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Latest Release DepMap Public 19Q1 | February 2019


The DepMap Public 19Q1 release includes CRISPR screen and omics datasets expanded to include new cell lines. The Avana CRISPR screen dataset is expanded to include 558 cell lines. Mutation, copy number and gene expression data are also expanded to include new lines in Avana dataset. As our screening efforts continue, we will be releasing additional cancer dependency data on a quarterly basis.

The CRISPR screen uses the Avana CRISPR-Cas9 genome-scale knockout library, and descriptions of the experimental methods and the CERES algorithm are published in here. This dataset also includes batch-corrected versions of gene_effect, gene_dependency, and pan_dependent_genes, and the portal shows this batch corrected version of gene_effect. See the README file for more detail.

Copy number and mutation data for some cell lines were based on Sanger Institute whole exome sequencing data (COSMIC: http://cancer.sanger.ac.uk/cell_lines, EGA accession number: EGAD00001001039), then reprocessed. Omics processing for 19Q1 has changed as follows.

Expression: We use the GTEx pipeline. Since last release, there have been some updates to the versions of different components of the pipeline and minor bug fixes on GTEx's end.

Mutation: In this release, we additionally process DepMap WES samples through the CGA mutation calling pipeline adapted for cell lines and available on FireCloud. We have added a column for the allele counts from this pipeline run (CGA_WES_AC). Previously, the WES_AC column was generated from running the Van Allen mutation calling pipeline for SNPs + Mutect2 for INDELs. This column is now named VA_WES_AC.

Copy Number: We have moved to using the newest version of the GATK pipeline.

Cellular Models

Mutation

Public 19Q1

Merged mutation calls (coding region, germline filtered)

Genes 18755
Cell Lines 1601
Primary Diseases 37
Lineages 33

Cellular Models

Copy Number

Public 19Q1

DepMap WES CN Data

Genes 23299
Cell Lines 1604
Primary Diseases 38
Lineages 33

Cellular Models

Cell Line Metadata

Metadata about cell lines in the 19Q1 release, including mapping between DepMap ID and CCLE names

Genes 0
Cell Lines 1677
Primary Diseases 38
Lineages 33

Genetic Dependency

CRISPR

(Avana) Public 19Q1

Batch corrected CERES inferred gene effect matrix

Genes 17634
Cell Lines 558
Primary Diseases 26
Lineages 28

Cellular Models

Expression

Public 19Q1

CCLE RNAseq gene expression data (log2(TPM+1)). The portal shows expression data for only protein coding genes; this download is the full dataset.

Genes 57820
Cell Lines 1165
Primary Diseases 33
Lineages 32

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Cellular Models

Protein Array

CCLE Reverse Phase Protein Array (RPPA) data

Genes 214
Cell Lines 899
Primary Diseases 28
Lineages 28

Genetic Dependency

Combined RNAi

(Broad, Novartis, Marcotte)
Genes 17309
Cell Lines 712
Primary Diseases 30
Lineages 31

Please cite the following publications when using these datasets

For CRISPR datasets:

DepMap, Broad (2019): DepMap Achilles 19Q1 Public. figshare. Fileset. doi:10.6084/m9.figshare.7655150

Robin M. Meyers, Jordan G. Bryan, James M. McFarland, Barbara A. Weir, ... David E. Root, William C. Hahn, Aviad Tsherniak. Computational correction of copy number effect improves specificity of CRISPR-Cas9 essentiality screens in cancer cells. Nature Genetics 2017 October 49:1779–1784. doi:10.1038/ng.3984

For omics datasets:

Cancer Cell Line Encyclopedia Consortium, and Genomics of Drug Sensitivity in Cancer Consortium. 2015. Pharmacogenomic Agreement between Two Cancer Cell Line Data Sets. Nature 528 (7580):84–87. https://doi.org/10.1038/nature15736.

Jordi Barretina, Giordano Caponigro, Nicolas Stransky, Kavitha Venkatesan, William R. Sellers, Robert Schlegel, Levi A. Garraway, et. al. 2012. The Cancer Cell Line Encyclopedia Enables Predictive Modelling of Anticancer Drug Sensitivity. Nature 483 (7391):603–7. https://doi.org/10.1038/nature11003.

For the RNAi dataset:

James M. McFarland, Zandra V. Ho, Guillaume Kugener, Joshua M. Dempster, Phillip G. Montgomery, Jordan G. Bryan, John M. Krill-Burger, Thomas M. Green, Francisca Vazquez, Jesse S. Boehm, Todd R. Golub, William C. Hahn, David E. Root, Aviad Tsherniak. (2018). Improved estimation of cancer dependencies from large-scale RNAi screens using model-based normalization and data integration. Nature Communications 9, 1. https://doi.org/10.1038/s41467-018-06916-5


This data was generated in part from funding from:

This project is partially funded by CTD2, the Achilles consortium, and The Carlos Slim Foundation in Mexico through the Slim Initiative for Genomic Medicine.