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Theme: Emerging Molecular Diagnostic Technologies - Review

Detection and interpretation of genomic structural variation in health and disease

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Pages 61-82 | Published online: 09 Jan 2014

References

  • Venter JC, Adams MD, Myers EW et al. The sequence of the human genome. Science 291(5507), 1304–1351 (2001).
  • Lander ES, Linton LM, Birren B et al.; International Human Genome Sequencing Consortium. Initial sequencing and analysis of the human genome. Nature 409(6822), 860–921 (2001).
  • International HapMap Consortium. The International HapMap Project. Nature 426(6968), 789–796 (2003).
  • Redon R, Ishikawa S, Fitch KR et al. Global variation in copy number in the human genome. Nature 444(7118), 444–454 (2006).
  • 1000 Genomes Project Consortium. A map of human genome variation from population-scale sequencing. Nature 467(7319), 1061–1073 (2010).
  • Cooper GM, Coe BP, Girirajan S et al. A copy number variation morbidity map of developmental delay. Nat. Genet. 43(9), 838–846 (2011).
  • Siva N. 1000 Genomes project. Nat. Biotechnol. 26(3), 256 (2008).
  • Lynch M. Rate, molecular spectrum, and consequences of human mutation. Proc. Natl Acad. Sci. USA 107(3), 961–968 (2010).
  • Sherry ST, Ward MH, Kholodov M et al. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 29(1), 308–311 (2001).
  • Iafrate AJ, Feuk L, Rivera MN et al. Detection of large-scale variation in the human genome. Nat. Genet. 36(9), 949–951 (2004).
  • Cooper GM, Nickerson DA, Eichler EE. Mutational and selective effects on copy-number variants in the human genome. Nat. Genet. 39(7 Suppl.), S22–S29 (2007).
  • Shaikh TH, Gai X, Perin JC et al. High-resolution mapping and analysis of copy number variations in the human genome: a data resource for clinical and research applications. Genome Res. 19(9), 1682–1690 (2009).
  • Itsara A, Cooper GM, Baker C et al. Population analysis of large copy number variants and hotspots of human genetic disease. Am. J. Hum. Genet. 84(2), 148–161 (2009).
  • Shaffer LG, Lupski JR. Molecular mechanisms for constitutional chromosomal rearrangements in humans. Annu. Rev. Genet. 34, 297–329 (2000).
  • Zhang F, Khajavi M, Connolly AM, Towne CF, Batish SD, Lupski JR. The DNA replication FoSTeS/MMBIR mechanism can generate genomic, genic and exonic complex rearrangements in humans. Nat. Genet. 41(7), 849–853 (2009).
  • Feinberg AP. Phenotypic plasticity and the epigenetics of human disease. Nature 447(7143), 433–440 (2007).
  • Feinberg AP. Genome-scale approaches to the epigenetics of common human disease. Virchows Arch. 456(1), 13–21 (2010).
  • Heulens I, Kooy F. Fragile X syndrome: from gene discovery to therapy. Front. Biosci. 16, 1211–1232 (2011).
  • Pober BR. Williams-Beuren syndrome. N. Engl. J. Med. 362(3), 239–252 (2010).
  • Neill NJ, Ballif BC, Lamb AN et al. Recurrence, submicroscopic complexity, and potential clinical relevance of copy gains detected by array CGH that are shown to be unbalanced insertions by FISH. Genome Res. 21(4), 535–544 (2011).
  • Ersfeld K. Fiber-FISH: fluorescence in situ hybridization on stretched DNA. Methods Mol. Biol. 270, 395–402 (2004).
  • Rooms L, Reyniers E, Kooy RF. Subtelomeric rearrangements in the mentally retarded: a comparison of detection methods. Hum. Mutat. 25(6), 513–524 (2005).
  • Rooms L, Vandeweyer G, Reyniers E et al. Array-based MLPA to detect recurrent copy number variations in patients with idiopathic mental retardation. Am. J. Med. Genet. A 155A(2), 343–348 (2011).
  • Kumps C, Van Roy N, Heyrman L et al. Multiplex Amplicon Quantification (MAQ), a fast and efficient method for the simultaneous detection of copy number alterations in neuroblastoma. BMC Genomics 11, 298 (2010).
