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Detection of structural variation using target captured next-generation sequencing data for genetic diagnostic testing.

Wenbo Mu | Bing Li | Sitao Wu | Jefferey Chen | Divya Sain | Dong Xu | Mary Helen Black | Rachid Karam | Katrina Gillespie | Kelly D Farwell Hagman | Lucia Guidugli | Melissa Pronold | Aaron Elliott | Hsiao-Mei Lu
Genetics in medicine : official journal of the American College of Medical Genetics | 2019

Structural variation (SV) is associated with inherited diseases. Next-generation sequencing (NGS) is an efficient method for SV detection because of its high-throughput, low cost, and base-pair resolution. However, due to lack of standard NGS protocols and a limited number of clinical samples with pathogenic SVs, comprehensive standards for SV detection, interpretation, and reporting are to be established.

Pubmed ID: 30563988

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This is a list of tools and resources that we have found mentioned in this publication.


Agilent Genomic Workbench (tool)

RRID:SCR_010918

A comprehensive design and analysis tool for setting up and interpreting your microarray experiments.

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Integrative Genomics Viewer (tool)

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aCGH (tool)

RRID:SCR_013232

Software functions for reading aCGH data from image analysis output files and clone information files, creation of aCGH S3 objects for storing these data. Basic methods for accessing/replacing, subsetting, printing and plotting aCGH objects.

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Agilent Feature Extraction Software (tool)

RRID:SCR_014963

Software that automatically reads and processes up to 100 raw microarray image files. The software finds and places microarray grids, rejects outlier pixels, accurately determines feature intensities and ratios, flags outlier pixels, and calculates statistical confidences.

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