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ConStrain-0.3.0
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مشاهده بیشترتوضیحات
A Python package for constructing microbial strains
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | ConStrain-0.3.0 |
| نام | ConStrain |
| نسخه کتابخانه | 0.3.0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Lucas Levassor |
| ایمیل نویسنده | lucaslevassor@gmail.com |
| آدرس صفحه اصلی | https://github.com/hiyama341/ConStrain |
| آدرس اینترنتی | https://pypi.org/project/ConStrain/ |
| مجوز | MIT license |
.. image:: https://raw.githubusercontent.com/hiyama341/ConStrain/main/pictures/constrain1.svg?token=GHSAT0AAAAAABTYCY2LIAJ7B7ZTWBR4NMHIYZUJQ4A
:width: 400
:alt: ConStrain logo
ConStrain: Literate programming can streamline bioengineering workflows
-----------------------------------------------------------------------
.. summary-start
.. image:: https://badge.fury.io/py/ConStrain.svg
:target: https://badge.fury.io/py/ConStrain
.. image:: https://github.com/hiyama341/ConStrain/actions/workflows/main.yml/badge.svg
:target: https://github.com/hiyama341/ConStrain/actions
.. image:: https://readthedocs.org/projects/constrain/badge/?version=latest
:target: https://constrain.readthedocs.io/en/latest/?version=latest
:alt: Documentation Status
.. image:: https://img.shields.io/github/license/hiyama341/ConStrain
:target: https://github.com/hiyama341/ConStrain/blob/main/LICENSE
.. image:: https://img.shields.io/pypi/pyversions/ConStrain.svg
:target: https://pypi.org/project/ConStrain/
:alt: Supported Python Versions
.. image:: https://codecov.io/gh/hiyama341/ConStrain/branch/main/graph/badge.svg?token=P4457QACUY
:target: https://codecov.io/gh/hiyama341/ConStrain
.. image:: https://img.shields.io/badge/code%20style-black-black
:target: https://black.readthedocs.io/en/stable/
What is ConStrain?
~~~~~~~~~~~~~~~~~~
**ConStrain** is an easy-to-use python package with functions that
can be used in literate programming to simulate steps of a strain
construction cycle from generating genetic parts, to designing a
combinatorial library along with instructions for the assembly.
A fully integrated LIMS system is presented to keep track of samples
and allocation through both a commercial Benchling API and a low-level CSV file database.
Here, we demonstrate the use of ConStrain in a complex machine learning-guided
metabolic engineering task. We envision that literate programming for biology
can be adapted for any experimental workflow and be mixed and matched for the
benefit of the user. As this tool is built to be flexible through its open-source
Python platform, future repetitive tasks can be automated and thus increase
the speed at which we engineer biology.
Curious about how you can build strains easier and faster? Head over to our `Google Colab notebooks <https://github.com/hiyama341/ConStrain/tree/main/colab_notebooks>`__
and give it a try.
Please cite our paper (link tba) if you've used ConStrain in a scientific publication.
.. summary-end
Features
--------
* Combinatorial library generation
* HT cloning and transformation workflows
* Flowbot One instructions
* CSV-based LIMS system as well as integration to Benchling
* Genotyping of microbial strains
* Advanced Machine Learning of biological datasets with the AutoML `H2O <https://docs.h2o.ai/h2o/latest-stable/h2o-docs/automl.html>`__
* Workflows for selecting enzyme homologs
* Promoter selection workflows from RNA-seq datasets
* Data analysis of large LC-MS datasets along with workflows for analysis
.. image:: https://raw.githubusercontent.com/hiyama341/ConStrain/main/pictures/DBTL_ConStrain_with_modules1.svg
:width: 800
:alt: Overview of ConStrain's features throughout the DBTL cycle.
Getting started
~~~~~~~~~~~~~~~
To get started with making microbial strains in an HT manner please follow the steps below:
1. Install ConStrain. You will find the necessary information below for installation.
2. Check out our `notebooks <https://github.com/hiyama341/ConStrain/tree/main/colab_notebooks>`__ for inspiration to make HT strain construction with ConStrain.
