Getting Started
Last updated
Last updated
SSBlazer represents a pioneering effort to harness deep learning techniques for the prediction of Single Strand Break (SSB) sites. It utilizes genome-wide single-strand break sequencing data with single-nucleotide resolution to make these predictions. For more information, to download the tool, or to contribute, visit the SSBlazer GitHub repository. For a web-based interface that enables easy use of this tool, check out the SSBlazer Web Server.
SSBlazer is designed to perform three primary functions:
Prediction of DNA Single Strand Break Sites SSBlazer can accurately predict the probability of single strand breaks at each nucleotide position within a DNA sequence provided in FASTA format. (Manual, Web Server)
Point Mutation Analysis: The software can also predict the impact of point mutations on nearby DNA break probability. (Manual, Web Server)
Training a New Model: Beyond SSB predictions, SSBlazer's framework allows for training on different lesion types, such as double-strand breaks, by supplying a dataset specific to the desired lesion type. (Manual)
For optimal performance, input lengths are limited in the web server. Please use our local software version for larger tasks.
To get started with SSBlazer, we need to set up the environment correctly.
First, download the repository by cloning it from the GitHub repository.
Once cloned, you need to navigate into the ssblazer directory.
Next, you will need to create a new environment. We use the conda package manager for this. If you don't have conda installed, you can download it from the official site.
With conda installed, create the environment using the following command:
Once the environment is created, you can activate it using the following command:
After these steps, your environment is set up and ready to go!
With SSBlazer, you can make predictions either from a local fasta file or using our online tool. You can use a local fasta file to make predictions. The following command uses a test fasta file and a batch size of 128:
After running the prediction script, the results will be saved in a .bed file format. This file, result.bed, contains the predicted SSB sites.
This software is developed by Sheng Xu, Junkang Wei and Yu Li. If you have any questions or need assistance with SSBlazer, you can contact Yu Li directly at liyu[AT]cse.cuhk.edu.hk. For bug reporting, feature requests, or general questions about the tool, you may also raise an issue on the SSBlazer GitHub repository.
If you use SSBlazer in your work, please cite the publication:
Xu S, Wei J, Li Y. Genome-wide nucleotide-resolution model of single-strand break site reveals species evolutionary hierarchy[J]. arXiv preprint arXiv:2208.09813, 2022.