  • Pinkel D, Segraves R, Sudar D et al. High resolution analysis of DNA copy number variation using comparative genomic hybridization to microarrays. Nat. Genet. 20(2), 207–211 (1998).
  • McCormick MR, Selzer RR, Richmond TA. Methods in high-resolution, array-based comparative genomic hybridization. Methods Mol. Biol. 381, 189–211 (2007).
  • Conrad DF, Pinto D, Redon R et al.; Wellcome Trust Case Control Consortium. Origins and functional impact of copy number variation in the human genome. Nature 464(7289), 704–712 (2010).
  • Greisman HA, Hoffman NG, Yi HS. Rapid high-resolution mapping of balanced chromosomal rearrangements on tiling CGH arrays. J. Mol. Diagn. 13(6), 621–633 (2011).
  • Peiffer DA, Le JM, Steemers FJ et al. High-resolution genomic profiling of chromosomal aberrations using Infinium whole-genome genotyping. Genome Res. 16(9), 1136–1148 (2006).
  • Shen F, Huang J, Fitch KR et al. Improved detection of global copy number variation using high density, non-polymorphic oligonucleotide probes. BMC Genet. 9, 27 (2008).
  • McMullan DJ, Bonin M, Hehir-Kwa JY et al. Molecular karyotyping of patients with unexplained mental retardation by SNP arrays: a multicenter study. Hum. Mutat. 30(7), 1082–1092 (2009).
  • Ting JC, Roberson ED, Miller ND et al. Visualization of uniparental inheritance, Mendelian inconsistencies, deletions, and parent of origin effects in single nucleotide polymorphism trio data with SNPtrio. Hum. Mutat. 28(12), 1225–1235 (2007).
  • Attiyeh EF, Diskin SJ, Attiyeh MA et al. Genomic copy number determination in cancer cells from single nucleotide polymorphism microarrays based on quantitative genotyping corrected for aneuploidy. Genome Res. 19(2), 276–283 (2009).
  • Greenman CD, Bignell G, Butler A et al. PICNIC: an algorithm to predict absolute allelic copy number variation with microarray cancer data. Biostatistics 11(1), 164–175 (2010).
  • Popova T, Manié E, Stoppa-Lyonnet D, Rigaill G, Barillot E, Stern MH. Genome Alteration Print (GAP): a tool to visualize and mine complex cancer genomic profiles obtained by SNP arrays. Genome Biol. 10(11), 128 (2009).
  • Tuzun E, Sharp AJ, Bailey JA et al. Fine-scale structural variation of the human genome. Nat. Genet. 37(7), 727–732 (2005).
  • Kidd JM, Cooper GM, Donahue WF et al. Mapping and sequencing of structural variation from eight human genomes. Nature 453(7191), 56–64 (2008).
  • Xi R, Kim TM, Park PJ. Detecting structural variations in the human genome using next generation sequencing. Brief. Funct. Genomics 9(5–6), 405–415 (2010).
  • Metzker ML. Sequencing technologies – the next generation. Nat. Rev. Genet. 11(1), 31–46 (2010).
  • Sulonen AM, Ellonen P, Almusa H et al. Comparison of solution-based exome capture methods for next generation sequencing. Genome Biol. 12(9), 94 (2011).
  • Dohm JC, Lottaz C, Borodina T, Himmelbauer H. Substantial biases in ultra-short read data sets from high-throughput DNA sequencing. Nucleic Acids Res. 36(16), e105 (2008).
  • Schneider GF, Dekker C. DNA sequencing with nanopores. Nat. Biotechnol. 30(4), 326–328 (2012).
  • Myllykangas S, Buenrostro J, Ji HP. Overview of Sequencing Technology Platforms Bioinformatics for High Throughput Sequencing. Rodríguez-Ezpeleta N, Hackenberg M, Aransay AM (Eds). Springer, NY, USA, 11–25 (2012).
  • Lejeune J, Gautier M, Turpin R. [Study of somatic chromosomes from 9 mongoloid children]. C. R. Hebd. Seances Acad. Sci. 248(11), 1721–1722 (1959).
  • Ford CE, Jones KW, Polani PE, De Almeida JC, Briggs JH. A sex-chromosome anomaly in a case of gonadal dysgenesis (Turner’s syndrome). Lancet 1(7075), 711–713 (1959).
