diff --git a/README.md b/README.md
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--- a/README.md
+++ b/README.md
@@ -1,8 +1,49 @@
 # JASS analysis pipeline
 contact hanna.julienne@pasteur.fr
+
+## Table of Content
+
+1. [Overview](##Overview)
+2. [Quick Start](#quick-start---run-pipeline-on-test-data)
+3. [Advanced Options](#optional-parameters)
+4. [Available Reference Panels](#available-reference-panels)
+5. [Usage on HPC and docker container](#usage-example-on-hpc-cluster)
+
 ## Overview
 
 We present here a nextflow pipeline to harmonize, impute and analyze jointly GWAS summary statistics.
+For more detail about the multi-trait GWAS or imputation method and to see example potential results from this type of analysis:
+
+**When referring to theoretical basis of JASS tests, cite :**
+
+* Julienne H, Laville V, McCaw ZR, He Z, Guillemot V, Lasry C, Ziyatdinov A, Nerin C, Vaysse A, Lechat P, Ménager H, Le Goff W, Dube MP, Kraft P, Ionita-Laza I, Vilhjálmsson BJ, Aschard H.
+Multitrait GWAS to connect disease variants and biological mechanisms.
+PLoS Genet. 2021 Aug 30;17(8):e1009713.
+doi: 10.1371/journal.pgen.1009713.
+
+**When using JASS software in publication, cite :**
+* Julienne H, Lechat P, Guillemot V, Lasry C, Yao C, Araud R, Laville V, Vilhjalmsson B, Ménager H, Aschard H.
+JASS: command line and web interface for the joint analysis of GWAS results.
+NAR Genom Bioinform. 2020 Mar;2(1):lqaa003.
+doi: 10.1093/nargab/lqaa003.
+
+**When referring to the imputation of summary statistics, cite :**
+* Julienne H, Shi H, Pasaniuc B, Aschard H.
+RAISS: robust and accurate imputation from summary statistics. Bioinformatics.
+Bioinformatics. 2019 Nov 1;35(22):4837-4839.
+doi: 10.1093/bioinformatics/btz466.
+
+**Additional publications tied to JASS**
+* Suzuki, Y., Ménager, H., Brancotte, B., Vernet, R., Nerin, C., Boetto, C., Auvergne, A., Linhard, C., Torchet, R., Lechat, P., Troubat, L., Cho, M.H., Bouzigon, E., Aschard, H., Julienne, H., 2023.
+Trait selection strategy in multi-trait GWAS: Boosting SNPs discoverability.
+doi.org/10.1101/2023.10.27.564319
+
+* Troubat, L., Fettahoglu, D., Henches, L., Aschard, H., Julienne, H., 2023.
+Multi-trait GWAS for diverse ancestries: Mapping the knowledge gap.
+doi.org/10.1101/2023.06.23.546248
+Auvergne, A., Traut, N., Henches, L., Troubat, L., Frouin, A., Boetto, C., Kazem, S., Julienne, H., Toro, R., Aschard, H., 2023.
+Linking the genetic structure of neuroanatomical phenotypes with psychiatric disorders.
+doi.org/10.1101/2023.11.01.564329
 
 The current pipeline integrate the following workflow:
 
@@ -26,8 +67,7 @@ Clone the current repository locally:
     git clone https://gitlab.pasteur.fr/statistical-genetics/jass_suite_pipeline.git
 ```
 
-[!NOTE]
-The pipeline has been upgraded to nextflow DSL2 syntax recently. If you wish to use the previous version in DSL1, you find it in ./old_versions and run it with previous version of nextflow ("NXF_VER=22.10.5 nextflow run jass_pipeline.nf ....") 
+[!NOTE] The pipeline has been upgraded to nextflow DSL2 syntax recently. If you wish to use the previous version in DSL1, you find it in ./old_versions and run it with previous version of nextflow ("NXF_VER=22.10.5 nextflow run jass_pipeline.nf ....") 
 
 
 Test data are located in the ${PATH_TO_PIPELINE_FOLDER}/test_data/hg38_EAS/ folder
@@ -36,7 +76,7 @@ These are extracts of summary statistics from a trans ancestry GWAS on blood tra
 
 They correspond to the chromosome 21 and 22 for the East asian ancestry.
