https://github.com/datngu/tagsnp_evaluation
Evaluation tag SNP selection strategies
https://github.com/datngu/tagsnp_evaluation
Last synced: 2 months ago
JSON representation
Evaluation tag SNP selection strategies
- Host: GitHub
- URL: https://github.com/datngu/tagsnp_evaluation
- Owner: datngu
- Created: 2021-08-29T02:55:08.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2022-01-13T07:31:51.000Z (over 3 years ago)
- Last Synced: 2025-01-21T20:48:47.182Z (4 months ago)
- Language: C++
- Size: 1.1 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# TagSNP_evaluation
Evaluation tag SNP selection strategies
These scipts tested in Ubuntu v18 environment.
## Software requirements
Leave one out imputation cross validation requires R, vcftools, bcftools, minimac3, minimac4, and plink.
- VCFtools 0.1.17 : https://vcftools.github.io
- bcftools 1.10.2 : https://github.com/samtools/bcftools
- plink1.9 : https://www.cog-genomics.org/plink/
- minimac3 : https://genome.sph.umich.edu/wiki/Minimac3
- minimac4 : https://genome.sph.umich.edu/wiki/Minimac4
- R packages version 3.6.0 or latter
## Clone this project and config pipeline:
```sh
git clone https://github.com/datngu/TagSNP_evaluation
```
# I. RUNNING PIPELINES
Assumming that you have a processed (Bialellic SNPs with MAF >= 1%) vcf.gz file named: chr10_EAS.vcf.gz and want to compare impuatation accuracy with 30000 tag SNP selected.
## 1. Running TagIt
```sh
cd TagSNP_evaluation/TagIt
bash configure.sh
bash TagIt_pipeline.sh ../chr10_EAS.vcf.gz chr10_EAS
# picking top 30000 tag SNP selected from
head -n 30000 chr10_EAS/chr10_EAS_tags_cleaned.txt > chr10_EAS_tags_30000_cleaned.txt
```
You output should be: `TagSNP_evaluation/TagIt/chr10_EAS_tags_30000_cleaned.txt`
## 2. Running FastTagger
```sh
cd TagSNP_evaluation/FastTagger
bash bash configure.sh
bash FastTagger_pipeline.sh ../chr10_EAS.vcf.gz chr10_EAS 30000 chr10_EAS
```
You output should be: `TagSNP_evaluation/FastTagger/cleaned_EAS_chr10_EAS_30000.tagSNP.txt`
## 3. Running EQ_uniform
```sh
cd TagSNP_evaluation/EQ_uniform
Rscript EQ_uniform.R vcf=../chr10_EAS.vcf.gz size=30000 out=EQ_uniform_array_30000.txt
```
You output should be: `TagSNP_evaluation/EQ_uniform/EQ_uniform_array_30000.txt`
## 4. Running EQ_MAF
```sh
cd TagSNP_evaluation/EQ_MAF
Rscript EQ_MAF.R vcf=../chr10_EAS.vcf.gz size=30000 out=EQ_MAF_array_30000.txt
```
You output should be: `TagSNP_evaluation/EQ_MAF/EQ_MAF_array_30000.txt`
# II. EVALUATION WITH LEAVE ONE OUT CROSS VALIDATION
## 1. Create imputation reference pannel
This step take very long time, depends on your size of chromosome and population.
```sh
cd TagSNP_evaluation
create_imputation_ref.sh -v chr10_EAS.vcf.gz -o chr10_EAS_imputation_ref -p 16
```
## 2. Leave one out imputation with your pre-buit reference panel and computing Pearson's correlation
```sh
cd TagSNP_evaluation
# 1. TagIt
## imputation
imputation_with_prebuilt_ref.sh -t TagIt/chr10_EAS_tags_30000_cleaned.txt -r chr10_EAS_imputation_ref -o TagIt_EAS -p 16
## computing Pearson's correlation
compute_imputation_accuracy.R imputation=TagIt_EAS out=TagIt_EAS.Rdata
# 2. FastTagger
## imputation
imputation_with_prebuilt_ref.sh -t FastTagger/cleaned_EAS_chr10_EAS_30000.tagSNP.txt -r chr10_EAS_imputation_ref -o FastTagger_EAS -p 16
## computing Pearson's correlation
compute_imputation_accuracy.R imputation=FastTagger_EAS out=FastTagger_EAS.Rdata
# 3. EQ_uniform
## imputation
imputation_with_prebuilt_ref.sh -t EQ_uniform/EQ_uniform_array_30000.txt -r chr10_EAS_imputation_ref -o EQ_uniform_EAS -p 16
## computing Pearson's correlation
compute_imputation_accuracy.R imputation=EQ_uniform_EAS out=EQ_uniform_EAS.Rdata
# 4. EQ_MAF
## imputation
imputation_with_prebuilt_ref.sh -t EQ_MAF/EQ_MAF_array_30000.txt -r chr10_EAS_imputation_ref -o EQ_MAF_EAS -p 16
## computing Pearson's correlation
compute_imputation_accuracy.R imputation=EQ_MAF_EAS out=EQ_MAF_EAS.Rdata
```
You output should be: `TagIt_EAS.Rdata, FastTagger_EAS.Rdata, EQ_uniform_EAS.Rdata, EQ_MAF_EAS.Rdata`
# III. REFERENCE
Nguyen, D. T., Dinh, H. Q., Vu, G. M., Nguyen, D. T., & Vo, N. S. (2021, November). A comprehensive imputation-based evaluation of tag SNP selection strategies. In 2021 13th International Conference on Knowledge and Systems Engineering (KSE) (pp. 1-6). IEEE. https://doi.org/10.1109/KSE53942.2021.9648614