

pyBigWig is also available as a package in [Galaxy]( You can find it in the toolshed and the [IUC]( is currently hosting the XML definition of this on [github](
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