3. You can start making your own workflows by importing ConStrain into either Google colab or Jupyter lab/notebooks.
Colab notebooks
---------------
As a proof of concept we show how ConStrain and literate programming can be used to streamline bioengineering workflows.
These workflows should serve as a guide or a help to build your own workflows and thereby harnessing the power of literate programming with ConStrain.
Specifically, in this study we present how ConStrain and literate programming to build simulation-guided, iterative,
and evolution-guided laboratory workflows for optimizing strictosidine production in yeast.
Below you can find all the notebooks developed in this work. Just click the Google colab badge to start the workflows.
First DBTL cycle
~~~~~~~~~~~~~~~~
**DESIGN:**
.. |Notebook 00| image:: https://colab.research.google.com/assets/colab-badge.svg
:alt: Notebook 00
:target: https://colab.research.google.com/github/hiyama341/ConStrain/blob/main/colab_notebooks/00_1_DESIGN_Homologs.ipynb
.. |Notebook 01| image:: https://colab.research.google.com/assets/colab-badge.svg
:alt: Notebook 01
:target: https://colab.research.google.com/github/hiyama341/ConStrain/blob/main/colab_notebooks/01_1_DESIGN_Promoters.ipynb
.. |Notebook 02| image:: https://colab.research.google.com/assets/colab-badge.svg
:alt: Notebook 02
:target: https://colab.research.google.com/github/hiyama341/ConStrain/blob/main/colab_notebooks/02_1_DESIGN_Combinatorial_library.ipynb
00. Describes how we can automatically fetch homologs from NCBI from a query in a standardizable and repeatable way |Notebook 00|.
01. Describes how promoters can be selected from RNAseq data and fetched from online database with various quality measurements implemented |Notebook 01|.
02. Describes how a combinatorial library can be generated with the DesignAssembly class along with robot executable intructions |Notebook 02|.
**BUILD:**
.. |Notebook 03| image:: https://colab.research.google.com/assets/colab-badge.svg
:alt: Notebook 03
:target: https://colab.research.google.com/github/hiyama341/ConStrain/blob/main/colab_notebooks/03_1_BUILD_USER_gRNA_plasmid.ipynb
.. |Notebook 04| image:: https://colab.research.google.com/assets/colab-badge.svg
:alt: Notebook 04
:target: https://colab.research.google.com/github/hiyama341/ConStrain/blob/main/colab_notebooks/04_1_BUILD_Background_strain.ipynb
.. |Notebook 05| image:: https://colab.research.google.com/assets/colab-badge.svg
:alt: Notebook 05
:target: https://colab.research.google.com/github/hiyama341/ConStrain/blob/main/colab_notebooks/05_1_BUILD_CombinatorialLibrary_AllStrains.ipynb
03. Describes the assembly of a CRISPR plasmid with USER cloning |Notebook 03|.
04. Describes the construction of the background strain by K/O of G8H and CPR |Notebook 04|.
05. Shows how the first combinatorial library was generated for 1280 possible combinations |Notebook 05|.
**TEST:**
.. |Notebook 06| image:: https://colab.research.google.com/assets/colab-badge.svg
:alt: Notebook 06
:target: https://colab.research.google.com/github/hiyama341/ConStrain/blob/main/colab_notebooks/06_1_TEST_LibraryCharacterisation.ipynb
06. Describes data processing of LC-MS data and genotyping of the generated strains |Notebook 06|.
**LEARN:**
.. |Notebook 07| image:: https://colab.research.google.com/assets/colab-badge.svg
:alt: Notebook 07
:target: https://colab.research.google.com/github/hiyama341/ConStrain/blob/main/colab_notebooks/07_1_LEARN_DataAnalysis.ipynb
07. Describes how we use AutoML to predict the best combinations for a targeted second round of library construction |Notebook 07|.
