.. image:: https://raw.githubusercontent.com/hiyama341/teemi/main/pictures/teemi_logo.svg
:width: 400
:alt: teemi logo
teemi: a python package designed to make high-throughput strain construction reproducible and FAIR
--------------------------------------------------------------------------------------------------
.. summary-start
.. image:: https://badge.fury.io/py/teemi.svg
:target: https://badge.fury.io/py/teemi
.. image:: https://github.com/hiyama341/teemi/actions/workflows/main.yml/badge.svg
:target: https://github.com/hiyama341/teemi/actions
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:target: https://teemi.readthedocs.io/en/latest/?version=latest
:alt: Documentation Status
.. image:: https://img.shields.io/github/license/hiyama341/teemi
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.. image:: https://img.shields.io/pypi/pyversions/teemi.svg
:target: https://pypi.org/project/teemi/
:alt: Supported Python Versions
.. image:: https://codecov.io/gh/hiyama341/teemi/branch/main/graph/badge.svg?token=P4457QACUY
:target: https://codecov.io/gh/hiyama341/teemi
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.. image:: https://img.shields.io/badge/DOI-10_1101_2023_06_18_545451
:target: https://doi.org/10.1101/2023.06.18.545451
What is teemi?
~~~~~~~~~~~~~~
**teemi**, named after the Greek goddess of fairness, is a python package designed
to make microbial strain construction reproducible and FAIR (Findable, Accessible,
Interoperable, and Reusable). With teemi, you can simulate all steps of
a strain construction cycle, from generating genetic parts to designing
a combinatorial library and keeping track of samples through a commercial
Benchling API and a low-level CSV file database.
This tool can be used in literate programming to
increase efficiency and speed in metabolic engineering tasks.
To try teemi, visit our `Google Colab notebooks <https://github.com/hiyama341/teemi/tree/main/colab_notebooks>`__.
teemi not only simplifies the strain construction process but also offers the
flexibility to adapt to different experimental workflows through its open-source
Python platform. This allows for efficient automation of repetitive tasks and
a faster pace in metabolic engineering.
Our demonstration of teemi in a complex machine learning-guided
metabolic engineering task showcases its efficiency
and speed by debottlenecking a crucial step in the strictosidine pathway.
This highlights the versatility and usefulness of this tool in various
biological applications.
Curious about how you can build strains easier and faster with teemi?
Head over to our `Google Colab notebooks <https://github.com/hiyama341/teemi/tree/main/colab_notebooks>`__
and give it a try.
For a quick introduction, check our quick guides:
- `A Quick Guide to Creating a Combinatorial Library`_
- `A Quick Guide to making a CRISPR plasmid with USER cloning (for the beginner)`_
Our pre-print `"Literate programming for iterative design-build-test-learn cycles in bioengineering" <https://www.biorxiv.org/content/10.1101/2023.06.18.545451v1>`__ is out now.
Please cite it if you've used teemi in a scientific publication.
.. summary-end
Overview
--------
- `Features`_
- `Getting started`_
- `A Quick Guide to Creating a Combinatorial Library`_
- `A Quick Guide to making a CRISPR plasmid with USER cloning (for the beginner)`_
- `Colab notebooks`_
- `Strictosidine case : First DBTL cycle`_
- `Strictosidine case : Second DBTL cycle`_
- `Installation`_
- `Documentation and Examples`_
- `Contributions`_
- `License`_
- `Credits`_
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
Getting started
~~~~~~~~~~~~~~~
To get started with making microbial strains in an HT manner please follow the steps below:
1. Install teemi. You will find the necessary information below for installation.
2. Check out our `notebooks <https://github.com/hiyama341/teemi/tree/main/colab_notebooks>`__ for inspiration to make HT strain construction with teemi.
3. You can start making your own workflows by importing teemi into either Google colab or Jupyter lab/notebooks.
A Quick Guide to Creating a Combinatorial Library
-------------------------------------------------
This guide provides a simple example of the power and ease of use of the teemi tool.
Let's take the example of creating a basic combinatorial library with the following design considerations:
- Four promoters
- Ten enzyme homologs
- A Kozak sequence integrated into the primers
Our goal is to assemble a library of promoters and enzymes into a genome via in vivo assembly.
We already have a CRISPR plasmid; all we need to do is amplify the promoters and enzymes for the transformation.
This requires generating primers and making PCRs. We'll use teemi for this process.
To begin, we load the genetic parts using Teemi's easy-to-use function ``read_genbank_files()``, specifying the path to the genetic parts.
.. code-block:: python
from teemi.design.fetch_sequences import read_genbank_files
path = '../data/genetic_parts/G8H_CYP_CPR_PARTS/'
pCPR_sites = read_genbank_files(path+'CPR_promoters.gb')
CPR_sites = read_genbank_files(path+'CPR_tCYC1.gb')
We have four promoters and ten CPR homologs (all with integrated terminators).
We want to convert them into ``pydna.Dseqrecord`` objects from their current form as ``Bio.Seqrecord``. We can do it this way:
.. code-block:: python
from pydna.dseqrecord import Dseqrecord
pCPR_sites = [Dseqrecord(seq) for seq in pCPR_sites]
CPR_sites = [Dseqrecord(seq) for seq in CPR_sites]
Next, we add these genetic parts to a list in the configuration we desire, with the promoters upstream of the enzyme homologs.
.. code-block:: python
list_of_seqs = [pCPR_sites, CPR_sites]
If we want to integrate a sgRNA site into the primers, we can do that. In this case, we want to integrate a Kozak sequence.
We can initialize it as shown below.
.. code-block:: python
kozak = [Dseqrecord('TCGGTC')]
Now we're ready to create a combinatorial library of our 4x10 combinations. We can import the Teemi class for this.
