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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
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Updated: Nov 4, 2025

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

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

Jonathan R Belyeu^1,2, Murad Chowdhury³, Joseph Brown^1,2

¹Department of Human Genetics, University of Utah, Salt Lake City, UT, USA.

|May 26, 2021

Summary

This summary is machine-generated.

Samplot is a new tool that creates images to help researchers validate structural variant (SV) calls. It reduces false positives in genomic data analysis, improving accuracy for disease studies and inherited variation.

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Accurate detection of structural variants (SVs) is crucial for understanding genetic diseases.
Visual validation of SV calls is essential to minimize false positives in genomic data.
Current methods for SV adjudication can be time-consuming and labor-intensive.

Purpose of the Study:

To introduce Samplot, a novel computational tool for the visual validation of structural variants.
To provide a method for rapidly curating large sets of SV calls across diverse samples and sequencing technologies.
To develop a machine learning component for automated reduction of false-positive SV predictions.

Main Methods:

Samplot generates images displaying read depth and sequence alignments for SV detection.
The tool facilitates the adjudication of potential SVs by enabling rapid visual review.
A machine learning package is integrated to automate the filtering of false-positive SVs.

Main Results:

Samplot enables efficient visual inspection of SV candidates, aiding in the curation of large call sets.
The tool is versatile and applicable to various genomic analyses, including disease studies and inherited variation.
The integrated machine learning model significantly reduces the number of false positives, minimizing the need for manual review.

Conclusions:

Samplot provides an effective solution for the visual validation and curation of structural variants.
The tool enhances the accuracy of SV detection by reducing false positives through both visual inspection and machine learning.
Samplot is a valuable resource for researchers in genomics, disease studies, and population genetics, available at https://github.com/ryanlayer/samplot.