  • Feuk L, Carson AR, Scherer SW. Structural variation in the human genome. Nat. Rev. Genet. 7(2), 85–97 (2006).
  • Vandeweyer G, Kooy RF. Balanced translocations in mental retardation. Hum. Genet. 126(1), 133–147 (2009).
  • Vandeweyer G, Van der Aa N, Reyniers E, Kooy RF. The contribution of CLIP2 haploinsufficiency to the clinical manifestations of the Williams–Beuren syndrome. Am. J. Hum. Genet. 90(6), 1071–1078 (2012).
  • Pinto D, Marshall C, Feuk L, Scherer SW. Copy-number variation in control population cohorts. Hum. Mol. Genet. 16 Spec No. 2, 168–173 (2007).
  • Perry GH, Ben-Dor A, Tsalenko A et al. The fine-scale and complex architecture of human copy-number variation. Am. J. Hum. Genet. 82(3), 685–695 (2008).
  • McCarroll SA, Kuruvilla FG, Korn JM et al. Integrated detection and population-genetic analysis of SNPs and copy number variation. Nat. Genet. 40(10), 1166–1174 (2008).
  • Mills RE, Pittard WS, Mullaney JM et al. Natural genetic variation caused by small insertions and deletions in the human genome. Genome Res. 21(6), 830–839 (2011).
  • Zogopoulos G, Ha KC, Naqib F et al. Germ-line DNA copy number variation frequencies in a large North American population. Hum. Genet. 122(3-4), 345–353 (2007).
  • Rooms L, Reyniers E, van Luijk R et al. Subtelomeric deletions detected in patients with idiopathic mental retardation using multiplex ligation-dependent probe amplification (MLPA). Hum. Mutat. 23(1), 17–21 (2004).
  • Koolen DA, Pfundt R, de Leeuw N et al. Genomic microarrays in mental retardation: a practical workflow for diagnostic applications. Hum. Mutat. 30(3), 283–292 (2009).
  • Buysse K, Delle Chiaie B, Van Coster R et al. Challenges for CNV interpretation in clinical molecular karyotyping: lessons learned from a 1001 sample experience. Eur. J. Med. Genet. 52(6), 398–403 (2009).
  • Wincent J, Anderlid BM, Lagerberg M, Nordenskjöld M, Schoumans J. High-resolution molecular karyotyping in patients with developmental delay and/or multiple congenital anomalies in a clinical setting. Clin. Genet. 79(2), 147–157 (2011).
  • Lupski JR. Genomic disorders: structural features of the genome can lead to DNA rearrangements and human disease traits. Trends Genet. 14(10), 417–422 (1998).
  • Koolen DA, Sharp AJ, Hurst JA et al. Clinical and molecular delineation of the 17q21.31 microdeletion syndrome. J. Med. Genet. 45(11), 710–720 (2008).
  • Sharp AJ, Selzer RR, Veltman JA et al. Characterization of a recurrent 15q24 microdeletion syndrome. Hum. Mol. Genet. 16(5), 567–572 (2007).
  • Lee C, Iafrate AJ, Brothman AR. Copy number variations and clinical cytogenetic diagnosis of constitutional disorders. Nat. Genet. 39(7 Suppl.), 48–54 (2007).
  • Vissers LE, de Vries BB, Veltman JA. Genomic microarrays in mental retardation: from copy number variation to gene, from research to diagnosis. J. Med. Genet. 47(5), 289–297 (2010).
  • Shaffer LG, Theisen A, Bejjani BA et al. The discovery of microdeletion syndromes in the post-genomic era: review of the methodology and characterization of a new 1q41q42 microdeletion syndrome. Genet. Med. 9(9), 607–616 (2007).
  • van Bon BW, Mefford HC, Menten B et al. Further delineation of the 15q13 microdeletion and duplication syndromes: a clinical spectrum varying from non-pathogenic to a severe outcome. J. Med. Genet. 46(8), 511–523 (2009).
  • Van der Aa N, Rooms L, Vandeweyer G et al. Fourteen new cases contribute to the characterization of the 7q11.23 microduplication syndrome. Eur. J. Med. Genet. 52(2-3), 94–100 (2009).
  • Somerville MJ, Mervis CB, Young EJ et al. Severe expressive-language delay related to duplication of the Williams–Beuren locus. N. Engl. J. Med. 353(16), 1694–1701 (2005).
