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Nexus copy number version 7

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Nexus-Copy-Number version 7.5 is a software platform for the analysis of copy number variations (CNVs) in genomic data. The core function of the software is to detect and visualize CNVs from next-generation sequencing (NGS) data.

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Lab products found in correlation

1m array, by Illumina (2 mentions) Snp arrays, by Illumina (2 mentions) Human cytosnp 12 array, by Illumina (2 mentions) Quantisnp70, by BioDiscovery (2 mentions)

Close spelling variants of this product

Nexus Copy Number (22 citations) Nexus 7.5 (9 citations) Nexus Copy Number Software version 7.0 (8 citations) Nexus Copy Number 7.5 (5 citations)

4 protocols using nexus copy number version 7

1

Tracking Genomic Stability in Cell Lines

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Illumina output files were analysed using Nexus-Copy-Number version 7.5 (BioDiscovery, CA, USA). Linear systematic correction and the ‘SNP-FASST2 segmentation' algorithm for CN-calling with default calling parameters was applied. A first CN analysis was performed on the autosomal chromosomes for each cell line after 2–5 passages. Strict CN-calling criteria (100% overlap of CN calls and P<0.05 in Nexus software) were applied. After this, each of the four cell lines was grown to 16–21 passages, applying either the E8:IαI or the E8:VN protocol. Subsequently, new CN-analyses using the same settings in Nexus software was performed and the results were compared to the CN calls from earlier. The number of shared CN calls between the early and later passages in each of the four cell lines was used to quantify changes introduced during culturing within each cell line under the E8:IαI and E8:VN protocols, respectively. We used Wilcoxon rank-sum-test in the statistical software R v3 to test for differences in the observed number of shared CN calls from the E8:IαI and E8:VN protocols.
Pijuan-Galitó S., Tamm C., Schuster J., Sobol M., Forsberg L., Merry C.L, & Annerén C. (2016). Human serum-derived protein removes the need for coating in defined human pluripotent stem cell culture. Nature Communications, 7, 12170.
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2

Identifying TP53 Mutation-Specific Aberrations

Cited 2 times
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As described previously [33] (link), DNA copy number aberrations were determined using TCGA level 1 Affymetrix SNP6.0 data, which were processed by Nexus Copy Number version 7.5 software (BioDiscovery, Inc, Hawthorne, CA) with allele-specific copy number analysis of tumors algorithm [34] . The frequency of each segmented region with DNA copy number aberration for each sample group with the p53 mutation at the same amino acid (e.g. R175) was compared with all the samples by Mann-Whitney U test to identify significant regions that are associated with specific p53 mutant. To identify potential upstream regulators for gene expression changes in samples with similar p53 mutations, gene expression profile analysis using RNAseq data from the TCGA data set was performed. Genes that are differentially expressed between the samples with specific p53 mutation and all other samples with missense p53 mutations were selected by simple t test (with Reads Per Kilobase Per Million mapped reads [RPKM] > 0.1 and P value < .05). The list of genes was then uploaded into Ingenuity Pathway Analysis for upstream regulator analysis as described [35] (link). The TP53 status of the 316 high-grade serous TCGA samples has been described previously [33] (link). The TP53 status of additional ovarian cancer samples was retrieved using the Catalogue of Somatic Mutations in Cancer database [36] (link).
Mullany L.K., Wong K.K., Marciano D.C., Katsonis P., King-Crane E.R., Ren Y.A., Lichtarge O, & Richards J.S. (2015). Specific TP53 Mutants Overrepresented in Ovarian Cancer Impact CNV, TP53 Activity, Responses to Nutlin-3a, and Cell Survival. Neoplasia (New York, N.Y.), 17(10), 789-803.
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3

