Click on a header on the header panel above to get directed to the main sections of this documentation
To navigate through more specific sections of this documentation, scroll through the grey navigation panel present at the right side of this page and click on a header.
To get a quick idea of how you can select the most relevant results from ORVAL, click on the Tutorials header above.
ORVAL is the first web bioinformatics platform for the exploration of predicted candidate disease-causing variant combinations, aiming to aid in uncovering the causes of oligogenic diseases (i.e. diseases caused by variants in a small number of genes). This tool integrates innovative machine learning methods for combinatorial variant pathogenicity prediction, further external annotations and interactive and exploratory visualisation techniques.
What can you do with ORVAL?
PREDICT CANDIDATE DISEASE-CAUSING VARIANT COMBINATIONS
NOTE: The main results of this platform are based on predictive tools. They are provided for research,
educational and informational purposes only and the pathogenicity predictions should be subject to further scientific and clinical investigation.
It is not in any way intended to be used as a substitute for professional medical advice, diagnosis, treatment or care.
The input data
ORVAL accepts a list of variants from a single individual only, as it creates all possible variant combinations between pairs assuming that these belong to the same individual.
You can provide either Single Nucleotide Variants (SNVs) or small insertions/deletions (indels).
NOTE: The use of ORVAL for the analysis of complete patient exomes is NOT recommended, as our methods are not fine-tuned for exomes. Please restrict your analysis to relevant gene panels for the disease of interest. If your VCF contains the complete exome of an individual, you can upload it in ORVAL as it is, and then use the Variant Filtering and Gene Filtering options before submitting your data.
Types of input files
There are two different types of variant input that you can use to upload your data: either a variant list or a VCF file. After uploading your data, you can start the analysis by clicking on the button.
Tab-delimited variant list
At the left panel of the Submit variants page you can insert/copy-paste a variant list. Each line should contain tab- or space-delimited information for one variant, in the corresponding order: chromosome, position, reference allele, alternative allele, zygosity.
No headers are needed.
The zygosity values should be either Heterozygous or Homozygous. During the analysis, ORVAL automatically converts X-linked variants in males as Hemizygous.
You can also manually insert a variant in this list by typing information:
- at the next line and making sure that you use the same delimiter for all columns
- at the corresponding chr, position, reference allele, alternative allele, Zygosity column fields and pressing the button.
NOTE: using the variant list panel, you can upload up to 80000 variants.
Alternatively, you can submit a VCF file (version 4.2) with your variants at the right panel of the submission page.
ORVAL requires as minimum the presence of:
- the #Header Line: #CHROM POS ID REF ALT etc... line
- the columns CHROM, POS, ID, REF, ALT, FORMAT, SAMPLE_NAME (patient information column containing values corresponding to
the FORMAT field).
- the genotype (GT) field for each variant at the FORMAT and SAMPLE_NAME columns. In case variants with GT: 0/0 ; 0|0 ; ./. ; . are present, these are discarded from the analysis.
Any other meta-information lines on the top of the file or any extra columns and fields (e.g. QUAL, INFO, etc.) can be present, but ORVAL will ignore them.
NOTE: if your VCF contains information for several individuals, you should separate the information of each individual in different VCF files and run them individually in ORVAL.
NOTE: in case of many alternative variants in some rows, we only consider the first of alternative variants for our analysis.
NOTE: you can upload a VCF file of size up to 50 MB. The file can also be compressed either with zip, gzip, bzip2 or xz.
In case you want to create your own VCF file, you can download and take a look at the example VCFs that are present at the VCF submission panel and/or consult the Samtools specification page on how to construct a proper VCF file.
You can either submit Single Nucleotide Variants (SNVs) or small insertions/deletions (indels). Other types of variants (e.g. CNVs) can be present in your list, but they will not be included in the analysis.
Specifically for indels, you can submit your variants in either one of the two different ways that are shown for a particular variant example (the VCF file can contain more columns).
|Tab-delimited list||VCF file|
|Example with dashes||16 3254468 CTT - Heterozygous||16 3254468 . CTT - PASS GT 1/0|
|Example without dashes||16 3254467 CCTT C Heterozygous||16 3254467 . CCTT C PASS GT 1/0|
At the moment ORVAL accepts and annotates variants using the GRCh37/hg19 human genome assembly.
We do not make conversions of genomic coordinates from different genome versions. In case you need to convert your variants, you are encouraged to use tools like the UCSC, Ensembl and NCBI assembly converters.
