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Project description

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

Table of Contents

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.
    • 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, the designated cells are clustered, and the similarity between cell clusters is analyzed from the functional point of view.

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

Usage

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

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