# pybbi #
[](https://zenodo.org/doi/10.5281/zenodo.10382980)
Python interface to Jim Kent's Big Binary Indexed file (BBI) \[[1](#ref1)\] library from the [UCSC Genome Browser source tree](https://github.com/ucscGenomeBrowser/kent) using Cython.
This provides read-level access to local and remote bigWig and bigBed files but no write capabilitites. The main feature is fast retrieval of range queries into numpy arrays.
## Installation ##
Wheels for `pybbi` are available on PyPI for Python 3.8, 3.9, 3.10, 3.11 on Linux (x86_64 and aarch64) and Mac OSX (x86_64/Intel). Apple Silicon (arm64) wheels will be made available once M1 runners are available in GitHub Actions.
```
$ pip install pybbi
```
## API ##
The `bbi.open` function returns a `BBIFile` object.
```
bbi.open(path) -> BBIFile
```
`path` can be a local file path (bigWig or bigBed) or a URL. `BBIFile` objects are context managers and can be used in a `with` statement to clean up resources without calling `BBIFile.close()`.
```python
>>> with bbi.open('bigWigExample.bw') as f:
... x = f.fetch('chr21', 1000000, 2000000, bins=40)
```
### Introspection
```
BBIFile.is_bigwig -> bool
BBIFile.is_bigbed -> bool
BBIFile.chromsizes -> OrderedDict
BBIFile.zooms -> list
BBIFile.info -> dict
BBIFile.schema -> dict
BBIFile.read_autosql() -> str
```
Note: `BBIFile.schema['dtypes']` provides numpy data types for the fields in a bigWig or bigBed (matched from the autoSql definition).
### Interval output
The actual interval records in a bigWig or bigBed can be retrieved as a pandas dataframe or as an iterator over records as tuples. The pandas output is parsed according to the file's schema.
```
BBIFile.fetch_intervals(chrom, start, end) -> pandas.DataFrame
BBIFile.fetch_intervals(chrom, start, end, iterator=True) -> interval iterator
```
Summary bin records at each zoom level are also accessible.
```
BBIFile.fetch_summaries(chrom, start, end, zoom) -> pandas.DataFrame
```
### Array output
Retrieve quantitative signal as an array. The signal of a bigWig file is obtained from its "value" field. The signal of a bigBed file is obtained from the genomic coverage of its intervals.
For a single range query:
```
BBIFile.fetch(chrom, start, end, [bins [, missing [, oob, [, summary]]]]) -> 1D numpy array
```
To produce a stacked heatmap from a list of (1) equal-length intervals or (2) arbitrary-length intervals with `bins` specified:
```
BBIFile.stackup(chroms, starts, ends, [bins [, missing [, oob, [, summary]]]]) -> 2D numpy array
```
* **Summary** querying is supported by specifying the number of `bins` for coarsening. The `summary` statistic can be one of: 'mean', 'min', 'max', 'cov', 'std', 'or 'sum'. (default = 'mean'). Intervals need not have the same length, in which case the data from each interval will be interpolated to the same number of bins (e.g., gene bodies).
* **Missing** data can be filled with a custom fill value, `missing` (default = 0).
* **Out-of-bounds** ranges (i.e. `start` less than zero or `end` greater than the chromosome length) are permitted because of their utility e.g., for generating vertical heatmap stacks centered at specific genomic features. A separate custom fill value, `oob` can be provided for out-of-bounds positions (default = NaN).
### Function API
The original function-based API is still available:
```python
bbi.is_bbi(path: str) -> bool
bbi.is_bigwig(path: str) -> bool
bbi.is_bigbed(path:str) -> bool
bbi.chromsizes(path: str) -> OrderedDict
bbi.zooms(path: str) -> list
bbi.info(path: str) -> dict
bbi.fetch_intervals(path: str, chrom: str, start: int, end: int, iterator: bool) -> Union[Iterable, pd.DataFrame]
bbi.fetch(path: str, chrom: str, start: int, end: int, [bins: int [, missing: float [, oob: float, [, summary: str]]]]) -> np.array[1, 'float64']
bbi.stackup(path: str, chroms: np.array, starts: np.array, ends: np.array, [bins: int [, missing: float [, oob: float, [, summary: str]]]]) -> np.array[2, 'float64']
```
See the docstrings for complete documentation.
