Somatic variant detection from multi-sampled genomic sequencing data of tumor specimens using the ith.Variant pipeline

Nicole Maeser; Aziz Khan; Ruping Sun

doi:10.1016/j.xpro.2022.101927

Somatic variant detection from multi-sampled genomic sequencing data of tumor specimens using the ith.Variant pipeline

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Abstract

A common technique for uncovering intra-tumor genomic heterogeneity (ITH) is variant detection. However, it can be challenging to reliably characterize ITH given uneven sample quality (e.g., depth of coverage, tumor purity, and subclonality). We describe a protocol for calling point mutations and copy number alterations using sequencing of multiple related clinical patient samples across diverse tissue, optimizing for sensitivity with specificity. The ith.Variant pipeline can be run on single- or multi-region whole-genome and whole-exome sequencing. For complete details on the use and execution of this protocol, please refer to Sun et al. (2017).¹

Original language	English (US)
Article number	101927
Journal	STAR Protocols
Volume	4
Issue number	1
DOIs	https://doi.org/10.1016/j.xpro.2022.101927
State	Published - Mar 17 2023

Bibliographical note

Funding Information:
This study was completed at the invitation of STAR Protocols for a special issue dedicated to genomics. This study uses the computing resources of the Minnesota Supercomputing Institute (MSI). This work was supported by the Department of Laboratory Medicine and Pathology ( https://med.umn.edu/pathology ) and the Masonic Cancer Center ( https://cancer.umn.edu/ ) at the University of Minnesota. This work was partially supported by the Karen Wyckoff Rein in Sarcoma Foundation ( https://www.reininsarcoma.org/ ). The funders had no role in study design, data collection, analysis, decision to publish, or preparation of the manuscript.

Funding Information:
This study was completed at the invitation of STAR Protocols for a special issue dedicated to genomics. This study uses the computing resources of the Minnesota Supercomputing Institute (MSI). This work was supported by the Department of Laboratory Medicine and Pathology (https://med.umn.edu/pathology) and the Masonic Cancer Center (https://cancer.umn.edu/) at the University of Minnesota. This work was partially supported by the Karen Wyckoff Rein in Sarcoma Foundation (https://www.reininsarcoma.org/). The funders had no role in study design, data collection, analysis, decision to publish, or preparation of the manuscript. Formal analysis, N.M. R.S.; Software, N.M. A.K. R.S.; Writing – original draft, N.M.; Writing – review & editing, N.M. A.K. R.S.; Visualization, N.M.; Supervision, R.S. The authors declare no competing interests.

Publisher Copyright:
© 2022 The Authors

Keywords

Bioinformatics
Cancer
Genomics
Health Sciences
Sequencing

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.xpro.2022.101927

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title = "Somatic variant detection from multi-sampled genomic sequencing data of tumor specimens using the ith.Variant pipeline",

abstract = "A common technique for uncovering intra-tumor genomic heterogeneity (ITH) is variant detection. However, it can be challenging to reliably characterize ITH given uneven sample quality (e.g., depth of coverage, tumor purity, and subclonality). We describe a protocol for calling point mutations and copy number alterations using sequencing of multiple related clinical patient samples across diverse tissue, optimizing for sensitivity with specificity. The ith.Variant pipeline can be run on single- or multi-region whole-genome and whole-exome sequencing. For complete details on the use and execution of this protocol, please refer to Sun et al. (2017).1",

keywords = "Bioinformatics, Cancer, Genomics, Health Sciences, Sequencing",

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note = "Funding Information: This study was completed at the invitation of STAR Protocols for a special issue dedicated to genomics. This study uses the computing resources of the Minnesota Supercomputing Institute (MSI). This work was supported by the Department of Laboratory Medicine and Pathology ( https://med.umn.edu/pathology ) and the Masonic Cancer Center ( https://cancer.umn.edu/ ) at the University of Minnesota. This work was partially supported by the Karen Wyckoff Rein in Sarcoma Foundation ( https://www.reininsarcoma.org/ ). The funders had no role in study design, data collection, analysis, decision to publish, or preparation of the manuscript. Funding Information: This study was completed at the invitation of STAR Protocols for a special issue dedicated to genomics. This study uses the computing resources of the Minnesota Supercomputing Institute (MSI). This work was supported by the Department of Laboratory Medicine and Pathology (https://med.umn.edu/pathology) and the Masonic Cancer Center (https://cancer.umn.edu/) at the University of Minnesota. This work was partially supported by the Karen Wyckoff Rein in Sarcoma Foundation (https://www.reininsarcoma.org/). The funders had no role in study design, data collection, analysis, decision to publish, or preparation of the manuscript. Formal analysis, N.M. R.S.; Software, N.M. A.K. R.S.; Writing – original draft, N.M.; Writing – review \& editing, N.M. A.K. R.S.; Visualization, N.M.; Supervision, R.S. The authors declare no competing interests. Publisher Copyright: {\textcopyright} 2022 The Authors",

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AU - Maeser, Nicole

AU - Khan, Aziz

AU - Sun, Ruping

N1 - Funding Information: This study was completed at the invitation of STAR Protocols for a special issue dedicated to genomics. This study uses the computing resources of the Minnesota Supercomputing Institute (MSI). This work was supported by the Department of Laboratory Medicine and Pathology ( https://med.umn.edu/pathology ) and the Masonic Cancer Center ( https://cancer.umn.edu/ ) at the University of Minnesota. This work was partially supported by the Karen Wyckoff Rein in Sarcoma Foundation ( https://www.reininsarcoma.org/ ). The funders had no role in study design, data collection, analysis, decision to publish, or preparation of the manuscript. Funding Information: This study was completed at the invitation of STAR Protocols for a special issue dedicated to genomics. This study uses the computing resources of the Minnesota Supercomputing Institute (MSI). This work was supported by the Department of Laboratory Medicine and Pathology (https://med.umn.edu/pathology) and the Masonic Cancer Center (https://cancer.umn.edu/) at the University of Minnesota. This work was partially supported by the Karen Wyckoff Rein in Sarcoma Foundation (https://www.reininsarcoma.org/). The funders had no role in study design, data collection, analysis, decision to publish, or preparation of the manuscript. Formal analysis, N.M. R.S.; Software, N.M. A.K. R.S.; Writing – original draft, N.M.; Writing – review & editing, N.M. A.K. R.S.; Visualization, N.M.; Supervision, R.S. The authors declare no competing interests. Publisher Copyright: © 2022 The Authors

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N2 - A common technique for uncovering intra-tumor genomic heterogeneity (ITH) is variant detection. However, it can be challenging to reliably characterize ITH given uneven sample quality (e.g., depth of coverage, tumor purity, and subclonality). We describe a protocol for calling point mutations and copy number alterations using sequencing of multiple related clinical patient samples across diverse tissue, optimizing for sensitivity with specificity. The ith.Variant pipeline can be run on single- or multi-region whole-genome and whole-exome sequencing. For complete details on the use and execution of this protocol, please refer to Sun et al. (2017).1

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KW - Bioinformatics

KW - Cancer

KW - Genomics

KW - Health Sciences

KW - Sequencing

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ER -

Somatic variant detection from multi-sampled genomic sequencing data of tumor specimens using the ith.Variant pipeline

Abstract

Bibliographical note

Keywords

UN SDGs

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