.. code-block:: python
from teemi.design.combinatorial_design import DesignAssembly
We initialize with the sequences, the pad (where we want the pad - in this case, between the promoters and CPRs), then select the overlap and the desired temperature for the primers.
Note that you can use your own primer calculator. Teemi has a function that can calculate primer Tm using NEB, for example, but for simplicity, we'll use the default calculator here.
.. code-block:: python
CPR_combinatorial_library = DesignAssembly(list_of_seqs, pad = kozak , position_of_pads =[1], overlap=35, target_tm = 55 )
Now, we can retrieve the library.
.. code-block:: python
CPR_combinatorial_library.primer_list_to_dataframe()
.. list-table::
:widths: 5 10 15 10 5 10 15 15 10
:header-rows: 1
* - id
- anneals to
- sequence
- annealing temperature
- length
- price(DKK)
- description
- footprint
- len_footprint
* - P001
- pMLS1
- ...
- 56.11
- 20
- 36.0
- Anneals to pMLS1
- ...
- 20
* - P002
- pMLS1
- ...
- 56.18
- 49
- 88.2
- Anneals to pMLS1, overlaps to 2349bp_PCR_prod
- ...
- 28
* - ...
- ...
- ...
- ...
- ...
- ...
- ...
- ...
- ...
The result of this operation is a pandas DataFrame which will look similar to the given example (note that the actual DataFrame have more rows).
To obtain a DataFrame detailing the steps required for each PCR, we can use the following:
.. code-block:: python
CPR_combinatorial_library.pcr_list_to_dataframe()
.. list-table::
:widths: 10 20 15 15 10 10
:header-rows: 1
* - pcr_number
- template
- forward_primer
- reverse_primer
- f_tm
- r_tm
* - PCR1
- pMLS1
- P001
- P002
- 56.11
- 56.18
* - PCR2
- AhuCPR_tCYC1
- P003
- P004
- 53.04
- 53.50
* - PCR3
- pMLS1
- P001
- P005
- 56.11
- 56.18
* - ...
- ...
- ...
- ...
- ...
- ...
The output is a pandas DataFrame. This is a simplified version and the actual DataFrame can have more rows.
Teemi has many more functionalities. For instance, we can easily view the different combinations in our library.
.. code-block:: python
CPR_combinatorial_library.show_variants_lib_df()
.. list-table::
:widths: 5 15 10 5
:header-rows: 1
* - 0
- 1
- Systematic_name
- Variant
* - pMLS1
- AhuCPR_tCYC1
- (1, 1)
- 0
* - pMLS1
- AanCPR_tCYC1
- (1, 2)
- 1
* - pMLS1
- CloCPR_tCYC1
- (1, 3)
- 2
* - ...
- ...
- ...
- ...
This command results in a pandas DataFrame, showing the combinations in the library. This is a simplified version and the actual DataFrame would have 40 rows for this example.
The next step is to head to the lab and build some strains. Luckily, we have many examples demonstrating how to do this for a large number of strains and a bigger library (1280 combinations).
Please refer to our `Colab notebooks <https://github.com/hiyama341/teemi/tree/main/colab_notebooks>`__ below where we look at optimizing strictosidine production in yeast with Teemi.
A Quick Guide to making a CRISPR plasmid with USER cloning (for the beginner)
-----------------------------------------------------------------------------
Here is a quick guide on how we simulate the assembly of a CRISPR plasmid with USER cloning.
Big thanks to `Björn Johansson <https://github.com/BjornFJohansson>`__ for the initial work with pydna that makes much of this possible.
Please check out `pydna <https://github.com/BjornFJohansson/pydna>`__ here.
Let's begin with the simple workflow:
.. code-block:: python
from pydna.primer import Primer
from pydna.dseqrecord import Dseqrecord
Step 1: Getting the fragments we want to integrate into our CRISPR plasmid.
Specifically, we aim to integrate sgRNAs to knock out two targets.
.. code-block:: python
# 1.1: Define the primers
U_pSNR52_Fw_1 = Primer('CGTGCGAUTCTTTGAAAAGATAATGTATGA')
TJOS_66_P2R = Primer('ACCTGCACUTAACTAATTACATGACTCGA')
U_pSNR52_Fw_2 = Primer('AGTGCAGGUTCTTTGAAAAGATAATGTATGA')
TJOS_65_P1R = Primer('CACGCGAUTAACTAATTACATGACTCGA')
Primers are short, single-stranded DNA sequences that are necessary for targeting the specific DNA region we want to amplify using PCR.
1.2: Get the gRNA template. We retrieve the gRNA template from plate we have in the lab with the following teemi function.
The gRNA template is the DNA sequence that encodes the guide RNA. This RNA molecule guides the Cas9 protein to the target DNA sequence, where it induces a cut.
.. code-block:: python
from teemi.lims.csv_database import get_dna_from_box_name
gRNA1_template = get_dna_from_plate_name('gRNA1_template (1).fasta', 'plasmid_plates', database_path="G8H_CPR_library/data/06-lims/csv_database/")
1.3: Perform a PCR to amplify the gRNA.
PCR (Polymerase Chain Reaction) is a technique used to amplify a specific DNA sequence. Here, we're amplifying our gRNA templates.
.. code-block:: python
from pydna.amplify import pcr
gRNA1_pcr_prod = pcr(U_pSNR52_Fw_1,TJOS_66_P2R, gRNA1_template)
gRNA2_pcr_prod = pcr(U_pSNR52_Fw_2,TJOS_65_P1R, gRNA2_template)
1.4: Use the USER enzyme to process the PCR products.
The USER enzyme is used to create single-stranded overhangs on the PCR products, which will facilitate their insertion into the plasmid.