## Related projects ##
- [bigtools](https://github.com/jackh726/bigtools): Alternative BBI library written in Rust with Python bindings; by Jack Huey
- [libBigWig](https://github.com/dpryan79/libBigWig): Alternative BBI library written in C; by Devon Ryan
- [pyBigWig](https://github.com/dpryan79/pyBigWig): Python bindings for `libBigWig`
- [bw-python](https://github.com/brentp/bw-python): Alternative Python wrapper to `libBigWig` by Brent Pederson
- [bx-python](https://github.com/bxlab/bx-python): Python bioinformatics library from James Taylor's group that includes tools for bbi files.
This library provides bindings to the reference UCSC bbi library code. Check out [@dpryan79](https://github.com/dpryan79)'s [libBigWig](https://github.com/dpryan79/libBigWig) for an alternative and dedicated C library for big binary files. pyBigWig also provides numpy-based retrieval and bigBed support.
## References ##
<a id="ref1">[1]</a>: http://bioinformatics.oxfordjournals.org/content/26/17/2204.full
## From source ##
If wheels for your platform or Python version aren't available or you want to develop, you'll need to install `pybbi` from source. The source distribution on PyPI ships with (slightly modified) kent utils source, which will compile before the extension module is built.
Requires
- Platform: Linux or Darwin (Windows Subsystem for Linux seems to work too)
- pthreads, zlib, libpng, openssl, make, pkg-config
- Python 3.6+
- `numpy` and `cython`
For example, on a fresh Ubuntu instance, you'll need `build-essential`, `make`, `pkg-config`, `zlib1g-dev`, `libssl-dev`, `libpng16-dev`.
On a Centos/RedHat (rpm) system you'll need `gcc`, `make`, `pkg-config`, `zlib-devel`, `openssl-devel`, `libpng-devel`.
On a Mac, you'll need Xcode and to `brew install pkg-config openssl libpng`.
For development, clone the repo and install in editable mode:
```
$ git clone https://github.com/nvictus/pybbi.git
$ cd pybbi
$ pip install -e .
```
You can use the `ARCH` environment variable to specify a target architecture or `ARCHFLAGS` on a Mac.
### Notes
Unfortunately, Kent's C source is not well-behaved library code, as it is littered with error calls that call `exit()`. `pybbi` will catch and pre-empt common input errors, but if somehow an internal error does get raised, it will terminate your interpreter instance.
Raw data
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"description": "# pybbi #\n\n\n[](https://zenodo.org/doi/10.5281/zenodo.10382980)\n\nPython interface to Jim Kent's Big Binary Indexed file (BBI) \\[[1](#ref1)\\] library from the [UCSC Genome Browser source tree](https://github.com/ucscGenomeBrowser/kent) using Cython.\n\nThis provides read-level access to local and remote bigWig and bigBed files but no write capabilitites. The main feature is fast retrieval of range queries into numpy arrays.\n\n\n## Installation ##\n\nWheels for `pybbi` are available on PyPI for Python 3.8, 3.9, 3.10, 3.11 on Linux (x86_64 and aarch64) and Mac OSX (x86_64/Intel). Apple Silicon (arm64) wheels will be made available once M1 runners are available in GitHub Actions.\n\n```\n$ pip install pybbi\n```\n\n## API ##\n\nThe `bbi.open` function returns a `BBIFile` object.\n\n```\nbbi.open(path) -> BBIFile\n```\n\n`path` can be a local file path (bigWig or bigBed) or a URL. `BBIFile` objects are context managers and can be used in a `with` statement to clean up resources without calling `BBIFile.close()`.\n\n```python\n>>> with bbi.open('bigWigExample.bw') as f:\n... x = f.fetch('chr21', 1000000, 2000000, bins=40)\n```\n\n### Introspection\n\n```\nBBIFile.is_bigwig -> bool\nBBIFile.is_bigbed -> bool\nBBIFile.chromsizes -> OrderedDict\nBBIFile.zooms -> list\nBBIFile.info -> dict\nBBIFile.schema -> dict\nBBIFile.read_autosql() -> str\n```\n\nNote: `BBIFile.schema['dtypes']` provides numpy data types for the fields in a bigWig or bigBed (matched from the autoSql definition).