 
-Once done you can launch the pipeline as:
+Once done you can launch the pipeline using by replacing {ABSOLUTE_PATH_TO_PIPELINE_FOLDER} by the absolute path to the folder where you cloned the JASS pipeline:
 ```
     nextflow run jass_pipeline.nf --ref_panel_WG {ABSOLUTE_PATH_TO_PIPELINE_FOLDER}Ref_Panel/1000G_EAS_0_01_chr22_21.csv --gwas_folder {ABSOLUTE_PATH_TO_PIPELINE_FOLDER}/test_data/hg38_EAS/ --meta-data {ABSOLUTE_PATH_TO_PIPELINE_FOLDER}/input_files/Data_test_EAS.csv --region {ABSOLUTE_PATH_TO_PIPELINE_FOLDER}/input_files/All_Regions_ALL_ensemble_1000G_hg38_EAS.bed --group {ABSOLUTE_PATH_TO_PIPELINE_FOLDER}/input_files/group.txt -with-report jass_report.html -c nextflow_local.config
 ```
@@ -62,7 +102,7 @@ See sections below, for running the imputation step and/or the LD-score step.
 
 The following Item are necessary to run JASS pipeline on real data
 
-1. --meta_data: A path toward a meta-data file describing GWAS (see example file in ./input_files/test1.tsv and [jass_preprocessing documentation](http://statistical-genetics.pages.pasteur.fr/jass_preprocessing/))
+1. --meta_data: A path toward a meta-data file describing GWAS (see example file in ./input_files/Data_test_EAS.csv and [jass_preprocessing documentation](http://statistical-genetics.pages.pasteur.fr/jass_preprocessing/))
 2. --gwas_folder: A path toward a folder containing the summary statistics to analyze
 3. --ref_panel_WG: a path toward a reference panel (all genome as 1 file). See below to download curated reference panels by ancestries derived from 1000G V3 on hg38 assembly
 4. --region: Quasi LD independent regions. These regions are used by JASS to determine quickly LD-independent hits accross the genome. The input_files folder contains one region file by ancestry on hg38 assembly. If working with a different assembly or population, you can provide 1Mb delimitations as a rough equivalent of these regions.
@@ -93,26 +133,12 @@ imputed files will be stored in
 
 ## Available reference panels
 
-To make reference panel readily available, we use git lfs.
-To download them, you can either install git lfs or simply download the file through this here and place it in the ./Ref_Panel folder.
-
-Solution with git LFS:
-
-```
-    git lfs pull --include 1000G_AFR_0_01.csv
-```
+Reference panels for JASS can be downloaded on Zenodo: https://zenodo.org/records/13940447
+You can then decompress than and place them in the ./Ref_Panel folder.
 
 We provide a reference panel for common SNPs (MAF > 1%) for the 
 East asian (EAS), African, South east Asian, Hispanic and European populations. They were built from 1000 Genomes consortium phase 3 data (hg38 build) for each ancestry. (The 1000 Genomes Project Consortium 2015).
