Welcome to the Exomiser documentation!¶
Introduction¶
The Exomiser is a Java program, developed as a collaboration between members of the Monarch Initiative for finding potential disease-causing variants in whole-exome or whole-genome sequencing data. It is available for use by all as an offline command-line tool, or on the web as a demo application/teaching tool.
It requires a VCF file and a set of phenotypes encoded using the Human Phenotype Ontology (HPO) from which 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 the Jannovar library and uses any of UCSC, RefSeq or Ensembl KnownGene transcript definitions and hg19 or hg38 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, TOPMed, UK10K, ExAC and gnomAD 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.
Installation¶
Software and Hardware requirements¶
- Minimum 8/16GB RAM (For an exome analysis of a 30,000 variant sample 4GB RAM should suffice. For a genome analysis of a 4,400,000 variant sample 12GB RAM should suffice.)
- Any 64-bit operating system
- Java 11 or above
- At least 100GB free disk space (SSD preferred for best performance)
- An internet connection is not required to run the Exomiser, although network access will be required if accessing a networked database (optional).
- By default the Exomiser is completely self-contained and is able to run on standard consumer laptops.
Pre-built Binaries¶
Note
This is the recommended way of installing for normal users.
Pre-built binaries are available from The Monarch Initiative or from the Exomiser repository on GitHub.
Exomiser requires 2-3 data files to be available as well - one for the phenotype data and one for each genome assembly required.
Windows install¶
- Install 7-Zip for unzipping the archive files. The built-in archiving software has issues extracting the zip files.
- Download the data and distribution files from https://data.monarchinitiative.org/exomiser/latest
- Extract the distribution files by right-clicking exomiser-cli-13.1.0-distribution.zip and selecting 7-Zip > Extract Here
- Extract the data files (e.g. 2109_phenotype.zip, 2109_hg19.zip) by right-clicking the archive and selecting 7-Zip > Extract files… into the exomiser data directory. By default exomiser expects this to be ‘exomiser-cli-13.1.0/data’, but this can be changed in the
application.properties - cd exomiser-cli-13.1.0
- java -Xmx4g -jar exomiser-cli-13.1.0.jar –analysis examples/test-analysis-exome.yml
Linux install¶
The following shell script should work-
# download the distribution (won't take long) wget https://data.monarchinitiative.org/exomiser/latest/exomiser-cli-13.1.0-distribution.zip # download the data (this is ~80GB and will take a while). If you only require a single assembly, only download the relevant file. wget https://data.monarchinitiative.org/exomiser/latest/2202_hg19.zip wget https://data.monarchinitiative.org/exomiser/latest/2202_hg38.zip wget https://data.monarchinitiative.org/exomiser/latest/2202_phenotype.zip # unzip the distribution and data files - this will create a directory called 'exomiser-cli-13.1.0' in the current working directory unzip exomiser-cli-13.1.0-distribution.zip unzip 2202_*.zip -d exomiser-cli-13.1.0/data # Check the application.properties are pointing to the correct versions # exomiser.hg19.data-version=2202 # exomiser.hg38.data-version=2202 # exomiser.phenotype.data-version=2202 # run a test exome analysis cd exomiser-cli-13.1.0 java -Xmx4g -jar exomiser-cli-13.1.0.jar --analysis examples/test-analysis-exome.yml
This script will download, verify and extract the exomiser files and then run the analysis contained in the file ‘test-analysis-exome.yml’ from the examples sub-directory. This contains a known pathogenic missense variant in the FGFR2 gene.
Genomiser / REMM data¶
In order to run the Genomiser you will also need to download the REMM data file
from [here](https://zenodo.org/record/4768448). Once downloaded you’ll need to add the path to the ReMM.v0.3.1.tsv.gz
file to the application.properties file. For example if you downloaded the file to the exomiser data directory you
could add the entry like this:
exomiser.hg19.remm-path=${exomiser.hg19.data-directory}/ReMM.v0.3.1.tsv.gz
If this step is omitted, the application will throw and error and stop any analysis which defines REMM in the pathogenicitySources section of an analysis yml file.
Having done this, run the analysis like this:
java -Xmx6g -jar exomiser-cli-13.1.0.jar --analysis examples/NA19722_601952_AUTOSOMAL_RECESSIVE_POMP_13_29233225_5UTR_38.yml
This is an analysis for an autosomal recessive 5’UTR variant located in POMP gene on chromosome 13. The phenotype HPO terms are taken from the clinical synopsis of OMIM #601952 (http://www.omim.org/clinicalSynopsis/601952)
CADD data¶
In order to use CADD you will need to download the CADD data files separately. These can be accessed from https://cadd.gs.washington.edu/download. Exomiser only requires the file with the score in, not the full annotations. For example, in release v1.4 Exomiser requires both the files All possible SNVs of GRCh38/hg38 and 80M InDels to initiate a local setup. Each genome assembly will require the relevant files. The direct links from the US site are shown below and are correct at the time of writing.
wget https://krishna.gs.washington.edu/download/CADD/v1.4/GRCh38/whole_genome_SNVs.tsv.gz
wget https://krishna.gs.washington.edu/download/CADD/v1.4/GRCh38/whole_genome_SNVs.tsv.gz.tbi
wget https://krishna.gs.washington.edu/download/CADD/v1.4/GRCh38/InDels.tsv.gz
wget https://krishna.gs.washington.edu/download/CADD/v1.4/GRCh38/InDels.tsv.gz.tbi
wget https://krishna.gs.washington.edu/download/CADD/v1.4/GRCh37/whole_genome_SNVs.tsv.gz
wget https://krishna.gs.washington.edu/download/CADD/v1.4/GRCh37/whole_genome_SNVs.tsv.gz.tbi
wget https://krishna.gs.washington.edu/download/CADD/v1.4/GRCh37/InDels.tsv.gz
wget https://krishna.gs.washington.edu/download/CADD/v1.4/GRCh37/InDels.tsv.gz.tbi
Enable Exomiser to use CADD by altering the application.properties file to enable these lines and ensure the
cadd.version property matches the version you downloaded.
cadd.version=1.4
exomiser.hg19.cadd-snv-path=${exomiser.data-directory}/cadd/${cadd.version}/hg19/whole_genome_SNVs.tsv.gz
exomiser.hg19.cadd-in-del-path=${exomiser.data-directory}/cadd/${cadd.version}/hg19/InDels.tsv.gz
# and/or for hg38
exomiser.hg38.cadd-snv-path=${exomiser.data-directory}/cadd/${cadd.version}/whole_genome_SNVs.tsv.gz
exomiser.hg38.cadd-in-del-path=${exomiser.data-directory}/cadd/${cadd.version}/InDels.tsv.gz
Exomiser will expect the tabix index .tbi file to be present in the same directory as the .tsv.gz files. To use
CADD scores in an analysis, the pathogenicitySources should contain the CADD property
#Possible pathogenicitySources: POLYPHEN, MUTATION_TASTER, SIFT, CADD, REMM
#REMM is trained on non-coding regulatory regions
#*WARNING* if you enable CADD or REMM ensure that you have downloaded and installed the CADD/REMM tabix files
#and updated their location in the application.properties. Exomiser will not run without this.
pathogenicitySources: [POLYPHEN, MUTATION_TASTER, SIFT, CADD]
Configuring the application.properties¶
Once you have downloaded and unzipped all the data, you will need to edit the exomiser-cli-13.1.0/application.properties file located in the main exomiser-cli directory. This file contains a lot of comments for optional data and assemblies.
If you want to run Exomiser using data from a different release directory edit the line in application.properties:
exomiser.data-directory=
with
exomiser.data-directory=/full/path/to/alternative/data/directory
For example, assuming you unzipped the contents of the 2202_hg38.zip data file into /data/exomiser-data:
exomiser.data-directory=/data/exomiser-data
where the contents of exomiser-data looks something like this:
$ tree -L 1 /data/exomiser-data/
/data/exomiser-data/
├── 2202_hg19
├── 2202_hg38
├── 2202_phenotype
├── cadd
└── remm
By default Exomiser will look for data located in the exomiser-cli-13.1.0/data directory.
After defining the a exomiser.data-directory, a minimal setup for exome analysis using GRCh37/hg19 would only require
the application.properties to contain this:
### hg19 assembly ###
exomiser.hg19.data-version=2109
exomiser.hg19.variant-white-list-path=2109_hg19_clinvar_whitelist.tsv.gz
### phenotypes ###
exomiser.phenotype.data-version=2109
For a GRCh38/hg38 only setup:
### hg38 assembly ###
exomiser.hg38.data-version=2109
exomiser.hg38.variant-white-list-path=2109_hg38_clinvar_whitelist.tsv.gz
### phenotypes ###
exomiser.phenotype.data-version=2109
Or an install supporting both assemblies:
### hg19 assembly ###
exomiser.hg19.data-version=2109
exomiser.hg19.variant-white-list-path=2109_hg19_clinvar_whitelist.tsv.gz
### hg38 assembly ###
exomiser.hg38.data-version=2109
exomiser.hg38.variant-white-list-path=2109_hg38_clinvar_whitelist.tsv.gz
### phenotypes ###
exomiser.phenotype.data-version=2109
n.b. each assembly will require approximately 1GB RAM to load. Attempting to analyse a VCF called using an unsupported/unloaded assembly data will result in an unrecoverable error being thrown.
Notice here that we are loading a whitelist created from ClinVar data. Exomiser will consider any variant on the whitelist to be maximally pathogenic, regardless of the underlying data (e.g. variant effect, allele frequency, predicted pathogenicity) and always included these in the results.
