Visual validation is an important step to minimize false-positive predictions from structural variant (SV) detection. We present Samplot, a tool for creating images that display the read depth and sequence alignments necessary to adjudicate purported SVs across samples and sequencing technologies. These images can be rapidly reviewed to curate large SV call sets. Samplot is applicable to many biological problems such as SV prioritization in disease studies, analysis of inherited variation, or de novo SV review. Samplot includes a machine learning package that dramatically decreases the number of false positives without human review. Samplot is available at https://github.com/ryanlayer/samplot.

Background Despite numerous molecular and computational advances, roughly half of patients with a rare disease remain undiagnosed after exome or genome sequencing. A particularly challenging barrier to diagnosis is identifying variants that cause deleterious alternative splicing at intronic or exonic loci outside of canonical donor or acceptor splice sites. Results Several existing tools predict the likelihood that a genetic variant causes alternative splicing. We sought to extend such methods by developing a new metric that aids in discerning whether a genetic variant leads to deleterious alternative splicing. Our metric combines genetic variation in the Genome Aggregate Database with alternative splicing predictions from SpliceAI to compare observed and expected levels of splice-altering genetic variation. We infer genic regions with significantly less splice-altering variation than expected to be constrained. The resulting model of regional splicing constraint captures differential splicing constraint across gene and exon categories, and the most constrained genic regions are enriched for pathogenic splice-altering variants. Building from this model, we developed ConSpliceML. This ensemble machine learning approach combines regional splicing constraint with multiple per-nucleotide alternative splicing scores to guide the prediction of deleterious splicing variants in protein-coding genes. ConSpliceML more accurately distinguishes deleterious and benign splicing variants than state-of-the-art splicing prediction methods, especially in “cryptic” splicing regions beyond canonical donor or acceptor splice sites. Conclusion Integrating a model of genetic constraint with annotations from existing alternative splicing tools allows ConSpliceML to prioritize potentially deleterious splice-altering variants in studies of rare human diseases.

Visual validation is an essential step in structural variant (SV) detection to eliminate false positives. We present Samplot, a tool for quickly creating images that display the read depth and sequence alignments necessary to adjudicate purported SVs across multiple samples and sequencing technologies, including short, long, and phased reads. These simple images can be rapidly reviewed to curate large SV call sets. Samplot is easily applicable to many biological problems such as prioritization of potentially causal variants in disease studies, family-based analysis of inherited variation, or de novo SV review. Samplot also includes a trained machine learning package that dramatically decreases the number of false positives without human review. Samplot is available via the conda package manager or at https://github.com/ryanlayer/samplot.