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Structural Variation detection by STAck and Tail SV-STAT

Quantifies evidence for structural variation in genomic regions suspected of harboring rearrangements. SV-STAT extends existing methods by adjusting a chimeric read’s support of a structural variation by (i) the number of its soft-clipped bases and (ii) the quality of its alignment to the junction. SV-STAT is more accurate than alternative methods for determining base-pair resolved breakpoints. SV-STAT is a significant advance towards accurate detection and genotyping of genomic rearrangements from DNA sequencing data.

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2 user reviews

2 user reviews

Ray Cui's avatar image Ray Cui's country flag

Ray Cui

Hello. We have 250x2 PE reads of the insert size ~400bp, and also mate pair libraries of 3kb, 7kb and 10kb inserts. Is it possible to use all of these reads jointly for detecting variations? I am mainly using this to perform a final polishing of our de novo genome assembly (from the same data).

Caleb Davis's avatar image

Caleb Davis

SV-STAT detects genomic rearrangements from NGS data with high accuracy and is complementary to existing tools. It is easy to use and has minimal dependencies.

I am the author of this tool. Please do not hesitate to contact me with any questions.

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SV-STAT classification

SV-STAT specifications

Software type:
Restrictions to use:
Programming languages:
Perl, Python, Shell (Bash)
Computer skills:
Dbfasta, BWA, Picard, Samtools, BioPerl, Bedtools
Command line interface
Operating system:
GNU General Public License version 3.0
Source code URL:

SV-STAT distribution


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SV-STAT support


  • Caleb Davis <>


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Structural and Computational Biology and Molecular Biophysics (SCBMB) Program, Baylor College of Medicine, Houston, TX, USA; Texas Children’s Cancer Center, Baylor College of Medicine, Houston, TX, USA; W. M. Keck Center for Interdisciplinary Bioscience Training, Houston, TX, USA; Human Genome Sequencing Center, Baylor College of Medicine, One Baylor Plaza, Houston, TX, USA

Funding source(s)

This work was supported by the following grants from the NIH: National Institute of General Medical Sciences (K12 GM084897), and a training fellowship from the Keck Center for Interdisciplinary Bioscience Training of the Gulf Coast Consortia (T15 LM007093).

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