The eQTac method.
Project description
eQTac
EQTac is a method to predict the potential regulatory elements (PREs) and their target genes, based on the eQTL datasets, the only additional data was ATAC-seq or ChIP-seq peak data.
Schematic
Dependence
python == 3.8 Conda enviroment is recommended:
conda create -n eqtac python=3.8 r-base=4.3 bioconda::r-gkmsvm=0.83 bioconda::bioconductor-bsgenome.hsapiens.ucsc.hg19.masked=1.3.993
Python packages
numpy == 1.22.4
pandas == 1.4.3
pybedtools == 0.8.2
pysam == 0.16.0.1
rpy2 == 3.5.11
scipy == 1.8.1
Other software (need manual installation)
plink == v1.90b6.24 (not plink2, plink should in $PATH)
bedtools == v2.30.0 (bedtools should in $PATH)
R = 4.3
gkmSVM == 0.8.3
bsgenome.hsapiens.ucsc.hg19.masked == 1.3.993
Installation & test example
# installation
pip install eQTac
# test examples
git clone https://github.com/JFF1594032292/eQTac.git # just for test
cd eQTac/Utilities_pipeline
nohup sh example_All_pipeline.sh &
Then it will generate an output_eQTac
folder, which contained results file test.geno.vcf.gz.PRE_score.eQTac_result.FDR.txt
. (example takes 3~5min)
Input data
- Data used in model training:
- Positive sets in bed format. It's usually the peak data from ATAC-seq or ChIP-seq, we recomended to trim peaks to the core region (e.g. summits $\pm$ 100bp). See
test_data/test.positive.bed
. - Excluded sets in bed format. It's usually the peak data from ATAC-seq or ChIP-seq, but with more relaxed thresholds (e.g. p=0.2). These region will be removed from generated negative regions, in order to remove potential positive sequences from negative sets. See
test_data/test
.exclude.bed. - Fasta file with .fai index. Usually the human genome sequnce file in fasta format. See
test_data/test.hg19.chr17.fa
.
- Positive sets in bed format. It's usually the peak data from ATAC-seq or ChIP-seq, we recomended to trim peaks to the core region (e.g. summits $\pm$ 100bp). See
- Data used in eQTac calculation.
- PRE.bed. The candidate regions used to assess chromatin accessibility scores across different individuals and then calculate correlation with target genes. See
test_data/test.pre.bed
. - Genotype data in plink format. Individual genotype in eQTL datasets. See
test_data/test.geno.bed, test_data/test.geno.bim, test_data/test.geno.fam
. - Expression file. The expresion values are normalized expression values (see GTEx) and already corrected for covariates. See
test_data/test.exp_residual
. - Snplist file. SNP list file used in eQTac analysis. Note: only single nucleotide mutations. See
test_data/test.geno.snplist
.
- PRE.bed. The candidate regions used to assess chromatin accessibility scores across different individuals and then calculate correlation with target genes. See
Usage pattern
We provided three level patterns: (1) pipeline level. (2) part level. (3) function level.
Pipeline-level pattern
For the function level pattern, we provide a script: Part-All-eQTac_pipeline.py.
It can be used as Utilities_pipeline/example_All_pipeline.sh
:
python Part-All-eQTac_pipeline.py \
-p test_data/test.positive.bed \
-ex test_data/test.exclude.bed \
-pre test_data/test.pre.bed \
--geno test_data/test.geno \
--snp test_data/test.geno.snplist \
-fa test_data/test.hg19.chr17.fa \
-exp test_data/test.exp_residual \
-n 100 \
-o output_eQTac \
-t 3 -l 10 -k 6 -c 10 -g 2 -e 0.01
Part-level pattern
For the function level pattern, we provide four scripts:
Part-1-Train_model.py
Part-2-Generate_PRE_fa.py
Part-3-Predict_PRE_score.py
Part-4-Calculate_eQTac_correlation.py
It can be used as Utilities_pipeline/example_Part_pipeline.sh
:
python Part-1-Train_model.py \
-p test_data/test.positive.bed \
-ex test_data/test.exclude.bed \
-o output_eQTac_part \
-t 3 -l 10 -k 6 -c 10 -g 2 -e 0.01 -f 2.5
python Part-2-Generate_PRE_fa.py \
-pre test_data/test.pre.bed \
--geno test_data/test.geno \
--snp test_data/test.geno.snplist \
-fa test_data/test.hg19.chr17.fa \
-o output_eQTac_part
python Part-3-Predict_PRE_score.py \
-m output_eQTac_part/test.positive.pos.svmmodel.3_10_6_0.01.model.txt \
-l output_eQTac_part/test.geno.snplist.bed--test.pre.bed.pre_snplist.ld_info \
-mfa output_eQTac_part/test.geno.snplist.bed--test.pre.bed.pre_snplist.ld_info.snplist.bed.mutate.fa \
-geno test_data/test.geno \
-snp output_eQTac_part/test.geno.snplist.bed--test.pre.bed.pre_snplist \
-T 1 \
-o output_eQTac_part
python Part-4-Calculate_eQTac_correlation.py \
-pre output_eQTac_part/test.geno.vcf.gz.PRE_score \
-exp test_data/test.exp_residual \
-n 20 \
-o output_eQTac_part
Function-level pattern
For the function level pattern, we provide a series of functions:
from eQTac.get_nullseq import get_nullseq
from eQTac.filter_bkg import filter_bkg
from eQTac.generate_snp_dict import generate_snp_dict
from eQTac.generate_PRE import generate_PRE
from eQTac.generate_mut_fa import generate_mut_fa
from eQTac.geno2score import geno2score
from eQTac.eQTac_correlation import eQTac_correlation
from eQTac.eQTac_permutation import eQTac_permutation
from eQTac.control_FDR import control_FDR
These functions can be used to construct the whole pipeline.
Recomend
We recomend to use the pipeline-level pattern at first to make sure that all input formats are valid.
Then use the part-level pattern to debug parameters. (e.g. training a best performance model). The first step is the most time-consuming step, we recomended to use the part-level pattern to save the SVM model xxx.svmmodel.3_10_6_0.01.model.txt
.
If you are familiar with this pipeline, you can directly use the function-level pattern to construct your own pipeline.
Notes
- The test result is very volatile, because of the small size of test dataset (only ~6MB length of sequences). The results will be stable with tens of thousands or more peaks used as positive set.
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