Indel variant analysis of short-read sequencing data with Scalpel

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As the second most common type of variation in the human genome, insertions and deletions (indels) have been linked to many diseases, but the discovery of indels of more than a few bases in size from short-read sequencing data remains challenging. Scalpel ( is an open-source software for reliable indel detection based on the microassembly technique. It has been successfully used to discover mutations in novel candidate genes for autism, and it is extensively used in other large-scale studies of human diseases. This protocol gives an overview of the algorithm and describes how to use Scalpel to perform highly accurate indel calling from whole-genome and whole-exome sequencing data. We provide detailed instructions for an exemplary family-based de novo study, but we also characterize the other two supported modes of operation: single-sample and somatic analysis. Indel normalization, visualization and annotation of the mutations are also illustrated. Using a standard server, indel discovery and characterization in the exonic regions of the example sequencing data can be completed in 5 h after read mapping.

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Figure 1: High accuracy of indel detection using Scalpel on WGS data.
Figure 2: Main steps in the Scalpel protocol.
Figure 3: Overview of the indel variant filtering cascade.
Figure 4: Higher coverage can improve Scalpel's sensitivity performance for indel detection with WGS data.
Figure 5: Comparison of standard WGS and PCR-free data based on indel quality.
Figure 6: Whole-genome mutational concordance.
Figure 7: Size distribution of inherited and de novo indels.
Figure 8: Histograms of low-quality homopolymer indels by category.
Figure 9: Variant allele fractions (VAF %) of the inherited indels.
Figure 10: Filtering cascade of inherited and de novo indel calls.
Figure 11: Frame-preserving indels are more abundant within coding sequences.
Figure 12: Screenshot of the alignment of the de novo deletion in the IGV browser.

Change history

  • 08 December 2016

    In the version of this article initially published, the affiliations of two authors, Esra Dikoglu and Vaidehi Jobanputra, were incorrectly reported. Corrected affiliations are as follows: Esra Dikoglu is affiliated with the New York Genome Center, New York, New York, USA. Vaidehi Jobanputra is affiliated with the New York Genome Center, New York, New York, USA; and Columbia University Medical Center, New York, New York, USA.


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The project was supported in part by grants from the US National Institutes of Health (R01-HG006677 and U01-CA168409) and the US National Science Foundation (DBI-1350041) to M.C.S. and by grants from the Cold Spring Harbor Laboratory (CSHL) Cancer Center Support (5P30CA045508), the Stanley Institute for Cognitive Genomics and the Simons Foundation (SF51 and SF235988) to M.W.

Author information




G.N. is the lead developer of Scalpel. M.C.S. contributed to the development of Scalpel and wrote the microsatellite detector scripts. H.F. contributed to enhance Scalpel, compiled the Scalpel resource bundle and generated the figures in this article. E.D. and V.J. performed the Sanger validation. G.N., M.C.S. and M.W. conceived the Scalpel software project. G.N., M.C.S. and H.F. wrote the initial draft of the manuscript. M.C.S. is the principal investigator. All authors contributed to the development and approval of the final manuscript.

Corresponding author

Correspondence to Giuseppe Narzisi.

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Competing interests

G.J.L. serves on advisory boards for GenePeeks and Omicia. The remaining authors declare no competing financial interests.

Supplementary information

Supplementary Methods and Supplementary Results

Supplementary Methods. Descriptions of Sanger validation experiments. Supplementary Results. Screenshots of images from Mutation Surveyor for each Sanger validation experiment. (PDF 1576 kb)

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Fang, H., Bergmann, E., Arora, K. et al. Indel variant analysis of short-read sequencing data with Scalpel. Nat Protoc 11, 2529–2548 (2016).

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