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Samplot: a platform for structural variant visual validation and automated filtering

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. T...

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Detalles Bibliográficos
Autores principales: Belyeu, Jonathan R., Chowdhury, Murad, Brown, Joseph, Pedersen, Brent S., Cormier, Michael J., Quinlan, Aaron R., Layer, Ryan M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8145817/
https://www.ncbi.nlm.nih.gov/pubmed/34034781
http://dx.doi.org/10.1186/s13059-021-02380-5
Descripción
Sumario: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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-021-02380-5.