  • Hartman JL 4th, Garvik B, Hartwell L. Principles for the buffering of genetic variation. Science 291(5506), 1001–1004 (2001).
  • Girirajan S, Rosenfeld JA, Cooper GM et al. A recurrent 16p12.1 microdeletion supports a two-hit model for severe developmental delay. Nat. Genet. 42(3), 203–209 (2010).
  • Poot M, Hochstenbach R. A three-step workflow procedure for the interpretation of array-based comparative genome hybridization results in patients with idiopathic mental retardation and congenital anomalies. Genet. Med. 12(8), 478–485 (2010).
  • Vermeesch JR, Balikova I, Schrander-Stumpel C, Fryns JP, Devriendt K. The causality of de novo copy number variants is overestimated. Eur. J. Hum. Genet. 19(11), 1112–1113 (2011).
  • Jacquemont S, Reymond A, Zufferey F et al. Mirror extreme BMI phenotypes associated with gene dosage at the chromosome 16p11.2 locus. Nature 478(7367), 97–102 (2011).
  • Lachman HM, Pedrosa E, Petruolo OA et al. Increase in GSK3beta gene copy number variation in bipolar disorder. Am. J. Med. Genet. B Neuropsychiatr. Genet. 144B(3), 259–265 (2007).
  • Le Maréchal C, Masson E, Chen JM et al. Hereditary pancreatitis caused by triplication of the trypsinogen locus. Nat. Genet. 38(12), 1372–1374 (2006).
  • Snape K, Hanks S, Ruark E et al. Mutations in CEP57 cause mosaic variegated aneuploidy syndrome. Nat. Genet. 43(6), 527–529 (2011).
  • Zhang L, Znoyko I, Costa LJ et al. Clonal diversity analysis using SNP microarray: a new prognostic tool for chronic lymphocytic leukemia. Cancer Genet. 204(12), 654–665 (2011).
  • Hoffmann R, Seidl T, Dugas M. Profound effect of normalization on detection of differentially expressed genes in oligonucleotide microarray data analysis. Genome Biol. 3(7), RESEARCH0033 (2002).
  • Quackenbush J. Microarray data normalization and transformation. Nat. Genet. 32 Suppl, 496–501 (2002).
  • Miecznikowski JC, Gaile DP, Liu S, Shepherd L, Nowak N. A new normalizing algorithm for BAC CGH arrays with quality control metrics. J. Biomed. Biotechnol. 2011, 860732 (2011).
  • Khojasteh M, Lam WL, Ward RK, MacAulay C. A stepwise framework for the normalization of array CGH data. BMC Bioinformatics 6, 274 (2005).
  • Staaf J, Jönsson G, Ringnér M, Vallon-Christersson J. Normalization of array-CGH data: influence of copy number imbalances. BMC Genomics 8, 382 (2007).
  • Bolstad BM, Irizarry RA, Astrand M, Speed TP. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19(2), 185–193 (2003).
  • Diskin SJ, Li M, Hou C et al. Adjustment of genomic waves in signal intensities from whole-genome SNP genotyping platforms. Nucleic Acids Res. 36(19), e126 (2008).
  • Nannya Y, Sanada M, Nakazaki K et al. A robust algorithm for copy number detection using high-density oligonucleotide single nucleotide polymorphism genotyping arrays. Cancer Res. 65(14), 6071–6079 (2005).
  • Matsuzaki H, Dong S, Loi H et al. Genotyping over 100,000 SNPs on a pair of oligonucleotide arrays. Nat. Methods 1(2), 109–111 (2004).
  • Staaf J, Vallon-Christersson J, Lindgren D et al. Normalization of Illumina Infinium whole-genome SNP data improves copy number estimates and allelic intensity ratios. BMC Bioinformatics 9, 409 (2008).
  • Bengtsson H, Neuvial P, Speed TP. TumorBoost: normalization of allele-specific tumor copy numbers from a single pair of tumor-normal genotyping microarrays. BMC Bioinformatics 11, 245 (2010).
  • Yu W, Ballif BC, Kashork CD et al. Development of a comparative genomic hybridization microarray and demonstration of its utility with 25 well-characterized 1p36 deletions. Hum. Mol. Genet. 12(17), 2145–2152 (2003).
  • Pollack JR, Sørlie T, Perou CM et al. Microarray analysis reveals a major direct role of DNA copy number alteration in the transcriptional program of human breast tumors. Proc. Natl Acad. Sci. USA 99(20), 12963–12968 (2002).
  • Hodgson G, Hager JH, Volik S et al. Genome scanning with array CGH delineates regional alterations in mouse islet carcinomas. Nat. Genet. 29(4), 459–464 (2001).
  • Venkatraman ES, Olshen AB. A faster circular binary segmentation algorithm for the analysis of array CGH data. Bioinformatics 23(6), 657–663 (2007).
  • Wang K, Li M, Hadley D et al. PennCNV: an integrated hidden Markov model designed for high-resolution copy number variation detection in whole-genome SNP genotyping data. Genome Res. 17(11), 1665–1674 (2007).
  • Colella S, Yau C, Taylor JM et al. QuantiSNP: an Objective Bayes Hidden-Markov Model to detect and accurately map copy number variation using SNP genotyping data. Nucleic Acids Res. 35(6), 2013–2025 (2007).
  • Chen J, Wang YP. A statistical change point model approach for the detection of DNA copy number variations in array CGH data. IEEE/ACM Trans. Comput. Biol. Bioinform. 6(4), 529–541 (2009).
  • Fridlyand J, Snijders AM, Pinkel D, Albertson DG, Jain AN. Hidden Markov models approach to the analysis of array CGH data. J. Multivariate Anal. 90(1), 132–153 (2004).
  • Wang H, Veldink JH, Blauw H, van den Berg LH, Ophoff RA, Sabatti C. Markov Models for inferring copy number variations from genotype data on Illumina platforms. Hum. Hered. 68(1), 1–22 (2009).
  • Alonso A, Julià A, Tortosa R et al. CNstream: a method for the identification and genotyping of copy number polymorphisms using Illumina microarrays. BMC Bioinformatics 11, 264 (2010).
  • Klijn C, Holstege H, de Ridder J et al. Identification of cancer genes using a statistical framework for multiexperiment analysis of nondiscretized array CGH data. Nucleic Acids Res. 36(2), e13 (2008).
  • Shah SP, Lam WL, Ng RT, Murphy KP. Modeling recurrent DNA copy number alterations in array CGH data. Bioinformatics 23(13), i450–i458 (2007).
  • Korn JM, Kuruvilla FG, McCarroll SA et al. Integrated genotype calling and association analysis of SNPs, common copy number polymorphisms and rare CNVs. Nat. Genet. 40(10), 1253–1260 (2008).
  • GoldenHelix. Science and Methodology Behind Copy Number Analysis in SVS 7 (2009).
  • Cardin N, Holmes C, Donnelly P, Marchini J; Wellcome Trust Case Control Consortium. Bayesian hierarchical mixture modeling to assign copy number from a targeted CNV array. Genet. Epidemiol. 35(6), 536–548 (2011).
  • Pique-Regi R, Ortega A, Asgharzadeh S. Joint estimation of copy number variation and reference intensities on multiple DNA arrays using GADA. Bioinformatics 25(10), 1223–1230 (2009).
  • Staaf J, Lindgren D, Vallon-Christersson J et al. Segmentation-based detection of allelic imbalance and loss-of-heterozygosity in cancer cells using whole genome SNP arrays. Genome Biol. 9(9), 136 (2008).
  • Yau C, Mouradov D, Jorissen RN et al. A statistical approach for detecting genomic aberrations in heterogeneous tumor samples from single nucleotide polymorphism genotyping data. Genome Biol. 11(9), 92 (2010).
  • Liu Z, Li A, Schulz V, Chen M, Tuck D. MixHMM: inferring copy number variation and allelic imbalance using SNP arrays and tumor samples mixed with stromal cells. PLoS One 5(6), e10909 (2010).
  • Medvedev P, Stanciu M, Brudno M. Computational methods for discovering structural variation with next-generation sequencing. Nat. Methods 6(11 Suppl.), 13–20 (2009).
  • Campbell PJ, Stephens PJ, Pleasance ED et al. Identification of somatically acquired rearrangements in cancer using genome-wide massively parallel paired-end sequencing. Nat. Genet. 40(6), 722–729 (2008).
  • Chiang DY, Getz G, Jaffe DB et al. High-resolution mapping of copy-number alterations with massively parallel sequencing. Nat. Methods 6(1), 99–103 (2009).
