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Items: 1 to 20 of 122

1.

Correcting for cancer genome size and tumour cell content enables better estimation of copy number alterations from next-generation sequence data.

Gusnanto A, Wood HM, Pawitan Y, Rabbitts P, Berri S.

Bioinformatics. 2012 Jan 1;28(1):40-7. doi: 10.1093/bioinformatics/btr593. Epub 2011 Oct 28.

PMID:
22039209
2.

cn.MOPS: mixture of Poissons for discovering copy number variations in next-generation sequencing data with a low false discovery rate.

Klambauer G, Schwarzbauer K, Mayr A, Clevert DA, Mitterecker A, Bodenhofer U, Hochreiter S.

Nucleic Acids Res. 2012 May;40(9):e69. doi: 10.1093/nar/gks003. Epub 2012 Feb 1.

3.

Stratifying tumour subtypes based on copy number alteration profiles using next-generation sequence data.

Gusnanto A, Tcherveniakov P, Shuweihdi F, Samman M, Rabbitts P, Wood HM.

Bioinformatics. 2015 Aug 15;31(16):2713-20. doi: 10.1093/bioinformatics/btv191. Epub 2015 Apr 5.

PMID:
25847006
4.

WaveCNV: allele-specific copy number alterations in primary tumors and xenograft models from next-generation sequencing.

Holt C, Losic B, Pai D, Zhao Z, Trinh Q, Syam S, Arshadi N, Jang GH, Ali J, Beck T, McPherson J, Muthuswamy LB.

Bioinformatics. 2014 Mar 15;30(6):768-74. doi: 10.1093/bioinformatics/btt611. Epub 2013 Nov 4.

5.

Control-FREEC: a tool for assessing copy number and allelic content using next-generation sequencing data.

Boeva V, Popova T, Bleakley K, Chiche P, Cappo J, Schleiermacher G, Janoueix-Lerosey I, Delattre O, Barillot E.

Bioinformatics. 2012 Feb 1;28(3):423-5. doi: 10.1093/bioinformatics/btr670. Epub 2011 Dec 6.

6.

Comparison of methods to detect copy number alterations in cancer using simulated and real genotyping data.

Mosén-Ansorena D, Aransay AM, Rodríguez-Ezpeleta N.

BMC Bioinformatics. 2012 Aug 7;13:192. doi: 10.1186/1471-2105-13-192.

7.

Estimating optimal window size for analysis of low-coverage next-generation sequence data.

Gusnanto A, Taylor CC, Nafisah I, Wood HM, Rabbitts P, Berri S.

Bioinformatics. 2014 Jul 1;30(13):1823-9. doi: 10.1093/bioinformatics/btu123. Epub 2014 Mar 5.

PMID:
24603986
8.

COPS: a sensitive and accurate tool for detecting somatic Copy Number Alterations using short-read sequence data from paired samples.

Krishnan NM, Gaur P, Chaudhary R, Rao AA, Panda B.

PLoS One. 2012;7(10):e47812. doi: 10.1371/journal.pone.0047812. Epub 2012 Oct 22.

9.

Control-free calling of copy number alterations in deep-sequencing data using GC-content normalization.

Boeva V, Zinovyev A, Bleakley K, Vert JP, Janoueix-Lerosey I, Delattre O, Barillot E.

Bioinformatics. 2011 Jan 15;27(2):268-9. doi: 10.1093/bioinformatics/btq635. Epub 2010 Nov 15.

10.

Deconvolving tumor purity and ploidy by integrating copy number alterations and loss of heterozygosity.

Li Y, Xie X.

Bioinformatics. 2014 Aug 1;30(15):2121-9. doi: 10.1093/bioinformatics/btu174. Epub 2014 Apr 2. Erratum in: Bioinformatics. 2015 Feb 15;31(4):618.

11.

OncoSNP-SEQ: a statistical approach for the identification of somatic copy number alterations from next-generation sequencing of cancer genomes.

Yau C.

Bioinformatics. 2013 Oct 1;29(19):2482-4. doi: 10.1093/bioinformatics/btt416. Epub 2013 Aug 7.

PMID:
23926227
12.

CLImAT: accurate detection of copy number alteration and loss of heterozygosity in impure and aneuploid tumor samples using whole-genome sequencing data.

Yu Z, Liu Y, Shen Y, Wang M, Li A.

Bioinformatics. 2014 Sep 15;30(18):2576-83. doi: 10.1093/bioinformatics/btu346. Epub 2014 May 19.

13.

VegaMC: a R/bioconductor package for fast downstream analysis of large array comparative genomic hybridization datasets.

Morganella S, Ceccarelli M.

Bioinformatics. 2012 Oct 1;28(19):2512-4. Epub 2012 Jul 18.

PMID:
22815357
14.

Genome-wide identification of significant aberrations in cancer genome.

Yuan X, Yu G, Hou X, Shih IeM, Clarke R, Zhang J, Hoffman EP, Wang RR, Zhang Z, Wang Y.

BMC Genomics. 2012 Jul 27;13:342. doi: 10.1186/1471-2164-13-342.

15.

The algorithm of equal acceptance region for detecting copy number alterations: applications to next-generation sequencing data.

Lin YX.

Int J Bioinform Res Appl. 2012;8(3-4):245-62. doi: 10.1504/IJBRA.2012.048969.

PMID:
22961454
16.

CONTRA: copy number analysis for targeted resequencing.

Li J, Lupat R, Amarasinghe KC, Thompson ER, Doyle MA, Ryland GL, Tothill RW, Halgamuge SK, Campbell IG, Gorringe KL.

Bioinformatics. 2012 May 15;28(10):1307-13. doi: 10.1093/bioinformatics/bts146. Epub 2012 Apr 2.

17.

SNVMix: predicting single nucleotide variants from next-generation sequencing of tumors.

Goya R, Sun MG, Morin RD, Leung G, Ha G, Wiegand KC, Senz J, Crisan A, Marra MA, Hirst M, Huntsman D, Murphy KP, Aparicio S, Shah SP.

Bioinformatics. 2010 Mar 15;26(6):730-6. doi: 10.1093/bioinformatics/btq040. Epub 2010 Feb 3.

18.

A regression model for estimating DNA copy number applied to capture sequencing data.

Rigaill GJ, Cadot S, Kluin RJ, Xue Z, Bernards R, Majewski IJ, Wessels LF.

Bioinformatics. 2012 Sep 15;28(18):2357-65. doi: 10.1093/bioinformatics/bts448. Epub 2012 Jul 13.

PMID:
22796958
19.

Determination of genomic copy number alteration emphasizing a restriction site-based strategy of genome re-sequencing.

Zheng C, Miao X, Li Y, Huang Y, Ruan J, Ma X, Wang L, Wu CI, Cai J.

Bioinformatics. 2013 Nov 15;29(22):2813-21. doi: 10.1093/bioinformatics/btt481. Epub 2013 Aug 20.

PMID:
23962614
20.

seqCNA: an R package for DNA copy number analysis in cancer using high-throughput sequencing.

Mosen-Ansorena D, Telleria N, Veganzones S, De la Orden V, Maestro ML, Aransay AM.

BMC Genomics. 2014 Mar 5;15:178. doi: 10.1186/1471-2164-15-178.

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