Introduction to HumanCRISPR B37 Land Content

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HumanCRISPR_B37

CRISPR-based screens have emerged as a high-throughput method of screening gene dependency on developmental processes and cellular survival. Based on customer requests, OmicSoft has generated a framework to visualize results from these screens in our 2019 Land Release 1. In this first release, data from Project Achilles using a subset of cell lines from the Cancer Cell Line Encyclopedia (CCLE) for CRISPR screening is available in the Land. By browsing the gene dependency data in this Land, users have a new opportunity to pre-screen cell lines or diseases to see if a gene/drug target would be a viable option for these types of diseases, or identify novel genes/drug targets likely to affect patient cohorts.

  • Note: This Land is a Beta release - we welcome customer feedback and will work to improve the usage and update the content and framework to support more projects and views.
Land Version Genome Build Gene Model
HumanCRISPR_B37 Human.B37.3 OmicsoftGene20130723

Data Source

https://portals.broadinstitute.org/achilles/datasets/all https://data.mendeley.com/datasets/y3ds55n88r/4

Achilles - The current version of HumanCRISPR_B37 land consists of data from Project Achilles from the following release from early 2018:

43 cell lines screened with GeCKO CRISPR-Cas9 library (Ach 3.3.8), and 391 cell lines screened with AVANA CRISPR-Cas9 library (avana_public_18Q1)

https://portals.broadinstitute.org/achilles/about


Data Types

  • CRISPR Gene Dependency Scores
  • sgRNA log2 fold change values


Processing Methods

The CERES method was used derive gene dependency from the Achilles CRISPR dataset.

Computational correction of copy number effect improves specificity of CRISPR-Cas9 essentiality screens in cancer cells. "CERES", a computational method to estimate gene-dependency levels from CRISPR-Cas9 essentiality screens while accounting for the copy number-specific effect (https://www.ncbi.nlm.nih.gov/pubmed/29083409).

Key Meta Data Columns (OmicSoft-controlled)

  • Tissue Category: The body site where the cell line sample is derived from.
  • Disease State: Histological types of cancer, such as carcinoma, glioma and sarcoma.
  • Gene Dependency Score: The algorithm used in the sample to score a gene dependency from the fold change values of the individual sgRNAs used.
  • CellLine: The identifiers of the cell line used in the experiment.
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Primary Grouping

Tissue Category

Sample Distribution by Tissue Category

CRISPRSampleDistribution.png

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Key Views

There are a number of views that help users identify whether 1) a gene is required in certain cell lines/types or 2) which cell lines likely have a dependency on the gene(s) of interest. This can viewed as a single gene search for either gene dependency or fold-change of a set of sgRNAs in a given experiment:

For example, users can identify cell lines for which a single gene is required using either a waterfall or box plot view:

Singlegene.png

For multi-gene searches, users can also view all genes/samples in a single view, including heatmap and boxplot:

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