  • McKernan KJ, Peckham HE, Costa GL et al. Sequence and structural variation in a human genome uncovered by short-read, massively parallel ligation sequencing using two-base encoding. Genome Res. 19(9), 1527–1541 (2009).
  • Yoon S, Xuan Z, Makarov V, Ye K, Sebat J. Sensitive and accurate detection of copy number variants using read depth of coverage. Genome Res. 19(9), 1586–1592 (2009).
  • Xie C, Tammi MT. CNV-seq, a new method to detect copy number variation using high-throughput sequencing. BMC Bioinformatics 10, 80 (2009).
  • Alkan C, Kidd JM, Marques-Bonet T et al. Personalized copy number and segmental duplication maps using next-generation sequencing. Nat. Genet. 41(10), 1061–1067 (2009).
  • Kim TM, Luquette LJ, Xi R, Park PJ. rSW-seq: algorithm for detection of copy number alterations in deep sequencing data. BMC Bioinformatics 11, 432 (2010).
  • Boeva V, Zinovyev A, Bleakley K et al. Control-free calling of copy number alterations in deep-sequencing data using GC-content normalization. Bioinformatics 27(2), 268–269 (2011).
  • Rozowsky J, Euskirchen G, Auerbach RK et al. PeakSeq enables systematic scoring of ChIP-seq experiments relative to controls. Nat. Biotechnol. 27(1), 66–75 (2009).
  • Korbel JO, Abyzov A, Mu XJ et al. PEMer: a computational framework with simulation-based error models for inferring genomic structural variants from massive paired-end sequencing data. Genome Biol. 10(2), 23 (2009).
  • Ye K, Schulz MH, Long Q, Apweiler R, Ning Z. Pindel: a pattern growth approach to detect break points of large deletions and medium sized insertions from paired-end short reads. Bioinformatics 25(21), 2865–2871 (2009).
  • Hormozdiari F, Hajirasouliha I, Dao P et al. Next-generation VariationHunter: combinatorial algorithms for transposon insertion discovery. Bioinformatics 26(12), 350–357 (2010).
  • Lee S, Hormozdiari F, Alkan C, Brudno M. MoDIL: detecting small indels from clone-end sequencing with mixtures of distributions. Nat. Methods 6(7), 473–474 (2009).
  • Hormozdiari F, Alkan C, Eichler EE, Sahinalp SC. Combinatorial algorithms for structural variation detection in high-throughput sequenced genomes. Genome Res. 19(7), 1270–1278 (2009).
  • Quinlan AR, Clark RA, Sokolova S et al. Genome-wide mapping and assembly of structural variant breakpoints in the mouse genome. Genome Res. 20(5), 623–635 (2010).
  • Chen K, Wallis JW, McLellan MD et al. BreakDancer: an algorithm for high-resolution mapping of genomic structural variation. Nat. Methods 6(9), 677–681 (2009).
  • Magi A, Benelli M, Yoon S, Roviello F, Torricelli F. Detecting common copy number variants in high-throughput sequencing data by using JointSLM algorithm. Nucleic Acids Res. 39(10), 65 (2011).
  • Brudno M, Lee S, Xing E. MoGUL: detecting common insertions and deletions in a population. Lect. N. Bioinformat. 6044, 357–368 (2010).
  • Schwarzbauer K, Klambauer G, Mayr A, Hochreiter S. Identifying copy number variations based on next generation sequencing data by a mixture of poisson model. Presented: 18th Annual International Conference on Intelligent Systems for Molecular Biology. MA, USA, 10–14 July 2010.
  • Hajirasouliha I, Hormozdiari F, Alkan C et al. Detection and characterization of novel sequence insertions using paired-end next-generation sequencing. Bioinformatics 26(10), 1277–1283 (2010).
  • Vandeweyer G, Reyniers E, Wuyts W, Rooms L, Kooy RF. CNV-WebStore: online CNV analysis, storage and interpretation. BMC Bioinformatics 12, 4 (2011).
  • Baross A, Delaney AD, Li HI et al. Assessment of algorithms for high throughput detection of genomic copy number variation in oligonucleotide microarray data. BMC Bioinformatics 8, 368 (2007).
  • Gai X, Perin JC, Murphy K et al. CNV Workshop: an integrated platform for high-throughput copy number variation discovery and clinical diagnostics. BMC Bioinformatics 11, 74 (2010).
  • Sanders MA, Verhaak RG, Geertsma-Kleinekoort WM et al. SNPExpress: integrated visualization of genome-wide genotypes, copy numbers and gene expression levels. BMC Genomics 9, 41 (2008).
  • Blankenberg D, Von Kuster G, Coraor N et al. Galaxy: a web-based genome analysis tool for experimentalists. Curr. Protoc. Mol. Biol. Chapter 19, Unit 19.10.1–Unit 19.10.21 (2010).
  • Goecks J, Nekrutenko A, Taylor J; Galaxy Team. Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences. Genome Biol. 11(8), 86 (2010).
  • Firth HV, Richards SM, Bevan AP et al. DECIPHER: Database of Chromosomal Imbalance and Phenotype in Humans Using Ensembl Resources. Am. J. Hum. Genet. 84(4), 524–533 (2009).
  • Feenstra I, Fang J, Koolen DA et al. European Cytogeneticists Association Register of Unbalanced Chromosome Aberrations (ECARUCA); an online database for rare chromosome abnormalities. Eur. J. Med. Genet. 49(4), 279–291 (2006).
  • Bugge M, Bruun-Petersen G, Brøndum-Nielsen K et al. Disease associated balanced chromosome rearrangements: a resource for large scale genotype-phenotype delineation in man. J. Med. Genet. 37(11), 858–865 (2000).
  • Church DM, Lappalainen I, Sneddon TP et al. Public data archives for genomic structural variation. Nat. Genet. 42(10), 813–814 (2010).
  • Sharp AJ. Emerging themes and new challenges in defining the role of structural variation in human disease. Hum. Mutat. 30(2), 135–144 (2009).
  • McCarroll SA, Altshuler DM. Copy-number variation and association studies of human disease. Nat. Genet. 39(7 Suppl.), 37–42 (2007).
  • Fujita PA, Rhead B, Zweig AS et al. The UCSC Genome Browser database: update 2011. Nucleic Acids Res. 39(Database issue), 876–882 (2011).
  • Flicek P, Amode MR, Barrell D et al. Ensembl 2011. Nucleic Acids Res. 39(Database issue), 800–806 (2011).
  • Crim J, McDonald R, Pereira F. Automatically annotating documents with normalized gene lists. BMC Bioinformatics 6(Suppl. 1), 13 (2005).
  • McDonald R, Pereira F. Identifying gene and protein mentions in text using conditional random fields. BMC Bioinformatics 6(Suppl. 1), 6 (2005).
  • Fryns JP, de Ravel TJ. London Dysmorphology Database, London Neurogenetics Database and Dysmorphology Photo Library on CD-ROM [Version 3] 2001R. M. Winter, M. Baraitser, Oxford University Press, ISBN 019851-780, pound sterling 1595. Hum. Genet. 111(1), 113 (2002).
  • Smith CL, Eppig JT. The mammalian phenotype ontology: enabling robust annotation and comparative analysis. Wiley Interdiscip. Rev. Syst. Biol. Med. 1(3), 390–399 (2009).
  • Robinson PN, Mundlos S. The human phenotype ontology. Clin. Genet. 77(6), 525–534 (2010).
  • Bodenreider O. The Unified Medical Language System (UMLS): integrating biomedical terminology. Nucleic Acids Res. 32(Database issue), 267–270 (2004).
  • Osborne JD, Lin S, Zhu L, Kibbe WA. Mining biomedical data using MetaMap Transfer (MMtx) and the Unified Medical Language System (UMLS). Methods Mol. Biol. 408, 153–169 (2007).
  • Webber C, Hehir-Kwa JY, Nguyen DQ, de Vries BB, Veltman JA, Ponting CP. Forging links between human mental retardation-associated CNVs and mouse gene knockout models. PLoS Genet. 5(6), e1000531 (2009).
  • von Mering C, Jensen LJ, Kuhn M et al. STRING 7 – recent developments in the integration and prediction of protein interactions. Nucleic Acids Res. 35(Database issue), 358–362 (2007).
  • Ashburner M, Ball CA, Blake JA et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25(1), 25–29 (2000).
