From 8e99a846fe0c4788ec0294fb49c7284105b64757 Mon Sep 17 00:00:00 2001
From: Jana Ebler <47976081+eblerjana@users.noreply.github.com>
Date: Tue, 21 Feb 2023 10:16:50 +0100
Subject: [PATCH] Update README.md

---
 README.md | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/README.md b/README.md
index 1187b08..5bd5311 100644
--- a/README.md
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@@ -78,6 +78,14 @@ We typically generate such VCFs from haplotype-resolved assemblies using this pi
 
 In this case you can run PanGenie using the Snakemake pipeline provided in ``pipelines/run-from-callset/``. This automatically merges overlapping alleles into mult-allelic VCF, runs PanGenie and later converts the output VCF back to the original representation.
 
+#### Existing reference panels to use with PanGenie
+
+We have already produced input reference panels for several datasets from high-quality, haplotype-resolved assemblies that can be used as input to PanGenie:
+
+- HGSVC (GRCh38, 64 haplotypes): http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data_collections/HGSVC2/release/v2.0/PanGenie_PAV-panel/20210311_pav-panel-freeze4.vcf.gz
+- HPRC (GRCh38, 88 haplotypes): https://zenodo.org/record/6797328/files/cactus_filtered_ids.vcf.gz?download=1
+- HPRC (CHM13, 88 haplotypes): https://zenodo.org/record/7660118/files/cactus_filtered_ids_chm13.vcf.gz?download=1
+
 ### Input reads
 
 PanGenie is k-mer based and thus expects **short reads** as input. Reads must be provided in a single FASTA or FASTQ file using the ``-i`` option.