Except from the variant list, you should also provide (if available) the sex information of the patient, i.e. if the person is a male or a female.
ORVAL handles differently X-linked variants in males (hemizygous variants) compared to females, and therefore this information is important in order to provide better predictions.
Example input files
You can try ORVAL with the two example VCF files that are present in the VCF file section of the variant submission page. These files give you the opportunity to test ORVAL on a small or large number of variants and see what the webserver has to offer.
- the Example_VCF_1 file contains 25 variants and its running time (with filtering) is 15 seconds.
- the Example_VCF_2 file contains 1800 variants and its running time (with filtering) is 18 seconds.
Every time you submit your data, you will first get directed to the Submitted ORVAL Job page where you can follow the status of your submission.
In this page you will also receive a Job Id, which you can use to re-access the results of that specific submission or report errors. That Job Id is also present in the Results site in the format: orval.ibsquare.be/results?id=YourJobID.
You can re-access your results by:
- saving the URL of the Job or the Results page
- typing on your browser https://orval.ibsquare.be/results?id= followed by the Job ID
Do you receive error or warning messages during your data submission? You can consult the Frequently Asked Questions (FAQ) section for suggestions on how to handle them.
NOTE: all result information is automatically deleted 7 days after the submission at 04.00 am (GMT+2). After this period, you have to re-submit your data and you will receive a new Job Id.
NOTE: for server monitoring purposes we allow every user (based on their IP address) to run up to 5 different Submission Jobs at the same time. In case you exceed this number, you will have to wait until at least one of the running Jobs is finished to launch a new one.
Data filtering and annotation
In the Submit variants page, ORVAL offers a recommended variant and gene filtering procedure that will automatically run when you submit your data. This procedure is highly recommended, as it will limit the amount of variant combinations to be tested and will restrict the analysis to the most relevant variants.
The variant filtering procedure ensures that your analysis will contain variants in accordance with the variant types used to train the predictive methods (VarCoPP and Digenic Effect predictor) integrated in ORVAL: exonic and splicing variants of MAF lower or equal than 3% in protein-coding genes.
The three different filtering options are already pre-selected in the Variant Filtering panel of the submission page. You can unselect a filtering option, by clicking on its corresponding check-box.
Select the minimum threshold of ExAC MAF for the variants. A MAF of ≤ 0.03 was used to train VarCoPP and is the recommended threshold.
Removes variants that are not inside the defined gene coordinates, based on the human assembly GRCh37/hg19.
Remove intronic and synonymous
- all intronic variants that have a distance bigger than 13 nucleotides from each exon edge, based on the exon coordinates of the canonical transcript of the gene.
- all synonymous variants that have a distance bigger than 7 nucleotides from each exon edge, based on the exon coordinates of the canonical transcript of the gene.
NOTE: apart from the requested filtering steps, ORVAL may also exclude some extra variants during the data annotation process. You can consult the complete list of variant exclusion cases during that process here.
The gene filtering option restricts the analysis to a specified list of relevant genes that can be present in your data. This procedure is highly recommended in case your VCF contains the complete exome of an individual, as it can dramatically limit the amount of False Positives that can be obtained.
To run your analysis only with a subset of genes, you can simply upload a .txt file with the gene symbols you are interested to include, each gene being in a different line. After submission, ORVAL will use this list to filter the genes that will be used in the analysis.
After you submit your data, ORVAL:
- automatically annotates them with the biological information needed for the integrated predictive methods (VarCoPP and the Digenic Effect predictor)
- creates all possible variant combinations between any pair of genes present in your variant input and
- orders the variants and genes inside each combination.
Below, you can find some important parameters for each process.
To map variants in genes, ORVAL uses at first the gene information that is present in the CADD annotation file for that variant and uses only the canonical transcripts of those genes, according to the Ensembl Grch37/hg19 genome version.
In cases where a variant can be mapped to multiple genes, ORVAL maps that variant to only one gene based on a set of priority rules that include (starting from higher to lower priority): valid gene IDs and canonical transcript, prioritisation of genes based on their biotype and the functional consequence of the variant, prioritisation of genes where the variant falls inside the gene and canonical transcript coordinates, presence of a CCDS, prioritisation of gene with the longest canonical transcript, etc.
ORVAL then annotates the genes with:
- the required gene features for VarCoPP (detailed description of the features and their sources in the link)
- the required gene features for the Digenic Effect predictor (detailed description of the features and their sources in the link)
- the Gene Damage Index (GDI), a metric that shows the susceptibility of a gene to disease. Lower values of GDI indicate greater susceptibility of a gene to candidate disease-causing mutations.
