SV-Autopilot: the new face of Structural Variant Detection

by | Sep 10, 2014

Although there are many tools designed for structural variation (SV) analysis there has been no bench mark study to guide scientists in choosing the best tool for their particular data set. In this test case, AllBio Test Case 2 – Identification of Large Structural Variants, a researcher is eager to identify large structural variants in multiple accessions of Arabidopsis. What is needed is a tool which can identify the type and quality of the sequencing reads, assemble them with regards to a reference genome, and predict large structural variants (>30nt). Lastly, it is desirable that the tool should have a user-friendly interface with which bench scientists can easily examine structural variants in their accession(s) of interest that need to be validated. Here we have not only created the benchmark data, but have also created a Virtual Machine framework that can automate this process using multiple SV prediction algorithms in parallel and refine the output to reduce false positive calls. We call this SV-AUTOPILOT, a Structural Variation AUTOmated PIpeLine Optimization Tool. This tool was tested by participants during the workshop. Link to SV-Autopilot: