Metadata-Version: 2.4
Name: metasag
Version: 1.3.0
Summary: A compiled and protected Python test package
Home-page: https://github.com/liangcheng-hrbmu/MetaSAG
Author: LiangCheng
Author-email: liangcheng@hrbmu.edu.cn
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE.txt
Requires-Dist: setuptools<60.0.0
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: openpyxl
Requires-Dist: matplotlib
Requires-Dist: seaborn
Requires-Dist: scipy
Requires-Dist: umap-learn
Requires-Dist: scikit-learn
Requires-Dist: statsmodels
Requires-Dist: biopython
Requires-Dist: tensorflow>=2.0
Requires-Dist: h5py
Dynamic: author
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# MetaSAG


## What is it?

In a single sample, **single-cell microbial genome droplet sequencing technology** can generate tens of thousands of droplet short-read sequencing data at a time.
**MetaSAG** enables **efficient and easy** procedural bioinformatics analysis of large raw sequencing data.

![Framework](READMESource/Framework2.png)


## Table of Contents

- [Main Features](#Main-Features)
- [Installation and requirements](#Installation-and-requirements)
- [Usage](#Usage)
- [FAQs](#FAQs)
- [License](#License)
- [Contact](#Contact)






## Main Features
- Here are just a few of the things that AAA does well:

  - According to the distribution of short reading segments of droplets in the sample,
    low-quality droplets are removed, and the soft threshold is more scientific.
  - The classification and annotation of a single cell are flexible, and it is not
    necessary to rely on the similarity between cells for clustering.
  - Annotation method has interpretable biological significance.
  - Annotations depend on [**MetaPhlAn4**][MetaPhlAn4].
  - Multi-cell droplets and unknown classified droplets can be identified.
  - The definition of cell category is flexible, and the default threshold or custom threshold can be used.
  - Assembling genomes of known classified cell boxes is efficient and accurate.
  - The main phage viruses in the sample can be identified.
  - Streamlined downstream functional analysis (phylogenetic tree, SNP classification strain, HGT level gene transfer)
  - According to Uniref90 features and using [**HuMann3 Tool**][HuMann3], the designated cells are clustered, and the similarity between
    cell clusters is analyzed from the functional point of view.


   [MetaPhlAn4]: https://github.com/biobakery/MetaPhlAn
   [HuMann3]: https://github.com/biobakery/humann


## Installation and requirements

- **Install**
```
pip install MetaSAG
```

- **Requirements**
```
MetaSAG requires Python version >= 3.8.0, R version >= 4.2.2, 
other tools or packages you need and their version we list here:
```
[Tools we recommand](READMESource/ReadMETool.md)









## Usage
-  [**Step 1. Distribute the reads in the sample to a file of individual droplets.**](READMESource/READMEUsage1.md)
-  [**Step 2. Filter low-quality cells**](READMESource/READMEUsage2.md)
-  [**Step 3. MetaPhlAn4 annotates the reads and classifies droplets**](READMESource/READMEUsage3.md)
-  [**Step 4. Quality control and integration annotation of assembled genome**](READMESource/READMEUsage4.md)
-  [**Step 5. Build phylogenetic tree**](READMESource/READMEUsage5.md)
-  [**Step 6. Species to Strain resolved genomes**](READMESource/READMEUsage6.md)
-  [**Step 7. Horizontal Gene Transfer**](READMESource/READMEUsage7.md)
-  [**Step 8. HUMAnN Path**](READMESource/READMEUsage8.md)
-  [**Step 9. Droplet clustering of potentially unknown species**](READMESource/READMEUsage9.md)
-  [**Step 10. SGB Strain Evolution Analysis Function**](READMESource/READMEUsage10.md)
-  [**Step 11. MetaK-Lytic**](READMESource/READMEUsage11.md)





## FAQs
This section answers some of the users' most recurrent doubts when running MetaSAG.


## License
MetaSAG is free for academic use only.


## Contact
If you have any comments or suggestions about MetaSAG please raise an issue or contact us:

Dumeiyu: 2023020560@hrbmu.edu.cn