Troubleshooting¶
Zip file reported as too big or corrupted¶
If, when running ‘unzip exomiser-cli-13.1.0-distribution.zip’, you see the following:
error: Zip file too big (greater than 4294959102 bytes) Archive: exomiser-cli-13.1.0-distribution.zip warning [exomiser-cli-13.1.0-distribution.zip]: 9940454202 extra bytes at beginning or within zipfile (attempting to process anyway) error [exomiser-cli-13.1.0-distribution.zip]: start of central directory not found; zipfile corrupt. (please check that you have transferred or created the zipfile in the appropriate BINARY mode and that you have compiled UnZip properly)
Check that your unzip version was compiled with LARGE_FILE_SUPPORT and ZIP64_SUPPORT. This is standard with UnZip 6.00 and can be checked by typing:
unzip -version
This shouldn’t be an issue with more recent linux distributions.
Running Exomiser¶
Usage¶
In general we recommend running Exomiser from the install directory. A single sample exome analysis can be run using the following command:
# run a test exome analysis cd exomiser-cli-13.1.0 java -jar exomiser-cli-13.1.0.jar --sample examples/pfeiffer-phenopacket.yml --vcf examples/Pfeiffer.vcf.gz --assembly hg19
This command prioritises variants from the input VCF file, called against the GRCh37/hg19 reference assembly in the context of the sample phenotypes encoded using Human Phenotype Ontology terms contained in the Phenopacket file.
Running a multi-sample VCF for trios also requires a PED file. e.g.
# run a test exome family analysis cd exomiser-cli-13.1.0 java -jar exomiser-cli-13.1.0.jar --sample examples/pfeiffer-family.yml --vcf examples/Pfeiffer-quartet.vcf.gz --assembly hg19 --ped examples/Pfeiffer-quartet.ped
By default there will be two output files written to the results directory using the same filename as the input VCF file but
with _exomiser appended before a file extension of .json or .html e.g.
ls results/Pfeiffer*
results/Pfeiffer_exomiser.html results/Pfeiffer_exomiser.json
The HTML file is for human use, whilst the JSON file is better read by machines, for instance by using jq. Details on how to interpret the output can be found in the Interpreting the Results section.
The examples directory contains a selection of single sample exome and genome analysis files, a multisample (family)
analysis with an associated pedigree in PED
format, and the respective Phenopacket representations of the proband or
family.
Want help?¶
java -jar exomiser-cli-13.1.0.jar --help
Running from alternate directory¶
If you’re running the exomiser from a different directory to the one the exomiser-cli-13.1.0.jar file is located,
you will need to specify the path to the application.properties file in the start-up command. For example:
java -Xmx4g -jar /full/path/to/your/exomiser-cli/directory/exomiser-cli-13.1.0.jar --analysis /full/path/to/your/exomiser-cli/directory/examples/test-analysis-exome.yml --spring.config.location=/full/path/to/your/exomiser-cli/directory/application.properties
n.b. the spring.config.location command must be the last argument in the input commands!
Troubleshooting¶
java.lang.UnsupportedClassVersionError:¶
If you get the following error message:
Exception in thread "main" java.lang.UnsupportedClassVersionError:
org/monarchinitiative/exomiser/cli/Main : Unsupported major.minor version
or
Error: A JNI error has occurred, please check your installation and try again
Exception in thread "main" java.lang.UnsupportedClassVersionError: org/monarchinitiative/exomiser/cli/Main has been
compiled by a more recent version of the Java Runtime (class file version 55.0), this version of the Java Runtime
only recognizes class file versions up to 52.0
You are running an older unsupported version of Java. Exomiser requires java version 11 or higher. This can be checked by running:
$ java -version
You should see something like this in response:
openjdk version "11.0.11" 2021-04-20
OpenJDK Runtime Environment (build 11.0.11+9-Ubuntu-0ubuntu2.20.04)
OpenJDK 64-Bit Server VM (build 11.0.11+9-Ubuntu-0ubuntu2.20.04, mixed mode, sharing)
Versions lower than 11 (e.g. 1.5, 1.6, 1.7, 1.8, 9, 10) will not run exomiser, so you will need to install the latest java version.
Input Files and Options¶
The Exomiser can be run via simply via a yaml analysis file. The extended cli capability was removed in version 10.0.0 as this was less capable than the yaml scripts and only supported hg19 exome analysis. Version 13.0.0 introduced a more flexible input using a GA4GH Phenopacket (v 1.0) for the sample data with the ability to specify the input pedigree, VCF and genome assembly independently; user-specified, preset or default analysis options and a new batch mode.
Sample, vcf, assembly, ped¶
It is recommended to provide Exomiser with the input sample as a Phenopacket.
Exomiser will accept this in either JSON or YAML format. The sample is provided using the sample switch and
the full path to the phenopacket file:
java -jar exomiser-cli-13.1.0.jar --sample path/to/phenopacket.json
Should the phenopacket either not specify a VCF file or specifies a file on another filesystem, the VCF file path can be
provided/overridden using the vcf option. This option requires that the genome assembly the VCF file was called against
is also specified using the assembly option:
java -jar exomiser-cli-13.1.0.jar --sample path/to/phenopacket.json --vcf path/to/genome.vcf --assembly hg19
or for hg38/ GRCh38:
java -jar exomiser-cli-13.1.0.jar --sample path/to/phenopacket.json --vcf path/to/genome.vcf --assembly hg38
Lastly, when analysing a multi-sample VCF a pedigree is required. This can be provided using a dedicated PED file. This
uses the ped switch and a full path to the PED file:
java -jar exomiser-cli-13.1.0.jar --sample path/to/phenopacket.json --vcf path/to/genome.vcf --assembly hg38 --ped path/to/pedigree.ped
or the pedigree, proband and family members can be provided as a phenopacket family message which can encode the pedigree.
java -jar exomiser-cli-13.1.0.jar --sample path/to/family.json --vcf path/to/genome.vcf --assembly hg38
Whatever the input used it is essential that the sample names used for the proband and other family members are consistent between the pedigree and the sample identifiers used in the VCF file. Exomiser will exit with an error explaining that they do not match. Examples of these can be found in the examples directory of the installation.
Preset¶
If no analysis is provided and no preset is specified, Exomiser will default to running the exome preset analysis.
If you want to run Genomiser, which will analyse non-coding regions of a WGS sample use --preset genome:
java -jar exomiser-cli-13.1.0.jar --sample path/to/phenopacket.json --preset genome
In order to run a genome preset you need to first ensure that the REMM score data has been downloaded for the relevant
genome assembly and is enabled in the application.properties see the Genomiser / REMM data section.
Analysis¶
Analysis files contain all possible options for running an analysis including the ability to specify variant frequency and pathogenicity data sources and the ability to tweak the order that analysis steps are performed.
See the test-analysis-exome.yml and test-analysis-genome.yml files located in the base install directory for examples. Details can be found in the Analysis section.
java -jar exomiser-cli-13.1.0.jar --analysis examples/test-analysis-exome.yml
These files an also be used to run full-genomes, however they will require substantially more RAM to do so. For example a 4.4 million variant analysis requires approximately 12GB RAM. However, RAM requirements can be greatly reduced by setting the analysisMode option to PASS_ONLY. This will also aid your ability to evaluate the results.
Analyses can be run in batch mode. Simply put the path to each analysis file in the batch file - one file path per line.
java -jar exomiser-cli-13.1.0.jar --analysis-batch examples/test-analysis-batch.txt
Output¶
By default Exomiser will write out any result files to the exomiser-cli-13.1.0/results sub-directory of the Exomiser installation directory. Unless specified in the output.yml or outputOptions section of the analysis YAML file, Exomiser will write out a .json and a .html file. These are for machine (JSON) and human (HTML) use. The filenames will match the input VCF filename. For example
java -jar exomiser-cli-13.1.0.jar --sample examples/pfeiffer-phenopacket.yml --vcf path/to/manuel.vcf.gz --assembly hg19
Would result in two files being output with the filename ‘manuel_exomiser’ and the ‘.json’ and ‘.html’ extensions:
exomiser-cli-13.1.0/results/manuel_exomiser.html exomiser-cli-13.1.0/results/manuel_exomiser.json
Users requiring more control over their output can use either the outputOptions section of an analysis file or a
specific Output options yaml file. An example of this can be found in the exomiser-cli-13.1.0/examples/output-options.yml
file:
---
outputContributingVariantsOnly: false
# numGenes options: 0 = all or specify a limit e.g. 500 for the first 500 results
numGenes: 10
minExomiserGeneScore: 0.7
# outputDirectory: (optional) (default: '{exomiserDir}/results/')
outputDirectory: results/
# outputFileName: (optional) (default: 'input-vcf-name-exomiser')
outputFileName: NA12345-exomiser-results
# out-format options: HTML, JSON, TSV_GENE, TSV_VARIANT, VCF (default: HTML)
outputFormats: [HTML, JSON, TSV_GENE]
This file is passed to Exomiser using the --output switch:
java -jar exomiser-cli-13.1.0.jar --sample examples/pfeiffer-phenopacket.yml --vcf path/to/manuel.vcf.gz --output path/to/output-options.yml
The output filename, directory and format can also be specified directly on the CLI (see the –help command for details).
Batch¶
The above commands can be added to a batch file for example in the file exomiser-cli-13.1.0/examples/test-analysis-batch-commands.txt
#This is an example analysis batch file to be run using the --analysis-batch command
#
#Each line should specify the path of a single analysis file, either relative to the directory the exomiser
#is being run from or the full system path. It will run any combination of exomiser commands listed using -h or --help.
