Detect loops (and other patterns) in Hi-C contact maps.
Project description
Chromosight
Detect chromatin loops (and other patterns) in Hi-C contact maps.
Installation
pip3 install -U chromosight
or, if you want to get the very latest version:
sudo pip3 install -e git+https://github.com/koszullab/chromosight.git@master#egg=chromosight
Usage
chromosight detect <contact_maps> [<output>] [--kernels=None] [--loops]
[--borders] [--precision=4] [--iterations=auto]
[--output]
Input
Input Hi-C contact maps can be either in bedgraph2d or cool format. Bedgraph2d is defined as a tab-separated text file with 7 columns: chr1 start1 end1 chr2 start2 end2 contacts. The cool format is an efficient and compact format for Hi-C data based on HDF5. It is maintained by the Mirny lab and documented here: https://mirnylab.github.io/cooler/
Output
Two files are generated in the output directory (replace pattern by the pattern used, e.g. loops or borders):
- pattern.txt: List of genomic coordinates, bin ids and correlation scores for the pattern identified
- pattern.json: JSON file containing the windows (of the same size as the kernel used) around the patterns from pattern.txt
Options
Pattern exploration and detection
Explore and detect patterns (loops, borders, centromeres, etc.) in Hi-C contact
maps with pattern matching.
Usage:
chromosight detect <contact_map> [<output>] [--kernel-config FILE]
[--pattern=loops] [--precision=auto] [--iterations=auto]
[--win-fmt={json,npy}] [--subsample=no]
[--inter] [--max-dist=auto] [--no-plotting] [--threads 1]
chromosight generate-config <prefix> [--preset loops]
detect:
performs pattern detection on a Hi-C contact map using kernel convolution
generate-config:
Generate pre-filled config files to use for `chromosight detect`.
A config consists of a JSON file describing analysis parameters for the
detection and path pointing to kernel matrices files. Those matrices
files are tsv files with numeric values ordered in a square dense matrix
to use for convolution.
Arguments for detect:
-h, --help Display this help message.
--version Display the program's current version.
contact_map The Hi-C contact map to detect patterns on, in
bedgraph2d or cool format.
output name of the output directory
-I, --inter Enable to consider interchromosomal contacts.
-i, --iterations auto How many iterations to perform after the first
template-based pass. Auto sets an appropriate
value loaded from the kernel configuration
file. [default: auto]
-k, --kernel-config FILE Optionally give a path to a custom JSON kernel
config path. Use this to override pattern if
you do not want to use one of the preset
patterns.
-m, --max-dist auto Maximum distance from the diagonal (in base pairs)
at which pattern detection should operate. Auto
sets a value based on the kernel configuration
file and the signal to noise ratio. [default: auto]
-n, --no-plotting Disable generation of pileup plots.
-P, --pattern loops Which pattern to detect. This will use preset
configurations for the given pattern. Possible
values are: loops, borders, hairpin. [default: loops]
-p, --precision auto Precision threshold when assessing pattern
probability in the contact map. A lesser value
leads to potentially more detections, but more
false positives. [default: auto]
-s, --subsample=INT Subsample contacts from the matrix to INT contacts.
This is useful when comparing matrices with different
coverages. [default: no]
-t, --threads 1 Number of CPUs to use in parallel. [default: 1]
-w, --win-fmt={json,npy} File format used to store individual windows
around each pattern. Window order match
patterns inside the associated text file.
Possible formats are json and npy. [default: json]
Arguments for generate-config:
prefix Path prefix for config files. If prefix is a/b,
files a/b.json and a/b.1.txt will be generated.
If a given pattern has N kernel matrices, N txt
files are created they will be named a/b.[1-N].txt.
-p, --preset loops Generate a preset config for the given pattern.
Preset configs available are "loops" and
"borders". [default: loops]
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