.. code-block:: python
from teemi.design.cloning import USER_enzyme
gRNA1_pcr_USER = USER_enzyme(gRNA1_pcr_prod)
gRNA2_pcr_USER = USER_enzyme(gRNA2_pcr_prod)
print(gRNA1_pcr_USER)
print(gRNA2_pcr_USER)
Output:
.. code-block::
Dseq(-425)
TCTT..GTTAAGTGCAGGT
GCACGCTAAGAA..CAAT
Dseq(-425)
TCTT..GTTAATCGCGTG
TCACGTCCAAGAA..CAAT
Step 2: Digesting the plasmid. The plasmid is a small, circular DNA molecule. We're importing a specific template that we'll use to integrate our gRNAs.
.. code-block:: python
# 2.1: Import the plasmid
vector = Dseqrecord(get_dna_from_plate_name('Backbone_template - p0056_(pESC-LEU-ccdB-USER) (1).fasta', 'plasmid_plates', database_path="G8H_CPR_library/data/06-lims/csv_database/"), circular = True)
2.2: Digest the plasmid with AsiSI enzyme.
Digestion with the AsiSI enzyme creates specific cuts in the plasmid, allowing us to insert our gRNAs at these locations.
.. code-block:: python
from Bio.Restriction import AsiSI
vector_asiSI, cCCDB = sorted( vector.cut(AsiSI), reverse=True)
print(vector_asiSI.seq)
Output:
.. code-block::
Dseq(-6972)
CGCG..TGCGAT
TAGCGC..ACGC
2.3: Nick the digested plasmid using a nicking enzyme
.. code-block:: python
from teemi.design.cloning import nicking_enzyme
vector_asiSI_nick = Dseqrecord(nicking_enzyme(vector_asiSI))
vector_asiSI_nick.seq
Nicking enzymes create single-stranded breaks in the DNA. This step prepares the plasmid for the insertion of the gRNAs.
Output:
.. code-block::
Dseq(-6972)
CATT..AATGCGTGCGAT
TAGCGCACGTAA..TTAC
Step 3: Assembling sgRNAs and vector
.. code-block:: python
# 3.1: Combine the nicked vector with the USER processed gRNAs and loop the resulting sequence
rec_vec = (vector_asiSI_nick + gRNA1_pcr_USER + gRNA2_pcr_USER).looped()
rec_vec.seq
In this final step, we're assembling the plasmid by combining the nicked vector with the processed gRNAs. The resulting molecule is a circular DNA plasmid containing our gRNAs.
Output:
.. code-block::
Dseq(o7797)
CATT..CGTG
GTAA..GCAC
For more real-life examples on how to use this in complex metabolic worklfows in a high-throughput manner pleas check our `Colab notebooks <https://github.com/hiyama341/teemi/tree/main/colab_notebooks>`__ .
Colab notebooks
---------------
As a proof of concept we show how teemi 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 teemi.
Specifically, in this first study we present how teemi and literate programming to build simulation-guided, iterative,
and evolution-guided laboratory workflows for optimizing strictosidine production in yeast.
If you wanna read the study you can find the pre-print `here <https://www.biorxiv.org/content/10.1101/2023.06.18.545451v1>`__.
Below you can find all the notebooks developed in this work.
Just click the Google colab badge to start the workflows.
Strictosidine case : First DBTL cycle
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
**The strictosidine pathway and short intro:**
Strictosidine is a crucial precursor for 3,000+ bioactive alkaloids found
in plants, used in medical treatments like cancer and malaria.
Chemically synthesizing or extracting them is challenging.
We're exploring biotechnological methods to produce them in yeast cell factories.
But complex P450-mediated hydroxylations limit production.
We're optimizing these reactions using combinatorial optimization, starting with geraniol hydroxylation(G8H) as a test case.
Feal free to check out the notebooks for more information on how we did it.
.. image:: https://raw.githubusercontent.com/hiyama341/teemi/fadcfe20e17e6b630280d38c624d1ad2e8838d5c/pictures/Petersend_Levassor_et_al_fig2A_strictosidine_pathway.png
:width: 700
:alt: strictosidine pathway
**DESIGN:**
.. |Notebook 00| image:: https://colab.research.google.com/assets/colab-badge.svg
:alt: Notebook 00
:target: https://colab.research.google.com/github/hiyama341/teemi/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/teemi/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/teemi/blob/main/colab_notebooks/02_1_DESIGN_Combinatorial_library.ipynb
1. Automatically fetch homologs from NCBI from a query in a standardizable and repeatable way
|Notebook 00|
01. Promoters can be selected from RNAseq data and fetched from online database with various quality measurements implemented
|Notebook 01|
02. Combinatorial libraries 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/teemi/blob/main/colab_notebooks/03_1_BUILD_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/teemi/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/teemi/blob/main/colab_notebooks/05_1_BUILD_Combinatorial_library.ipynb
03. Assembly of a CRISPR plasmid with USER cloning
|Notebook 03|
04. Construction of the background strain by K/O of G8H and CPR
|Notebook 04|
05. 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/teemi/blob/main/colab_notebooks/06_1_TEST_Library_characterisation.ipynb
06. 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/teemi/blob/main/colab_notebooks/07_1_LEARN_Modelling_and_predictions.ipynb
07. Use AutoML to predict the best combinations for a targeted second round of library construction
|Notebook 07|
Strictosidine case : 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/teemi/blob/main/colab_notebooks/08_2_DESIGN_Model_recommended_combinatiorial_library.ipynb
08. Results from the ML can be translated into making a targeted 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/teemi/blob/main/colab_notebooks/09_2_BUILD_Combinatorial_library.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/teemi/blob/main/colab_notebooks/10_2_TEST_Library_characterization.ipynb
10. Data processing of LC-MS data like in notebook 6
|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/teemi/blob/main/colab_notebooks/11_2_LEARN_Modelling_and_predictions.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 teemi from `PyPI <https://pypi.org/project/teemi/>`__.
