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FsStationB Builds

Station B Libraries for Biological Computation

Overview

This open source repository contains various libraries and wrappers that can enable a variety of computational workflows in the field of synthetic biology, genetic engineering, computational biology, and lab automation.

Station B Libraries is divided into two main implementations:

  • PyStationB: Libraries and Projects written in Python
  • FsStationB: Libraries and Projects written in F#

The workflows and libraries in repository can be broadly categorized into the following projects:

  • Biological Knowledge Graph (BCKG): A framework to ingest, structure, store, and retrieve biological knowledge and experimental data. The BCKG project comprises of the following:
    • BCKG: An F# library structure biological data and store and retrieve biological and experimental data using Azure Table and Blob Storage.
    • BCKG REST API Server: A SAFE Stack implementation to host a REST API server for BCKG.
    • PyBCKG: An extensive Python library for BCKG, to represent biological data as structured python objects, and to retrieve data via wrappers for the Azure Storage Rest API. The library also contains functions to query the knowledge graph and plot timeseries data and observations.
  • Automated Biological Experimentation (ABEX): A framework to enable Gaussian process modelling and Bayesian optimization. The ABEX project seeks to reduce the number of experiments required to optimize a biological protocol. The goal of this project is to explore and compare the usage of frameworks like Bayesian optimization to effectively traverse a solution space against traditional block-design strategies. This project includes:
    • Global Penalisation: a library that provides an implementation of the moment-matched approximation of qEI as well as benchmarks against other Bayesian Optimization acquisition functions.
    • ABEX: a wrapper around the publicly available Emukit library
    • Cell Signalling: a project to demonstrate an end-to-end semi-automated pipeline that used Bayesian optimization, static characterization, and lab automation to optimize biological protocols for a desired objective.
    • Malvern App: a webapp/GUI interface for the Cell Signalling project.
  • Time-series modelling and characterization: This includes the following:
    • StaticCharacterization: A Python library to enable the static characterization of gene expression. To characterize how circuit activity depends on different conditions, the static characterization library seeks to summarize the whole time series into a single number.
    • FSTL: An F# library to specify properties of time-series traces using Signal Temporal Logic.
  • Lab Automation: Libraries and scripts to enable end-to-end lab automation. This includes:
    • Barcoder: A Python library to annotate a Barcoded plate with reagent information based on the barcodes stored in the BCKG database.
    • Plate Reader Loader: An F# library to parse Plate Reader data into time-series files and automate the process of uploading raw and processed plate-reader files to an Azure Blob storage instance.
  • Miscellaneous Utilities:

Various projects and libraries in this repository also contain wrappers around plotting libraries to visualize and generate images for workflows in computational biology.

Contributions

There have been several contributors to this codebase, prior to its migration to this location on GitHub. Significant contributions have come from the following (listed in alphabetical order):

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.

About

Python and F# libraries developed for biological computation as part of the Station-B project.

Resources

Readme
MIT license

Code of conduct

Code of conduct

Security policy

Security policy
Activity
Custom properties

Stars

7 stars

Watchers

1 watching

Forks

4 forks
Report repository

Releases

Packages

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content

This repository was archived by the owner on Jun 14, 2024. It is now read-only.

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FsStationB Builds
Station B Libraries for Biological Computation
Overview
This open source repository contains various libraries and wrappers that can enable a variety of computational workflows in the field of synthetic biology, genetic engineering, computational biology, and lab automation.
Station B Libraries is divided into two main implementations:
  • PyStationB: Libraries and Projects written in Python
  • FsStationB: Libraries and Projects written in F#
The workflows and libraries in repository can be broadly categorized into the following projects:
  • Biological Knowledge Graph (BCKG): A framework to ingest, structure, store, and retrieve biological knowledge and experimental data. The BCKG project comprises of the following:

    • BCKG: An F# library structure biological data and store and retrieve biological and experimental data using Azure Table and Blob Storage.
    • BCKG REST API Server: A SAFE Stack implementation to host a REST API server for BCKG.
    • PyBCKG: An extensive Python library for BCKG, to represent biological data as structured python objects, and to retrieve data via wrappers for the Azure Storage Rest API. The library also contains functions to query the knowledge graph and plot timeseries data and observations.
  • Automated Biological Experimentation (ABEX): A framework to enable Gaussian process modelling and Bayesian optimization. The ABEX project seeks to reduce the number of experiments required to optimize a biological protocol. The goal of this project is to explore and compare the usage of frameworks like Bayesian optimization to effectively traverse a solution space against traditional block-design strategies. This project includes:

