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GitHub - hunt-genes/ncls: The Nested Containment List for Python. Basically an i...

 5 years ago
source link: https://github.com/hunt-genes/ncls
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README.md

Nested containment list

Build Status PyPI version

The Nested Containment List is a datastructure for interval overlap queries, like the interval tree. It is usually an order of magnitude faster than the interval tree both for building and query lookups.

The implementation here is a revived version of the one used in the now defunct PyGr library, which died of bitrot. I have made it less memory-consuming and created wrapper functions which allows batch-querying the NCLS for further speed gains.

It was implemented to be the cornerstone of the PyRanges project, but I have made it available to the Python community as a stand-alone library. Enjoy.

Paper: https://academic.oup.com/bioinformatics/article/23/11/1386/199545

Install

pip install ncls

Changelog

# 2018.10.12 (0.0.33)
- speedups

# 2018.13.11 (0.0.29)
- add 32-bit methods

# 2018.01.06 (0.0.23)
- NCLS started segfaulting in travis build for pyranges. Probably due to bad string definition files in travis.

# 2018.05.09 (0.0.16-18)
- add Cython/C helper code for pyranges

# 2018.05.09 (0.0.15)
- add faster method has_overlap that returns True/False

# 2018.05.09 (0.0.11)
- empty NCLS returns [] instead of raising IndexError

Usage

# see the examples/ folder for more examples
from ncls import NCLS

import pandas as pd

starts = pd.Series(range(0, 5))
ends = starts + 100
ids = starts

ncls = NCLS(starts.values, ends.values, ids.values)

# python API, slower
it = ncls.find_overlap(0, 2)
for i in it:
    print(i)
# (0, 100, 0)
# (1, 101, 1)

starts_query = pd.Series([1, 3])
ends_query = pd.Series([52, 14])
indexes_query = pd.Series([10000, 100])

# everything done in C/Cython; faster
ncls.all_overlaps_both(starts_query.values, ends_query.values, indexes_query.values)
# (array([10000, 10000, 10000, 10000, 10000,   100,   100,   100,   100,
#          100]), array([0, 1, 2, 3, 4, 0, 1, 2, 3, 4]))

Benchmark

Test file of 100 million intervals (created by subsetting gencode gtf with replacement):

Library Function Time (s) Memory (GB) bx-python build 161.7 2.5 ncls build 3.15 0.5 bx-python overlap 148.4 4.3 ncls overlap 7.2 0.5

Building is 50 times faster and overlap queries are 20 times faster. Memory usage is one fifth and one ninth.

Citation

Alexander V. Alekseyenko, Christopher J. Lee; Nested Containment List (NCList): a new algorithm for accelerating interval query of genome alignment and interval databases, Bioinformatics, Volume 23, Issue 11, 1 June 2007, Pages 1386–1393, https://doi.org/10.1093/bioinformatics/btl647


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