CLISGen

Cell LInes SNP Genotypes

CLISGen is a web application designed for fast and flexible SNP genotype querying across 1,065 cancer cell lines from the Cancer Cell Line Encyclopedia (CCLE). It integrates data from WGS, WES, and RNA-seq using a pileup-based method anchored to dbSNP, enabling robust genotype calls across over 34 million common coding and non-coding SNPs. CLISGen provides sequencing depth, copy number status, and genetic ancestry information, making it a valuable tool for applications like CRISPR guide design, allele-specific expression analysis, and pharmacogenomic studies.

Explore More Than 1K Cancer Cell Lines

A Database of Over 34M SNPs

More Than 1.5K NGS Files Analyzed

Department of Cellular, Computational and Integrative Biology CIBIO

Laboratory of Bioinformatics and Computational Genomics

Select Search Mode:

SNP rsID Gene Genomic Region

Region Variant Result
SNP Result

A homozygote

T homozygote

G homozygote

C homozygote

AT heterozygote

AG heterozygote

AC heterozygote

TG heterozygote

TC heterozygote

GC heterozygote

No Coverage

Not Defined

Error

How to Search the Database?

You can access the search tool via the button on the CLISGen homepage or using the Search tab in the navigation bar.
Choose one of the three available search modes:

By rsID

Enter a specific rsID (e.g., rs11208299) and The application will automatically retrieve and display the results for that variant.

By Gene

Input a Gene Symbol(e.g.EXO5) or an Ensembl ID (e.g. ENSG00000164002): the application will display a table containing all SNPs found within the gene boundaries.
Individual variants within the table can be selected for further details.

By Region

Input a Genomic Region, up to 0.5 Mbp, (e.g. chr1:40515050-40515059): the application will display a table containing all SNPs found within the specified chromosomal boundaries.
Individual variants within the table can be selected for further details.

How to read Gene/Region search results?

When searching by gene or region, an intermediate page displays a table listing all SNPs from our database located within the selected region. The first column contains the rsIDs, which link to the respective data. Subsequent columns provide genomic coordinates and annotations, while the final three columns present summary information respectively from RNA-seq, WGS, and WXS data, specifying the cell lines associated with each genotype

How to explore individual SNP results?

On the Search page, all data related to the queried SNP are displayed, including Pileup information, copy number details (when available), and associated metadata. The initial results view provides a comprehensive overview of genotype distributions across approximately 1,000 cell lines in the database. A default coverage filter is applied to ensure high-quality results, but you can modify or remove it as needed.

In the main section of the page, a series of pie charts illustrate the distribution of observed genotypes within each dataset. Beneath these visual summaries, a detailed data table lists information for each cell line with coverage at the queried SNP locus. This table serves as the most informative component of the interface, supporting in-depth data interpretation. For each sample, it reports the unique identifier, cell line name, ancestry group (if available), copy number (CN) status, loss of heterozygosity (LOH) annotation, genotype call, sequencing platform, and per-base read counts (A, T, C, G), along with the total coverage.

How to refine the search using filters?

The left panel provides several filters to narrow your results:

Restrict results by data type or tissue type
Adjust the minimum coverage threshold
Exclude cell lines lacking SNP coverage (enabled by default)

As you apply filters, the results, including plots and tables, update dynamically, enabling rapid exploration of SNP-level variation across different biological and technical subsets.

For example, to contextualize the variant within a disease-relevant model system, you can apply the tissue type filter.

The table will then update automatically, displaying only the cell lines belonging to that specific tissue, while the plots above will refresh to show histograms highlighting the cell lines with the highest coverage values.

Can I export the results?

All displayed results can be downloaded in CSV format using the CSV button located at the top of the results table, or copied to the clipboard using the Copy button.

