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 . For a web-based interface that enables easy use of this tool, check out the .
SSBlazer is designed to perform three primary functions:
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.
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.
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.
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. (, )
Point Mutation Analysis: The software can also predict the impact of point mutations on nearby DNA break probability. (, )
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. ()
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 .
This software is developed by , and . 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 .
