https://github.com/JXing-Lab/nanopore-sv-evaluation

Repository for the paper "Evaluating nanopore sequencing data processing pipelines for structure variation identification"
https://github.com/JXing-Lab/nanopore-sv-evaluation

Last synced: 26 days ago
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Repository for the paper "Evaluating nanopore sequencing data processing pipelines for structure variation identification"

• Host: GitHub
• URL: https://github.com/JXing-Lab/nanopore-sv-evaluation
• Owner: JXing-Lab
• License: mit
• Created: 2019-05-17T01:10:00.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2019-10-04T19:11:10.000Z (about 5 years ago)
• Last Synced: 2024-02-15T09:36:22.082Z (10 months ago)
• Language: Shell
• Homepage:
• Size: 47.9 KB
• Stars: 5
• Watchers: 2
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• Awesome-Bioinformatics-Benchmarks - all code used in the study - installed programs and all seven pipeline. (Variant Callers / SV callers)

README

        
[![DOI](https://zenodo.org/badge/187123521.svg)](https://zenodo.org/badge/latestdoi/187123521)

# nanopore-sv-evaluation

This is the code repository for the paper "Evaluating nanopore sequencing data processing pipelines for structure variation identification".

- scripts/download.sh: code for downloading the online data

- scripts/simulation.sh: code for genreating simulation datasets

- scripts/workflow.sh: code for running the aligners and SV callers

- scripts/integration.sh: code for consensus call set generation

- scripts/comparison.md: code for call set and true set comparisons

- scripts/coverage.sh: code for subsetting the reads and coverages analysis

- scripts/ML/gen_label.sh: code for generating labels for the random forest classifier

- scripts/ML/random_forest.py: code for the random forest classifier training, predicting, and evaluating