Validating Genotyping and Familial Relationships

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Illumina SNP arrays were run on some WGS500 samples and other relatives. This was to check the genotyping accuracy of our sequencing pipeline, to refine linkage regions, to confirm familial relationships, and, in two cases, to investigate whether large stretches of homozygosity were likely due to uniparental disomy or unreported consanguinity. We ran 200 ng of DNA on the Illumina Human CytoSNP12 array or on the 1M array (Illumina Inc.), following the manufacturer’s guidelines. Concordance between the CytoSNP12 genotypes and the WGS data is shown in Supplementary Tables 1 and 2, and the dependence on coverage in Supplementary Figure 2. In most cases, array-CGH had already been performed prior to submission of samples, but we also used QuantiSNP70 to check for CNVs, as well as Nexus Copy Number version 7 (BioDiscovery, Hawthorn, CA). We used MERLIN71 in familial studies to identify regions identical-by-descent.
Taylor J.C., Martin H.C., Lise S., Broxholme J., Cazier J.B., Rimmer A., Kanapin A., Lunter G., Fiddy S., Allan C., Aricescu A.R., Attar M., Babbs C., Becq J., Beeson D., Bento C., Bignell P., Blair E., Buckle V.J., Bull K., Cais O., Cario H., Chapel H., Copley R.R., Cornall R., Craft J., Dahan K., Davenport E.E., Dendrou C., Devuyst O., Fenwick A.L., Flint J., Fugger L., Gilbert R.D., Goriely A., Green A., Greger I.H., Grocock R., Gruszczyk A.V., Hastings R., Hatton E., Higgs D., Hill A., Holmes C., Howard M., Hughes L., Humburg P., Johnson D., Karpe F., Kingsbury Z., Kini U., Knight J.C., Krohn J., Lamble S., Langman C., Lonie L., Luck J., McCarthy D., McGowan S.J., McMullin M.F., Miller K.A., Murray L., Németh A.H., Nesbit M.A., Nutt D., Ormondroyd E., Oturai A.B., Pagnamenta A., Patel S.Y., Percy M., Petousi N., Piazza P., Piret S.E., Polanco-Echeverry G., Popitsch N., Powrie F., Pugh C., Quek L., Robbins P.A., Robson K., Russo A., Sahgal N., van Schouwenburg P.A., Schuh A., Silverman E., Simmons A., Sørensen P.S., Sweeney E., Taylor J., Thakker R.V., Tomlinson I., Trebes A., Twigg S.R., Uhlig H.H., Vyas P., Vyse T., Wall S.A., Watkins H., Whyte M.P., Witty L., Wright B., Yau C., Buck D., Humphray S., Ratcliffe P.J., Bell J.I., Wilkie A.O., Bentley D., Donnelly P, & McVean G. (2015). Factors influencing success of clinical genome sequencing across a broad spectrum of disorders. Nature genetics, 47(7), 717-726.
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4

Validating Genotyping and Familial Relationships

Check if the same lab product or an alternative is used in the 5 most similar protocols
Illumina SNP arrays were run on some WGS500 samples and other relatives. This was to check the genotyping accuracy of our sequencing pipeline, to refine linkage regions, to confirm familial relationships, and, in two cases, to investigate whether large stretches of homozygosity were likely due to uniparental disomy or unreported consanguinity. We ran 200 ng of DNA on the Illumina Human CytoSNP12 array or on the 1M array (Illumina Inc.), following the manufacturer’s guidelines. Concordance between the CytoSNP12 genotypes and the WGS data is shown in Supplementary Tables 1 and 2, and the dependence on coverage in Supplementary Figure 2. In most cases, array-CGH had already been performed prior to submission of samples, but we also used QuantiSNP70 to check for CNVs, as well as Nexus Copy Number version 7 (BioDiscovery, Hawthorn, CA). We used MERLIN71 in familial studies to identify regions identical-by-descent.
Taylor J.C., Martin H.C., Lise S., Broxholme J., Cazier J.B., Rimmer A., Kanapin A., Lunter G., Fiddy S., Allan C., Aricescu A.R., Attar M., Babbs C., Becq J., Beeson D., Bento C., Bignell P., Blair E., Buckle V.J., Bull K., Cais O., Cario H., Chapel H., Copley R.R., Cornall R., Craft J., Dahan K., Davenport E.E., Dendrou C., Devuyst O., Fenwick A.L., Flint J., Fugger L., Gilbert R.D., Goriely A., Green A., Greger I.H., Grocock R., Gruszczyk A.V., Hastings R., Hatton E., Higgs D., Hill A., Holmes C., Howard M., Hughes L., Humburg P., Johnson D., Karpe F., Kingsbury Z., Kini U., Knight J.C., Krohn J., Lamble S., Langman C., Lonie L., Luck J., McCarthy D., McGowan S.J., McMullin M.F., Miller K.A., Murray L., Németh A.H., Nesbit M.A., Nutt D., Ormondroyd E., Oturai A.B., Pagnamenta A., Patel S.Y., Percy M., Petousi N., Piazza P., Piret S.E., Polanco-Echeverry G., Popitsch N., Powrie F., Pugh C., Quek L., Robbins P.A., Robson K., Russo A., Sahgal N., van Schouwenburg P.A., Schuh A., Silverman E., Simmons A., Sørensen P.S., Sweeney E., Taylor J., Thakker R.V., Tomlinson I., Trebes A., Twigg S.R., Uhlig H.H., Vyas P., Vyse T., Wall S.A., Watkins H., Whyte M.P., Witty L., Wright B., Yau C., Buck D., Humphray S., Ratcliffe P.J., Bell J.I., Wilkie A.O., Bentley D., Donnelly P, & McVean G. (2015). Factors influencing success of clinical genome sequencing across a broad spectrum of disorders. Nature genetics, 47(7), 717-726.
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