Data Used

To build the SNP genotype database integrated into the CLISGen web application, we leveraged publicly available genomic data from the Cancer Cell Line Encyclopedia (CCLE) project. Specifically, we analyzed sequencing data from 1,065 cancer cell lines, incorporating over 2,000 samples from RNA sequencing (RNA-seq), whole genome sequencing (WGS), and whole exome sequencing (WXS) to compute common SNP genotypes across all lines. Additionally, copy number status information was derived from Affymetrix SNP array data, which also served as input for the EthSEQ tool (Dalfovo et al., 2023) to infer genetic ancestry for each cell line.

CCLE Project European Nucleotide Archive: PRJNA523380 Analysis of Genetic Ancestry from NGS Data Using EthSEQ

Common SNPs Analyzed

Approximately 34 million single nucleotide polymorphisms (SNPs) were selected for analysis based on the dbSNP build 151, aligned to the GRCh37 (hg19) human genome reference. These SNPs represent a comprehensive catalog of common genetic variants, enabling robust and consistent genotyping across diverse sequencing data types.

NCBI FTP repository

Pileup Analysis

To perform SNP genotyping, we employed ASEQ (Romanel et al., 2015), a tool which features an efficient and scalable module for pileup-based genotype calling at specified genomic positions. In our workflow, this functionality was used to extract base-level read information from BAM files aligned to the GRCh37 reference genome. By supplying ASEQ with a curated list of common SNP positions, we conducted pileup analysis across these loci using all available sequencing data from the CCLE project—including RNA-seq, WGS and WES samples. The resulting base call information was then used to compute genotype calls for the selected SNPs across all 1,065 cancer cell lines.

ASEQ: fast allele-specific studies from next-generation sequencing data

Genotype-Calls

We used a threshold-based method to assign genotypes, applying allele frequency cutoffs to identify homozygous and heterozygous variants. This approach, previously shown to be highly accurate for known variant genotyping (Romanel et al., 2015), calls a site homozygous alternative if the alternative allele frequency is above 80%, and considers alleles above 20% frequency for variant detection. Ambiguous cases with multiple alleles passing the threshold are marked as undetermined and logged for further analysis. Importantly, the per-base depth of coverage information is always provided, allowing users to assess the quality and reliability of each genotype call.

Matteo Marchesin | Email | GitHub | LinkedIn

Alessandro Romanel | Email | GitHub | LinkedIn

Laboratory of Bioinformatics and Computational Genomics, CIBIO UniTN

If you use CLISGen, please cite:

Matteo Marchesin, Davide Dalfovo, Alessandro Romanel, CLISGen: A Comprehensive Resource of SNP Genotypes for Human Cell Lines, Journal of Molecular Biology, 2026

DOI: 10.1016/j.jmb.2026.169681

Licensing
Versions

Licensing

This project is licensed under a Creative Commons Attribution 4.0 International License.

CLISGen Version History

Version	Date	Address	Content
1.8	2026-01-06	bcglab.cibio.unitn.it/clisgen/	Error on table update fixed.
1.7	2026-01-05	bcglab.cibio.unitn.it/clisgen/	Updated bar chart layout to stacked bar charts.
1.6	2026-01-04	bcglab.cibio.unitn.it/clisgen/	Added gnomAD global MAF data and a ploidy column to the results table. Fixed typos and updated plot color palettes.
1.4	2025-12-30	bcglab.cibio.unitn.it/clisgen/	Updated search page navigation. Revised table tooltip descriptions.
1.3	2025-12-23	bcglab.cibio.unitn.it/clisgen/	Added search functionality by region and gene.Added a download section for analysis results.
1.2	2025-10-6	bcglab.cibio.unitn.it/clisgen/	Reordered navigation tabs to improve user flow.
1.1	2025-09-30	bcglab.cibio.unitn.it/clisgen/	Added Tabs and bugs Fixed.
1.0	2025-06-09	bcglab.cibio.unitn.it/clisgen/	Initial release.
-	2025-02-20	-	Database completed.

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