Exomiser

Overview

The Exomiser is a Java program that finds potential disease-causing variants from whole-exome or whole-genome sequencing data.

Starting from a VCF file and a set of phenotypes encoded using the Human Phenotype Ontology (HPO) it will annotate, filter and prioritise likely causative variants. The program does this based on user-defined criteria such as a variant's predicted pathogenicity, frequency of occurrence in a population and also how closely the given phenotype matches the known phenotype of diseased genes from human and model organism data.

The functional annotation of variants is handled by Jannovar and uses UCSC KnownGene transcript definitions and hg19 genomic coordinates.

Variants are prioritised according to user-defined criteria on variant frequency, pathogenicity, quality, inheritance pattern, and model organism phenotype data. Predicted pathogenicity data is extracted from the dbNSFP resource. Variant frequency data is taken from the 1000 Genomes, ESP and ExAC datasets. Subsets of these frequency and pathogenicity data can be defined to further tune the analysis. Cross-species phenotype comparisons come from our PhenoDigm tool powered by the OWLTools OWLSim algorithm.

The Exomiser was developed by the Computational Biology and Bioinformatics group at the Institute for Medical Genetics and Human Genetics of the Charité - Universitätsmedizin Berlin, the Mouse Informatics Group at the Sanger Institute and other members of the Monarch initiative.

Download and Installation

The Exomiser and supporting files can be downloaded from the Exomiser FTP site.

For instructions on installing and running please refer to the README.md file.

An online demo version of the exomiser can be found here.

Learn and Support

Please refer to the README file on the FTP site for instructions on running the Exomiser.

License and Citation

Published under the GNU Affero General Public License, version 3

Please cite the Exomiser using:

Improved exome prioritization of disease genes through cross-species phenotype comparison.

Robinson PN, Köhler S, Oellrich A, Sanger Mouse Genetics Project, Wang K, Mungall CJ, Lewis SE, Washington N, Bauer S, Seelow D, Krawitz P, Gilissen C, Haendel M and Smedley D

Genome research 2014;24;2;340-8

PUBMED: 24162188; PMC: 3912424; DOI: 10.1101/gr.160325.113

Contact

Damian Smedley or Jules Jacobsen

Authors

Sanger Contributors

Publications

  • Next-generation diagnostics and disease-gene discovery with the Exomiser.

    Smedley D, Jacobsen JO, Jäger M, Köhler S, Holtgrewe M et al.

    Nature protocols 2015;10;12;2004-15

  • Computational evaluation of exome sequence data using human and model organism phenotypes improves diagnostic efficiency.

    Bone WP, Washington NL, Buske OJ, Adams DR, Davis J et al.

    Genetics in medicine : official journal of the American College of Medical Genetics 2015

  • Walking the interactome for candidate prioritization in exome sequencing studies of Mendelian diseases.

    Smedley D, Köhler S, Czeschik JC, Amberger J, Bocchini C et al.

    Bioinformatics (Oxford, England) 2014;30;22;3215-22

  • Effective diagnosis of genetic disease by computational phenotype analysis of the disease-associated genome.

    Zemojtel T, Köhler S, Mackenroth L, Jäger M, Hecht J et al.

    Science translational medicine 2014;6;252;252ra123

  • Improved exome prioritization of disease genes through cross-species phenotype comparison.

    Robinson PN, Köhler S, Oellrich A, Sanger Mouse Genetics Project, Wang K et al.

    Genome research 2014;24;2;340-8

Tool type
Screenshots
  • Screenshot of the Exomiser running on a test dataset from the command-line along with the accompanying YAML configuration file used for the analysis.

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