\n\n\n### Interval output\n\nThe actual interval records in a bigWig or bigBed can be retrieved as a pandas dataframe or as an iterator over records as tuples. The pandas output is parsed according to the file's schema.\n\n```\nBBIFile.fetch_intervals(chrom, start, end) -> pandas.DataFrame\nBBIFile.fetch_intervals(chrom, start, end, iterator=True) -> interval iterator\n```\n\nSummary bin records at each zoom level are also accessible.\n\n```\nBBIFile.fetch_summaries(chrom, start, end, zoom) -> pandas.DataFrame\n```\n\n### Array output\n\nRetrieve quantitative signal as an array. The signal of a bigWig file is obtained from its \"value\" field. The signal of a bigBed file is obtained from the genomic coverage of its intervals.\n\nFor a single range query:\n```\nBBIFile.fetch(chrom, start, end, [bins [, missing [, oob, [, summary]]]]) -> 1D numpy array\n```\n\nTo produce a stacked heatmap from a list of (1) equal-length intervals or (2) arbitrary-length intervals with `bins` specified:\n```\nBBIFile.stackup(chroms, starts, ends, [bins [, missing [, oob, [, summary]]]]) -> 2D numpy array\n```\n\n* **Summary** querying is supported by specifying the number of `bins` for coarsening. The `summary` statistic can be one of: 'mean', 'min', 'max', 'cov', 'std', 'or 'sum'. (default = 'mean'). Intervals need not have the same length, in which case the data from each interval will be interpolated to the same number of bins (e.g., gene bodies).\n\n* **Missing** data can be filled with a custom fill value, `missing` (default = 0). \n\n* **Out-of-bounds** ranges (i.e. `start` less than zero or `end` greater than the chromosome length) are permitted because of their utility e.g., for generating vertical heatmap stacks centered at specific genomic features. A separate custom fill value, `oob` can be provided for out-of-bounds positions (default = NaN).\n\n### Function API\n\nThe original function-based API is still available:\n\n```python\nbbi.is_bbi(path: str) -> bool\nbbi.is_bigwig(path: str) -> bool\nbbi.is_bigbed(path:str) -> bool\nbbi.chromsizes(path: str) -> OrderedDict\nbbi.zooms(path: str) -> list\nbbi.info(path: str) -> dict\nbbi.fetch_intervals(path: str, chrom: str, start: int, end: int, iterator: bool) -> Union[Iterable, pd.DataFrame]\nbbi.fetch(path: str, chrom: str, start: int, end: int, [bins: int [, missing: float [, oob: float, [, summary: str]]]]) -> np.array[1, 'float64']\nbbi.stackup(path: str, chroms: np.array, starts: np.array, ends: np.array, [bins: int [, missing: float [, oob: float, [, summary: str]]]]) -> np.array[2, 'float64']\n```\n\nSee the docstrings for complete documentation.\n\n## Related projects ##\n\n- [bigtools](https://github.com/jackh726/bigtools): Alternative BBI library written in Rust with Python bindings; by Jack Huey\n- [libBigWig](https://github.com/dpryan79/libBigWig): Alternative BBI library written in C; by Devon Ryan\n- [pyBigWig](https://github.com/dpryan79/pyBigWig): Python bindings for `libBigWig`\n- [bw-python](https://github.com/brentp/bw-python): Alternative Python wrapper to `libBigWig` by Brent Pederson\n- [bx-python](https://github.com/bxlab/bx-python): Python bioinformatics library from James Taylor's group that includes tools for bbi files.\n\nThis library provides bindings to the reference UCSC bbi library code. Check out [@dpryan79](https://github.com/dpryan79)'s [libBigWig](https://github.com/dpryan79/libBigWig) for an alternative and dedicated C library for big binary files. pyBigWig also provides numpy-based retrieval and bigBed support.\n\n## References ##\n\n<a id=\"ref1\">[1]</a>: http://bioinformatics.oxfordjournals.org/content/26/17/2204.full\n\n## From source ##\n\nIf wheels for your platform or Python version aren't available or you want to develop, you'll need to install `pybbi` from source. 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