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Exome sequence read depth methods for identifying copy number changes

Exome sequence read depth methods for identifying copy number changes
Exome sequence read depth methods for identifying copy number changes
Copy number variants (CNVs) play important roles in a number of human diseases and in pharmacogenetics. Powerful methods exist for CNV detection in whole genome sequencing (WGS) data, but such data are costly to obtain. Many disease causal CNVs span or are found in genome coding regions (exons), which makes CNV detection using whole exome sequencing (WES) data attractive. If reliably validated against WGS-based CNVs, exome-derived CNVs have potential applications in a clinical setting. Several algorithms have been developed to exploit exome data for CNV detection and comparisons made to find the most suitable methods for particular data samples. The results are not consistent across studies. Here, we review some of the exome CNV detection methods based on depth of coverage profiles and examine their performance to identify problems contributing to discrepancies in published results. We also present a streamlined strategy that uses a single metric, the likelihood ratio, to compare exome methods, and we demonstrated its utility using the VarScan 2 and eXome Hidden Markov Model (XHMM) programs using paired normal and tumour exome data from chronic lymphocytic leukaemia patients. We use array-based somatic CNV (SCNV) calls as a reference standard to compute prevalence-independent statistics, such as sensitivity, specificity and likelihood ratio, for validation of the exome-derived SCNVs. We also account for factors known to influence the performance of exome read depth methods, such as CNV size and frequency, while comparing our findings with published results.
1467-5463
380-392
Kadalayil, L.
e620b801-844a-45d9-acaf-e0a58acd7cf2
Rafiq, S.
fce4d7bf-fc11-474c-9d30-a0f8ab8beafe
Rose-Zerilli, M. J. J.
08b3afa4-dbc2-4c0d-a852-2a9f33431199
Pengelly, R.J.
af97c0c1-b568-415c-9f59-1823b65be76d
Parker, H.
33e0cd81-d45f-49bc-9539-09345d79d895
Oscier, D.
c2620a1d-25bb-48f7-9651-f5d023636381
Strefford, J. C.
3782b392-f080-42bf-bdca-8aa5d6ca532f
Tapper, W. J.
9d5ddc92-a8dd-4c78-ac67-c5867b62724c
Gibson, J.
855033a6-38f3-4853-8f60-d7d4561226ae
Ennis, S.
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Collins, A.
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Kadalayil, L.
e620b801-844a-45d9-acaf-e0a58acd7cf2
Rafiq, S.
fce4d7bf-fc11-474c-9d30-a0f8ab8beafe
Rose-Zerilli, M. J. J.
08b3afa4-dbc2-4c0d-a852-2a9f33431199
Pengelly, R.J.
af97c0c1-b568-415c-9f59-1823b65be76d
Parker, H.
33e0cd81-d45f-49bc-9539-09345d79d895
Oscier, D.
c2620a1d-25bb-48f7-9651-f5d023636381
Strefford, J. C.
3782b392-f080-42bf-bdca-8aa5d6ca532f
Tapper, W. J.
9d5ddc92-a8dd-4c78-ac67-c5867b62724c
Gibson, J.
855033a6-38f3-4853-8f60-d7d4561226ae
Ennis, S.
7b57f188-9d91-4beb-b217-09856146f1e9
Collins, A.
7daa83eb-0b21-43b2-af1a-e38fb36e2a64

Kadalayil, L., Rafiq, S., Rose-Zerilli, M. J. J., Pengelly, R.J., Parker, H., Oscier, D., Strefford, J. C., Tapper, W. J., Gibson, J., Ennis, S. and Collins, A. (2015) Exome sequence read depth methods for identifying copy number changes. Briefings in Bioinformatics, 16 (3), 380-392, [bbu027]. (doi:10.1093/bib/bbu027).

Record type: Article

Abstract

Copy number variants (CNVs) play important roles in a number of human diseases and in pharmacogenetics. Powerful methods exist for CNV detection in whole genome sequencing (WGS) data, but such data are costly to obtain. Many disease causal CNVs span or are found in genome coding regions (exons), which makes CNV detection using whole exome sequencing (WES) data attractive. If reliably validated against WGS-based CNVs, exome-derived CNVs have potential applications in a clinical setting. Several algorithms have been developed to exploit exome data for CNV detection and comparisons made to find the most suitable methods for particular data samples. The results are not consistent across studies. Here, we review some of the exome CNV detection methods based on depth of coverage profiles and examine their performance to identify problems contributing to discrepancies in published results. We also present a streamlined strategy that uses a single metric, the likelihood ratio, to compare exome methods, and we demonstrated its utility using the VarScan 2 and eXome Hidden Markov Model (XHMM) programs using paired normal and tumour exome data from chronic lymphocytic leukaemia patients. We use array-based somatic CNV (SCNV) calls as a reference standard to compute prevalence-independent statistics, such as sensitivity, specificity and likelihood ratio, for validation of the exome-derived SCNVs. We also account for factors known to influence the performance of exome read depth methods, such as CNV size and frequency, while comparing our findings with published results.

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More information

Accepted/In Press date: 28 August 2014
e-pub ahead of print date: 28 August 2014
Published date: May 2015
Organisations: Chemistry, Cancer Sciences, Human Development & Health, Centre for Biological Sciences

Identifiers

Local EPrints ID: 368514
URI: http://eprints.soton.ac.uk/id/eprint/368514
ISSN: 1467-5463
PURE UUID: 956e6aa1-5bf4-4fe6-b928-2e4e7bf01f6d
ORCID for M. J. J. Rose-Zerilli: ORCID iD orcid.org/0000-0002-1064-5350
ORCID for R.J. Pengelly: ORCID iD orcid.org/0000-0001-7022-645X
ORCID for H. Parker: ORCID iD orcid.org/0000-0001-8308-9781
ORCID for J. C. Strefford: ORCID iD orcid.org/0000-0002-0972-2881
ORCID for W. J. Tapper: ORCID iD orcid.org/0000-0002-5896-1889
ORCID for J. Gibson: ORCID iD orcid.org/0000-0002-0973-8285
ORCID for S. Ennis: ORCID iD orcid.org/0000-0003-2648-0869
ORCID for A. Collins: ORCID iD orcid.org/0000-0001-7108-0771

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Date deposited: 09 Sep 2014 13:37
Last modified: 18 Feb 2021 17:22

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Contributors

Author: L. Kadalayil
Author: S. Rafiq
Author: R.J. Pengelly ORCID iD
Author: H. Parker ORCID iD
Author: D. Oscier
Author: J. C. Strefford ORCID iD
Author: W. J. Tapper ORCID iD
Author: J. Gibson ORCID iD
Author: S. Ennis ORCID iD
Author: A. Collins ORCID iD

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