#
# Original format exomiser analysis containing all the sample and analysis information
--analysis test-analysis-exome.yml
# New preset exome analysis using a v1 phenopacket to submit the phenotype information and adding/overriding the VCF input
--preset exome --sample pfeiffer-phenopacket.yml --vcf Pfeiffer.vcf.gz
# Using the default analysis (exome) with a v1 phenopacket containing the phenotype information and adding/overriding the VCF input
--sample pfeiffer-phenopacket.yml --vcf Pfeiffer.vcf.gz
# Using a user-defined analysis with a v1 phenopacket containing the phenotype information and adding/overriding the VCF input
--analysis preset-exome-analysis.yml --sample pfeiffer-phenopacket.yml --vcf Pfeiffer.vcf.gz
# Using a user-defined analysis with a v1 phenopacket containing the phenotype information and adding/overriding the VCF input
--analysis preset-exome-analysis.yml --sample pfeiffer-phenopacket.yml --vcf Pfeiffer.vcf.gz --output output-options.yml
then run using the --batch command:
java -jar exomiser-cli-13.1.0.jar --batch path/to/exomiser-cli-13.1.0/examples/test-analysis-batch-commands.txt
The advantage of this is that a single command will be able to analyse many samples in far less time than starting a new JVM for each as there will be no start-up penalty after the initial start and the Java JIT compiler will be able to take advantage of a longer-running process to optimise the runtime code. For maximum throughput on a cluster consider splitting your batch jobs over multiple nodes.
Analysis/Job File Configuration¶
Exomiser analyses are defined using a yml format configuration file(s). Examples can be found in the unpacked exomiser-cli-13.1.0/examples/ directory. Here you will find a mixture of samples, jobs, analyses, outputOptions, phenopackets, phenopacket family, vcf and ped files.
Job¶
An Exomiser job contains three sections:
These fields define all the input data, analysis steps and output requirements for a complete Exomiser analysis. They can be used as a record of how a sample was analysed. Alternatively the various sections can all be defined at runtime using the CLI arguments as explained in the Input Files and Options section.
which defines run mode, filters and prioritisers, and the output options section that defines the output format, output file and number of results that should be returned. Each of these sections can be defined independently on the command line or provided as a single file as shown in the job section.
## Exomiser Job Template.
# The job is split into three sections:
# sample: describes the proband
# analysis: details the steps exomiser will take to analyse the sample
# outputOptions: specifies what files to output and where with filtering options for the number of genes and the exomiser score
#
# These can be input separately on the command line e.g. --sample sample.yml --analysis analysis.yml
# In this example the 'sample' is replaced with a 'phenopacket' see https://phenopacket-schema.readthedocs.io/en/1.0.0/phenopacket.html
---
phenopacket:
id: manuel
subject:
id: manuel
sex: MALE
phenotypicFeatures:
- type:
id: HP:0001159
label: Syndactyly
- type:
id: HP:0000486
label: Strabismus
- type:
id: HP:0000327
label: Hypoplasia of the maxilla
- type:
id: HP:0000520
label: Proptosis
- type:
id: HP:0000316
label: Hypertelorism
- type:
id: HP:0000244
label: Brachyturricephaly
htsFiles:
- uri: examples/Pfeiffer.vcf
htsFormat: VCF
genomeAssembly: hg19
metaData:
created: '2019-11-12T13:47:51.948Z'
createdBy: julesj
resources:
- id: hp
name: human phenotype ontology
url: http://purl.obolibrary.org/obo/hp.owl
version: hp/releases/2019-11-08
namespacePrefix: HP
iriPrefix: 'http://purl.obolibrary.org/obo/HP_'
phenopacketSchemaVersion: 1.0
analysis:
#FULL or PASS_ONLY
analysisMode: PASS_ONLY
# In cases where you do not want any cut-offs applied an empty map should be used e.g. inheritanceModes: {}
# These are the default settings, with values representing the maximum minor allele frequency in percent (%) permitted for an
# allele to be considered as a causative candidate under that mode of inheritance.
# If you just want to analyse a sample under a single inheritance mode, delete/comment-out the others. For AUTOSOMAL_RECESSIVE
# or X_RECESSIVE ensure *both* relevant HOM_ALT and COMP_HET modes are present.
inheritanceModes: {
AUTOSOMAL_DOMINANT: 0.1,
AUTOSOMAL_RECESSIVE_COMP_HET: 2.0,
AUTOSOMAL_RECESSIVE_HOM_ALT: 0.1,
X_DOMINANT: 0.1,
X_RECESSIVE_COMP_HET: 2.0,
X_RECESSIVE_HOM_ALT: 0.1,
MITOCHONDRIAL: 0.2
}
#Possible frequencySources:
#Thousand Genomes project http://www.1000genomes.org/
# THOUSAND_GENOMES,
#ESP project http://evs.gs.washington.edu/EVS/
# ESP_AFRICAN_AMERICAN, ESP_EUROPEAN_AMERICAN, ESP_ALL,
#ExAC project http://exac.broadinstitute.org/about
# EXAC_AFRICAN_INC_AFRICAN_AMERICAN, EXAC_AMERICAN,
# EXAC_SOUTH_ASIAN, EXAC_EAST_ASIAN,
# EXAC_FINNISH, EXAC_NON_FINNISH_EUROPEAN,
# EXAC_OTHER
#Possible frequencySources:
#Thousand Genomes project - http://www.1000genomes.org/ (THOUSAND_GENOMES)
#TOPMed - https://www.nhlbi.nih.gov/science/precision-medicine-activities (TOPMED)
#UK10K - http://www.uk10k.org/ (UK10K)
#ESP project - http://evs.gs.washington.edu/EVS/ (ESP_)
# ESP_AFRICAN_AMERICAN, ESP_EUROPEAN_AMERICAN, ESP_ALL,
#ExAC project http://exac.broadinstitute.org/about (EXAC_)
# EXAC_AFRICAN_INC_AFRICAN_AMERICAN, EXAC_AMERICAN,
# EXAC_SOUTH_ASIAN, EXAC_EAST_ASIAN,
# EXAC_FINNISH, EXAC_NON_FINNISH_EUROPEAN,
# EXAC_OTHER
#gnomAD - http://gnomad.broadinstitute.org/ (GNOMAD_E, GNOMAD_G)
frequencySources: [
THOUSAND_GENOMES,
TOPMED,
UK10K,
ESP_AFRICAN_AMERICAN, ESP_EUROPEAN_AMERICAN, ESP_ALL,
EXAC_AFRICAN_INC_AFRICAN_AMERICAN, EXAC_AMERICAN,
EXAC_SOUTH_ASIAN, EXAC_EAST_ASIAN,
EXAC_FINNISH, EXAC_NON_FINNISH_EUROPEAN,
EXAC_OTHER,
GNOMAD_E_AFR,
GNOMAD_E_AMR,
# GNOMAD_E_ASJ,
GNOMAD_E_EAS,
GNOMAD_E_FIN,
GNOMAD_E_NFE,
GNOMAD_E_OTH,
GNOMAD_E_SAS,
GNOMAD_G_AFR,
GNOMAD_G_AMR,
# GNOMAD_G_ASJ,
GNOMAD_G_EAS,
GNOMAD_G_FIN,
GNOMAD_G_NFE,
GNOMAD_G_OTH,
GNOMAD_G_SAS
]
# Possible pathogenicitySources: (POLYPHEN, MUTATION_TASTER, SIFT), (REVEL, MVP), CADD, REMM
# REMM is trained on non-coding regulatory regions
# *WARNING* if you enable CADD or REMM ensure that you have downloaded and installed the CADD/REMM tabix files
# and updated their location in the application.properties. Exomiser will not run without this.
pathogenicitySources: [ REVEL, MVP ]
#this is the standard exomiser order.
#all steps are optional
steps: [
#hiPhivePrioritiser: {},
#priorityScoreFilter: {priorityType: HIPHIVE_PRIORITY, minPriorityScore: 0.500},
#intervalFilter: {interval: 'chr10:123256200-123256300'},
# or for multiple intervals:
#intervalFilter: {intervals: ['chr10:123256200-123256300', 'chr10:123256290-123256350']},
# or using a BED file - NOTE this should be 0-based, Exomiser otherwise uses 1-based coordinates in line with VCF
#intervalFilter: {bed: /full/path/to/bed_file.bed},
#genePanelFilter: {geneSymbols: ['FGFR1','FGFR2']},
failedVariantFilter: { },
#qualityFilter: {minQuality: 50.0},
variantEffectFilter: {
remove: [
FIVE_PRIME_UTR_EXON_VARIANT,
FIVE_PRIME_UTR_INTRON_VARIANT,
THREE_PRIME_UTR_EXON_VARIANT,
THREE_PRIME_UTR_INTRON_VARIANT,
NON_CODING_TRANSCRIPT_EXON_VARIANT,
UPSTREAM_GENE_VARIANT,
INTERGENIC_VARIANT,
REGULATORY_REGION_VARIANT,
CODING_TRANSCRIPT_INTRON_VARIANT,
NON_CODING_TRANSCRIPT_INTRON_VARIANT,
DOWNSTREAM_GENE_VARIANT
]
},
# removes variants represented in the database
#knownVariantFilter: {},
frequencyFilter: {maxFrequency: 2.0},
pathogenicityFilter: {keepNonPathogenic: true},
# inheritanceFilter and omimPrioritiser should always run AFTER all other filters have completed
# they will analyse genes according to the specified modeOfInheritance above- UNDEFINED will not be analysed.
inheritanceFilter: {},
# omimPrioritiser isn't mandatory.
omimPrioritiser: {},
#priorityScoreFilter: {minPriorityScore: 0.4},
# Other prioritisers: Only combine omimPrioritiser with one of these.
# Don't include any if you only want to filter the variants.
hiPhivePrioritiser: {},
# or run hiPhive in benchmarking mode:
#hiPhivePrioritiser: {runParams: 'mouse'},
#phivePrioritiser: {}
#phenixPrioritiser: {}
#exomeWalkerPrioritiser: {seedGeneIds: [11111, 22222, 33333]}
]
outputOptions:
outputContributingVariantsOnly: false
#numGenes options: 0 = all or specify a limit e.g. 500 for the first 500 results
numGenes: 0
# Path to the desired output directory. Will default to the 'results' subdirectory of the exomiser install directory
#outputDirectory: results
# Filename for the output files. Will default to {input-vcf-filename}-exomiser
outputFileName: Pfeiffer-hiphive-exome
#out-format options: HTML, JSON, TSV_GENE, TSV_VARIANT, VCF (default: HTML)
outputFormats: [HTML, JSON, TSV_GENE, TSV_VARIANT, VCF]
Sample¶
It is recommended to provide Exomiser the input sample as in Phenopacket format. Exomiser will accept this in either JSON or YAML format.
probandId:¶
hpoIds:¶
Input of the HPO identifiers/terms. You can select them using the HPO browser. Input must be in array format. Terms are comma separated and delimited by single quotes. For example [‘HP:0001156’, ‘HP:0001363’, ‘HP:0011304’, ‘HP:0010055’]. It is critical that these are as detailed as possible and describe the observed phenotypes as fully and precisely as possible. These are used by the phenotype matching algorithms and will heavily influence the outcome of an analysis.
vcf:¶
The variant file in VCF format. There can be variants of multiple samples from one family in the file.
ped:¶
If you have multiple samples as input you have to define the pedigree using the ped format.
It is important that you correctly define affected and unaffected individuals. If you use X_RECESSIVE as mode of inheritance
be sure that the sex is correct (unknown is also fine).
Preset¶
# one of EXOME or GENOME. GENOME will require REMM to be available. Default is EXOME.
preset: EXOME
Analysis¶
The analysis shown below is equivalent to the --preset exome argument or adding
preset: EXOME
to an Exomiser job file. In most cases it is recommended to use the default presets as these have been thoroughly tested on the UK 100,000 genome rare-disease cohort. In case the user requires anything different, it is possible to manually define the data sources and steps in an Exomiser analysis.
---
analysisMode: PASS_ONLY
inheritanceModes: {
AUTOSOMAL_DOMINANT: 0.1,
AUTOSOMAL_RECESSIVE_HOM_ALT: 0.1,
AUTOSOMAL_RECESSIVE_COMP_HET: 2.0,
X_DOMINANT: 0.1,
X_RECESSIVE_HOM_ALT: 0.1,
X_RECESSIVE_COMP_HET: 2.0,
MITOCHONDRIAL: 0.2
}
frequencySources: [
THOUSAND_GENOMES,
TOPMED,
UK10K,
ESP_AFRICAN_AMERICAN, ESP_EUROPEAN_AMERICAN, ESP_ALL,
EXAC_AFRICAN_INC_AFRICAN_AMERICAN, EXAC_AMERICAN,
EXAC_SOUTH_ASIAN, EXAC_EAST_ASIAN,
EXAC_FINNISH, EXAC_NON_FINNISH_EUROPEAN,
EXAC_OTHER,
GNOMAD_E_AFR,
GNOMAD_E_AMR,
# GNOMAD_E_ASJ,
GNOMAD_E_EAS,
GNOMAD_E_FIN,
GNOMAD_E_NFE,
GNOMAD_E_OTH,
GNOMAD_E_SAS,
GNOMAD_G_AFR,
GNOMAD_G_AMR,
# GNOMAD_G_ASJ,
GNOMAD_G_EAS,
GNOMAD_G_FIN,
GNOMAD_G_NFE,
GNOMAD_G_OTH,
GNOMAD_G_SAS
]
# Possible pathogenicitySources: (POLYPHEN, MUTATION_TASTER, SIFT), (REVEL, MVP), CADD, REMM
# REMM is trained on non-coding regulatory regions
# *WARNING* if you enable CADD or REMM ensure that you have downloaded and installed the CADD/REMM tabix files
# and updated their location in the application.properties. Exomiser will not run without this.
pathogenicitySources: [ REVEL, MVP ]
#this is the standard exomiser order.
steps: [
failedVariantFilter: { },
variantEffectFilter: {
remove: [
FIVE_PRIME_UTR_EXON_VARIANT,
FIVE_PRIME_UTR_INTRON_VARIANT,
THREE_PRIME_UTR_EXON_VARIANT,
THREE_PRIME_UTR_INTRON_VARIANT,
NON_CODING_TRANSCRIPT_EXON_VARIANT,
NON_CODING_TRANSCRIPT_INTRON_VARIANT,
CODING_TRANSCRIPT_INTRON_VARIANT,
UPSTREAM_GENE_VARIANT,
DOWNSTREAM_GENE_VARIANT,
INTERGENIC_VARIANT,
REGULATORY_REGION_VARIANT
]
},
frequencyFilter: { maxFrequency: 2.0 },
pathogenicityFilter: { keepNonPathogenic: true },
inheritanceFilter: { },
omimPrioritiser: { },
hiPhivePrioritiser: { }
]
analysisMode:¶
Can be FULL or PASS_ONLY. We highly recommend PASS_ONLY on genomes for because of memory issues. It will only
keep variants that passes all filters. FULL will keep all variants, using the most memory and requiring the most time,
but it can be useful for troubleshooting why a particular variant of interest might not have been returned in the results
of an analysis.
analysisMode: PASS_ONLY
inheritanceModes:¶
Can be AUTOSOMAL_DOMINANT, AUTOSOMAL_RECESSIVE, X_RECESSIVE or UNDEFINED. This is a functionality of Jannovar.
See its inheritance documentation for further information.
# These are the default settings, with values representing the maximum minor allele frequency in percent (%) permitted for an
# allele to be considered as a causative candidate under that mode of inheritance.
# If you just want to analyse a sample under a single inheritance mode, delete/comment-out the others. For AUTOSOMAL_RECESSIVE
# or X_RECESSIVE ensure *both* relevant HOM_ALT and COMP_HET modes are present.
# In cases where you do not want any cut-offs applied an empty map should be used e.g. inheritanceModes: {}
inheritanceModes: {
AUTOSOMAL_DOMINANT: 0.1,
AUTOSOMAL_RECESSIVE_HOM_ALT: 0.1,
AUTOSOMAL_RECESSIVE_COMP_HET: 2.0,
X_DOMINANT: 0.1,
X_RECESSIVE_HOM_ALT: 0.1,
X_RECESSIVE_COMP_HET: 2.0,
MITOCHONDRIAL: 0.2
}
frequencySources:¶
Here you can specify which variant frequency databases you want to use. You can add multiple databases using the same array format as the HPO IDs.
The data sources used are from 1000 genomes (via DBSNP), DBSNP, ESP, ExAC, gnomAD exomes and gnomAD genomes, UK10K (via DBSNP), TOPMed (via DBSNP).
- DBSNP:
THOUSAND_GENOMES,UK10K,TOPMED- ESP:
ESP_AFRICAN_AMERICAN,ESP_EUROPEAN_AMERICAN,ESP_ALL- ExAC:
EXAC_AFRICAN_INC_AFRICAN_AMERICAN,EXAC_AMERICAN,EXAC_SOUTH_ASIAN,EXAC_EAST_ASIAN,EXAC_FINNISH,EXAC_NON_FINNISH_EUROPEAN,EXAC_OTHER- gnomAD exomes:
GNOMAD_E_AFR,GNOMAD_E_AMR,GNOMAD_E_ASJ,GNOMAD_E_EAS,GNOMAD_E_FIN,GNOMAD_E_NFE,GNOMAD_E_OTH,GNOMAD_E_SAS,- gnomAD genomes:
GNOMAD_G_AFR,GNOMAD_G_AMR,GNOMAD_G_ASJ,GNOMAD_G_EAS,GNOMAD_G_FIN,GNOMAD_G_NFE,GNOMAD_G_OTH,GNOMAD_G_SAS
We recommend using all databases if the proband population background is unknown, although removing the GNOMAD_E_ASJ and GNOMAD_G_ASJ, unless your proband is
known to come from an Ashkenazi population e.g.
frequencySources: [
THOUSAND_GENOMES,
TOPMED,
UK10K,
ESP_AFRICAN_AMERICAN, ESP_EUROPEAN_AMERICAN, ESP_ALL,
EXAC_AFRICAN_INC_AFRICAN_AMERICAN, EXAC_AMERICAN,
EXAC_SOUTH_ASIAN, EXAC_EAST_ASIAN,
EXAC_FINNISH, EXAC_NON_FINNISH_EUROPEAN,
EXAC_OTHER,
GNOMAD_E_AFR,
GNOMAD_E_AMR,
# GNOMAD_E_ASJ,
GNOMAD_E_EAS,
GNOMAD_E_FIN,
GNOMAD_E_NFE,
GNOMAD_E_OTH,
GNOMAD_E_SAS,
GNOMAD_G_AFR,
GNOMAD_G_AMR,
# GNOMAD_G_ASJ,
GNOMAD_G_EAS,
GNOMAD_G_FIN,
GNOMAD_G_NFE,
GNOMAD_G_OTH,
GNOMAD_G_SAS
]
pathogenicitySources:¶
Possible pathogenicitySources: POLYPHEN, MUTATION_TASTER, SIFT, REVEL, MVP, CADD, REMM. REMM is trained on
non-coding regulatory regions. WARNING if you enable CADD, ensure that you have downloaded and installed the CADD
tabix files and updated their location in the application.properties (see CADD data). Exomiser will not run
without this.
