diff --git a/README.md b/README.md
index cdbbafe9791ff6d0f30f55b2a2d68fd963e0df57..fbb9d6cdd2f596b5f3ec20d7b3fa07abb41dabb3 100644
--- a/README.md
+++ b/README.md
@@ -18,8 +18,5 @@ sudo /home/gonzalez/Software/micromamba/envs/m2bsgreprod/bin/apptainer  build re
 
 micromamba activate m2bsgreprod
 
-apptainer exec results/containers/m2bsgreprod.sif Rscript src/download_data.R
+apptainer exec results/containers/m2bsgreprod3.sif make -f workflows/makefile
 
-apptainer exec results/containers/m2bsgreprod.sif Rscript src/tp1.R
-
-make
diff --git a/Release.md b/Release.md
new file mode 100644
index 0000000000000000000000000000000000000000..1c5a717c55b4994b57c4e71181f8034f19ae2634
--- /dev/null
+++ b/Release.md
@@ -0,0 +1 @@
+Version for evaluation
diff --git a/results/tp1/TP1_asbvg.RData b/results/tp1/TP1_asbvg.RData
index 12b56cfb2fbbe9e0f3087b2e245332191016fd5e..3bc0a32960c517ce7de5b801685fe35d6a76ff31 100644
Binary files a/results/tp1/TP1_asbvg.RData and b/results/tp1/TP1_asbvg.RData differ
diff --git a/results/tp1/plink_base.bed b/results/tp1/plink_base.bed
index 15d24eaf7339f0d5cf08792c98c065fa58279454..d6901a7dbfcd6c10f2bf78354eee9352c37555d7 100644
Binary files a/results/tp1/plink_base.bed and b/results/tp1/plink_base.bed differ
diff --git a/results/tp1/plink_base.bim b/results/tp1/plink_base.bim
index f81c97f10824ffc0ba3bab523312c2b65c78bfca..1c5f39b6d0be914117e3d996113c6185f0a8707a 100644
--- a/results/tp1/plink_base.bim
+++ b/results/tp1/plink_base.bim
@@ -1,25000 +1,25000 @@
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diff --git a/src/TP1.v2.R b/src/TP1.v2.R
index 5bb53cd9367251bb9fae37a49d20b538dc442fa2..0db5778f338ab9aedbca089f7b894ee1a86837c0 100644
--- a/src/TP1.v2.R
+++ b/src/TP1.v2.R
@@ -8,20 +8,26 @@ output_dir <- file.path(m2bsgreprod, "results", "TP1")
 # Créer les répertoires si nécessaire
 dir.create(output_dir, showWarnings = FALSE, recursive = TRUE)
 
+# Les données analysées nécessitant beaucoup de RAM, nous allons sélectionner aléatoirement 250000 SNPs et réecrire des fichiers bed, bim, fam
+penncath_bed_path = file.path(data.dir, "penncath.bed")
+penncath_bim_path = file.path(data.dir, "penncath.bim")
+penncath_fam_path = file.path(data.dir, "penncath.fam")
+
+clinical_csv_path = "data/GWAStutorial_clinical.csv"
+
 # Fichiers d'entrées
 gwas.fn<-lapply(c(bed='bed', fam='fam', bim ='bim', gds='gds'), function(n) sprintf("%s/GWAStutorial.%s", data.dir, n))
+
 gwas.fn.2<-lapply(c(bed='bed', fam='fam', bim ='bim', gds='gds'), function(n) sprintf("%s/data.%s", data.dir, n))
-clinical.fn<-sprintf("%s/GWAStutorial_clinical.csv", data.dir)
+
+# clinical.fn<-sprintf("%s/GWAStutorial_clinical.csv", data.dir)
 onethou.fn<-lapply(c(info='info', ped='ped'), function(n) sprintf("%s/chr16_1000g_CEU.%s", data.dir, n))
 protein.coding.coords.fname<-sprintf("%s/ProCodgene_coords.csv", data.dir)
 
 # Sauvegarde des "modules"
 working.data.fname <- function(num) { sprintf("%s/working_%s.Rdata", output_dir, num) }
 
-# Les données analysées nécessitant beaucoup de RAM, nous allons sélectionner aléatoirement 250000 SNPs et réecrire des fichiers bed, bim, fam
-penncath_bed_path = file.path(data.dir, "penncath.bed")
-penncath_bim_path = file.path(data.dir, "penncath.bim")
-penncath_fam_path = file.path(data.dir, "penncath.fam")
+
 
 geno <- snpStats::read.plink(penncath_bed_path, penncath_bim_path, penncath_fam_path, select.snps=sample(1:861473, 25000, replace = FALSE ), na.strings = ("-9"))
 
@@ -39,7 +45,7 @@ genoFam<-geno$fam
 rm(geno)
 
 # On charge le fichier clinique
-clinical<- read.csv(clinical.fn, colClasses = c("character", "factor", "factor", rep("numeric", 4)))
+clinical<- read.csv(clinical_csv_path, colClasses = c("character", "factor", "factor", rep("numeric", 4)))
 rownames(clinical)<-clinical$FamID
 print(head(clinical))
 
diff --git a/src/TP1.v3.R b/src/TP1.v3.R
index 15b1f1a9b55d579c72a69ff487853acd0a9612a9..abefe186331485710056cc4677639ded84e0841f 100644
--- a/src/TP1.v3.R
+++ b/src/TP1.v3.R
@@ -9,7 +9,6 @@ penncath_fam_path = "results/data/penncath.fam"
 
 geno <- snpStats::read.plink(penncath_bed_path, penncath_bim_path, penncath_fam_path, select.snps=sample(1:861473, 25000, replace = FALSE ), na.strings = ("-9"))
 
-
 plink_base=file.path(output_dir, "plink_base")
 snpStats::write.plink(plink_base, snps=geno$genotypes, pedigree=geno$fam[,1], id=geno$fam[,1], mother=geno$fam[,4], sex=geno$fam[,5], phenotype=geno$fam[,6], chromosome = geno$map[,1], genetic.distance = geno$map[,3], position = geno$map[,4], allele.1 = geno$map[,5], allele.2 = geno$map[,6], na.code = ("-9"))
 
@@ -26,6 +25,15 @@ genoFam<-geno$fam
 # On commence par libérer de l'espace
 rm(geno)
 
+# On charge le fichier clinique
+clinical_csv_path = "data/GWAStutorial_clinical.csv"
+clinical<- read.csv(clinical_csv_path, colClasses = c("character", "factor", "factor", rep("numeric", 4)))
+rownames(clinical)<-clinical$FamID
+#print(head(clinical))
+
+protein.coding.coords.fname<-"data/ProCodgene_coords.csv"
+
+
 rdata_path = file.path(output_dir, "TP1_asbvg.RData")
 save.image(rdata_path)