 
-You can download the five panel using the command:
-
-```
-    git lfs fetch --all
-```
-or manualy through the gitlab interface:
-![workflow image](./doc/download_test_files.png)
-
 ## Running the LDSC regression covariance step
 ### To infer multi-trait z-scores null distribution, heritabilities, genetic correlations using the LDscore regression
 
diff --git a/Ref_Panel/1000G_AFR_0_01.csv b/Ref_Panel/1000G_AFR_0_01.csv
deleted file mode 100755
index 34732ab61353114c4ab406dce02a6e50b3520077..0000000000000000000000000000000000000000
--- a/Ref_Panel/1000G_AFR_0_01.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:934c63984d760943b0228802f1fcbf3a778d8a3c525e3b78138948b4c07f4ab4
-size 515852597
diff --git a/Ref_Panel/1000G_AMR_0_01.csv b/Ref_Panel/1000G_AMR_0_01.csv
deleted file mode 100755
index 8517b1b84654f4b2357560d32ca1e78526789b6f..0000000000000000000000000000000000000000
Binary files a/Ref_Panel/1000G_AMR_0_01.csv and /dev/null differ
diff --git a/Ref_Panel/1000G_EAS_0_01.csv b/Ref_Panel/1000G_EAS_0_01.csv
deleted file mode 100644
index fb62efacb3efe39254248a5285f9a696aeb40a4e..0000000000000000000000000000000000000000
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diff --git a/Ref_Panel/1000G_EUR_0_01.csv b/Ref_Panel/1000G_EUR_0_01.csv
deleted file mode 100755
index 44d786177f9c6b6ca56f0f79753640021af39bd1..0000000000000000000000000000000000000000
Binary files a/Ref_Panel/1000G_EUR_0_01.csv and /dev/null differ
diff --git a/Ref_Panel/1000G_SAS_0_01.csv b/Ref_Panel/1000G_SAS_0_01.csv
deleted file mode 100755
index 52db85218eeb10bd8be57ca2584ff77a3af68527..0000000000000000000000000000000000000000
Binary files a/Ref_Panel/1000G_SAS_0_01.csv and /dev/null differ
diff --git a/input_files/Data_test_EAS.csv b/input_files/Data_test_EAS.csv
index 39596cb6da016424d3a85faf08193d4ec571b555..11be861682923eefd7e2da9dd450a0caaad61a1b 100644
--- a/input_files/Data_test_EAS.csv
+++ b/input_files/Data_test_EAS.csv
@@ -1,4 +1,4 @@
-"filename"	"Consortium"	"Outcome"	"FullName"	"internalDataLink"	"Type"	"Reference"	"ReferenceLink"	"dataLink"	"Nsample"	"Ncase"	"Ncontrol"	"Nsnp"	"snpid"	"POS"	"CHR"	"a1"	"a2"	"freq"	"pval"	"n"	"z"	"OR"	"se"	"index_type"	"imp"	"ncas"	"ncont"
+"filename"	"Consortium"	"Outcome"	"FullName"	"internalDataLink"	"Type"	"Reference"	"ReferenceLink"	"dataLink"	"Nsample"	"Ncase"	"Ncontrol"	"Nsnp"	"snpid"	"POS"	"CHR"	"a1"	"a2"	"freq"	"pval"	"n"	"beta_or_Z"	"OR"	"se"	"index_type"	"imp"	"ncas"	"ncont"
 "WBC_EAS_chr22.tsv"	"BCT"	"WBC"	"White blood cell count"		"Cellular"	"Chen MH et al. 2020"	"https://pubmed.ncbi.nlm.nih.gov/32888493/"	"http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90002001-GCST90003000/GCST90002373/harmonised/32888493-GCST90002373-EFO_0007988.h.tsv.gz"	15061			36864690	"hm_rsid"	"hm_pos"	"hm_chrom"	"hm_effect_allele"	"hm_other_allele"	"hm_effect_allele_frequency"	"p_value"		"hm_beta"		"standard_error"	"rs-number"
 "RBC_EAS_chr22.tsv"	"BCT"	"RBC"	"Red blood cell count"		"Cellular"	"Chen MH et al. 2020"	"https://pubmed.ncbi.nlm.nih.gov/32888493/"	"http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90002001-GCST90003000/GCST90002373/harmonised/32888493-GCST90002373-EFO_0007988.h.tsv.gz"	15061			36864690	"hm_rsid"	"hm_pos"	"hm_chrom"	"hm_effect_allele"	"hm_other_allele"	"hm_effect_allele_frequency"	"p_value"		"hm_beta"		"standard_error"	"rs-number"
 "PLT_EAS_chr22.tsv"	"BCT"	"PLT"	"Platelet cell count"		"Cellular"	"Chen MH et al. 2020"	"https://pubmed.ncbi.nlm.nih.gov/32888493/"	"http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90002001-GCST90003000/GCST90002373/harmonised/32888493-GCST90002373-EFO_0007988.h.tsv.gz"	15061			36864690	"hm_rsid"	"hm_pos"	"hm_chrom"	"hm_effect_allele"	"hm_other_allele"	"hm_effect_allele_frequency"	"p_value"		"hm_beta"		"standard_error"	"rs-number"