We recommend using either [REVEL, MVP] OR [POLYPHEN, MUTATION_TASTER, SIFT] as REVEL and MVP are newer
predictors which have been shown to have better performance and are more nuanced. Mixing them with the Polyphen2,
MutationTaster or SIFT will give worse performance.
pathogenicitySources: [REVEL, MVP, REMM]
steps¶
This section instructs exomiser which analysis steps should be run and with which criteria. _n.b._ the order in which the steps are declared is important - exomiser will run them in the order declared, although certain optimisations will happen automatically. We recommend using the [standard settings](../example/test-analysis-genome) for genome wide analysis as these have been optimised for both speed and memory performance. Nonetheless all steps are optional. Being an array of steps, this section must be enclosed in square brackets. Steps are comma separated and written in hash format name: {options}. All steps are optional - comment them out or delete them if you do not want to use them.
Analysis steps are defined in terms of variant filters, gene filters or prioritisers. The inheritanceFilter and omimPrioritiser are both somewhat anomalous as they operate on genes but also require the variants to have already been filtered. The optimiser will ensure that these are run at the correct time if they have been incorrectly placed.
Using these it is possible to create artificial exomes, define gene panels or only examine specific regions, for example.
Variant filters¶
These operate on variants and will produce a pass or fail result for each variant run through the filter.
failedVariantFilter:¶
Removes variants which are not marked as PASS or . in the VCF file. This is a highly recommended filter which will remove a lot of noise.
failedVariantFilter: {}
qualityFilter:¶
Removes variants with VCF QUAL scores lower than the given minQuality.
qualityFilter: {minQuality: 50.0}
intervalFilter:¶
Defines an interval of interest. Only variants within this interval will be passed. Currently only single intervals are possible.
intervalFilter: {interval: 'chr10:123256200-123256300'}
geneIdFilter:¶
You can define entrez-gene-ids for genes of interest. Only variants associated with these genes will be analyzed.
geneIdFilter: {geneIds: [12345, 34567, 98765]}
variantEffectFilter:¶
If you are interested only in specific functional classes of variants you can define a set of classes you want to remove from the output. Variant effects are generated by Jannovar. Jannovar uses Sequence Ontology (SO) terms and are listed in their manual.
variantEffectFilter: {remove: [SYNONYMOUS_VARIANT]}
regulatoryFeatureFilter:¶
If included it removes all non-regulatory, non-coding variants over 20Kb from a known gene. Intergenic and upstream variants in known enhancer regions over 20kb from a known gene are associated with genes in their TAD and not effected by this filter. This is an important filter to include when analysing whole-genome data.
regulatoryFeatureFilter: {}
knownVariantFilter:¶
Removes variants represented in the databases set in the frequencySources section. E.g. if you define
frequencySources: [THOUSAND_GENOMES]
every variant with an RS number will be removed. Variants without an RSid will be removed/failed if they are represented in any database defined in the frequencySources section. We do not recommend this option on recessive diseases.
knownVariantFilter: {}
frequencyFilter:¶
Frequency cutoff of a variant in percent. Frequencies are derived from the databases defined in the frequencySources section. We recommend a value below 5.0 % depending on the disease. Variants will be removed/failed if they have a frequency higher than the stated percentage in any database defined in the frequencySources section. _n.b_ Not defining this filter will result in all variants having no frequency data, even if the frequencySources contains values.
frequencyFilter: {maxFrequency: 1.0}
pathogenicityFilter:¶
Will apply the pathogenicity scores defined in the pathogenicitySources section to variants.
If the keepNonPathogenic field is set to true then all variants will be kept. Setting this to false will set
the filter to fail non-missense variants with pathogenicity scores lower than a score cutoff of 0.5.
This filter is meant to be quite permissive and we recommend it be set to true.
pathogenicityFilter: {keepNonPathogenic: true}
Gene filters¶
These act at the gene-level and therefore may also refer to the variants associated with the gene. As a rule this is discouraged, although is broken by the inheritanceFiler.
priorityScoreFilter:¶
Running the prioritizer followed by a priorityScoreFilter will remove genes which are least likely to contribute to the
phenotype defined in hpoIds, this will dramatically reduce the time and memory required to analyze a genome. 0.501
is a good compromise to select good phenotype matches and the best protein-protein interactions hits using the hiPhive
prioritiser. PriorityType can be one of HIPHIVE_PRIORITY, PHIVE_PRIORITY, PHENIX_PRIORITY, OMIM_PRIORITY, EXOMEWALKER_PRIORITY.
priorityScoreFilter: {priorityType: HIPHIVE_PRIORITY, minPriorityScore: 0.501}
inheritanceFilter:¶
inheritanceFilter and omimPrioritiser should always run AFTER all other filters have completed. They will analyze
genes according to the specified modeOfInheritance above. If set to UNDEFINED no filtering will be done.
You can read more in the Jannovar inheritance documentation how exactly this filter works.
inheritanceFilter: {}
Prioritisers¶
These work on the gene-level and will produce the semantic-similarity scores for how well a gene matches the sample’s HPO profile. We recommend using a combination of the OMIM and hiPHIVE prioritisers only. These have been tested on the UK’s 100,000 genomes pilot project on rare disease diagnoses. Using other prioritisers may miss diagnoses as the data could be out of date or the algorithm’s effectiveness may have been superseded. They are retained for historical interest.
omimPrioritiser:¶
inheritanceFilter and omimPrioritiser should always run AFTER all other filters have completed. Other prioritisers: Only combine omimPrioritiser with one of the next filters. The OMIM prioritiser adds known disease information from OMIM to genes including the inheritance mode and then checks this inheritance mode with the compatible inheritance modes of the gene. Genes with incompatible modes will have their scores halved.
omimPrioritiser: {}
hiPhivePrioritiser:¶
Scores genes using phenotype comparisons to human, mouse and fish involving disruption of the gene or nearby genes in the interactome using a random walk.
See the hiPHIVE publication for details.
# Using the default
hiPhivePrioritiser: {}
is the same as
hiPhivePrioritiser: {runParams: 'human, mouse, fish, ppi'}
It is possible to only run comparisons agains a given organism/set of organisms human,mouse,fish and include/exclude
protein-protein interaction proximities ppi. e.g. only using human and mouse data -
hiPhivePrioritiser: {runParams: 'human,mouse'}
phenixPrioritiser:¶
Scores genes using phenotype comparisons to known human disease genes. See the PhenIX publication for details.
phenixPrioritiser: {}
phivePrioritiser:¶
Scores genes using phenotype comparisons to mice with disruption of the gene. This is equivalent to running hiPhivePrioritiser: {runParams: 'mouse'}.
See the PHIVE publication for details.
phivePrioritiser: {}
exomeWalkerPrioritiser:¶
Scores genes by proximity in interactome to the seed genes. Gene identifiers are required to be genbank identifiers. See the ExomeWalker publication for details.
exomeWalkerPrioritiser: {seedGeneIds: [11111, 22222, 33333]}
Output options¶
When specified as part of a command-line argument, the file should be a properly formed YAML object:
# Exomiser outputOptions
---
outputContributingVariantsOnly: true
numGenes: 20
minExomiserGeneScore: 0.7
outputDirectory: /analyses/sample-12345/
outputFileName: sample-12345
outputFormats: [HTML, JSON]
When included as part of an exomiser job the fields are inlined in the exomiser-job.yaml file like so:
# other analysis and sample options above
outputOptions:
outputContributingVariantsOnly: true
numGenes: 20
minExomiserGeneScore: 0.7
outputDirectory: /analyses/sample-12345/
outputFileName: sample-12345
outputFormats: [HTML, JSON]
outputContributingVariantsOnly:¶
Can be true or false. Setting this to true will make the TSV_VARIANT and VCF output file only contain
PASS variants which contribute to the gene score. Defaults to true.
numGenes:¶
Maximum number of genes listed in the results. If set to 0 all are listed. In most cases a limit of 50 is more
than adequate as typically a good result will be found in the top 5. Not enabled by
default.
minExomiserGeneScore¶
The mimimum gene (combined phenotype and variant) score required to be returned. As a rule of thumb scores >= 0.7 are a good score however, depending on the proband phenotype and the phenotype of best matching condition although it is not a hard-and-fast number. In our testing 0.7 gave the best performance in terms or recall and sensitivity. Not enabled by default.
outputPrefix:¶
Specify the path/filename without an extension and this will be added according to the
outputFormats option. If unspecified this will default to the following:
{exomiserDir}/results/input-vcf-name-exomiser-results.html. Alternatively, specify a fully qualified path
only. e.g. /home/jules/exomes/analysis.
DEPRECATED - replaced by explicit outputDirectory and outputFileName options. Users are strongly advised to
migrate any existing scripts to use the new options (below), as this will be removed in a future version.
outputDirectory:¶
Optional path indicating where the output files for the analysis should be written. Will attempt to create any missing
directories. Using this without the outputFileName option will result in a default filename being used which will be
output to the specified directory.
Default value: {runDirectory}/results
outputFileName:¶
Optional filename prefix to be used for the output files. Can be combined with the outputDirectory option to specify
a custom location and filename. Used alone will result in files with the specified filename being written to the default
results directory.
Default value: {input-vcf-filename}-exomiser
outputFormats:¶
Array to define the output formats. can be [TSV-GENE], [TSV-VARIANT], [VCF], JSON or HTML or any
combination like [TSV-GENE, TSV-VARIANT, VCF]. JSON is the most informative output option and suitable for use in
downstream computational analyses or manually queried using something like jq. The
HTML output is most suitable for manual inspection / human use.
Default value: [JSON, HTML].
Structural Variant Prioritisation¶
Exomiser v13.0.0 is capable of jointly prioritising structural and sequence variants from a combined VCF file. For the
purposes of the analysis a structural variant is defined as a variant >= 50 nucleotides in length or a symbolic variant
(e.g. a variant with a VCF ALT allele of the form <DEL> instead of an actual sequence). There are many, many callers
to choose from so performance will depend heavily on these as well as the underlying sequencing technology (e.g. Illumina
short-read vs PacBio or Oxford Nanopore long-read sequencing). Exomiser has been tested on Manta and Canvas calls from Illumina short-reads
produced for the 100K genomes project, with some compatibility testing against PacBio pbsv.
Prioritisation Overview¶
Assuming the recommended analysis steps are being used, Exomiser will broadly consider structural variants in a similar manner to sequence variants, with the major difference being that the precise size, position and sequence change is not known, especially for symbolic variants. Briefly, Exomiser performs these steps on each SV:
- Predict variant effects based on overlapping transcripts
- Assign variant to all genes for which it overlaps a transcript
- Assign variant pathogenicity score according to variant effect and known similar ClinVar variants
- Assign variant frequency score according to similar alleles in gnomAD-SV, DECIPHER, dbVar, DGV, GoNL
Following these SV-specific steps, the variant is considered in the same way as a sequence variant and is filtered and prioritised accordingly. This allows compound heterozygous genotypes of structural and sequence variants to be considered during an analysis.
Multiple Gene Overlaps¶
If a variant overlaps more than one gene, the variant will be associated with all of these genes and reported with the most severe variant effect it is predicted to have on each gene in the results.
For example, if a variant 1-23456-78910-N-<DEL> completely deletes GENE:1 and deletes the first exon of GENE:2, the variant will be reported twice, once for each associated gene. The following pseudo-code tries to illustrate this.
GENE1:
variants:
- 1-23456-78910-N-<DEL>
variantEffect: TRANSCRIPT_ABLATION
GENE2:
variants:
- 1-23456-78910-N-<DEL>
variantEffect: EXON_LOSS_VARIANT
SV Similarity¶
Given their imprecise nature, SVs are not looked-up in the database using precise coordinates, instead they are considered ‘equal’ if their genomic coordinates have a jaccard similarity of 0.8 and they constitute an equivalent broad type - gain, loss, me_gain, me_loss, inversion or complex.
Interpreting the Results¶
Depending on the output options provided, Exomiser will write out at least an HTML results file in the results sub-directory of the Exomiser installation.
As a general rule all output files contain a ranked list of genes and/or variants with the top-ranked gene/variant displayed first. The exception being the VCF output which, since version 13.1.0, is sorted according to VCF convention and tabix indexed.
Exomiser attempts to predict the variant or variants likely to be causative of a patient’s phenotype and does so by associating them with the gene (or genes in the case of large structural variations) they intersect with on the genomic sequence. Variants occurring in intergenic regions are associated to the closest gene and those overlapping two genes are associated with the gene in which they are predicted to have the largest consequence.
Once associated with a gene, Exomiser uses the compatible modes of inheritance for a variant to assess it in the context of any diseases associated with the gene or any mouse knockout models of that gene. These are all bundled together into a GeneScore which features filtered variants located in that gene compatible with a given mode of inheritance. After the filtering steps Exomiser ranks these GeneScores according to descending combined score. The results are then written out to the files and formats specified in the output settings.
As of release 13.2.0 the output files only feature a single, combined output file of ranked genes/variants e.g. when supplying the output options (via an output-options.yaml file) outputFileName: Pfeiffer-hiphive-exome-PASS_ONLY and outputFormats: [TSV_VARIANT, TSV_GENE, VCF], the following files will be written out: Pfeiffer-hiphive-exome-PASS_ONLY.variants.tsv Pfeiffer-hiphive-exome-PASS_ONLY.genes.tsv, Pfeiffer-hiphive-exome-PASS_ONLY.vcf
These formats are detailed below.
JSON¶
The JSON file represents the most accurate representation of the data, as it is referenced internally by Exomiser. As such, we don’t provide a schema for this, but it has been pretty stable and breaking changes will only occur with major version changes to the software. Minor additions are to be expected for minor releases, as per the SemVer specification.
We recommend using Python or JQ to extract data from this file.
TSV GENES¶
In the genes.tsv file it is possible for a gene to appear multiple times, depending on the MOI it is compatible with, given the filtered variants. For example in the example below MUC6 is ranked 7th under the AD model and 8th under an AR model.
#RANK ID GENE_SYMBOL ENTREZ_GENE_ID MOI P-VALUE EXOMISER_GENE_COMBINED_SCORE EXOMISER_GENE_PHENO_SCORE EXOMISER_GENE_VARIANT_SCORE HUMAN_PHENO_SCORE MOUSE_PHENO_SCORE FISH_PHENO_SCORE WALKER_SCORE PHIVE_ALL_SPECIES_SCORE OMIM_SCORE MATCHES_CANDIDATE_GENE HUMAN_PHENO_EVIDENCE MOUSE_PHENO_EVIDENCE FISH_PHENO_EVIDENCE HUMAN_PPI_EVIDENCE MOUSE_PPI_EVIDENCE FISH_PPI_EVIDENCE
1 FGFR2_AD FGFR2 2263 AD 0.0000 0.9981 1.0000 1.0000 0.8808 1.0000 0.0000 0.5095 1.0000 1.0000 0 Jackson-Weiss syndrome (OMIM:123150): Brachydactyly (HP:0001156)-Broad hallux (HP:0010055), Craniosynostosis (HP:0001363)-Craniosynostosis (HP:0001363), Broad thumb (HP:0011304)-Broad metatarsal (HP:0001783), Broad hallux (HP:0010055)-Broad hallux (HP:0010055), Brachydactyly (HP:0001156)-abnormal sternum morphology (MP:0000157), Craniosynostosis (HP:0001363)-premature cranial suture closure (MP:0000081), Broad thumb (HP:0011304)-abnormal sternum morphology (MP:0000157), Broad hallux (HP:0010055)-abnormal sternum morphology (MP:0000157), Proximity to FGF14 associated with Spinocerebellar ataxia 27 (OMIM:609307): Broad hallux (HP:0010055)-Pes cavus (HP:0001761), Proximity to FGF14 Brachydactyly (HP:0001156)-abnormal digit morphology (MP:0002110), Broad thumb (HP:0011304)-abnormal digit morphology (MP:0002110), Broad hallux (HP:0010055)-abnormal digit morphology (MP:0002110),
2 ENPP1_AD ENPP1 5167 AD 0.0049 0.8690 0.5773 0.9996 0.6972 0.5773 0.5237 0.5066 0.6972 1.0000 0 Autosomal recessive hypophosphatemic rickets (ORPHA:289176): Brachydactyly (HP:0001156)-Genu varum (HP:0002970), Craniosynostosis (HP:0001363)-Craniosynostosis (HP:0001363), Broad thumb (HP:0011304)-Tibial bowing (HP:0002982), Broad hallux (HP:0010055)-Genu varum (HP:0002970), Brachydactyly (HP:0001156)-fused carpal bones (MP:0008915), Craniosynostosis (HP:0001363)-abnormal nucleus pulposus morphology (MP:0006392), Broad thumb (HP:0011304)-fused carpal bones (MP:0008915), Broad hallux (HP:0010055)-fused carpal bones (MP:0008915), Craniosynostosis (HP:0001363)-ceratohyal cartilage premature perichondral ossification, abnormal (ZP:0012007), Broad thumb (HP:0011304)-cleithrum nodular, abnormal (ZP:0006782), Proximity to PAPSS2 associated with Brachyolmia 4 with mild epiphyseal and metaphyseal changes (OMIM:612847): Brachydactyly (HP:0001156)-Brachydactyly (HP:0001156), Broad thumb (HP:0011304)-Brachydactyly (HP:0001156), Broad hallux (HP:0010055)-Brachydactyly (HP:0001156), Proximity to PAPSS2 Brachydactyly (HP:0001156)-abnormal long bone epiphyseal plate morphology (MP:0003055), Craniosynostosis (HP:0001363)-domed cranium (MP:0000440), Broad thumb (HP:0011304)-abnormal long bone epiphyseal plate morphology (MP:0003055), Broad hallux (HP:0010055)-abnormal long bone epiphyseal plate morphology (MP:0003055),
//
7 MUC6_AD MUC6 4588 AD 0.0096 0.7532 0.5030 0.9990 0.0000 0.0000 0.0000 0.5030 0.5030 1.0000 0 Proximity to GKN2 Brachydactyly (HP:0001156)-brachydactyly (MP:0002544), Broad thumb (HP:0011304)-brachydactyly (MP:0002544), Broad hallux (HP:0010055)-brachydactyly (MP:0002544),
8 MUC6_AR MUC6 4588 AR 0.0096 0.7531 0.5030 0.9990 0.0000 0.0000 0.0000 0.5030 0.5030 1.0000 0 Proximity to GKN2 Brachydactyly (HP:0001156)-brachydactyly (MP:0002544), Broad thumb (HP:0011304)-brachydactyly (MP:0002544), Broad hallux (HP:0010055)-brachydactyly (MP:0002544),
TSV VARIANTS¶
In the variants.tsv file it is possible for a variant, like a gene, to appear multiple times, depending on the MOI it is compatible with. For example in the example below MUC6 has two variants ranked 7th under the AD model and two ranked 8th under an AR (compound heterozygous) model. In the AD case the CONTRIBUTING_VARIANT column indicates whether the variant was (1) or wasn’t (0) used for calculating the EXOMISER_GENE_COMBINED_SCORE and EXOMISER_GENE_VARIANT_SCORE.
