Managing Filtering Protocols

Charlene Son-Rigby -


Managing Filtering Protocols

Filtering Protocols are reusable collections of filters that teams can use to help standardize the analysis of genome variants. Filtering protocols are automatically shared with everyone in your Workspace, so establishing filtering protocols helps ensure that interpretations of genome variants are performed more consistently.

To manage your Filtering Protocols, on the Home Page select “Filtering Protocols”.

To create a new filtering protocol, click the “New Filtering Protocol” button. In the resulting “New Filtering Protocol” page you can enter a name and description, as well as any number of the available filters.


When creating a filtering protocol, users can choose any number of individual filters. Available filters are:


Coverage The number of sequencing reads spanning a variant’s genomic location. You can focus on higher quality variants by setting this value.
Quality Phred-like base level quality value as reported in the variant file.
Genotype Quality Phred-like score measuring the confidence in the called genotype. 
Omicia Score  Proprietary impact assessment score that provides a rational aggregation of other variant scoring algorithms. Values range from 0 to 1, the greater the value the more likely that a variant is deleterious or is located in a highly conserved region. (See Appendix 2). Values >0.8 typically agree with other algorithms in suggesting that a variant is deleterious.
CADD   The CADD score combines information from 63 different annotations including PhastCons, GERP, PhyloP, SIFT and PolyPhen, using a support vector machine classifier (Kircher et al, 2013). It measures deleteriousness by using observed variant frequency as the basis for its calculation. The C score ranges from 1 to 99, with a higher score indicating greater deleteriousness. Values >= 10 are predicted to be the 10% most deleterious substitutions, >= 20 indicate the 1% most deleterious.
VVP The VAAST Variant Prioritization (VVP) score applies the VAAST algorithm at the variant level. VAAST takes predicted protein impact, conservation and allele frequency into consideration in its deleteriousness assessment. VVP provides normalized scores for variants in genes, enabling direct comparison of variants in conserved and polymorphic genes. VVP scores are provided for coding, non-coding regions, and intergenic variant categories, allowing comparison of these types of variants across genes.
1KG Frequency Filter out higher frequency or “common” variants using the 1K Genomes project allele frequency data. Uses the dbSNP global MAF table. Note: this filter does not exclude variants when MAF data is not available. 
EVS Frequency Filter using the Exome Variant Server (EVS) allele frequency data
ExAC Frequency Filter using the ExAC project allele frequency data
SIFT Focus on variants that produce amino-acid changes predicted to be deleterious from an evolutionary perspective. The SIFT score is a p-value, and variants with scores <0.05 are considered to be likely deleterious.
Zygosity Select only to show variants that match a particular zygosity, either Homozygous or Heterozygous.
Chromosome Filter by chromosome.
Evidence Show only variants that have evidence from variant annotation databases (i.e. ClinVar OMIM, LSDB, GWAS or COSMIC); from OMIM only; or from ClinVar only. The Require ClinVar option is only applied as a filter criterion for genomes that have been run through Opal Annotation Engine.
Allelic Balance Variant allele fraction. 
Consequence Variants by the consequences they have for the protein. Options are: Stop Gained or Lost; Missense; Frameshift Indel; In-frame Indel; Splice Site; Start Lost or Retained; Splice Region; and Structural Variant. Note that Start Lost or Retained; Splice Region; and Structural Variant are only applied as filter criteria for genomes that have been through Opal Annotation Engine.
Regulatory Variants with regulatory consequences.
ClinVar Classification

View variants with selected ClinVar Classifications. In addition to allele matches, you can select a filter to match position/codon and Pathogenic or Likely Pathogenic RCV, or overlaps a Pathogenic or Likely Pathogenic RCV.

Gene Model Overlap View variants overlapping RefSeq (default) or CCDS gene models. Selecting both gene models means the variant must overlap both gene models to meet the filter criteria.
Exclude Leave out some variant types from your report.


Click on the “Save Filtering Protocol” button to save and return to the listing. If you later would like to view, edit, or delete your filtering protocol, click the “Actions” dropdown menu adjacent to your filtering protocol and select the “Delete” item.



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