Leveraging long and short read sequencing technologies to provide a comprehensive resource for benchmarking variant calling methods

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Publication

            If you use this resource in your work, please cite the following:

            John C Mu, Pegah Tootoonchi Afshar, Marghoob Mohiyuddin, Xi Chen, Jian Li, Narges Bani Asadi, Mark B Gerstein, Wing H Wong, and Hugo Y.K. Lam.

            Leveraging long read sequencing from a single individual to provide a comprehensive resource for benchmarking variant calling methods.

            Nature Scientific Reports, published online September 28, 2015 http://dx.doi.org/10.1038/srep14493

High-confidence variants and regions for HuRef

We identified high-confidence variants for HuRef using multiple detection and validation methodologies. The list of files below contains the various outputs from our analyses.

High-confidence SNPs: SNPs identified as high-confidence for HuRef.
High-confidence INDELs: INDELs identified as high-confidence for HuRef.
High-confidence deletion SVs: Large (structural-variant) high-confidence deletions for HuRef.
High-confidence insertion SVs: Large (structural-variant) high-confidence insertions for HuRef.
All deletion SVs: Large (structural-variant) deletions for HuRef. This is a more comprehensive set of deletion SVs identified using a relaxed detection and validation methodology.
All insertion SVs: Large (structural-variant) insertions for HuRef. This is a more comprehensive set of insertion SVs identified using a relaxed detection and validation methodology.
No structural variant regions (NSVR): This BED file contains regions which have been identified as having no structural variants with high-confidence.