- the protein sequences from Uniprot using the Ensembl canonical identifiers, as these are needed to calculate some of the features of our predictive methods
Gene pair annotation
ORVAL annotates a gene pair with:
- their Biological Distance, a metric of biological relatedness between any two genes, based on protein-protein interaction information, which is used as a feature for the VarCoPP .
- involvement in the same pathway information from Reactome, which is used as a feature for the Digenic Effect predictor
- protein-protein interaction (PPI) and cell co-localisation information from the comPPI database
ORVAL uses the Gene Damage Index (GDI) metric to order the appearance of genes inside each digenic variant combination, with gene A being always the gene with the lower GDI value, and thus more probable to be have a disturbed function due to the presence of a variant. You can find more details about how ORVAL creates digenic variant combinations and orders variants and genes in the Creating digenic variant combinations section of the Documentation page.
ORVAL first maps a variant in a gene based on the Gene annotation process described above. It then annotates each variant with:
- the required variant features for VarCoPP, the most important being the CADD score (detailed description of the features and their sources in the link)
- the required variant features for the Digenic Effect predictor (detailed description of the features and their sources in the link)
When ORVAL creates digenic variant combinations, it uses the CADD score to order the appearance of variants that are present inside the same gene (i.e. in cases of heterozygous compound variants). You can find more details about how ORVAL creates digenic variant combinations and orders variants and genes in the Creating digenic variant combinations section of the Documentation page.
In some situations during the data annotation process ORVAL excludes variants from the analysis and you will not find them in the results:
- Variant not exonic in canonical transcript
We use only the canonical Ensembl transcript identifiers to annotate our variants. If you have selected to exclude intronic variants from your analysis, if the variant is not exonic in the canonical transcript of the gene, even if it may be exonic in an alternative transcript, it will be excluded.
- Variant with invalid zygosity
Variants with GT:0/0 or GT:0|0 in a VCF file are considered invalid and are excluded from the analysis.
- Alternative variant
In case multiple alternative variants are present in a row in a VCF file, we only take into account the first alternative variant. The rest of the variants are excluded from the analysis.
- CADD score not available
ORVAL annotates variants with a CADD score, which is a feature required for the pathogenicity predictions. As this feature is important for the predictions, if a CADD score is not available for a variant, that variant is excluded for the analysis, as a missing value may severely alter the results.
- Variants only in one gene
As ORVAL creates combinations between gene pairs, if your input data includes variants from one gene only, you will not get any results.
- The variant is a CNV
ORVAL analyses only SNVs and small insertions and deletions. Any other variant type in your data is automatically excluded from the analysis.
Creating digenic variant combinations
After annotation, VarCoPP creates all possible variant combinations between any gene pair present in your input, taking into consideration any filtering options you have included during your variant submission.
You can find below a list of details and constraints that take place during this procedure.
Number of variants per combination
ORVAL creates for any gene pair variant combinations that can be:
- bi-allelic (i.e. one mutated allele at each gene)
e.g.: one heterozygous variant per gene
- tri-allelic (i.e. three mutated alleles in total)
e.g.: an homozygous variant at gene A and an heterozygous variant in gene B
- tetra-allelic (i.e. four mutated alleles in total)
e.g.: one homozygous variant per gene
In the tri-allelic and tetra-allelic cases, a digenic combination can also include heterozygous compound variants (i.e. two different mutated alleles in the same gene), along with the presence of variant(s) in another gene.
NOTE: Tetra-allelic variant combinations with heterozygous compound variants in BOTH genes are not created.
Order of variant alleles inside the gene
In case of two different mutated alleles in the same gene (heterozygous compound cases), the variant allele 1 is always the variant allele with the highest CADD score.
A graphical representation of a digenic combination
The predictive methods of ORVAL
VarCoPP: the variant combination pathogenicity predictor
VarCoPP stands for Variant Combination Pathogenicity Predictor. It is a machine-learning method that predicts the pathogenicity of any bi-locus variant combination (i.e. a combination of two to four variant alleles between two genes).
Based on VarCoPP, a bi-locus variant combination can either be candidate disease-causing or neutral.
IMPORTANT UPDATE: An update of VarCoPP has been made using up-to-date versions of all required features (check the Updates page). The confidence zones have been updated and the probability thresholds are slightly different than those in the corresponding publication (see below for more information).
