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Structural Variant Machine SV-M

Detects indel candidates using a discriminative classifier based on features of split read alignment profiles and trained on true and false indel candidates that were validated by Sanger sequencing. SV-M is able to discover and distinguish true from false indel candidates in order to reduce the false positive rate. The key benefit of using a discriminative model is to learn to distinguish between true and false candidates based on a Sanger validated ground truth, thereby reducing the false positive rate among predicted indels.

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SV-M classification

SV-M specifications

Software type:
Restrictions to use:
Programming languages:
C, C++
Command line interface
Operating system:
Computer skills:

SV-M distribution


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SV-M support


  • Dominik Grimm <>


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Machine Learning and Computational Biology Research Group, Max Planck Institute for Developmental Biology and Max Planck Institute for Intelligent Systems, Tübingen, Germany; Department of Molecular Biology, Max Planck Institute for Developmental Biology, Tübingen, Germany; Center for Bioinformatics, Eberhard Karls Universität, Tübingen, Germany

Funding source(s)

Supported by Transnational Plant Alliance for Novel Technologies – Towards Implementing the Knowledge-based Bioeconomy in Europe (PLANT-KBBE) project Transcriptional Networks and Their Evolution in the Brassicaceae (TRANSNET), funded by the Bundesministerium für Bildung und Forschung, by a Gottfried Wilhelm Leibniz Award of the Deutsche Forschungsgemeinschaft, and the Max Planck Society.

Link to literature

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