::
$ pip install teemi
If you want to develop or if you cloned the repository from our `GitHub <https://github.com/hiyama341/teemi/>`__
you can install teemi in the following way.
::
$ pip install -e <path-to-teemi-repo>
Or if you are in the teemi repository:
::
$ pip install -e .
For those who want to contribute or develop further, you can install the development version with:
::
$ pip install -e .[dev]
Or directly from PyPI:
::
$ pip install teemi[dev]
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://teemi.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 teemi and how we use these notebooks for strain
construnction. The Colab notebooks can be found here
`teemi.notebooks <https://github.com/hiyama341/teemi/tree/main/colab_notebooks>`__.
* Documentation: https://teemi.readthedocs.io
Contributions
~~~~~~~~~~~~~
Contributions are very welcome! Check our `guidelines <https://teemi.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
- teemis logo was made by Jonas Krogh Fischer. Check out his `website <http://jkfischerproductions.com/kea/portfolio/index.html>`__.
History
-------
0.3.3 (2023-11-08)
- Updated readme file with extra examples
- Fixed setup.py file for installation of development packages like: pip install teemi[dev]
0.3.2 (2023-01-08)
This release features a re-factored DesignAssembly class with:
- Simplified methods i.e. redundant methods have been removed.
- The ability to add more than one pad, which can be used to make constructs with overlapping ends for for plasmid cloning.
0.3.1 (2023-31-07)
- This release failed due to a bug in the readme file.
0.3.0 (2023-22-06)
~~~~~~~~~~~~~~~~~~
* New submodules: gibson_cloning
This module is used to perform simple Gibson cloning workflows.
While the addition of the "gibson_cloning" submodule is an exciting development, this module is still a work in progress.
Next, a golden gate module. Keep posted on the progress.
0.2.0 (2023-31-05)
~~~~~~~~~~~~~~~~~~
* New submodules: CRISPRsequencecutter, sequence_finder.
CRISPRSequenceCutter is a dataclass that is used to cut DNA through CRISPR-cas9 double-stranded break.
SequenceFinder is a dataclass that finds upstream and downstream sequences from a sequence input, annotates them and saves them.
0.1.0 (2023-01-02)
~~~~~~~~~~~~~~~~~~
* First release on PyPI.
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"description": ".. image:: https://raw.githubusercontent.com/hiyama341/teemi/main/pictures/teemi_logo.svg\n :width: 400\n :alt: teemi logo \n\nteemi: a python package designed to make high-throughput strain construction reproducible and FAIR\n--------------------------------------------------------------------------------------------------\n\n.. summary-start\n\n.. image:: https://badge.fury.io/py/teemi.svg\n :target: https://badge.fury.io/py/teemi\n\n.. image:: https://github.com/hiyama341/teemi/actions/workflows/main.yml/badge.svg\n :target: https://github.com/hiyama341/teemi/actions\n\n.. image:: https://readthedocs.org/projects/teemi/badge/?version=latest\n :target: https://teemi.readthedocs.io/en/latest/?version=latest\n :alt: Documentation Status\n\n.. image:: https://img.shields.io/github/license/hiyama341/teemi\n :target: https://github.com/hiyama341/teemi/blob/main/LICENSE\n\n.. image:: https://img.shields.io/pypi/pyversions/teemi.svg\n :target: https://pypi.org/project/teemi/\n :alt: Supported Python Versions\n\n.. image:: https://codecov.io/gh/hiyama341/teemi/branch/main/graph/badge.svg?token=P4457QACUY \n :target: https://codecov.io/gh/hiyama341/teemi\n\n.. image:: https://img.shields.io/badge/code%20style-black-black\n :target: https://black.readthedocs.io/en/stable/\n\n.. image:: https://img.shields.io/github/last-commit/hiyama341/teemi\n\n.. image:: https://img.shields.io/badge/DOI-10_1101_2023_06_18_545451\n :target: https://doi.org/10.1101/2023.06.18.545451\n \n\n\nWhat is teemi?\n~~~~~~~~~~~~~~\n\n**teemi**, named after the Greek goddess of fairness, is a python package designed\nto make microbial strain construction reproducible and FAIR (Findable, Accessible, \nInteroperable, and Reusable). With teemi, you can simulate all steps of \na strain construction cycle, from generating genetic parts to designing \na combinatorial library and keeping track of samples through a commercial\nBenchling API and a low-level CSV file database. \nThis tool can be used in literate programming to \nincrease efficiency and speed in metabolic engineering tasks. \nTo try teemi, visit our `Google Colab notebooks <https://github.com/hiyama341/teemi/tree/main/colab_notebooks>`__.\n\n\nteemi not only simplifies the strain construction process but also offers the \nflexibility to adapt to different experimental workflows through its open-source\nPython platform. This allows for efficient automation of repetitive tasks and\na faster pace in metabolic engineering.\n\nOur demonstration of teemi in a complex machine learning-guided\nmetabolic engineering task showcases its efficiency \nand speed by debottlenecking a crucial step in the strictosidine pathway. \nThis highlights the versatility and usefulness of this tool in various \nbiological applications. \n\nCurious about how you can build strains easier and faster with teemi? \nHead over to our `Google Colab notebooks <https://github.com/hiyama341/teemi/tree/main/colab_notebooks>`__\nand give it a try.