ailist 2.1.7

Python package for Augmented Interval List

Augmented Interval List

Augmented interval list (AIList) is a data structure for enumerating intersections between a query interval and an interval set. AILists have previously been shown to be faster than interval tree, NCList, and BEDTools.

This implementation is a Python wrapper of the one used in the original AIList library.

Additonal wrapper functions have been created which allow easy user interface.

All citations should reference to original paper.

For full usage and installation documentation

Install

If you dont already have numpy and scipy installed, it is best to download Anaconda, a python distribution that has them included.

    https://continuum.io/downloads

Dependencies can be installed by:

    pip install -r requirements.txt

PyPI install, presuming you have all its requirements installed:

    pip install ailist

Benchmark

Test numpy random integers:

# ailist version: 0.1.7
from ailist import AIList
# ncls version: 0.0.53
from ncls import NCLS
# numpy version: 1.18.4
import numpy as np
# pandas version: 1.0.3
import pandas as pd
# quicksect version: 0.2.2
import quicksect

# Set seed
np.random.seed(100)


# First values
starts1 = np.random.randint(0, 100000, 100000)
ends1 = starts1 + np.random.randint(1, 10000, 100000)
ids1 = np.arange(len(starts1))
values1 = np.ones(len(starts1))

# Second values
starts2 = np.random.randint(0, 100000, 100000)
ends2 = starts2 + np.random.randint(1, 10000, 100000)
ids2 = np.arange(len(starts2))
values2 = np.ones(len(starts2))

Library	Function	Time (µs)
ncls	single overlap	1170
pandas	single overlap	924
quicksect	single overlap	550
ailist	single overlap	73

Library	Function	Time (s)	Max Memory (GB)
ncls	bulk overlap	151 s	>50
ailist	bulk overlap	17.8 s	~9

Usage

from ailist import AIList
import numpy as np

i = AIList()
i.add(15, 20)
i.add(10, 30)
i.add(17, 19)
i.add(5, 20)
i.add(12, 15)
i.add(30, 40)

# Print intervals
i.display()
# (15-20) (10-30) (17-19) (5-20) (12-15) (30-40)

# Find overlapping intervals
o = i.intersect(6, 15)
o.display()
# (5-20) (10-30) (12-15)

# Find index of overlaps
i.intersect_index(6, 15)
# array([3, 1, 4])

# Now i has been constructed/sorted
i.display()
# (5-20) (10-30) (12-15) (15-20) (17-19) (30-40)

# Can be done manually as well at any time
i.construct()

# Iterate over intervals
for x in i:
   print(x)
# Interval(5-20, 3)
# Interval(10-30, 1)
# Interval(12-15, 4)
# Interval(15-20, 0)
# Interval(17-19, 2)
# Interval(30-40, 5)

# Interval comparisons
j = AIList()
j.add(5, 15)
j.add(50, 60)

# Subtract regions
s = i - j #also: i.subtract(j)
s.display()
# (15-20) (15-30) (15-20) (17-19) (30-40) 

# Common regions
i + j #also: i.common(j)
# AIList
#  range: (5-15)
#    (5-15, 3)
#    (10-15, 1)
#    (12-15, 4)

# AIList can also add to from arrays
starts = np.arange(10,1000,100)
ends = starts + 50
ids = starts
values = np.ones(10)
i.from_array(starts, ends, ids, values)
i.display()
# (5-20) (10-30) (12-15) (15-20) (17-19) (30-40) 
# (10-60) (110-160) (210-260) (310-360) (410-460) 
# (510-560) (610-660) (710-760) (810-860) (910-960)

# Merge overlapping intervals
m = i.merge(gap=10)
m.display()
# (5-60) (110-160) (210-260) (310-360) (410-460) 
# (510-560) (610-660) (710-760) (810-860) (910-960)

# Find array of coverage
c = i.coverage()
c.head()
# 5    1.0
# 6    1.0
# 7    1.0
# 8    1.0
# 9    1.0
# dtype: float64

# Calculate window protection score
w = i.wps(5)
w.head()
# 5   -1.0
# 6   -1.0
# 7    1.0
# 8   -1.0
# 9   -1.0
# dtype: float64

# Filter to interval lengths between 3 and 20
fi = i.filter(3,20)
fi.display()
# (5-20) (10-30) (15-20) (30-40)

# Query by array
i.intersect_from_array(starts, ends, ids)
# (array([ 10,  10,  10,  10,  10,  10,  10, 110, 210, 310, 410, 510, 610,
#         710, 810, 910]),
# array([  5,   2,   0,   4,  10,   1,   3, 110, 210, 310, 410, 510, 610,
#        710, 810, 910]))

Original paper

Jianglin Feng, Aakrosh Ratan, Nathan C Sheffield; Augmented Interval List: a novel data structure for efficient genomic interval search, Bioinformatics, btz407, https://doi.org/10.1093/bioinformatics/btz407