Structure of VarCoPP
VarCoPP is an ensemble predictor that consists of 500 individual predictors, and more specifically, 500 classification Random Forest (RF) algorithms.
Each predictor of VarCoPP has been trained on the pathogenic variant combinations present in the Digenic Diseases Database (DIDA) against a different subset, each time, of variant data derived from control individuals of the 1000 Genomes Project (1KGP).
The variant types that were used for training were the same for both DIDA and 1KGP: exonic and splicing variants of up to 3% MAF, while all genes were protein coding genes.
When a bi-locus variant combination is tested with VarCoPP, each individual RF provides a probability on that combination to be candidate disease-causing. If the probability is above 0.532, then the RF predicts that this combination is candidate disease-causing. The final prediction is based on a majority vote: if 50% or more of the RFs agree that a bi-locus combination is candidate disease-causing, then the final prediction is that it belongs to the candidate disease-causing class.
Therefore, in general, a bi-locus combination is predicted as candidate disease-causing if ≥50% of the predictors agree that it is candidate disease-causing and the median probability for this prediction among all predictors will be, consequently, ≥0.532.
A graphical representation of the structure of VarCoPP
VarCoPP uses different variant, genes and gene pairs biological features to make the predictions.
|Feature||Feature abbreviation||Gene / Variant allele|
|CADD raw score
|Gene A / Variant allele 1
Gene A / Variant allele 2
Gene B / Variant allele 1
Gene B / Variant allele 2
|Amino acid hydrophobicity difference
|Hydr1||Gene A / Variant allele 1|
|Amino acid flexibility difference||Flex1||Gene A / Variant allele 1|
|Gene haploinsufficiency probability
|Gene recessiveness probability
|Biol_Dist||Gene pair AB|
For each bi-locus combination VarCoPP provides two prediction scores, based on the way it makes the predictions. These scores are also used to rank the bi-locus combinations in the output files.
Support score (SS)
The Support score (SS) of a bi-locus combination indicates the percentage of RFs that agree that the combination is candidate disease-causing. It can therefore take values between 0 (no RF predicted that the combination is pathogenic) to 100 (all RFs predicted that the combination is pathogenic).
For candidate disease-causing combinations, SS is always equal or larger than 50.0.
Classification score (CS)
The classification score (CS) of a bi-locus variant combination is defined as the median probability of that combination being disease-causing among all RFs. It can take values between 0 and 1.
For candidate disease-causing combinations, CS is always larger than 0.532.
In general, the higher these scores are, the more confident VarCoPP is for the disease-causing class. These scores can be used for a prioritisation of candidate disease-causing variant combinations, you can further consult our tutorial.
95% and 99% confidence zones
With VarCoPP we have defined 95%- and 99% confidence zones, delimited by minimal Classification (CS) and Support scores (SS), which provide a probability of whether a particular combination predicted as candidate disease-causing, is actually a True Positive (TP) result. This indication can be useful for further evaluation and filtering of the predictions.
These confidence zones were created by testing neutral bi-locus combinations from the 1000 Genomes Project and obtaining the minimal CS and SS scores that gave 5% and 1% False Positives respectively. If a combination falls into either one of the two zones, a coloured indication will appear in the summary results.
IMPORTANT UPDATE: An update of VarCoPP has been made using up-to-date versions of all required features (check the Updates page). The confidence zone probability thresholds are now slightly different than those in the corresponding publication.
Requires CS≥0.64 and SS≥83.2. If a digenic combination falls inside this zone, it has 95% probability of being a TP result.
Requires CS≥0.83 and SS=99.8. If a digenic combination falls inside this zone, it has 99% probability of being a TP result.
The Digenic Effect Predictor
The Digenic Effect predictor is a machine-learning method that predicts the type, or else the digenic effect of a pathogenic digenic variant combination. This information could be useful in case there is no pedigree information or parent genotypes available, as it could give a predictive indication of the effect of a predicted as pathogenic variant combination. As this is a machine-learning approach, again, a manual investigation by the user can confirm or reject the assigned digenic effect class.
The Digenic Effect predictor has been published in the Artificial Intelligence in Medicine journal: https://doi.org/10.1016/j.artmed.2019.06.006 and the Nucleic Acids Research journal: https://doi.org/10.1093/nar/gkx557. See also the Cite us section in the About page, for a list of all relevant citations.
