Remove Background Mutations

The Remove background mutations task is a variants filtering task that enables you to filter variants against built-in databases, to clean up noise from common polymorphisms and benign mutations. The available built-in databases are:

• Primate AI [1]: Prediction of pathogenicity on missense mutations. Percentile score ranges from 0 to 1, with 0 being benign, 1 being most pathogenic. Global percentile is based on global ranking of all missense variants in the human genome, while Percentile is based on ranking of all missense variants within the same gene.

• Promoter AI [2]: Prediction of expression-altering consequences of variants at promoter regions. Scores range from -1 to 1. Positive score indicates variant likely enhances transcription, negative score indicates variant likely repress transcription.

• gnomAD [3]: The Genome Aggregation Database (gnomAD) is a resource developed by an international coalition of investigators, with the goal of aggregating and harmonizing both exome and genome sequencing data from a wide variety of large-scale sequencing projects, and making summary data available for the wider scientific community. Filtering by minor allele frequency (MAF) is supported.

• DRAGEN Haplotype Database: Proprietary haplotype database built from a panel of 256 population haplotypes. Filtering by minor allele frequency (MAF) is supported.

The databases in the Remove background mutations task support only human hg38 assembly.

Running Remove background mutations

The Remove background mutations can be invoked from a Variants data node:

• Click to select a Variants data node.

• Select Variant analysis from context-sensitive menu, select Remove background mutations.

• Select a database for filtering, click Next.

• Set filtering criteria, then click Finish.

If you need to filter variants on more than one databases, run the Remove background mutations tasks sequentially.

References

1. Hong Gao et al. ,The landscape of tolerated genetic variation in humans and primates.Science380, eabn8153(2023). DOI:10.1126/science.abn8197

2. Kishore Jaganathan et al. ,Predicting expression-altering promoter mutations with deep learning. Science389, eads7373(2025). DOI:10.1126/science.ads7373

3. Karczewski, K.J., Francioli, L.C., Tiao, G. et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature 581, 434–443 (2020). https://doi.org/10.1038/s41586-020-2308-7. https://gnomad.broadinstitute.org/