#RANK ID GENE_SYMBOL ENTREZ_GENE_ID MOI P-VALUE EXOMISER_GENE_COMBINED_SCORE EXOMISER_GENE_PHENO_SCORE EXOMISER_GENE_VARIANT_SCORE EXOMISER_VARIANT_SCORE CONTRIBUTING_VARIANT WHITELIST_VARIANT VCF_ID RS_ID CONTIG START END REF ALT CHANGE_LENGTH QUAL FILTER GENOTYPE FUNCTIONAL_CLASS HGVS EXOMISER_ACMG_CLASSIFICATION EXOMISER_ACMG_EVIDENCE EXOMISER_ACMG_DISEASE_ID EXOMISER_ACMG_DISEASE_NAME CLINVAR_ALLELE_ID CLINVAR_PRIMARY_INTERPRETATION CLINVAR_STAR_RATING GENE_CONSTRAINT_LOEUF GENE_CONSTRAINT_LOEUF_LOWER GENE_CONSTRAINT_LOEUF_UPPER MAX_FREQ_SOURCE MAX_FREQ ALL_FREQ MAX_PATH_SOURCE MAX_PATH ALL_PATH
1 10-123256215-T-G_AD FGFR2 2263 AD 0.0000 0.9981 1.0000 1.0000 1.0000 1 1 rs121918506 10 123256215 123256215 T G 0 100.0000 PASS 1|0 missense_variant FGFR2:ENST00000346997.2:c.1688A>C:p.(Glu563Ala) LIKELY_PATHOGENIC PM2,PP3_Strong,PP4,PP5 OMIM:123150 Jackson-Weiss syndrome 28333 LIKELY_PATHOGENIC 1 0.13692 0.074 0.27 REVEL 0.965 REVEL=0.965,MVP=0.9517972
2 6-132203615-G-A_AD ENPP1 5167 AD 0.0049 0.8690 0.5773 0.9996 0.9996 1 0 rs770775549 6 132203615 132203615 G A 0 922.9800 PASS 0/1 splice_donor_variant ENPP1:ENST00000360971.2:c.2230+1G>A:p.? UNCERTAIN_SIGNIFICANCE PVS1_Strong OMIM:615522 Cole disease NOT_PROVIDED 0 0.41042 0.292 0.586 GNOMAD_E_SAS 0.0032486517 TOPMED=7.556E-4,EXAC_NON_FINNISH_EUROPEAN=0.0014985314,GNOMAD_E_NFE=0.0017907989,GNOMAD_E_SAS=0.0032486517
//
7 11-1018088-TG-T_AD MUC6 4588 AD 0.0096 0.7532 0.5030 0.9990 0.9990 1 0 rs765231061 11 1018088 1018089 TG T -1 441.8100 PASS 0/1 frameshift_variant MUC6:ENST00000421673.2:c.4712del:p.(Pro1571Hisfs*21) UNCERTAIN_SIGNIFICANCE NOT_PROVIDED 0 0.79622 0.656 0.971 GNOMAD_G_NFE 0.0070363074 GNOMAD_E_AMR=0.0030803352,GNOMAD_G_NFE=0.0070363074
7 11-1018093-G-GT_AD MUC6 4588 AD 0.0096 0.7532 0.5030 0.9990 0.9989 0 0 rs376177791 11 1018093 1018093 G GT 1 592.4500 PASS 0/1 frameshift_elongation MUC6:ENST00000421673.2:c.4707dup:p.(Pro1570Thrfs*136) NOT_AVAILABLE NOT_PROVIDED 0 0.79622 0.656 0.971 GNOMAD_G_NFE 0.007835763 GNOMAD_G_NFE=0.007835763
8 11-1018088-TG-T_AR MUC6 4588 AR 0.0096 0.7531 0.5030 0.9990 0.9990 1 0 rs765231061 11 1018088 1018089 TG T -1 441.8100 PASS 0/1 frameshift_variant MUC6:ENST00000421673.2:c.4712del:p.(Pro1571Hisfs*21) UNCERTAIN_SIGNIFICANCE NOT_PROVIDED 0 0.79622 0.656 0.971 GNOMAD_G_NFE 0.0070363074 GNOMAD_E_AMR=0.0030803352,GNOMAD_G_NFE=0.0070363074
8 11-1018093-G-GT_AR MUC6 4588 AR 0.0096 0.7531 0.5030 0.9990 0.9989 1 0 rs376177791 11 1018093 1018093 G GT 1 592.4500 PASS 0/1 frameshift_elongation MUC6:ENST00000421673.2:c.4707dup:p.(Pro1570Thrfs*136) UNCERTAIN_SIGNIFICANCE NOT_PROVIDED 0 0.79622 0.656 0.971 GNOMAD_G_NFE 0.007835763 GNOMAD_G_NFE=0.007835763
VCF¶
In the VCF file it is possible for a variant, like a gene, to appear multiple times, depending on the MOI it is
compatible with. For example in the example below MUC6 has two variants ranked 7th under the AD model and two ranked 8th
under an AR (compound heterozygous) model. In the AD case the CONTRIBUTING_VARIANT column indicates whether the variant
was (1) or wasn’t (0) used for calculating the EXOMISER_GENE_COMBINED_SCORE and EXOMISER_GENE_VARIANT_SCORE. The INFO
field with the ID=Exomiser describes the internal format of this sub-field. Be aware that for multi-allelic sites,
Exomiser will decompose and trim them for the proband sample and this is what will be displayed in the Exomiser ID
sub-field e.g. 11-1018088-TG-T_AD.
##INFO=<ID=Exomiser,Number=.,Type=String,Description="A pipe-separated set of values for the proband allele(s) from the record with one per compatible MOI following the format: {RANK|ID|GENE_SYMBOL|ENTREZ_GENE_ID|MOI|P-VALUE|EXOMISER_GENE_COMBINED_SCORE|EXOMISER_GENE_PHENO_SCORE|EXOMISER_GENE_VARIANT_SCORE|EXOMISER_VARIANT_SCORE|CONTRIBUTING_VARIANT|WHITELIST_VARIANT|FUNCTIONAL_CLASS|HGVS|EXOMISER_ACMG_CLASSIFICATION|EXOMISER_ACMG_EVIDENCE|EXOMISER_ACMG_DISEASE_ID|EXOMISER_ACMG_DISEASE_NAME}">
#CHROM POS ID REF ALT QUAL FILTER INFO sample
10 123256215 . T G 100 PASS Exomiser={1|10-123256215-T-G_AD|FGFR2|2263|AD|0.0000|0.9981|1.0000|1.0000|1.0000|1|1|missense_variant|FGFR2:ENST00000346997.2:c.1688A>C:p.(Glu563Ala)|LIKELY_PATHOGENIC|PM2,PP3_Strong,PP4,PP5|OMIM:123150|"Jackson-Weiss syndrome"};GENE=FGFR2;INHERITANCE=AD;MIM=101600 GT:DS:PL 1|0:2.000:50,11,0
11 1018088 . TG T 441.81 PASS AC=1;AF=0.50;AN=2;BaseQRankSum=7.677;DP=162;DS;Exomiser={7|11-1018088-TG-T_AD|MUC6|4588|AD|0.0096|0.7532|0.5030|0.9990|0.9990|1|0|frameshift_variant|MUC6:ENST00000421673.2:c.4712del:p.(Pro1571Hisfs*21)|UNCERTAIN_SIGNIFICANCE|||""},{8|11-1018088-TG-T_AR|MUC6|4588|AR|0.0096|0.7531|0.5030|0.9990|0.9990|1|0|frameshift_variant|MUC6:ENST00000421673.2:c.4712del:p.(Pro1571Hisfs*21)|UNCERTAIN_SIGNIFICANCE|||""};FS=25.935;HRun=3;HaplotypeScore=1327.2952;MQ=43.58;MQ0=6;MQRankSum=-5.112;QD=2.31;ReadPosRankSum=2.472;set=variant GT:AD:DP:GQ:PL 0/1:146,45:162:99:481,0,5488
11 1018093 . G GT 592.45 PASS AC=1;AF=0.50;AN=2;BaseQRankSum=8.019;DP=157;Exomiser={7|11-1018093-G-GT_AD|MUC6|4588|AD|0.0096|0.7532|0.5030|0.9990|0.9989|0|0|frameshift_elongation|MUC6:ENST00000421673.2:c.4707dup:p.(Pro1570Thrfs*136)|NOT_AVAILABLE|||""},{8|11-1018093-G-GT_AR|MUC6|4588|AR|0.0096|0.7531|0.5030|0.9990|0.9989|1|0|frameshift_elongation|MUC6:ENST00000421673.2:c.4707dup:p.(Pro1570Thrfs*136)|UNCERTAIN_SIGNIFICANCE|||""};FS=28.574;HRun=1;HaplotypeScore=1267.6968;MQ=44.06;MQ0=4;MQRankSum=-5.166;QD=3.26;ReadPosRankSum=1.328;set=variant GT:AD:DP:GQ:PL 0/1:140,42:157:99:631,0,4411
6 132203615 . G A 922.98 PASS AC=1;AF=0.50;AN=2;BaseQRankSum=-0.671;DP=94;Dels=0.00;Exomiser={2|6-132203615-G-A_AD|ENPP1|5167|AD|0.0049|0.8690|0.5773|0.9996|0.9996|1|0|splice_donor_variant|ENPP1:ENST00000360971.2:c.2230+1G>A:p.?|UNCERTAIN_SIGNIFICANCE|PVS1_Strong|OMIM:615522|"Cole disease"};FS=0.805;HRun=0;HaplotypeScore=3.5646;MQ=56.63;MQ0=0;MQRankSum=1.807;QD=9.82;ReadPosRankSum=-0.900;set=variant2 GT:AD:DP:GQ:PL 0/1:53,41:94:99:953,0,1075
The VCF file is tabix-indexed and exomiser ranked alleles can be extracted using grep. For example, to display the
top 5 ranked variants, you would issue the command:
zgrep -E '\{[1-5]{1}\|' Pfeiffer-hiphive-exome-PASS_ONLY.vcf.gz
ACMG Assignment¶
Starting with version 13.1.0, Exomiser performs a partial categorisation of the variants contributing to the gene score for a mode of inheritance using the ACMG/AMP Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. The criteria are assigned and combined according to the UK ACGS 2020 guidelines.
