semvec/pypi-packages huggingface dataset:
phigaro 2.4.0
Phigaro is a scalable command-line tool for predicting phages and prophages.
License: MIT

# Phigaro v2.4.0
[![PyPI version](https://badge.fury.io/py/phigaro.svg)](https://badge.fury.io/py/phigaro)
![Conda installation](https://anaconda.org/bioconda/phigaro/badges/version.svg)
![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)

Phigaro is a standalone command-line application that is able to detect prophage regions taking raw genome and metagenome assemblies as an input. It also produces dynamic annotated “prophage genome maps” and marks possible transposon insertion spots inside prophages. It is applicable for mining prophage regions from large metagenomic datasets.

## Updates tracker
You can find the information about updates and releases by [link.](https://github.com/bobeobibo/phigaro/blob/master/version_tracker.md)

## Documentation: Installation & Usage
Please, follow [the documentation link](https://phigaro.readthedocs.io/) to find installation and usage information.

## Methods overview
Open-reading frames (i.e. proteins) are predicted from the input FASTA file using Prodigal. Phage genes are annotated with prokaryotic viral orthologous groups (pVOGs) profile Hidden Markov Models (HMMs), which can be downloaded stand-alone from http://dmk-brain.ecn.uiowa.edu/pVOGs/. Each contig is represented as a sequence of phage and non-phage genes. A smoothing window algorithm (a triangular window function) determines regions with a high density of phage genes and therefore the prophage regions and boundaries, considering the pVOG annotations and the GC content.

## Known issues
Phigaro is tested on Linux systems. For MacOS, you may need to add the following softlink `ln -s /usr/libexec/locate.updatedb /usr/local/bin/updated` and run `brew install wget`. If you encounter any issues while running Phigaro on test data, please report them to us at estarikova@rcpcm.org.

## Publication
Elizaveta V. Starikova, Polina O. Tikhonova, Nikita A. Prianichnikov, Chris M. Rands, Evgeny M. Zdobnov, Vadim M. Govorun <br>Phigaro: high throughput prophage sequence annotation

- Bioinformatics, 2020; doi: https://doi.org/10.1093/bioinformatics/btaa250
- bioRxiv, 2019; doi: https://doi.org/10.1101/598243

(c) E.Starikova, P. Tikhonova, N.Pryanichnikov, 2019
pca-pwa 1.0.5
simplified manner for insights and decision-making, by visualizing complex relationships with PCA web application
License: MIT
Requires Python: >=3.11,<4.0
Keywords: pca,danymukesha

# pca-pwa
`pca-pwa`, a simplified manner for insights and decision-making by visualizing complex relationships with PCA web application.

### The Purpose of the Package
- The purpose of the package is to offer a simple way of visualizing relatationships between items of any given dataset. 
The user could easily obtain a pca plot without needing to configure or compile the application.

### Installation
To install `pca_pwa`, you can use pip. Open your terminal and run:

```sh
pip install pca_pwa
```
Open `IPython` or `Jupyter Notebook`
```python
>>> from pca_pwa import app
>>> app.app.run(debug=True, use_reloader=True, host='0.0.0.0', port=8082)
>>> # * Serving Flask app 'app'
>>> # * Debug mode: on
>>> # * Running on http://127.0.0.1:8082
```

Open the url: http://127.0.0.1:8082

![image](https://github.com/danymukesha/pca-pwa/assets/45208254/03c32efa-3873-4173-9682-877c51aefdd6)

Upload `xslx/slx` file (Excel) 

- e.g.:
  - Click [here](https://github.com/danymukesha/pca-pwa/raw/main/tests/samples_file.xlsx) to download the excel file
    * Items/Observations should be in rows
    * Variables/Features should in columns

Choose a method of imputation for missing values

Then run the pca by clicking ``Perform PCA`` button.

![image](https://github.com/danymukesha/pca-pwa/assets/45208254/a25bf538-599e-4353-80e4-a26963e4d721)

---------

Otherwise you can use `git clone`:

Here is the [Usage](https://github.com/danymukesha/pca-pwa/blob/main/Usage.md):

Clone the github repository

```git
git clone https://github.com/danymukesha/pca-pwa.git
```

Run the app

```sh
cd pca-pwa
python3.1 pca-pwa/app.y

# * Serving Flask app 'app'
# * Debug mode: on
# * Running on http://127.0.0.1:8082
```

Open the url: http://127.0.0.1:8082

### License
This project is licensed under the MIT License.

### Credits
Author: MIT © [Dany Mukesha](https://danymukesha.github.io/)

Email: danymukesha@gmail.com

Thank you for using `pca_pwa`!
magCalEM 1.3.6
A program to calibrate the pixel size of cryoEM data
License: MIT
Requires Python: >=3.6

#MagCalibration

This is a program to calibrate the effective pixel size for cryoEM data collected on gold foils.
Documentation and source code is availbale at www.mrc-lmb.cam.ac.uk/crusso/
If the program is useful in your work, please cite: 
Joshua L. Dickerson, Erin Leahy, Mathew J. Peet, Katerina Naydenova, Christopher J. Russo. "Accurate magnification determination for cryoEM using gold". Ultramicroscopy 2023:113883.