DOI: 10.5281/zenodo.17219899
title: Optimized SNP Reference VCFs for FACETS Analysis (hg38/GRCh38)

description: #### **1. Summary**

This deposit contains two reference VCF files for the human genome (hg38/GRCh38). They list common Single Nucleotide Polymorphisms (SNPs) and are specifically designed for use with allele-specific copy number analysis tools, such as **FACETS**.

The main feature of these files is a **uniform SNP density of approximately 1 SNP per kilobase (kb)**, which significantly improves the performance and robustness of the analysis.

#### **2. Rationale**

Official SNP files (e.g., from dbSNP) present two challenges for FACETS-like analyses:

  * **Excessive file size:** They contain millions of rare variants, which considerably slows down the pre-processing step (`snp-pileup`).  * **Non-uniform density:** SNP 'hotspots' with very high density can introduce bias into segmentation algorithms.

These optimized reference files solve both problems by providing a lightweight, clean, and evenly distributed set of markers.

#### **3. Contents of the Deposit**

  * `facets_reference_snps_hg38_uniform_1kb.chr_style.vcf.gz`

      * The SNP reference for use with BAM files where chromosomes are named **'chr1', 'chr2', etc.**

  * `facets_reference_snps_hg38_uniform_1kb.chr_style.vcf.gz.tbi`

      * The Tabix index for the above file.

  * `facets_reference_snps_hg38_uniform_1kb.no_chr_style.vcf.gz`

      * The SNP reference for use with BAM files where chromosomes are named **'1', '2', etc.**

  * `facets_reference_snps_hg38_uniform_1kb.no_chr_style.vcf.gz.tbi`

      * The Tabix index for the above file.

  * `uniformize_vcf_density.py`

      * The Python script used to generate these reference files.

  * `README.md`

      * This information file.

#### **4. Generation Workflow (Transparency)**

The generation process for these files is fully reproducible:

1.  **Primary Source:** Data was derived from the official **dbSNP_BUILD_ID=157** VCF for the **GRCh38/hg38** assembly (`GCF_000001405.40.gz`), downloaded from the NCBI FTP server.

2.  **Initial Filtering:** The source VCF was filtered using `bcftools` to retain only **common (INFO/COMMON=1), bi-allelic SNPs**.

3.  **Chromosome Renaming:** NCBI-style chromosome names (`NC_...`) were converted to the two standard nomenclatures (`chr` and `no-chr`) using the official GRCh38.p14 assembly report.

4.  **Density Uniformization:** The `uniformize_vcf_density.py` script was run on the filtered files to select the most informative SNP (allele frequency closest to 0.5) within each **1 kb** window.

#### **5. Recommended Usage**

1.  Download the VCF file (and its `.tbi` index) that matches the chromosome naming style of your BAM files.2.  Provide this VCF file as the SNP reference to the `snp-pileup` tool or a Galaxy wrapper for FACETS.

*Example command:*

```bashsnp-pileup -q15 -Q20 facets_reference_snps_hg38_uniform_1kb.chr_style.vcf.gz normal.bam tumor.bam | gzip > pileup.csv.gz```

#### **6. Authors and Citation**

This resource was prepared by `drosofff@gmail.com` (ARTbio project, https://github.com/ARTbio/tools-artbio) in collaboration with the Gemini language model (Google). Source data is derived from NCBI dbSNP. If you use these files in your work, please cite this Zenodo deposit.