\n\nFor a quick introduction, check our quick guides:\n\n- `A Quick Guide to Creating a Combinatorial Library`_\n- `A Quick Guide to making a CRISPR plasmid with USER cloning (for the beginner)`_\n\nOur pre-print `\"Literate programming for iterative design-build-test-learn cycles in bioengineering\" <https://www.biorxiv.org/content/10.1101/2023.06.18.545451v1>`__ is out now. \nPlease cite it if you've used teemi in a scientific publication.\n\n.. summary-end\n\nOverview\n--------\n- `Features`_\n- `Getting started`_\n- `A Quick Guide to Creating a Combinatorial Library`_\n- `A Quick Guide to making a CRISPR plasmid with USER cloning (for the beginner)`_\n- `Colab notebooks`_\n- `Strictosidine case : First DBTL cycle`_\n- `Strictosidine case : Second DBTL cycle`_\n- `Installation`_\n- `Documentation and Examples`_\n- `Contributions`_\n- `License`_\n- `Credits`_\n\nFeatures\n--------\n\n* Combinatorial library generation\n* HT cloning and transformation workflows\n* Flowbot One instructions\n* CSV-based LIMS system as well as integration to Benchling\n* Genotyping of microbial strains\n* Advanced Machine Learning of biological datasets with the AutoML `H2O <https://docs.h2o.ai/h2o/latest-stable/h2o-docs/automl.html>`__\n* Workflows for selecting enzyme homologs\n* Promoter selection workflows from RNA-seq datasets\n* Data analysis of large LC-MS datasets along with workflows for analysis\n\n\nGetting started\n~~~~~~~~~~~~~~~\nTo get started with making microbial strains in an HT manner please follow the steps below: \n\n1. Install teemi. You will find the necessary information below for installation.\n\n2. Check out our `notebooks <https://github.com/hiyama341/teemi/tree/main/colab_notebooks>`__ for inspiration to make HT strain construction with teemi.\n\n3. You can start making your own workflows by importing teemi into either Google colab or Jupyter lab/notebooks.\n\n\n\nA Quick Guide to Creating a Combinatorial Library\n-------------------------------------------------\n\nThis guide provides a simple example of the power and ease of use of the teemi tool. \nLet's take the example of creating a basic combinatorial library with the following design considerations:\n\n- Four promoters\n- Ten enzyme homologs\n- A Kozak sequence integrated into the primers\n\nOur goal is to assemble a library of promoters and enzymes into a genome via in vivo assembly. \nWe already have a CRISPR plasmid; all we need to do is amplify the promoters and enzymes for the transformation. \nThis requires generating primers and making PCRs. We'll use teemi for this process.\n\nTo begin, we load the genetic parts using Teemi's easy-to-use function ``read_genbank_files()``, specifying the path to the genetic parts.\n\n.. code-block:: python\n\n from teemi.design.fetch_sequences import read_genbank_files\n path = '../data/genetic_parts/G8H_CYP_CPR_PARTS/'\n pCPR_sites = read_genbank_files(path+'CPR_promoters.gb')\n CPR_sites = read_genbank_files(path+'CPR_tCYC1.gb')\n\nWe have four promoters and ten CPR homologs (all with integrated terminators). \nWe want to convert them into ``pydna.Dseqrecord`` objects from their current form as ``Bio.Seqrecord``. We can do it this way:\n\n.. code-block:: python\n\n from pydna.dseqrecord import Dseqrecord\n pCPR_sites = [Dseqrecord(seq) for seq in pCPR_sites]\n CPR_sites = [Dseqrecord(seq) for seq in CPR_sites]\n\nNext, we add these genetic parts to a list in the configuration we desire, with the promoters upstream of the enzyme homologs.\n\n.. code-block:: python\n\n list_of_seqs = [pCPR_sites, CPR_sites]\n\nIf we want to integrate a sgRNA site into the primers, we can do that. In this case, we want to integrate a Kozak sequence.\nWe can initialize it as shown below.\n\n.. code-block:: python\n\n kozak = [Dseqrecord('TCGGTC')]\n\nNow we're ready to create a combinatorial library of our 4x10 combinations. We can import the Teemi class for this.\n\n.. code-block:: python\n\n from teemi.design.combinatorial_design import DesignAssembly\n\nWe initialize with the sequences, the pad (where we want the pad - in this case, between the promoters and CPRs), then select the overlap and the desired temperature for the primers. \nNote that you can use your own primer calculator. Teemi has a function that can calculate primer Tm using NEB, for example, but for simplicity, we'll use the default calculator here.\n\n.. code-block:: python\n\n CPR_combinatorial_library = DesignAssembly(list_of_seqs, pad = kozak , position_of_pads =[1], overlap=35, target_tm = 55 )\n\nNow, we can retrieve the library.\n\n.. code-block:: python\n\n CPR_combinatorial_library.primer_list_to_dataframe()\n\n\n.. list-table::\n :widths: 5 10 15 10 5 10 15 15 10\n :header-rows: 1\n\n * - id\n - anneals to\n - sequence\n - annealing temperature\n - length\n - price(DKK)\n - description\n - footprint\n - len_footprint\n * - P001\n - pMLS1\n - ...\n - 56.11\n - 20\n - 36.0\n - Anneals to pMLS1\n - ...\n - 20\n * - P002\n - pMLS1\n - ...\n - 56.18\n - 49\n - 88.2\n - Anneals to pMLS1, overlaps to 2349bp_PCR_prod\n - ...\n - 28\n * - ...\n - ...\n - ...\n - ...\n - ...\n - ...\n - ...\n - ...\n - ...