MTase Classification Pipeline

This repository provides a pipeline for the classification of Methyltransferases (MTases) using Hidden Markov Models (HMMs). The pipeline consists of three main steps: installing the required packages, conducting HMMer searches, and detecting regions for classification.

Table of Contents

1. Installation
2. HMMer Search
3. Region Detection
4. Classification
5. Web Application
6. Additional Information

Installation

Before running the pipeline, you will need to install the required packages. Execute the following commands in your Colab Notebook:

!git clone https://github.com/MVolobueva/MTase-classification.git
!sudo apt-get -y install hmmer
!git clone https://github.com/isrusin/etsv
!python3 -m pip install -e etsv

The visualization of the steps in the pipeline is shown in the image below:

HMMer Search

Conduct the HMMer search to identify Methyltransferase sequences. Use the following command:

!hmmsearch --cpu 3 -E 0.01 --domE 0.01 --incE 0.01 --incdomE 0.01 \
        -o /dev/null --noali -A file.stk \
        /content/MTase-classification/HMM_profiles/selected_profiles.hmm /content/MTase-classification/Sample_MTases/MTase_sequences.fasta

Region Detection

After running the HMMer search, the next step is to detect regions in the alignment. Use this command:

!./MTase-classification/Scripts/get_aln_regions.py \
  /content/MTase-classification/profile-markup/All_profile_region.csv \
  /content/file.stk > region_alignments.tsv

Classification

Finally, perform the classification of the detected regions with the following command:

!python ./MTase-classification/Scripts/classification.py \
  --t /content/region_alignments.tsv \
  --m several_cat_domains.tsv \
  --c class.tsv

Web Application

To illustrate the workings of the pipeline, we have developed a web application. You can access it at the following link:

MTase Classification Web Application

Additional Information

For detailed information about the classification method used in this pipeline, please refer to the latest version of the manuscript available at: Classification Manuscript.

Contributing

Contributions to this project are welcome! Please feel free to fork the repository and submit pull requests.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

We would like to acknowledge the developers and contributors of the libraries and tools used in this project. Special thanks to the HMMer team for their contribution to bioinformatics.