Detecting copy-number variations in whole-exome sequencing data using the eXome Hidden Markov Model: an ‘exome-first’ approach View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2015-04

AUTHORS

Satoko Miyatake, Eriko Koshimizu, Atsushi Fujita, Ryoko Fukai, Eri Imagawa, Chihiro Ohba, Ichiro Kuki, Megumi Nukui, Atsushi Araki, Yoshio Makita, Tsutomu Ogata, Mitsuko Nakashima, Yoshinori Tsurusaki, Noriko Miyake, Hirotomo Saitsu, Naomichi Matsumoto

ABSTRACT

Whole-exome sequencing (WES) is becoming a standard tool for detecting nucleotide changes, and determining whether WES data can be used for the detection of copy-number variations (CNVs) is of interest. To date, several algorithms have been developed for such analyses, although verification is needed to establish if they fit well for the appropriate purpose, depending on the characteristics of each algorithm. Here, we performed WES CNV analysis using the eXome Hidden Markov Model (XHMM). We validated its performance using 27 rare CNVs previously identified by microarray as positive controls, finding that the detection rate was 59%, or higher (89%) with three or more targets. XHMM can be effectively used, especially for the detection of >200 kb CNVs. XHMM may be useful for deletion breakpoint detection. Next, we applied XHMM to genetically unsolved patients, demonstrating successful identification of pathogenic CNVs: 1.5-1.9-Mb deletions involving NSD1 in patients with unknown overgrowth syndrome leading to the diagnosis of Sotos syndrome, and 6.4-Mb duplication involving MECP2 in affected brothers with late-onset spasm and progressive cerebral/cerebellar atrophy confirming the clinical suspect of MECP2 duplication syndrome. The possibility of an 'exome-first' approach for clinical genetic investigation may be considered to save the cost of multiple investigations. More... »

PAGES

175

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/jhg.2014.124

DOI

http://dx.doi.org/10.1038/jhg.2014.124

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1050739581

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/25608832


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Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1038/jhg.2014.124'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/jhg.2014.124'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/jhg.2014.124'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/jhg.2014.124'


 

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