IMPORTANT UPDATE: An update of the Digenic Effect predictor has been made using up-to-date versions of all required features (check the Updates page). This means that the obtained results may differ than those from the corresponding publication.
The Digenic Effect predictor can distinguish between three classes of pathogenic variant combinations:
Variants at both genes are needed to show the disease phenotype.
Monogenic + Modifier
The variant at the first gene acts as the major monogenic variant that can trigger disease symptoms, while the second variant acts as a modifier of symptoms severity or age of onset.
Dual Molecular Diagnosis
Conjunction of variants that trigger two independent monogenic disorders that occur simultaneously within a single patient.
The three types of digenic effects.
Combination a, a True Digenic combination, where the simultaneous presence of a pathogenic allele in each gene is necessary for the individual to express the disease. phenotype.
Combination b, a Monogenic plus Modifier combination, where a variant on the major gene induces a disease phenotype, while a mutation in the modifier gene modifies it, either by rendering it more severe or producing an early onset.
Combination c, a Dual Molecular Diagnosis combination, where both loci are responsible for either distinct or overlapping phenotypes for two different diseases.
The structure of the Digenic Effect predictor
The Digenic Effect predictor is a classification Random Forest (RF) algorithm.
The Digenic Effect predictor was trained on 240 pathogenic variant combinations.
More specifically, it has been trained on 90 True Digenic and 75 Monogenic+Modifier variant combinations present in the Digenic Diseases Database (DIDA) and 75 Dual Molecular Diagnosis combinations derived from the work of Posey et al.
The variant types were single nucleotide variations and small insertions/deletions.
The Digenic Effect predictor provides probabilities (from 0 to 1) for all three digenic effect classes for a variant combination.
The final digenic effect class is the class with the highest probability among the three.
The Digenic Effect predictor uses different variant, genes and gene pairs biological features to make the predictions.
|Feature||Feature abbreviation||Gene / Variant allele|
|CADD raw score
|GeneA / Variant allele 1
Gene A / Variant allele 2
Gene B / Variant allele 1
Gene B / Variant allele 2
|Gene recessiveness probability
|Essential in mouse
|Pathway||Gene pair AB|
In case you would like to see a specific tutorial in ORVAL, you can contact us.
Prioritisation of digenic variant combinations
Depending on the size of the data you are analysing you may end up with many digenic variant combinations predicted as candidate disease-causing. ORVAL is not a prioritisation tool per se, however, it is possible to limit your analysis to those combinations that could potentially be more interesting for your research. For this, you can follow the next steps:
- 1. Overview and filtering of the digenic variant combinations
The combinations in the Summary Table are ranked based on their Classification Score (CS) and Support Score (SS), with those at the top having the highest scores. The higher the CS and SS assigned to a digenic combination, the more confident VarCoPP is for the disease-causing class (consult the VarCoPP evaluation scores section for a detailed explanation).
- The strictest way to filter your combinations is by focusing on those falling in the 99%-confidence zone, in dark red colour (the first two combinations in this example). These have 1% probability of being False Positives. Keep in mind that this selection can be quite strict and can lead to False Negatives.
- To lessen your criteria, you can also include the combinations falling in the 95%-confidence zone in red colour (the next five combinations in this example), which have 5% probability of being FPs.
- If the steps described below do not yield convincing results, you can also try to include the combinations that are predicted as disease-causing but do not fall in any of the two confidence zones, and these are depicted in orange.
For more information, consult the VarCoPP Confidence Zones section of our Documentation page.
- 2. Find relevant gene modules in the predicted pathogenic gene network
Are the genes relevant? You can explore the predicted pathogenic gene network in ORVAL, which appears first in the results page. In this network, two genes are connected with an edge only if they contain at least one predicted pathogenic digenic combination.
You can first filter the network to keep the most relevant gene pairs, based on the pathogenicity cutoff that you choose. You can use the Filtering panel on the left, to keep only the gene pairs containing combinations falling in the 95%- and 99%-confidence zones,
by selecting 0.64 as the minimum Gene Pair Pathogenicity Score threshold (i.e., this is the minimum Classification Score for the 95%-confidence zone, see
the VarCoPP Confidence Zones section of our Documentation page).
For a stricter analysis, you can use the 0.83 value as a cutoff to include only gene pairs with combinations falling in the 99%-confidence zone.
Once you are satisfied with your network, click on a gene in the network to make the gene module panel appear on the right and click on the link that appears to get directed to another page the shows PPI and pathway information for those genes.