It is important to be aware that these scores are not a substitute for manual assignment by a qualified clinical geneticist or clinician - The scores displayed utilise the data found in the Exomiser database and are a subset of the possible criteria by which to assess a variant. Nonetheless, in our benchmarking on the returned cases from the 100K Genomes Project, restricting to variants with these automated P/LP classifications increases precision (positive predictive value) markedly without excluding many real diagnoses. For example, on a cohort of 742 solved cases where the top 5 Exomiser candidates were considered, including the P/LP criteria increased precision 3.8-fold from 15% to 57% with only a small drop in the recall of the diagnoses from 94% to 83%. An even larger 5.7-fold increase of precision from 3% to 17% was observed when considering a larger cohort of 31k cases where only 17% had received a positive diagnosis (again with a modest drop in recall from 91% to 75%).
Exomiser is capable of assigning the following ACMG categories:
Computational and Predictive Data¶
PVS1¶
Variants must have a predicted loss of function effect, be in a gene with known disease associations and have a gene constraint LOF O/E < 0.7635 (gnomAD 2.1.1) to suggest that a gene is LoF intolerant. Variants not predicted to lead to NMD (those located in the last exon) will have the modifier downgraded to Strong.
PM4¶
Stop-loss and in-frame insertions or deletions, not previously assigned a PVS1 criterion are assigned PM4.
PP3 / BP4¶
If REVEL is chosen as a pathogenicity predictor for missense variants, PP3 and BP4 are assigned using the modifiers according to table 2 of Evidence-based calibration of computational tools for missense variant pathogenicity classification and ClinGen recommendations for clinical use of PP3/BP4 criteria. Note that this suggests the use of modifiers up to Strong in the case of pathogenic or Very Strong in the case of benign predictions. Otherwise, an ensemble-based approach will be used for other pathogenicity predictors as per the original 215 guidelines. It should be noted we found better performance using the REVEL-based approach when testing against the 100K genomes data.
Segregation Data¶
BS4¶
If a pedigree with two or more members, including the proband is provided, Exomiser will assign BS4 for variants not segregating with affected members of the family.
De novo Data¶
PS2¶
Exomiser assigns the PS2 criterion for variants compatible with a dominant mode of inheritance, with a pedigree containing at least two ancestors of the proband, none of whom are affected and none of whom share the same allele as the proband.
Population Data¶
BA1¶
Given Exomiser will filter out alleles with an allele frequency of >= 2.0%, this is unlikely to be seen. However, alleles with a maximum frequency >= 5.0% in the frequency sources specified will be assigned the BA1 criterion.
PM2¶
Alleles not present in the ESP, ExAC and 1000 Genomes data sets (i.e. the allele must be absent from all three) are assigned the PM2 criterion.
Allelic Data¶
PM3 / BP2¶
If Exomiser is provided with a phased VCF and a variant is found to be in-trans with a ClinVar Pathogenic variant and associated with a recessive disorder, the PM3 criterion will be applied. However, in cases where variant is being considered for a recessive disorder and is in-cis or a dominant disorder and in-trans with another pathogenic variant the BP2 criterion is applied.
Phenotype¶
PP4¶
Given Exomiser’s focus on phenotype-driven variant prioritisation, variants in a gene associated with a disorder with a phenotype match score > 0.6 to the patient’s phenotype are assigned the PP4 criterion at the Moderate, rather than Supporting level.
Clinical¶
PP5 / BP6¶
If a variant is previously reported as P/LP in ClinVar with a 1-start rating, it will be assigned PP5, those with >= 2 stars (multiple submitters, criteria provided, no conflicts / reviewed by expert panel / practice guideline) will be assigned a Strong level. Conversely, if the variant is previously reported as B/LB it will be assigned BP6 with the same modification criteria. Typically these P/LP variants will be in the Exomiser ClinVar ‘whitelist’, and will have a very high variant score irrespective of the predicted variant effect and always survive any filtering criteria.
Transcript Selection¶
Transcripts will be selected using the most deleterious predicted variant effect from Jannovar according to the transcript-source property set in the application.properties. We recommend using the Ensembl transcript datasource as the Exomiser build uses the GENCODE basic set of transcripts. Future versions should use MANE transcripts.
ACMG assignments will be reported for a variant on a transcript consistent with a particular mode of inheritance in conjunction with a disorder, the assigned criteria with any modifiers and the final classification e.g.
1-12335-A-T, NC_000001.10:g.12335A>T, GENE1(ENST12345678):c.2346A>T:p.1234A>-, PATHOGENIC, [PVS1, PS1, PP4_Strong], Disease (OMIM:12345), AUTOSOMAL_DOMINANT
"acmgAssignments": [
{
"variantEvaluation": {
"genomeAssembly": "HG19",
"contigName": "10",
"start": 123256215,
"end": 123256215,
"ref": "T",
"alt": "G",
"type": "SNV",
"length": 1,
"phredScore": 100,
"variantEffect": "MISSENSE_VARIANT",
"whiteListed": true,
"filterStatus": "PASSED",
"contributesToGeneScore": true,
"variantScore": 1,
"frequencyScore": 1,
"pathogenicityScore": 1,
"predictedPathogenic": true,
"passedFilterTypes": [
"FAILED_VARIANT_FILTER",
"PATHOGENICITY_FILTER",
"FREQUENCY_FILTER",
"VARIANT_EFFECT_FILTER",
"INHERITANCE_FILTER"
],
"frequencyData": {
"rsId": "rs121918506",
"score": 1
},
"pathogenicityData": {
"clinVarData": {
"alleleId": "28333",
"primaryInterpretation": "LIKELY_PATHOGENIC",
"reviewStatus": "criteria provided, single submitter"
},
"score": 0.965,
"predictedPathogenicityScores": [
{
"source": "REVEL",
"score": 0.965
},
{
"source": "MVP",
"score": 0.9517972
}
],
"mostPathogenicScore": {
"source": "REVEL",
"score": 0.965
}
},
"compatibleInheritanceModes": [
"AUTOSOMAL_DOMINANT"
],
"contributingInheritanceModes": [
"AUTOSOMAL_DOMINANT"
],
"transcriptAnnotations": [
{
"variantEffect": "MISSENSE_VARIANT",
"geneSymbol": "FGFR2",
"accession": "ENST00000346997.2",
"hgvsGenomic": "g.12278533A>C",
"hgvsCdna": "c.1688A>C",
"hgvsProtein": "p.(Glu563Ala)",
"rankType": "EXON",
"rank": 12,
"rankTotal": 17
},
{
"variantEffect": "MISSENSE_VARIANT",
"geneSymbol": "FGFR2",
"accession": "ENST00000351936.6",
"hgvsGenomic": "g.12278533A>C",
"hgvsCdna": "c.1688A>C",
"hgvsProtein": "p.(Glu563Ala)",
"rankType": "EXON",
"rank": 13,
"rankTotal": 18
}
]
},
"geneIdentifier": {
"geneId": "ENSG00000066468",
"geneSymbol": "FGFR2",
"hgncId": "HGNC:3689",
"hgncSymbol": "FGFR2",
"entrezId": "2263",
"ensemblId": "ENSG00000066468",
"ucscId": "uc057wle.1"
},
"modeOfInheritance": "AUTOSOMAL_DOMINANT",
"disease": {
"diseaseId": "OMIM:123150",
"diseaseName": "Jackson-Weiss syndrome",
"associatedGeneId": 2263,
"diseaseType": "DISEASE",
"inheritanceMode": "AUTOSOMAL_DOMINANT",
"phenotypeIds": [
"HP:0000006",
"HP:0000272",
"HP:0001363",
"HP:0001783",
"HP:0004691",
"HP:0008080",
"HP:0008122",
"HP:0010055",
"HP:0010743",
"HP:0011800"
],
"id": "OMIM:123150",
"associatedGeneSymbol": "FGFR2"
},
"acmgEvidence": {
"evidence": {
"PM2": "MODERATE",
"PP3": "STRONG",
"PP4": "SUPPORTING",
"PP5": "SUPPORTING"
}
},
"acmgClassification": "LIKELY_PATHOGENIC"
}
]
}
Publications¶
Collaborations¶
Publications for other projects and resources in which the Exomiser is used as a component: