CADD specifications

Unique identifier:
OMICS_02627
Software type:
Package/Module
Restrictions to use:
Academic or non-commercial use
Input format:
VCF
Programming languages:
Python
Version:
1.3
Requirements:
SAMtools, tabix, bx-Python, PySam, perl, Ensembl VEP
Name:
Combined Annotation Dependent Depletion
Interface:
Command line interface
Input data:
Nucleotide variants
Operating system:
Unix/Linux, Mac OS
Computer skills:
Advanced
Stability:
Stable
Maintained:
Yes
  • Animals
    • Homo sapiens

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CADD distribution

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CADD specifications

Unique identifier:
OMICS_02627
Interface:
Web user interface
Input data:
Nucleotide variants
Programming languages:
Python
Version:
1.3
Maintained:
Yes
Name:
Combined Annotation Dependent Depletion
Restrictions to use:
Academic or non-commercial use
Input format:
VCF
Computer skills:
Basic
Stability:
Stable
  • Animals
    • Homo sapiens

CADD support

Maintainer

  • CADD <>
  • CADD <>

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Credits

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Publications

Institution(s)

Department of Genome Sciences, University of Washington, Seattle, WA, USA; Department of Biostatistics, University of Washington, Seattle, WA, USA; HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA

Funding source(s)

This work was supported by National Institutes of Health (N.I.H.) grants U54HG006493, DP5OD009145 and DP1HG007811.

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Pierre-Julien VIAILLY's avatar image

Pierre-Julien VIAILLY

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Combined Annotation Dependent Depletion (CADD) is a new tool for scoring the deleteriousness of single nucleotide variants as well as insertion/deletions variants in the human genome. Most of pathogenicity prediction algorithms tend to exploit a single information such as conservation to create a score. CADD give a broadly applicable metric that weights and integrated diverse information using a SVM approach. It integrates multiple annotations into one metric by contrasting variants that survived natural selection with simulated mutations.

Precomputed CADD scores can be downloaded on CADD website (http://cadd.gs.washington.edu/download). Scores have been included too in dbNSFP databases.

We have demonstrated that CADD is the most sensitive and specific prediction algorithms by comparison with the ClinVar, a database which aggregates information about genomic variation and its relationship to human health.

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