Skip to content

Latest commit

 

History

History
130 lines (98 loc) · 3.54 KB

README.md

File metadata and controls

130 lines (98 loc) · 3.54 KB
Raw

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.

Original Paper: https://academic.oup.com/bioinformatics/article/23/11/1386/199545 Cite: http://dx.doi.org/10.1093/bioinformatics/btz615

Cite

If you use this library in published research cite

http://dx.doi.org/10.1093/bioinformatics/btz615

Install

pip install ncls

Usage

from ncls import NCLS

import pandas as pd

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

subject_df = pd.DataFrame({"Start": starts, "End": ends}, index=ids)

print(subject_df)
#    Start  End
# 0      0  100
# 1      1  101
# 2      2  102
# 3      3  103
# 4      4  104

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])

query_df = pd.DataFrame({"Start": starts_query.values, "End": ends_query.values}, index=indexes_query.values)

query_df
#        Start  End
# 10000      1   52
# 100        3   14


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

print(query_df.loc[l_idxs])
#        Start  End
# 10000      1   52
# 10000      1   52
# 10000      1   52
# 10000      1   52
# 10000      1   52
# 100        3   14
# 100        3   14
# 100        3   14
# 100        3   14
# 100        3   14
print(subject_df.loc[r_idxs])
#    Start  End
# 0      0  100
# 1      1  101
# 2      2  102
# 3      3  103
# 4      4  104
# 0      0  100
# 1      1  101
# 2      2  102
# 3      3  103
# 4      4  104

# return intervals in python (slow/mem-consuming)
intervals = ncls.intervals()
intervals
# [(0, 100, 0), (1, 101, 1), (2, 102, 2), (3, 103, 3), (4, 104, 4)]

There is also an experimental floating point version of the NCLS called FNCLS. See the examples folder.

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.

Original paper

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