omicseye/deepbreaks-dc repository overview

⁠deepBreaks

deepBreaks , a computational method, aims to identify important changes in association with the phenotype of interest using multi-alignment sequencing data from a population.

Key features:

• Generality: deepBreaks is a new computational tool for identifying genomic regions and genetic variants significantly associated with phenotypes of interest.
• Validation: A comprehensive evaluation of deepBreaks performance using synthetic data generation with known ground truth for genotype-phenotype association testing.
• Interpretation: Rather than checking all possible mutations (breaks), deepBreaks prioritizes only statistically promising candidate mutations.
• Elegance: User-friendly, open-source software allowing for high-quality visualization and statistical tests.
• Optimization: Since sequence data are often very high volume (next-generation DNA sequencing reads typically in the millions), all modules have been written and benchmarked for computing time.
• Documentation: Open-source GitHub repository of code complete with tutorials and a wide range of real-world applications.

Citation:

Mahdi Baghbanzadeh, Tyson Dawson, Bahar Sayoldin, Todd H. Oakley, Keith A. Crandall, Ali Rahnavard (2023). deepBreaks: a machine learning tool for identifying and prioritizing genotype-phenotype associations , https://github.com/omicsEye/deepBreaks/⁠.

To use this docker image, assuming your datasets are stored in dir:

• you have a MSA file (here sf.fasta)
• a meta data (here meta.tsv)
• the phenotype column name in the meta.tsv here is LambdaMax
• the phenotype is a continues measure (reg) use the following command to run the image:

docker run --rm -v ~/Desktop/mahdi/lite_mar:/app/data deepbreaks-dc -sf /app/data/sf.fasta -st aa -md /app/data/meta.tsv -mv LambdaMax -a reg