\n\nThe result of this operation is a pandas DataFrame which will look similar to the given example (note that the actual DataFrame have more rows).\n\n\nTo obtain a DataFrame detailing the steps required for each PCR, we can use the following:\n\n.. code-block:: python\n\n CPR_combinatorial_library.pcr_list_to_dataframe()\n.. list-table::\n :widths: 10 20 15 15 10 10\n :header-rows: 1\n\n * - pcr_number\n - template\n - forward_primer\n - reverse_primer\n - f_tm\n - r_tm\n * - PCR1\n - pMLS1\n - P001\n - P002\n - 56.11\n - 56.18\n * - PCR2\n - AhuCPR_tCYC1\n - P003\n - P004\n - 53.04\n - 53.50\n * - PCR3\n - pMLS1\n - P001\n - P005\n - 56.11\n - 56.18\n * - ...\n - ...\n - ...\n - ...\n - ...\n - ...\n\n\nThe output is a pandas DataFrame. This is a simplified version and the actual DataFrame can have more rows.\n\nTeemi has many more functionalities. For instance, we can easily view the different combinations in our library.\n\n.. code-block:: python\n\n CPR_combinatorial_library.show_variants_lib_df()\n\n.. list-table::\n :widths: 5 15 10 5\n :header-rows: 1\n\n * - 0\n - 1\n - Systematic_name\n - Variant\n * - pMLS1\n - AhuCPR_tCYC1\n - (1, 1)\n - 0\n * - pMLS1\n - AanCPR_tCYC1\n - (1, 2)\n - 1\n * - pMLS1\n - CloCPR_tCYC1\n - (1, 3)\n - 2\n * - ...\n - ...\n - ...\n - ...\n\n\nThis command results in a pandas DataFrame, showing the combinations in the library. This is a simplified version and the actual DataFrame would have 40 rows for this example.\n\nThe next step is to head to the lab and build some strains. Luckily, we have many examples demonstrating how to do this for a large number of strains and a bigger library (1280 combinations). \nPlease refer to our `Colab notebooks <https://github.com/hiyama341/teemi/tree/main/colab_notebooks>`__ below where we look at optimizing strictosidine production in yeast with Teemi.\n\n\nA Quick Guide to making a CRISPR plasmid with USER cloning (for the beginner)\n-----------------------------------------------------------------------------\nHere is a quick guide on how we simulate the assembly of a CRISPR plasmid with USER cloning. \nBig thanks to `Bj\u00f6rn Johansson <https://github.com/BjornFJohansson>`__ for the initial work with pydna that makes much of this possible. \nPlease check out `pydna <https://github.com/BjornFJohansson/pydna>`__ here.\n\nLet's begin with the simple workflow:\n\n.. code-block:: python\n\n from pydna.primer import Primer\n from pydna.dseqrecord import Dseqrecord\n\nStep 1: Getting the fragments we want to integrate into our CRISPR plasmid. \nSpecifically, we aim to integrate sgRNAs to knock out two targets. \n\n.. code-block:: python\n\n # 1.1: Define the primers\n U_pSNR52_Fw_1 = Primer('CGTGCGAUTCTTTGAAAAGATAATGTATGA')\n TJOS_66_P2R = Primer('ACCTGCACUTAACTAATTACATGACTCGA')\n U_pSNR52_Fw_2 = Primer('AGTGCAGGUTCTTTGAAAAGATAATGTATGA')\n TJOS_65_P1R = Primer('CACGCGAUTAACTAATTACATGACTCGA')\n\nPrimers are short, single-stranded DNA sequences that are necessary for targeting the specific DNA region we want to amplify using PCR.\n\n1.2: Get the gRNA template. We retrieve the gRNA template from plate we have in the lab with the following teemi function.\nThe gRNA template is the DNA sequence that encodes the guide RNA. This RNA molecule guides the Cas9 protein to the target DNA sequence, where it induces a cut.\n\n.. code-block:: python\n\n from teemi.lims.csv_database import get_dna_from_box_name\n gRNA1_template = get_dna_from_plate_name('gRNA1_template (1).fasta', 'plasmid_plates', database_path=\"G8H_CPR_library/data/06-lims/csv_database/\")\n\n\n1.3: Perform a PCR to amplify the gRNA. \nPCR (Polymerase Chain Reaction) is a technique used to amplify a specific DNA sequence. Here, we're amplifying our gRNA templates.\n\n\n.. code-block:: python\n\n from pydna.amplify import pcr\n gRNA1_pcr_prod = pcr(U_pSNR52_Fw_1,TJOS_66_P2R, gRNA1_template)\n gRNA2_pcr_prod = pcr(U_pSNR52_Fw_2,TJOS_65_P1R, gRNA2_template)\n\n\n1.4: Use the USER enzyme to process the PCR products.\nThe USER enzyme is used to create single-stranded overhangs on the PCR products, which will facilitate their insertion into the plasmid.\n\n\n.. code-block:: python\n\n from teemi.design.cloning import USER_enzyme\n gRNA1_pcr_USER = USER_enzyme(gRNA1_pcr_prod)\n gRNA2_pcr_USER = USER_enzyme(gRNA2_pcr_prod)\n print(gRNA1_pcr_USER)\n print(gRNA2_pcr_USER)\n\n\nOutput:\n\n.. code-block::\n\n Dseq(-425)\n TCTT..GTTAAGTGCAGGT\n GCACGCTAAGAA..CAAT \n\n Dseq(-425)\n TCTT..GTTAATCGCGTG\n TCACGTCCAAGAA..CAAT \n\nStep 2: Digesting the plasmid. The plasmid is a small, circular DNA molecule. We're importing a specific template that we'll use to integrate our gRNAs.\n\n\n.. code-block:: python\n\n # 2.1: Import the plasmid\n vector = Dseqrecord(get_dna_from_plate_name('Backbone_template - p0056_(pESC-LEU-ccdB-USER) (1).fasta', 'plasmid_plates', database_path=\"G8H_CPR_library/data/06-lims/csv_database/\"), circular = True)\n\n\n2.2: Digest the plasmid with AsiSI enzyme.\nDigestion with the AsiSI enzyme creates specific cuts in the plasmid, allowing us to insert our gRNAs at these locations.\n\n\n.. code-block:: python\n \n from Bio.Restriction import AsiSI\n vector_asiSI, cCCDB = sorted( vector.cut(AsiSI), reverse=True)\n print(vector_asiSI.seq)\n\nOutput:\n\n.. code-block::\n\n Dseq(-6972)\n CGCG..TGCGAT\n TAGCGC..ACGC \n\n2.3: Nick the digested plasmid using a nicking enzyme\n\n.. code-block:: python\n\n from teemi.design.cloning import nicking_enzyme\n vector_asiSI_nick = Dseqrecord(nicking_enzyme(vector_asiSI))\n vector_asiSI_nick.seq\n\nNicking enzymes create single-stranded breaks in the DNA. This step prepares the plasmid for the insertion of the gRNAs.\n\nOutput:\n\n.. code-block::\n\n Dseq(-6972)\n CATT..AATGCGTGCGAT\n TAGCGCACGTAA..TTAC \n\nStep 3: Assembling sgRNAs and vector\n\n.. code-block:: python\n\n # 3.1: Combine the nicked vector with the USER processed gRNAs and loop the resulting sequence\n rec_vec = (vector_asiSI_nick + gRNA1_pcr_USER + gRNA2_pcr_USER).looped()\n rec_vec.seq\n\nIn this final step, we're assembling the plasmid by combining the nicked vector with the processed gRNAs. The resulting molecule is a circular DNA plasmid containing our gRNAs.\n\nOutput:\n\n.. code-block::\n\n Dseq(o7797)\n CATT..CGTG\n GTAA..GCAC\n\n\nFor more real-life examples on how to use this in complex metabolic worklfows in a high-throughput manner pleas check our `Colab notebooks <https://github.com/hiyama341/teemi/tree/main/colab_notebooks>`__ .\n\n\nColab notebooks\n---------------\nAs a proof of concept we show how teemi and literate programming can be used to streamline bioengineering workflows.\nThese workflows should serve as a guide or a help to build your own workflows and thereby harnessing the power of literate programming with teemi. \n\nSpecifically, in this first study we present how teemi and literate programming to build simulation-guided, iterative,\nand evolution-guided laboratory workflows for optimizing strictosidine production in yeast. \nIf you wanna read the study you can find the pre-print `here <https://www.biorxiv.org/content/10.1101/2023.06.18.545451v1>`__.\n\nBelow you can find all the notebooks developed in this work. \nJust click the Google colab badge to start the workflows. \n\nStrictosidine case : First DBTL cycle\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n**The strictosidine pathway and short intro:**\nStrictosidine is a crucial precursor for 3,000+ bioactive alkaloids found\nin plants, used in medical treatments like cancer and malaria. \nChemically synthesizing or extracting them is challenging. \nWe're exploring biotechnological methods to produce them in yeast cell factories. \nBut complex P450-mediated hydroxylations limit production. \nWe're optimizing these reactions using combinatorial optimization, starting with geraniol hydroxylation(G8H) as a test case.\nFeal free to check out the notebooks for more information on how we did it. \n\n\n.. image:: https://raw.githubusercontent.com/hiyama341/teemi/fadcfe20e17e6b630280d38c624d1ad2e8838d5c/pictures/Petersend_Levassor_et_al_fig2A_strictosidine_pathway.png\n :width: 700\n :alt: strictosidine pathway \n\n\n**DESIGN:**\n\n.. |Notebook 00| image:: https://colab.research.google.com/assets/colab-badge.svg\n :alt: Notebook 00\n :target: https://colab.research.google.com/github/hiyama341/teemi/blob/main/colab_notebooks/00_1_DESIGN_Homologs.ipynb \n\n.. |Notebook 01| image:: https://colab.research.google.com/assets/colab-badge.svg\n :alt: Notebook 01\n :target: https://colab.research.google.com/github/hiyama341/teemi/blob/main/colab_notebooks/01_1_DESIGN_Promoters.ipynb\n\n.. |Notebook 02| image:: https://colab.research.google.com/assets/colab-badge.svg\n :alt: Notebook 02\n :target: https://colab.research.google.com/github/hiyama341/teemi/blob/main/colab_notebooks/02_1_DESIGN_Combinatorial_library.ipynb\n \n\n1. Automatically fetch homologs from NCBI from a query in a standardizable and repeatable way \n\n|Notebook 00| \n\n\n01. Promoters can be selected from RNAseq data and fetched from online database with various quality measurements implemented \n\n|Notebook 01|\n\n\n\n02. Combinatorial libraries can be generated with the DesignAssembly class along with robot executable intructions \n\n|Notebook 02| \n\n\n\n**BUILD:**\n\n.. |Notebook 03| image:: https://colab.research.google.com/assets/colab-badge.svg\n :alt: Notebook 03\n :target: https://colab.research.google.com/github/hiyama341/teemi/blob/main/colab_notebooks/03_1_BUILD_gRNA_plasmid.ipynb\n\n\n.. |Notebook 04| image:: https://colab.research.google.com/assets/colab-badge.svg\n :alt: Notebook 04\n :target: https://colab.research.google.com/github/hiyama341/teemi/blob/main/colab_notebooks/04_1_BUILD_Background_strain.ipynb\n\n\n.. |Notebook 05| image:: https://colab.research.google.com/assets/colab-badge.svg\n :alt: Notebook 05\n :target: https://colab.research.google.com/github/hiyama341/teemi/blob/main/colab_notebooks/05_1_BUILD_Combinatorial_library.ipynb\n\n\n03. Assembly of a CRISPR plasmid with USER cloning \n\n|Notebook 03|\n\n04. Construction of the background strain by K/O of G8H and CPR \n\n|Notebook 04|\n\n05. First combinatorial library was generated for 1280 possible combinations \n\n|Notebook 05| \n\n\n\n**TEST:**\n\n\n.. |Notebook 06| image:: https://colab.research.google.com/assets/colab-badge.svg\n :alt: Notebook 06\n :target: https://colab.research.google.com/github/hiyama341/teemi/blob/main/colab_notebooks/06_1_TEST_Library_characterisation.ipynb\n\n\n06. Data processing of LC-MS data and genotyping of the generated strains \n\n|Notebook 06| \n\n\n**LEARN:**\n\n.. |Notebook 07| image:: https://colab.research.google.com/assets/colab-badge.svg\n :alt: Notebook 07\n :target: https://colab.research.google.com/github/hiyama341/teemi/blob/main/colab_notebooks/07_1_LEARN_Modelling_and_predictions.ipynb\n\n\n07. Use AutoML to predict the best combinations for a targeted second round of library construction \n\n|Notebook 07|\n\n\n\nStrictosidine case : Second DBTL cycle\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n\n\n**DESIGN:**\n\n.. |Notebook 08| image:: https://colab.research.google.com/assets/colab-badge.svg\n :alt: Notebook 08\n :target: https://colab.research.google.com/github/hiyama341/teemi/blob/main/colab_notebooks/08_2_DESIGN_Model_recommended_combinatiorial_library.ipynb\n\n08. Results from the ML can be translated into making a targeted library of strains \n\n|Notebook 08| \n\n\n\n**BUILD:**\n\n\n.. |Notebook 09| image:: https://colab.research.google.com/assets/colab-badge.svg\n :alt: Notebook 09\n :target: https://colab.research.google.com/github/hiyama341/teemi/blob/main/colab_notebooks/09_2_BUILD_Combinatorial_library.ipynb\n\n\n09. Shows the construction of a targeted library of strains \n\n|Notebook 09| \n\n\n\n\n**TEST:**\n\n.. |Notebook 10| image:: https://colab.research.google.com/assets/colab-badge.svg\n :alt: Notebook 10\n :target: https://colab.research.google.com/github/hiyama341/teemi/blob/main/colab_notebooks/10_2_TEST_Library_characterization.ipynb\n\n\n\n10. Data processing of LC-MS data like in notebook 6 \n\n|Notebook 10|\n\n\n\n\n**LEARN:**\n\n.. |Notebook 11| image:: https://colab.research.google.com/assets/colab-badge.svg\n :alt: Notebook 11\n :target: https://colab.research.google.com/github/hiyama341/teemi/blob/main/colab_notebooks/11_2_LEARN_Modelling_and_predictions.ipynb\n\n\n11. Second ML cycle of ML showing how the model increased performance and saturation of best performing strains \n\n|Notebook 11| \n\n\n\nInstallation\n~~~~~~~~~~~~\n\n.. installation-start\n\nUse pip to install teemi from `PyPI <https://pypi.org/project/teemi/>`__.\n\n::\n\n $ pip install teemi\n\n\nIf you want to develop or if you cloned the repository from our `GitHub <https://github.com/hiyama341/teemi/>`__\nyou can install teemi in the following way.\n\n::\n\n $ pip install -e <path-to-teemi-repo> \n\nOr if you are in the teemi repository:\n\n::\n\n $ pip install -e .\n\n\nFor those who want to contribute or develop further, you can install the development version with:\n\n::\n\n $ pip install -e .[dev]\n\nOr directly from PyPI:\n\n::\n\n $ pip install teemi[dev]\n\n\nYou might need to run these commands with administrative\nprivileges if you're not using a virtual environment (using ``sudo`` for example).\nPlease check the `documentation <https://teemi.readthedocs.io/en/latest/installation.html#>`__\nfor further details.\n\n.. installation-end\n\nDocumentation and Examples\n~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nDocumentation is available on through numerous Google Colab notebooks with\nexamples on how to use teemi and how we use these notebooks for strain\nconstrunction. The Colab notebooks can be found here \n`teemi.notebooks <https://github.com/hiyama341/teemi/tree/main/colab_notebooks>`__. \n\n* Documentation: https://teemi.readthedocs.io\n\n\nContributions\n~~~~~~~~~~~~~\n\nContributions are very welcome! Check our `guidelines <https://teemi.readthedocs.io/en/latest/contributing.html>`__ for instructions how to contribute.\n\n\nLicense\n~~~~~~~\n* Free software: MIT license\n\nCredits\n-------\n- This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.\n\n.. _Cookiecutter: https://github.com/audreyr/cookiecutter\n\n.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage\n\n- teemis logo was made by Jonas Krogh Fischer. Check out his `website <http://jkfischerproductions.com/kea/portfolio/index.html>`__. \n\n\nHistory\n-------\n\n0.3.3 (2023-11-08)\n\n- Updated readme file with extra examples\n\n- Fixed setup.py file for installation of development packages like: pip install teemi[dev]\n\n\n0.3.2 (2023-01-08)\n\nThis release features a re-factored DesignAssembly class with: \n\n- Simplified methods i.e. redundant methods have been removed.\n\n- The ability to add more than one pad, which can be used to make constructs with overlapping ends for for plasmid cloning.\n \n\n0.3.1 (2023-31-07)\n- This release failed due to a bug in the readme file.\n\n\n0.3.0 (2023-22-06)\n~~~~~~~~~~~~~~~~~~\n\n* New submodules: gibson_cloning\n\nThis module is used to perform simple Gibson cloning workflows. \nWhile the addition of the \"gibson_cloning\" submodule is an exciting development, this module is still a work in progress.\nNext, a golden gate module. Keep posted on the progress. \n\n\n0.2.0 (2023-31-05)\n~~~~~~~~~~~~~~~~~~\n\n* New submodules: CRISPRsequencecutter, sequence_finder. \n\nCRISPRSequenceCutter is a dataclass that is used to cut DNA through CRISPR-cas9 double-stranded break.\nSequenceFinder is a dataclass that finds upstream and downstream sequences from a sequence input, annotates them and saves them.\n\n0.1.0 (2023-01-02)\n~~~~~~~~~~~~~~~~~~\n\n* First release on PyPI.\n\n\n",
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