Recombination_rate_calculate.sh.txt

SIMULATE="/project-whj/software/ReLERNN/ReLERNN/ReLERNN_SIMULATE"
TRAIN="/project-whj/software/ReLERNN/ReLERNN/ReLERNN_TRAIN"
PREDICT="/project-whj/software/ReLERNN/ReLERNN/ReLERNN_PREDICT"
BSCORRECT="/project-whj/software/ReLERNN/ReLERNN/ReLERNN_BSCORRECT"
SEED="42"
MU="1.91e-9"
URTR="1"
DIR="/project-whj/lzx/0516_chicken_recombrate/CH_result/CH_Chr1_RelERNN_output"
VCF="/project-whj/lzx/0516_chicken_recombrate/VCF/CH_20sample_0516-160sample.chr1-39_20230306.Galgal7b.SNP.filtered.PASS.geno02maf005.vcf.recode.vcf"
GENOME="/project-whj/lzx/00_0410_reocomb_result/02_Hotspot_motif/genome_bed/Chr1.genome.bed.txt"
MASK="/project-whj/lzx/00_0410_reocomb_result/02_Hotspot_motif/accessibility_mask.bed"

# Simulate data
${SIMULATE} \
    --vcf ${VCF} \
    --genome ${GENOME} \
    --mask ${MASK} \
    --projectDir ${DIR} \
    --assumedMu ${MU} \
    --assumedGenTime 1 \
    --upperRhoThetaRatio ${URTR} \
    --nTrain 100000 \
    --nVali 1000 \
    --nTest 1000 \
    --seed ${SEED}
# Train network
${TRAIN} \
    --projectDir ${DIR} \
    --nEpochs 1000 \
    --nValSteps 20 \
    --seed ${SEED} \
    -t 16 \
    --gpuID 1
# Predict
${PREDICT} \
    --vcf ${VCF} \
    --projectDir ${DIR} \
    --seed ${SEED}

# Parametric Bootstrapping
${BSCORRECT} \
    --projectDir ${DIR} \
    --nSlice 100 \
    --nReps 1000 \
    --seed ${SEED}