SVSR: A Program to Simulate Structural Variations and Generate Sequencing Reads for Multiple Platforms.

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Structural variations (SVs) are significant contributors to human genome mutations and disease susceptibility.
• Next-generation sequencing (NGS) and computational tools enable cost-effective SV detection.
• Accurate simulation of SVs and sequencing reads is crucial for benchmarking detection algorithms.

Purpose of the Study:

• To develop SVSR, a novel program for simulating diverse structural variations and sequencing reads.
• To generate realistic datasets for evaluating and advancing computational methods for SV detection.

Main Methods:

• SVSR simulates five types of SVs (indels, tandem duplications, CNVs, inversions, translocations) and SNPs.
• It generates sequencing reads mimicking popular platforms (Illumina, SOLiD, 454, Ion Torrent) with platform-specific errors.
• Utilizes a selection model for copy number state prediction and probabilistic models for complex SVs.

Main Results:

• SVSR generates realistic datasets with satisfactory quality scores.
• The simulation captures key features relevant to SV detection.
• Experimental results validate SVSR's capability in evaluating detection methods.

Conclusions:

• SVSR provides a valuable tool for assessing the performance of structural variation detection algorithms.
• It can guide the development of new computational approaches for analyzing genomic variations.