coolpuppy 0.8.1

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# coolpup.py
[](https://zenodo.org/badge/latestdoi/147190130)

.**cool** file **p**ile-**up**s with **py**thon.

# Introduction

A versatile tool to perform pile-up analysis on Hi-C data in .cool format (https://github.com/mirnylab/cooler). And who doesn't like cool pupppies?

.cool is a modern and flexible (and the best, in my opinion) format to store Hi-C data.
It uses HDF5 to store sparse a representation of the Hi-C data, which allows low memory requirements when dealing with high resolution datasets. Another popular format to store Hi-C data, .hic, can be converted into .cool files using `hic2cool` (https://github.com/4dn-dcic/hic2cool).

See for details:

Abdennur, N., and Mirny, L. (2019). Cooler: scalable storage for Hi-C data and other genomically-labeled arrays. BioRxiv, 557660. doi: [10.1101/557660](https://doi.org/10.1101/557660)

## What are pileups?

This is the idea of how pileups work to check whether certain regions tend to interacts with each other:

<img src="https://github.com/Phlya/coolpuppy/blob/master/loop_quant.svg" width="400">

What's not shown here is normalization to the expected values. This can be done in two ways: either using a provided file with expected values of interactions at different distances (output of `cooltools compute-expected`), or directly from Hi-C data by dividing the pileups over randomly shifted control regions. If neither expected normalization approach is used (just set `--nshifts 0`), this becomes essentially identical to the APA approach (Rao et al., 2014), which can be used for averaging strongly interacting regions, e.g. annotated loops. For weaker interactors, decay of contact probability with distance will hide any focal enrichment that could be observed otherwise.

`coolpup.py` is particularly well suited performance-wise for analysing huge numbers of potential interactions, since it loads whole chromosomes into memory one by one (or in parallel to speed it up) to extract small submatrices quickly. Having to read everything into memory makes it relatively slow for small numbers of loops, but performance doesn't decrease until you reach a huge number of interactions.

# Getting started

## Installation
All requirements apart from `cooltools` are available from PyPI or conda. For `cooltools`, do

`
pip install https://github.com/mirnylab/cooltools/archive/master.zip
`

For coolpuppy (and other dependencies) simply do:

`pip install coolpuppy`

or

`pip install https://github.com/Phlya/coolpuppy/archive/master.zip`

to get the latest version from GitHub. This will make `coolpup.py` callable in your terminal, and importable in python as `coolpup`.

## Usage

Help message should help you get started to use the tool. It is a single command that has a lot of options and can do a lot of things!

```
Usage: coolpup.py [-h] [--pad PAD] [--minshift MINSHIFT] [--maxshift MAXSHIFT]
[--nshifts NSHIFTS] [--expected EXPECTED]
[--mindist MINDIST] [--maxdist MAXDIST] [--minsize MINSIZE]
[--maxsize MAXSIZE] [--excl_chrs EXCL_CHRS]
[--incl_chrs INCL_CHRS] [--subset SUBSET] [--anchor ANCHOR]
[--by_window] [--save_all] [--local] [--unbalanced]
[--coverage_norm] [--rescale] [--rescale_pad RESCALE_PAD]
[--rescale_size RESCALE_SIZE] [--n_proc N_PROC]
[--outdir OUTDIR] [--outname OUTNAME]
coolfile baselist

positional arguments:
coolfile Cooler file with your Hi-C data
baselist A 3-column tab-delimited bed file with coordinates
which intersections to pile-up. Alternatively, a
6-column double-bed file (i.e.
chr1,start1,end1,chr2,start2,end2) with coordinates of
centers of windows that will be piled-up. Can be piped
in via stdin, then use "-".

optional arguments:
-h, --help show this help message and exit
--pad PAD Padding of the windows (i.e. final size of the matrix
is 2×pad+res), in kb
--minshift MINSHIFT Shortest distance for randomly shifting coordinates
when creating controls
--maxshift MAXSHIFT Longest distance for randomly shifting coordinates
when creating controls
--nshifts NSHIFTS Number of control regions per averaged window
--expected EXPECTED File with expected (output of cooltools compute-
expected). If None, don't use expected and use
randomly shifted controls
--mindist MINDIST Minimal distance of intersections to use. If not
specified, uses --pad as mindist
--maxdist MAXDIST Maximal distance of intersections to use
--minsize MINSIZE Minimal length of features to use for local analysis
--maxsize MAXSIZE Maximal length of features to use for local analysis
--excl_chrs EXCL_CHRS
Exclude these chromosomes from analysis
--incl_chrs INCL_CHRS
Include these chromosomes; default is all. excl_chrs
overrides this.
--subset SUBSET Take a random sample of the bed file - useful for
files with too many featuers to run as is, i.e. some
repetitive elements. Set to 0 or lower to keep all
data.
--anchor ANCHOR A UCSC-style coordinate to use as an anchor to create
intersections with coordinates in the baselist
--by_window Create a pile-up for each coordinate in the baselist.
Will save a master-table with coordinates, their
enrichments and cornerCV, which is reflective of
noisiness
--save_all If by-window, save all individual pile-ups in a
separate json file
--local Create local pileups, i.e. along the diagonal
--unbalanced Do not use balanced data. Useful for single-cell Hi-C
data together with --coverage_norm, not recommended
otherwise.
--coverage_norm If --unbalanced, also add coverage normalization based
on chromosome marginals
--rescale Do not use centres of features and pad, and rather use
the actual feature sizes and rescale pileups to the
same shape and size
--rescale_pad RESCALE_PAD
If --rescale, padding in fraction of feature length
--rescale_size RESCALE_SIZE
If --rescale, this is used to determine the final size
of the pileup, i.e. it ill be size×size. Due to
technical limitation in the current implementation,
has to be an odd number
--n_proc N_PROC Number of processes to use. Each process works on a
separate chromosome, so might require quite a bit more
memory, although the data are always stored as sparse
matrices
--outdir OUTDIR Directory to save the data in
--outname OUTNAME Name of the output file. If not set, is generated
automatically to include important information.

```

Currently, `coolpup.py` doesn't support inter-chromosomal pileups, but this is an addition that is planned for the future.

## Citing coolpup.py

Until it has been published in a peer-reviewed journal, please cite our preprint

**Coolpup.py - a versatile tool to perform pile-up analysis of Hi-C data**

Ilya M. Flyamer, Robert S. Illingworth, Wendy A. Bickmore

https://www.biorxiv.org/content/10.1101/586537v1

## This tool has been used in the following publications

**DNA methylation directs polycomb-dependent 3D genome re- organisation in naive pluripotency**

Katy A McLaughlin, Ilya M Flyamer, John P Thomson, Heidi K Mjoseng, Ruchi Shukla, Iain Williamson, Graeme R Grimes, Robert S Illingworth, Ian R Adams, Sari Pennings, Richard R Meehan, Wendy A Bickmore

https://www.biorxiv.org/content/10.1101/527309v1