Figure legends
Figure 1. A: The DECIPHER community is a global network of
academic clinical centres with expertise in genetics. Depositing centres
are able to send messages directly to other registered users about
patient matches through DECIPHER. Since October 2014 over 4,500 such
messages have been sent. Here, each line represents a collaboration
request sent between depositing centres. Unregistered users’ messages,
sent through DECIPHER, are not included in this image. B: The
DECIPHER database currently openly shares approx. 40,000 rare disease
patient records, built up over time.
Figure 2. DECIPHER supports the deposition and sharing of
almost all types of genetic variation.
Figure 3. All genomic data is visualised in GRCh38, but
deposition is still supported in GRCh37/hg19. Tools are provided to
visualise the differences between assemblies. These include comparative
genome browsers and gene lists for variants lifted over by DECIPHER, and
a liftover mapping genome browser track.
Figure 4. A: DECIPHER enables the deposition of phenotypes
using HPO terms. B: DECIPHER supports the deposition of
developmental milestones and anthropometric measurements, e.g.
occipitofrontal (head) circumference.
Figure 5. A: DECIPHER has developed a protein browser which
summarises genotypic data. Tracks include: Pfam domains, DECIPHER and
ClinVar variants, gnomAD variants, and region of predicted nonsense
mediated decay (NMD) escape. B: DECIPHER supports the
annotation and sharing of sequence variant pathogenicity assessments
using ACMG guidelines. A pathogenicity evidence interface is available
for depositors. Relevant criteria are selected by clicking on the
criteria displayed on the left under “Available evidence types”.
“Selected criteria” are displayed on the right, along with “Evidence
to consider”. “Further information” links provide recommendations for
the use of criteria. In this example, a variant in SLC9A6 is
being annotated and ClinGen Variant Curation Expert Panel Specifications
exist for this gene. Detailed information about these recommendations
are displayed by clicking on the “Gene recommendation” links - expert
panel recommendations for de novo criterion PS2 are displayed. As
criteria are added, DECIPHER calculates the variant pathogenicity
according to criteria-combining rules detailed in the original 2015
guidelines, and according to the ClinGen SVI Working Group’s Bayesian
classification framework. C: DECIPHER supports the annotation
of copy-number variants according to ACMG/ClinGen technical standards.
Similar to the sequence variant interface, “Available evidence types”
are displayed on the left, with “Selected evidence” and “Evidence to
consider” displayed on the right. As criteria are selected, the
classification score and pathogenicity are calculated and displayed at
the bottom of the interface. D: An assessment interface is
provided which is designed to be used in a multidisciplinary team
meeting to evaluate whether one or more variants explain the clinical
features seen in a patient, and record if a diagnosis has been made (or
excluded). Depositors can report several lines of evidence, to weigh
evidence for or against a genotype-phenotype relationship. An OMIM
gene-disease pair and assertion is recorded.
Figure 6. A: Quantitative phenotype data (such as developmental
milestones or anthropometric measurements) is recorded in DECIPHER, and
aggregated on a gene-by-gene basis. The data is shared openly
in a series of graphs which displays expectations for the healthy
population (‘Normal’), the DECIPHER population as a whole, and the
gene-specific data. For certain genes, such as EP300 (displayed
here), there are composite faces, which highlight facial
dysmorphologies. B: The matching patient interface allows users
to view DECIPHER records which overlap a deposited copy-number,
sequence, or insertion variant, or a gene. In this example, the matching
patients overlap EP300 . Summary information is shown in a series
of pie charts, along with phenotypes present in multiple matching
patients. The individual patient records are displayed at the bottom of
the interface. Filters are available to assist in finding the most
relevant patient matches. C: Within DECIPHER, aggregated
phenotype data is used to identify the most discriminating phenotypes
associated with disease genes. A table shows the percentage of
phenotyped patients with sequence variants in a gene of interest, with a
particular phenotype, compared with the percentage of phenotyped
patients in DECIPHER with the same phenotype. The odds ratio andp -value from a Fisher’s exact test are displayed. In this
example, data for KMT2A is displayed and sorted byp -value. D : Users with write access to an open-access
patient record are able to query the MatchMaker Exchange to search for
potential patient matches. DECIPHER is currently connected to
Broad-seqr, GeneMatcher, MyGene2, PhenomeCentral and RD-Connect. Details
of potential patient matches are displayed within DECIPHER (patient IDs
have been removed in this example).
Figure 7. A: Since its inception in 2004, DECIPHER has been
cited in more than 2,600 publications. B: The genes with the
most open access sequence variants in DECIPHER (at the time of writing).C: DECIPHER openly shares variants of unknown significance
identified in undiagnosed probands in the Deciphering Developmental
Disorders study (research variants). For each variant a page provides
details of the variant and high-level phenotype terms. The number of
patients with each variant in the DDD dataset is displayed, in addition
to the number of patients identified in the GeneDx and Radboud
University Medical Center de novo variant dataset as described by
Kaplanis et al ., 2020.