    • Global Penalisation: a library that provides an implementation of the moment-matched approximation of qEI as well as benchmarks against other Bayesian Optimization acquisition functions.
    • ABEX: a wrapper around the publicly available Emukit library
    • Cell Signalling: a project to demonstrate an end-to-end semi-automated pipeline that used Bayesian optimization, static characterization, and lab automation to optimize biological protocols for a desired objective.
    • Malvern App: a webapp/GUI interface for the Cell Signalling project.
  • Time-series modelling and characterization: This includes the following:

    • StaticCharacterization: A Python library to enable the static characterization of gene expression. To characterize how circuit activity depends on different conditions, the static characterization library seeks to summarize the whole time series into a single number.
    • FSTL: An F# library to specify properties of time-series traces using Signal Temporal Logic.
  • Lab Automation: Libraries and scripts to enable end-to-end lab automation. This includes:

    • Barcoder: A Python library to annotate a Barcoded plate with reagent information based on the barcodes stored in the BCKG database.
    • Plate Reader Loader: An F# library to parse Plate Reader data into time-series files and automate the process of uploading raw and processed plate-reader files to an Azure Blob storage instance.
  • Miscellaneous Utilities:

Various projects and libraries in this repository also contain wrappers around plotting libraries to visualize and generate images for workflows in computational biology.
Contributions
There have been several contributors to this codebase, prior to its migration to this location on GitHub. Significant contributions have come from the following (listed in alphabetical order):
Contributing
This project welcomes contributions and suggestions. Most contributions require you to agree to a
Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
When you submit a pull request, a CLA bot will automatically determine whether you need to provide
a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions
provided by the bot. You will only need to do this once across all repos using our CLA.
This project has adopted the Microsoft Open Source Code of Conduct.
For more information see the Code of Conduct FAQ or
contact opencode@microsoft.com with any additional questions or comments.
Trademarks
This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft
trademarks or logos is subject to and must follow
Microsoft's Trademark & Brand Guidelines.
Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship.
Any use of third-party trademarks or logos are subject to those third-party's policies.
About
Python and F# libraries developed for biological computation as part of the Station-B project.
Resources
Readme
MIT license
Code of conduct
Code of conduct
Security policy
Security policy
Activity
Custom properties
Stars
7 stars
Watchers
1 watching
Forks
4 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content

This repository was archived by the owner on Jun 14, 2024. It is now read-only.

BranchesTags
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FsStationB Builds
Station B Libraries for Biological Computation
Overview
This open source repository contains various libraries and wrappers that can enable a variety of computational workflows in the field of synthetic biology, genetic engineering, computational biology, and lab automation.
Station B Libraries is divided into two main implementations:
  • PyStationB: Libraries and Projects written in Python
  • FsStationB: Libraries and Projects written in F#
The workflows and libraries in repository can be broadly categorized into the following projects:
  • Biological Knowledge Graph (BCKG): A framework to ingest, structure, store, and retrieve biological knowledge and experimental data. The BCKG project comprises of the following:

    • BCKG: An F# library structure biological data and store and retrieve biological and experimental data using Azure Table and Blob Storage.
    • BCKG REST API Server: A SAFE Stack implementation to host a REST API server for BCKG.
    • PyBCKG: An extensive Python library for BCKG, to represent biological data as structured python objects, and to retrieve data via wrappers for the Azure Storage Rest API. The library also contains functions to query the knowledge graph and plot timeseries data and observations.
  • Automated Biological Experimentation (ABEX): A framework to enable Gaussian process modelling and Bayesian optimization. The ABEX project seeks to reduce the number of experiments required to optimize a biological protocol. The goal of this project is to explore and compare the usage of frameworks like Bayesian optimization to effectively traverse a solution space against traditional block-design strategies. This project includes:

    • Global Penalisation: a library that provides an implementation of the moment-matched approximation of qEI as well as benchmarks against other Bayesian Optimization acquisition functions.
    • ABEX: a wrapper around the publicly available Emukit library
    • Cell Signalling: a project to demonstrate an end-to-end semi-automated pipeline that used Bayesian optimization, static characterization, and lab automation to optimize biological protocols for a desired objective.
    • Malvern App: a webapp/GUI interface for the Cell Signalling project.
  • Time-series modelling and characterization: This includes the following:

    • StaticCharacterization: A Python library to enable the static characterization of gene expression. To characterize how circuit activity depends on different conditions, the static characterization library seeks to summarize the whole time series into a single number.
    • FSTL: An F# library to specify properties of time-series traces using Signal Temporal Logic.
  • Lab Automation: Libraries and scripts to enable end-to-end lab automation. This includes:

    • Barcoder: A Python library to annotate a Barcoded plate with reagent information based on the barcodes stored in the BCKG database.
    • Plate Reader Loader: An F# library to parse Plate Reader data into time-series files and automate the process of uploading raw and processed plate-reader files to an Azure Blob storage instance.
  • Miscellaneous Utilities:

Various projects and libraries in this repository also contain wrappers around plotting libraries to visualize and generate images for workflows in computational biology.
Contributions
There have been several contributors to this codebase, prior to its migration to this location on GitHub. Significant contributions have come from the following (listed in alphabetical order):
Contributing
This project welcomes contributions and suggestions. Most contributions require you to agree to a
Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
When you submit a pull request, a CLA bot will automatically determine whether you need to provide
a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions
provided by the bot. You will only need to do this once across all repos using our CLA.
This project has adopted the Microsoft Open Source Code of Conduct.
For more information see the Code of Conduct FAQ or
contact opencode@microsoft.com with any additional questions or comments.
Trademarks
This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft
trademarks or logos is subject to and must follow
Microsoft's Trademark & Brand Guidelines.
Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship.
Any use of third-party trademarks or logos are subject to those third-party's policies.
About
Python and F# libraries developed for biological computation as part of the Station-B project.
Resources
Readme
MIT license
Code of conduct
Code of conduct
Security policy
Security policy
Activity
Custom properties
Stars
7 stars
Watchers
1 watching
Forks
4 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content

This repository was archived by the owner on Jun 14, 2024. It is now read-only.

BranchesTags
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FsStationB Builds
Station B Libraries for Biological Computation
Overview
This open source repository contains various libraries and wrappers that can enable a variety of computational workflows in the field of synthetic biology, genetic engineering, computational biology, and lab automation.
Station B Libraries is divided into two main implementations:
  • PyStationB: Libraries and Projects written in Python
  • FsStationB: Libraries and Projects written in F#
The workflows and libraries in repository can be broadly categorized into the following projects:
  • Biological Knowledge Graph (BCKG): A framework to ingest, structure, store, and retrieve biological knowledge and experimental data. The BCKG project comprises of the following:

    • BCKG: An F# library structure biological data and store and retrieve biological and experimental data using Azure Table and Blob Storage.
    • BCKG REST API Server: A SAFE Stack implementation to host a REST API server for BCKG.
    • PyBCKG: An extensive Python library for BCKG, to represent biological data as structured python objects, and to retrieve data via wrappers for the Azure Storage Rest API. The library also contains functions to query the knowledge graph and plot timeseries data and observations.
  • Automated Biological Experimentation (ABEX): A framework to enable Gaussian process modelling and Bayesian optimization. The ABEX project seeks to reduce the number of experiments required to optimize a biological protocol. The goal of this project is to explore and compare the usage of frameworks like Bayesian optimization to effectively traverse a solution space against traditional block-design strategies. This project includes:

    • Global Penalisation: a library that provides an implementation of the moment-matched approximation of qEI as well as benchmarks against other Bayesian Optimization acquisition functions.
    • ABEX: a wrapper around the publicly available Emukit library
    • Cell Signalling: a project to demonstrate an end-to-end semi-automated pipeline that used Bayesian optimization, static characterization, and lab automation to optimize biological protocols for a desired objective.
    • Malvern App: a webapp/GUI interface for the Cell Signalling project.
  • Time-series modelling and characterization: This includes the following:

    • StaticCharacterization: A Python library to enable the static characterization of gene expression. To characterize how circuit activity depends on different conditions, the static characterization library seeks to summarize the whole time series into a single number.
    • FSTL: An F# library to specify properties of time-series traces using Signal Temporal Logic.
  • Lab Automation: Libraries and scripts to enable end-to-end lab automation. This includes:

    • Barcoder: A Python library to annotate a Barcoded plate with reagent information based on the barcodes stored in the BCKG database.
    • Plate Reader Loader: An F# library to parse Plate Reader data into time-series files and automate the process of uploading raw and processed plate-reader files to an Azure Blob storage instance.
  • Miscellaneous Utilities:

Various projects and libraries in this repository also contain wrappers around plotting libraries to visualize and generate images for workflows in computational biology.
Contributions
There have been several contributors to this codebase, prior to its migration to this location on GitHub. Significant contributions have come from the following (listed in alphabetical order):
Contributing
This project welcomes contributions and suggestions. Most contributions require you to agree to a
Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
When you submit a pull request, a CLA bot will automatically determine whether you need to provide
a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions
provided by the bot. You will only need to do this once across all repos using our CLA.
This project has adopted the Microsoft Open Source Code of Conduct.
For more information see the Code of Conduct FAQ or
contact opencode@microsoft.com with any additional questions or comments.
Trademarks
This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft
trademarks or logos is subject to and must follow
Microsoft's Trademark & Brand Guidelines.
Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship.
Any use of third-party trademarks or logos are subject to those third-party's policies.
About
Python and F# libraries developed for biological computation as part of the Station-B project.
Resources
Readme
MIT license
Code of conduct
Code of conduct
Security policy
Security policy
Activity
Custom properties
Stars
7 stars
Watchers
1 watching
Forks
4 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content

This repository was archived by the owner on Jun 14, 2024. It is now read-only.

BranchesTags
Repository files navigation
FsStationB Builds
Station B Libraries for Biological Computation
Overview
This open source repository contains various libraries and wrappers that can enable a variety of computational workflows in the field of synthetic biology, genetic engineering, computational biology, and lab automation.
Station B Libraries is divided into two main implementations:
  • PyStationB: Libraries and Projects written in Python
  • FsStationB: Libraries and Projects written in F#
The workflows and libraries in repository can be broadly categorized into the following projects:
  • Biological Knowledge Graph (BCKG): A framework to ingest, structure, store, and retrieve biological knowledge and experimental data. The BCKG project comprises of the following:

    • BCKG: An F# library structure biological data and store and retrieve biological and experimental data using Azure Table and Blob Storage.
    • BCKG REST API Server: A SAFE Stack implementation to host a REST API server for BCKG.
    • PyBCKG: An extensive Python library for BCKG, to represent biological data as structured python objects, and to retrieve data via wrappers for the Azure Storage Rest API. The library also contains functions to query the knowledge graph and plot timeseries data and observations.
  • Automated Biological Experimentation (ABEX): A framework to enable Gaussian process modelling and Bayesian optimization. The ABEX project seeks to reduce the number of experiments required to optimize a biological protocol. The goal of this project is to explore and compare the usage of frameworks like Bayesian optimization to effectively traverse a solution space against traditional block-design strategies. This project includes:

    • Global Penalisation: a library that provides an implementation of the moment-matched approximation of qEI as well as benchmarks against other Bayesian Optimization acquisition functions.
    • ABEX: a wrapper around the publicly available Emukit library
    • Cell Signalling: a project to demonstrate an end-to-end semi-automated pipeline that used Bayesian optimization, static characterization, and lab automation to optimize biological protocols for a desired objective.
    • Malvern App: a webapp/GUI interface for the Cell Signalling project.
  • Time-series modelling and characterization: This includes the following:

    • StaticCharacterization: A Python library to enable the static characterization of gene expression. To characterize how circuit activity depends on different conditions, the static characterization library seeks to summarize the whole time series into a single number.
    • FSTL: An F# library to specify properties of time-series traces using Signal Temporal Logic.
  • Lab Automation: Libraries and scripts to enable end-to-end lab automation. This includes:

    • Barcoder: A Python library to annotate a Barcoded plate with reagent information based on the barcodes stored in the BCKG database.
    • Plate Reader Loader: An F# library to parse Plate Reader data into time-series files and automate the process of uploading raw and processed plate-reader files to an Azure Blob storage instance.
  • Miscellaneous Utilities:

Various projects and libraries in this repository also contain wrappers around plotting libraries to visualize and generate images for workflows in computational biology.
Contributions
There have been several contributors to this codebase, prior to its migration to this location on GitHub. Significant contributions have come from the following (listed in alphabetical order):
Contributing
This project welcomes contributions and suggestions. Most contributions require you to agree to a
Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
When you submit a pull request, a CLA bot will automatically determine whether you need to provide
a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions
provided by the bot. You will only need to do this once across all repos using our CLA.
This project has adopted the Microsoft Open Source Code of Conduct.
For more information see the Code of Conduct FAQ or
contact opencode@microsoft.com with any additional questions or comments.
Trademarks
This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft
trademarks or logos is subject to and must follow
Microsoft's Trademark & Brand Guidelines.
Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship.
Any use of third-party trademarks or logos are subject to those third-party's policies.
About
Python and F# libraries developed for biological computation as part of the Station-B project.
Resources
Readme
MIT license
Code of conduct
Code of conduct
Security policy
Security policy
Activity
Custom properties
Stars
7 stars
Watchers
1 watching
Forks
4 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
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} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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FsStationB Builds
Station B Libraries for Biological Computation
Overview
This open source repository contains various libraries and wrappers that can enable a variety of computational workflows in the field of synthetic biology, genetic engineering, computational biology, and lab automation.
Station B Libraries is divided into two main implementations:
  • PyStationB: Libraries and Projects written in Python
  • FsStationB: Libraries and Projects written in F#
The workflows and libraries in repository can be broadly categorized into the following projects:
  • Biological Knowledge Graph (BCKG): A framework to ingest, structure, store, and retrieve biological knowledge and experimental data. The BCKG project comprises of the following:

    • BCKG: An F# library structure biological data and store and retrieve biological and experimental data using Azure Table and Blob Storage.
    • BCKG REST API Server: A SAFE Stack implementation to host a REST API server for BCKG.
    • PyBCKG: An extensive Python library for BCKG, to represent biological data as structured python objects, and to retrieve data via wrappers for the Azure Storage Rest API. The library also contains functions to query the knowledge graph and plot timeseries data and observations.
  • Automated Biological Experimentation (ABEX): A framework to enable Gaussian process modelling and Bayesian optimization. The ABEX project seeks to reduce the number of experiments required to optimize a biological protocol. The goal of this project is to explore and compare the usage of frameworks like Bayesian optimization to effectively traverse a solution space against traditional block-design strategies. This project includes:

    • Global Penalisation: a library that provides an implementation of the moment-matched approximation of qEI as well as benchmarks against other Bayesian Optimization acquisition functions.
    • ABEX: a wrapper around the publicly available Emukit library
    • Cell Signalling: a project to demonstrate an end-to-end semi-automated pipeline that used Bayesian optimization, static characterization, and lab automation to optimize biological protocols for a desired objective.
    • Malvern App: a webapp/GUI interface for the Cell Signalling project.
  • Time-series modelling and characterization: This includes the following:

    • StaticCharacterization: A Python library to enable the static characterization of gene expression. To characterize how circuit activity depends on different conditions, the static characterization library seeks to summarize the whole time series into a single number.
    • FSTL: An F# library to specify properties of time-series traces using Signal Temporal Logic.
  • Lab Automation: Libraries and scripts to enable end-to-end lab automation. This includes:

    • Barcoder: A Python library to annotate a Barcoded plate with reagent information based on the barcodes stored in the BCKG database.
    • Plate Reader Loader: An F# library to parse Plate Reader data into time-series files and automate the process of uploading raw and processed plate-reader files to an Azure Blob storage instance.
  • Miscellaneous Utilities:

Various projects and libraries in this repository also contain wrappers around plotting libraries to visualize and generate images for workflows in computational biology.
Contributions
There have been several contributors to this codebase, prior to its migration to this location on GitHub. Significant contributions have come from the following (listed in alphabetical order):
Contributing
This project welcomes contributions and suggestions. Most contributions require you to agree to a
Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
When you submit a pull request, a CLA bot will automatically determine whether you need to provide
a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions
provided by the bot. You will only need to do this once across all repos using our CLA.
This project has adopted the Microsoft Open Source Code of Conduct.
For more information see the Code of Conduct FAQ or
contact opencode@microsoft.com with any additional questions or comments.
Trademarks
This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft
trademarks or logos is subject to and must follow
Microsoft's Trademark & Brand Guidelines.
Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship.
Any use of third-party trademarks or logos are subject to those third-party's policies.
About
Python and F# libraries developed for biological computation as part of the Station-B project.
Resources
Readme
MIT license
Code of conduct
Code of conduct
Security policy
Security policy
Activity
Custom properties
Stars
7 stars
Watchers
1 watching
Forks
4 forks
Report repository
Releases
Packages
Contributors
Languages

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var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content

This repository was archived by the owner on Jun 14, 2024. It is now read-only.

BranchesTags
Repository files navigation
FsStationB Builds
Station B Libraries for Biological Computation
Overview
This open source repository contains various libraries and wrappers that can enable a variety of computational workflows in the field of synthetic biology, genetic engineering, computational biology, and lab automation.
Station B Libraries is divided into two main implementations:
  • PyStationB: Libraries and Projects written in Python
  • FsStationB: Libraries and Projects written in F#
The workflows and libraries in repository can be broadly categorized into the following projects:
  • Biological Knowledge Graph (BCKG): A framework to ingest, structure, store, and retrieve biological knowledge and experimental data. The BCKG project comprises of the following:

    • BCKG: An F# library structure biological data and store and retrieve biological and experimental data using Azure Table and Blob Storage.
    • BCKG REST API Server: A SAFE Stack implementation to host a REST API server for BCKG.
    • PyBCKG: An extensive Python library for BCKG, to represent biological data as structured python objects, and to retrieve data via wrappers for the Azure Storage Rest API. The library also contains functions to query the knowledge graph and plot timeseries data and observations.
  • Automated Biological Experimentation (ABEX): A framework to enable Gaussian process modelling and Bayesian optimization. The ABEX project seeks to reduce the number of experiments required to optimize a biological protocol. The goal of this project is to explore and compare the usage of frameworks like Bayesian optimization to effectively traverse a solution space against traditional block-design strategies. This project includes:

    • Global Penalisation: a library that provides an implementation of the moment-matched approximation of qEI as well as benchmarks against other Bayesian Optimization acquisition functions.
    • ABEX: a wrapper around the publicly available Emukit library
    • Cell Signalling: a project to demonstrate an end-to-end semi-automated pipeline that used Bayesian optimization, static characterization, and lab automation to optimize biological protocols for a desired objective.
    • Malvern App: a webapp/GUI interface for the Cell Signalling project.
  • Time-series modelling and characterization: This includes the following:

    • StaticCharacterization: A Python library to enable the static characterization of gene expression. To characterize how circuit activity depends on different conditions, the static characterization library seeks to summarize the whole time series into a single number.
    • FSTL: An F# library to specify properties of time-series traces using Signal Temporal Logic.
  • Lab Automation: Libraries and scripts to enable end-to-end lab automation. This includes:

    • Barcoder: A Python library to annotate a Barcoded plate with reagent information based on the barcodes stored in the BCKG database.
    • Plate Reader Loader: An F# library to parse Plate Reader data into time-series files and automate the process of uploading raw and processed plate-reader files to an Azure Blob storage instance.
  • Miscellaneous Utilities:

Various projects and libraries in this repository also contain wrappers around plotting libraries to visualize and generate images for workflows in computational biology.
Contributions
There have been several contributors to this codebase, prior to its migration to this location on GitHub. Significant contributions have come from the following (listed in alphabetical order):
Contributing
This project welcomes contributions and suggestions. Most contributions require you to agree to a
Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
When you submit a pull request, a CLA bot will automatically determine whether you need to provide
a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions
provided by the bot. You will only need to do this once across all repos using our CLA.
This project has adopted the Microsoft Open Source Code of Conduct.
For more information see the Code of Conduct FAQ or
contact opencode@microsoft.com with any additional questions or comments.
Trademarks
This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft
trademarks or logos is subject to and must follow
Microsoft's Trademark & Brand Guidelines.
Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship.
Any use of third-party trademarks or logos are subject to those third-party's policies.
About
Python and F# libraries developed for biological computation as part of the Station-B project.
Resources
Readme
MIT license
Code of conduct
Code of conduct
Security policy
Security policy
Activity
Custom properties
Stars
7 stars
Watchers
1 watching
Forks
4 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content

This repository was archived by the owner on Jun 14, 2024. It is now read-only.

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Repository files navigation
FsStationB Builds
Station B Libraries for Biological Computation
Overview
This open source repository contains various libraries and wrappers that can enable a variety of computational workflows in the field of synthetic biology, genetic engineering, computational biology, and lab automation.
Station B Libraries is divided into two main implementations:
  • PyStationB: Libraries and Projects written in Python
  • FsStationB: Libraries and Projects written in F#
The workflows and libraries in repository can be broadly categorized into the following projects:
  • Biological Knowledge Graph (BCKG): A framework to ingest, structure, store, and retrieve biological knowledge and experimental data. The BCKG project comprises of the following:

    • BCKG: An F# library structure biological data and store and retrieve biological and experimental data using Azure Table and Blob Storage.
    • BCKG REST API Server: A SAFE Stack implementation to host a REST API server for BCKG.
    • PyBCKG: An extensive Python library for BCKG, to represent biological data as structured python objects, and to retrieve data via wrappers for the Azure Storage Rest API. The library also contains functions to query the knowledge graph and plot timeseries data and observations.
  • Automated Biological Experimentation (ABEX): A framework to enable Gaussian process modelling and Bayesian optimization. The ABEX project seeks to reduce the number of experiments required to optimize a biological protocol. The goal of this project is to explore and compare the usage of frameworks like Bayesian optimization to effectively traverse a solution space against traditional block-design strategies. This project includes:

    • Global Penalisation: a library that provides an implementation of the moment-matched approximation of qEI as well as benchmarks against other Bayesian Optimization acquisition functions.
    • ABEX: a wrapper around the publicly available Emukit library
    • Cell Signalling: a project to demonstrate an end-to-end semi-automated pipeline that used Bayesian optimization, static characterization, and lab automation to optimize biological protocols for a desired objective.
    • Malvern App: a webapp/GUI interface for the Cell Signalling project.
  • Time-series modelling and characterization: This includes the following:

    • StaticCharacterization: A Python library to enable the static characterization of gene expression. To characterize how circuit activity depends on different conditions, the static characterization library seeks to summarize the whole time series into a single number.
    • FSTL: An F# library to specify properties of time-series traces using Signal Temporal Logic.
  • Lab Automation: Libraries and scripts to enable end-to-end lab automation. This includes:

    • Barcoder: A Python library to annotate a Barcoded plate with reagent information based on the barcodes stored in the BCKG database.
    • Plate Reader Loader: An F# library to parse Plate Reader data into time-series files and automate the process of uploading raw and processed plate-reader files to an Azure Blob storage instance.
  • Miscellaneous Utilities:

Various projects and libraries in this repository also contain wrappers around plotting libraries to visualize and generate images for workflows in computational biology.
Contributions
There have been several contributors to this codebase, prior to its migration to this location on GitHub. Significant contributions have come from the following (listed in alphabetical order):
Contributing
This project welcomes contributions and suggestions. Most contributions require you to agree to a
Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
When you submit a pull request, a CLA bot will automatically determine whether you need to provide
a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions
provided by the bot. You will only need to do this once across all repos using our CLA.
This project has adopted the Microsoft Open Source Code of Conduct.
For more information see the Code of Conduct FAQ or
contact opencode@microsoft.com with any additional questions or comments.
Trademarks
This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft
trademarks or logos is subject to and must follow
Microsoft's Trademark & Brand Guidelines.
Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship.
Any use of third-party trademarks or logos are subject to those third-party's policies.
About
Python and F# libraries developed for biological computation as part of the Station-B project.
Resources
Readme
MIT license
Code of conduct
Code of conduct
Security policy
Security policy
Activity
Custom properties
Stars
7 stars
Watchers
1 watching
Forks
4 forks
Report repository
Releases
Packages
Contributors
Languages