Second DBTL cycle
~~~~~~~~~~~~~~~~~
**DESIGN:**
.. |Notebook 08| image:: https://colab.research.google.com/assets/colab-badge.svg
:alt: Notebook 08
:target: https://colab.research.google.com/github/hiyama341/ConStrain/blob/main/colab_notebooks/09_BUILD_Library2.ipynb
08. Shows how results from the ML can be translated into making a target library of strains |Notebook 08|.
**BUILD:**
.. |Notebook 09| image:: https://colab.research.google.com/assets/colab-badge.svg
:alt: Notebook 09
:target: https://colab.research.google.com/github/hiyama341/ConStrain/blob/main/colab_notebooks/09_2_BUILD_CombinatorialLibrary.ipynb
09. Shows the construction of a targeted library of strains |Notebook 09|.
**TEST:**
.. |Notebook 10| image:: https://colab.research.google.com/assets/colab-badge.svg
:alt: Notebook 10
:target: https://colab.research.google.com/github/hiyama341/ConStrain/blob/main/colab_notebooks/10_2_TEST_Library_Characterization.ipynb
10. Describes the data processing of LC-MS data like in notebook 7 |Notebook 10|.
**LEARN:**
.. |Notebook 11| image:: https://colab.research.google.com/assets/colab-badge.svg
:alt: Notebook 11
:target: https://colab.research.google.com/github/hiyama341/ConStrain/blob/main/colab_notebooks/11_2_LEARN_DataAnalysisML.ipynb
11. Second ML cycle of ML showing how the model increased performance and saturation of best performing strains |Notebook 11|.
Installation
~~~~~~~~~~~~
.. installation-start
Use pip to install ConStrain from `PyPI <https://pypi.org/project/ConStrain/>`__.
::
$ pip install constrain
If you want to develop or if you cloned the repository from our `GitHub <https://github.com/hiyama341/ConStrain/>`__
you can install ConStrain in the following way.
::
$ pip install -e <path-to-constrain-repo>
You might need to run these commands with administrative
privileges if you're not using a virtual environment (using ``sudo`` for example).
Please check the `documentation <https://constrain.readthedocs.io/en/latest/installation.html#>`__
for further details.
.. installation-end
Documentation and Examples
~~~~~~~~~~~~~~~~~~~~~~~~~~
Documentation is available on through numerous Google Colab notebooks with
examples on how to use ConStrain and how we use these notebooks for strain
construnction. The Colab notebooks can be found here
`constrain.notebooks <https://github.com/hiyama341/ConStrain/tree/main/colab_notebooks>`__.
* Documentation: https://constrain.readthedocs.io.
Contributions
~~~~~~~~~~~~~
Contributions are very welcome! Check our `guidelines <https://constrain.readthedocs.io/en/latest/contributing.html>`__ for instructions how to contribute.
License
~~~~~~~
* Free software: MIT license
Credits
-------
- This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.
.. _Cookiecutter: https://github.com/audreyr/cookiecutter
.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage
- ConStrains logo was made by Jonas Krogh Fischer. Check out his `website <http://jkfischerproductions.com/kea/portfolio/index.html>`__.
History
-------
0.1.0 (2022-07-11)
------------------
* First release on PyPI.
0.2.0 (2022-08-08)
------------------
* New genotyping module release release on PyPI.
0.3.0 (2023-17-01)
------------------
* New ML module
* New utils module
* New plotting module
* New files in the design module including: fetch_sequences.py, retrieve_gene_homologs.py, teselagen_helpers.py
* New functions in transformation.py and PCR.py
نیازمندی
| مقدار | نام |
|---|---|
| >=4.0.7 | pydna |
| >=1.3.0 | pandas |
| >=2.1.12 | benchlingapi |
| >=1.21.0 | numpy |
| >=1.79 | biopython |
| >=0.20.0 | python-dotenv |
| >=3.0.9 | openpyxl |
| >=0.37.1 | wheel |
| >=3.5.1 | matplotlib |
| >=1.12.0 | intermine |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.6 | Python |
نحوه نصب
نصب پکیج whl ConStrain-0.3.0:
pip install ConStrain-0.3.0.whl
نصب پکیج tar.gz ConStrain-0.3.0:
pip install ConStrain-0.3.0.tar.gz