NOTE: if a gene is shown as a hub in the gene network, meaning that it is highly connected with other genes, it may mean that it contains a variant there that drives the predictions higher. This may indicate that a Monogenic plus Modifier concept may be present in your data. However, if this gene does not seem relevant for the phenotype or seems unrelated to the rest of the genes, you could try to remove it from the network and consider the rest of the genes. You can use a general Centrality threshold of your choice or unselect genes manually in the gene table on the left.
Detailed information on ways to filter your network can be found in the Oligogenic network section of our Documentation.
- 3. Explore the relationship between the genes in the gene module
Once you click on the gene module link, ORVAL offers PPI, cellular location and molecular pathway information as a starting (and definitely not exhaustive) point to understand the relevance and interactions of genes in-silico. For an explanation of these graphs, consult the Oligogenic gene module, PPI network and Pathway information sections of our Documentation.
Are the genes in your module related in terms of their PPIs? In the PPI network (the first picture in this example) you can see whether the proteins of this module (purple nodes) directly interact (purple edges) or indirectly interact with one external protein in between, which are depicted as grey nodes and are linked with grey edges. In this example, the proteins of your module TRIM54 and TRIM63 directly interact, but also indirectly interact with several other external proteins found in the comPPI database.
Are the genes in your module involved in similar molecular pathways? You can consult the Reactome pathway treemap and the pathway table to find genes involved in similar pathways (the second picture in this example).
This is a starting point for you to explore the relationships between the genes present in the selected module. Based on this information, you can start limiting the results to the gene pairs that seem to be most relevant.
- 4. Explore the variant combinations of the selected gene pairs
You can now go back to the digenic variant combinations Summary Table (first picture) and the S-plot, and explore the variant combinations that are linked to the gene pairs that you have selected based on the previous steps. Especially if multiple variant combinations are linked with the same gene pair, it will be interesting to further explore their predictions to see which are relevant. A summary statistics for each gene pair is also available at the Gene pair ranking table of the Main Results page.
Once you find a combination of interest, you can click on it in the Summary Table (second column) or in the S-plot, and you will be directed to a page with information specific for that combination. There, you can first see a summary of the variants and the predictions of both VarCoPP and the Digenic Effect predictor.
You can explore the Feature preference boxplot of VarCoPP (second picture). How do the features vote for either the disease-causing (features in red colour), or neutral (features in blue colour) class? The more these feature preferences deviate from zero, the stronger their vote for a particular class is. In this example, we see that the CADD score of the variant alleles of gene A(CADD1 and CADD2) vote the strongest for the the disease-causing , whereas the CADD score of the first variant allele of gene B (CADD3), as well as its recessiveness probability (RecB) vote the strongest for the neutral class. You can further evaluate the values of these features and why they tend to vote for either class in the Annotations section of that page.
You can further get an indication of the Digenic Effect of that particular combination, i.e. whether it has a True Digenic , Monogenic gene plus Modifier or Dual Molecular Diagnosis effect (third picture). These predictions are indicative and further inspection or confirmation is required. In this example, the variant combination has a very strong prediction for the True Digenic class.
The information in this page can help you evaluate whether the specific variant combination seems relevant for your analysis.
- 5. Examine the relevance of selected gene pairs and combinations for the phenotype
Do the selected gene pairs and variant combinations make sense to you as a clinical researcher and are they in accordance or could they explain the patient's phenotype? At this step, you have a filtered set of gene pairs and variant combinations that could be potentially relevant, based on in silico information.
- 6. Optional: Repeat steps 1-5 with less strict criteria
If at this point the information seems incomplete or you still do not obtain promising results, you could lessen the strictness of your criteria and repeat the steps 1-5. For example, if you have selected only gene pairs with combinations falling into the 99%-confidence zone, you could now also allow gene pairs with combinations falling also into the 95%-confidence zone. Furthermore, if combinations falling into these confidence zones are not available or they do not seem convincing, you can try to include all gene pairs and variant combinations predicted as disease-causing.
- 7. Explore familial and functional evidence
The previous steps provide a way to limit your analysis to those variant combinations that seem to be more relevant or more promising for further research and experiments. ORVAL cannot, in any way, provide a definite way for diagnosis or medical advice. Further evidence is needed to show whether they can indeed be True Positives based on segregation analyses and functional experiments.
You can find below which browsers are suitable for ORVAL based on your operational system:
Frequently Asked Questions
If answers to your questions are not provided in this section and no information about your question is mentioned in the Documentation page, you can contact us.