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autolamella

autolamella is a python package for automated cryo-lamella preparation with focused ion beam milling.

https://github.com/demarcolab/autolamella

Science Score: 57.0%

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    Low similarity (9.6%) to scientific vocabulary
Last synced: 10 months ago · JSON representation ·

Repository

autolamella is a python package for automated cryo-lamella preparation with focused ion beam milling.

Basic Info
Statistics
  • Stars: 19
  • Watchers: 4
  • Forks: 15
  • Open Issues: 1
  • Releases: 16
Created almost 7 years ago · Last pushed 11 months ago
Metadata Files
Readme Changelog License Citation

README.md

image

Overview

AutoLamella is a python package for automated cryo-lamella preparation with focused ion beam milling. It is based on openFIBSEM, and currently supports the TESCAN Automation SDK and ThermoFisher AutoScript. Support for other FIBSEM systems is planned.

Documentation

Documentation Site

Install

Recommended Installation Guide

Create a new virtual environment from the Anaconda Prompt terminal:

bash conda create -n fibsem python=3.9 pip conda activate fibsem pip install autolamella

Running Autolammela

Open the Anaconda Prompt terminal and run the following commands.

``` bash conda activate fibsem autolamella_ui

```

ui_new

To launch liftout related methods:

```bash conda activate fibsem autoliftout_ui

```

Citation

If you find this useful, please cite our work.

Genevieve Buckley, Gediminas Gervinskas, Cyntia Taveneau, Hariprasad Venugopal, James C. Whisstock, Alex de Marco, Automated cryo-lamella preparation for high-throughput in-situ structural biology, Journal of Structural Biology, Volume 210, Issue 2, 2020 https://doi.org/10.1016/j.jsb.2020.107488.

See CITATION for more details.

Owner

  • Name: DeMarcoLab
  • Login: DeMarcoLab
  • Kind: organization

Citation (CITATION.md) 

# Citation

If you find this useful, please cite our work.

Genevieve Buckley, Gediminas Gervinskas, Cyntia Taveneau, Hariprasad Venugopal, James C. Whisstock, Alex de Marco,
**Automated cryo-lamella preparation for high-throughput in-situ structural biology**,
*Journal of Structural Biology*,
Volume 210, Issue 2,
2020
https://doi.org/10.1016/j.jsb.2020.107488.

BibTex:
```
@article{BUCKLEY2020107488,
title = "Automated cryo-lamella preparation for high-throughput in-situ structural biology",
journal = "Journal of Structural Biology",
volume = "210",
number = "2",
pages = "107488",
year = "2020",
issn = "1047-8477",
doi = "https://doi.org/10.1016/j.jsb.2020.107488",
url = "http://www.sciencedirect.com/science/article/pii/S104784772030054X",
author = "Genevieve Buckley and Gediminas Gervinskas and Cyntia Taveneau and Hariprasad Venugopal and James C. Whisstock and Alex {de Marco}",
keywords = "Cryo-FIB, Cryo-lamella, Automation, cryo-EM, In situ structural biology",
abstract = "Cryo-transmission electron tomography (cryo-ET) in association with cryo-focused ion beam (cryo-FIB) milling enables structural biology studies to be performed directly within the cellular environment. Cryo-preserved cells are milled and a lamella with a typical thickness of 200–300 nm provides an electron transparent window suitable for cryo-ET imaging. Cryo-FIB milling is an effective method, but it is a tedious and time-consuming process, which typically results in ~10 lamellae per day. Here, we introduce an automated method to reproducibly prepare cryo-lamellae on a grid and reduce the amount of human supervision. We tested the routine on cryo-preserved Saccharomyces cerevisiae, mammalian 293 T cells, and lysozyme protein crystals. Here we demonstrate that our method allows an increased throughput, achieving a rate of 5 lamellae/hour without the need to supervise the FIB milling. We demonstrate that the quality of the lamellae is consistent throughout the preparation and their compatibility with cryo-ET analyses."
}
```

You can download a PDF of our published work at: https://doi.org/10.1016/j.jsb.2020.107488

There is also a bioRxiv preprint of this work available at: https://doi.org/10.1101/797506

GitHub Events

Total
  • Create event: 8
  • Issues event: 1
  • Release event: 2
  • Delete event: 3
  • Issue comment event: 2
  • Push event: 104
  • Pull request review comment event: 1
  • Pull request review event: 3
  • Pull request event: 9
  • Fork event: 1
Last Year
  • Create event: 8
  • Issues event: 1
  • Release event: 2
  • Delete event: 3
  • Issue comment event: 2
  • Push event: 104
  • Pull request review comment event: 1
  • Pull request review event: 3
  • Pull request event: 9
  • Fork event: 1

Issues and Pull Requests

Last synced: 10 months ago

All Time
  • Total issues: 2
  • Total pull requests: 4
  • Average time to close issues: about 1 year
  • Average time to close pull requests: 7 months
  • Total issue authors: 1
  • Total pull request authors: 2
  • Average comments per issue: 0.0
  • Average comments per pull request: 0.5
  • Merged pull requests: 2
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 1
  • Pull requests: 3
  • Average time to close issues: 3 months
  • Average time to close pull requests: 2 months
  • Issue authors: 1
  • Pull request authors: 2
  • Average comments per issue: 0.0
  • Average comments per pull request: 0.67
  • Merged pull requests: 2
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • patrickcleeve2 (2)
Pull Request Authors
  • patrickcleeve2 (14)
  • thomasmfish (1)
Top Labels
Issue Labels
feature (2)
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 202 last-month
  • Total dependent packages: 1
  • Total dependent repositories: 0
  • Total versions: 22
  • Total maintainers: 1
pypi.org: autolamella

Automatated ion beam milling for cryo-electron microscopy sample preparation.

  • Versions: 22
  • Dependent Packages: 1
  • Dependent Repositories: 0
  • Downloads: 202 Last month
Rankings
Dependent packages count: 7.6%
Average: 38.5%
Dependent repos count: 69.4%
Maintainers (1)
patrickcleeve
Last synced: 11 months ago

Dependencies

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