pastis

Poisson-based algorithm for stable inference of DNA Structure

https://github.com/hiclib/pastis

Science Score: 44.0%

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Repository

Poisson-based algorithm for stable inference of DNA Structure

Basic Info

Host: GitHub
Owner: hiclib
License: other
Language: Python
Default Branch: master
Homepage: http://hiclib.github.io/pastis/
Size: 144 MB

Statistics

Stars: 39
Watchers: 7
Forks: 15
Open Issues: 27
Releases: 6

Created almost 12 years ago · Last pushed 5 months ago

Metadata Files

Readme License Citation

README.rst

PASTIS: Poisson-based Algorithm for STable Inference of DNA Structure
=====================================================================


Dependencies
------------

For Pastis:

- python (>= 2.7)
- numpy
- scipy
- scikit-learn
- pandas
- iced

Additional dependencies for new features (diploid inference
multiscale optimization, etc):
- python (>= 3.6)
- autograd (>= 1.3)

Most of these dependencies can be installed at once using conda:
`http://conda.pydata.org/miniconda.html `_

Once conda is installed, just type the following::

  conda install numpy scipy scikit-learn pandas

Or, to include the new features::

    conda install numpy scipy scikit-learn pandas autograd

`iced` can be installed via::

  pip install iced

Install PASTIS
--------------

This package uses distutils, which is the default way of installing
python modules.

To install in your home directory, use::

    python setup.py install --user

or using pip::

    pip install --user pastis

To install for all users on Unix/Linux::

    python setup.py build
    sudo python setup.py install

or using pip::

  pip install pastis

This will install a python package ``pastis``, and five programs:
``pastis-mds``, ``pastis-nmds``, ``pastis-pm1``, ``pastis-pm2``, and
``pastis-poisson``. Calling any of those five programs will display the help.

Owner

Name: hiclib
Login: hiclib
Kind: organization

Repositories: 4
Profile: https://github.com/hiclib

Citation (CITATION)

To reference Pastis in publication, please cite the following:

@InProceedings{cauer:inferring,
  author =  {A. G. Cauer and G. Yardimci and J.-P. Vert and N. Varoquaux and W. S. Noble},
  title = {Inferring Diploid {3D} Chromatin Structures from {Hi-C} Data},
  booktitle = {19th International Workshop on Algorithms in Bioinformatics (WABI 2019)},
  pages = {11:1--11:13},
  series =  {Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =  {978-3-95977-123-8},
  ISSN =  {1868-8969},
  year =  {2019},
  volume =  {143},
  editor =  {Katharina T. Huber and Dan Gusfield},
  publisher = {Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address = {Dagstuhl, Germany},
  URL =   {http://drops.dagstuhl.de/opus/volltexte/2019/11041},
  URN =   {urn:nbn:de:0030-drops-110418},
  doi =   {10.4230/LIPIcs.WABI.2019.11},
  annote =  {Keywords: Genome 3D architecture, chromatin structure, Hi-C, 3D modeling}
}

@article{Varoquaux15062014,
  author = {Varoquaux, Nelle and Ay, Ferhat and Noble, William Stafford and
	    Vert, Jean-Philippe}, 
  title = {A statistical approach for inferring the 3D structure of the genome},
  volume = {30}, 
  number = {12}, 
  pages = {i26-i33}, 
  year = {2014}, 
  doi = {10.1093/bioinformatics/btu268}, 
  URL = {http://bioinformatics.oxfordjournals.org/content/30/12/i26.abstract}, 
  journal = {Bioinformatics} 
}

GitHub Events

Total

Issues event: 10
Watch event: 4
Issue comment event: 24
Push event: 68
Pull request event: 10

Last Year

Issues event: 10
Watch event: 4
Issue comment event: 24
Push event: 68
Pull request event: 10

Issues and Pull Requests

Last synced: 4 months ago

All Time

Total issues: 37
Total pull requests: 71
Average time to close issues: 3 months
Average time to close pull requests: 3 months
Total issue authors: 35
Total pull request authors: 4
Average comments per issue: 1.59
Average comments per pull request: 0.99
Merged pull requests: 49
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 7
Pull requests: 10
Average time to close issues: about 2 months
Average time to close pull requests: about 1 month
Issue authors: 5
Pull request authors: 1
Average comments per issue: 1.0
Average comments per pull request: 1.3
Merged pull requests: 7
Bot issues: 0
Bot pull requests: 0

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Pull Request Authors

NelleV (40)
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Dependencies

.binder/requirements.txt pypi

iced *
matplotlib *
numpy *
pandas *
scipy *

.github/workflows/build-html-and-deploy.yml actions

JamesIves/github-pages-deploy-action 3.7.1 composite
actions/checkout v4 composite
conda-incubator/setup-miniconda v3 composite

.github/workflows/test.yml actions

actions/checkout v4 composite
conda-incubator/setup-miniconda v3 composite

environment.yml conda

numpy
pip
python
setuptools >=77

pyproject.toml pypi

autograd *
iced *
numpy >=1.16.0
pandas *
scikit-learn *
scipy >=0.19.0

setup.py pypi

ecosyste.ms

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