  • Kanehisa M, Goto S, Furumichi M, Tanabe M, Hirakawa M. KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucleic Acids Res. 38(Database issue), 355–360 (2010).
  • Blake JA, Bult CJ, Kadin JA, Richardson JE, Eppig JT; Mouse Genome Database Group. The Mouse Genome Database (MGD): premier model organism resource for mammalian genomics and genetics. Nucleic Acids Res. 39(Database issue), 842–848 (2011).
  • Chang JT, Nevins JR. GATHER: a systems approach to interpreting genomic signatures. Bioinformatics 22(23), 2926–2933 (2006).
  • Wu C, Orozco C, Boyer J et al. BioGPS: an extensible and customizable portal for querying and organizing gene annotation resources. Genome Biol. 10(11), 130 (2009).
  • Dennis G Jr, Sherman BT, Hosack DA et al. DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biol. 4(5), 3 (2003).
  • Lukashin I, Novichkov P, Boffelli D et al. VISTA Region Viewer (RViewer) – a computational system for prioritizing genomic intervals for biomedical studies. Bioinformatics 27(18), 2595–2597 (2011).
  • Smith NG, Eyre-Walker A. Human disease genes: patterns and predictions. Gene 318, 169–175 (2003).
  • Tranchevent LC, Capdevila FB, Nitsch D, De Moor B, De Causmaecker P, Moreau Y. A guide to web tools to prioritize candidate genes. Brief. Bioinformatics 12(1), 22–32 (2011).
  • Liekens AM, De Knijf J, Daelemans W, Goethals B, De Rijk P, Del-Favero J. BioGraph: unsupervised biomedical knowledge discovery via automated hypothesis generation. Genome Biol. 12(6), 57 (2011).
  • Maya I, Davidov B, Gershovitz L et al. Diagnostic utility of array-based comparative genomic hybridization (aCGH) in a prenatal setting. Prenat. Diagn. 30(12–13), 1131–1137 (2010).
  • Srebniak M, Boter M, Oudesluijs G et al. Application of SNP array for rapid prenatal diagnosis: implementation, genetic counselling and diagnostic flow. Eur. J. Hum. Genet. 19(12), 1230–1237 (2011).
  • Vissers LE, de Ligt J, Gilissen C et al. A de novo paradigm for mental retardation. Nat. Genet. 42(12), 1109–1112 (2010).
  • Sanders SJ, Murtha MT, Gupta AR et al. De novo mutations revealed by whole-exome sequencing are strongly associated with autism. Nature 485(7397), 237–241 (2012).
  • Xu B, Roos JL, Dexheimer P et al. Exome sequencing supports a de novo mutational paradigm for schizophrenia. Nat. Genet. 43(9), 864–868 (2011).
  • Sahoo T, del Gaudio D, German JR et al. Prader–Willi phenotype caused by paternal deficiency for the HBII-85 C/D box small nucleolar RNA cluster. Nat. Genet. 40(6), 719–721 (2008).
  • Geschwind DH. Autism: many genes, common pathways? Cell 135(3), 391–395 (2008).
  • Weiss MM, Snijders AM, Kuipers EJ et al. Determination of amplicon boundaries at 20q13.2 in tissue samples of human gastric adenocarcinomas by high-resolution microarray comparative genomic hybridization. J. Pathol. 200(3), 320–326 (2003).
  • Daruwala RS, Rudra A, Ostrer H, Lucito R, Wigler M, Mishra B. A versatile statistical analysis algorithm to detect genome copy number variation. Proc. Natl Acad. Sci. USA 101(46), 16292–16297 (2004).
  • Jong K, Marchiori E, van der Vaart A, Ylstra B, Weiss M, Meijer G. Chromosomal breakpoint detection in human cancer. Lect. Notes Comput. Sc. 2611, 54–65 (2003).
  • Jong K, Marchiori E, Meijer G, Vaart AV, Ylstra B. Breakpoint identification and smoothing of array comparative genomic hybridization data. Bioinformatics 20(18), 3636–3637 (2004).
  • Lipson D, Aumann Y, Ben-Dor A, Linial N, Yakhini Z. Efficient calculation of interval scores for DNA copy number data analysis. J. Comput. Biol. 13(2), 215–228 (2006).
  • Kim SY, Nam SW, Lee SH et al. ArrayCyGHt: a web application for analysis and visualization of array-CGH data. Bioinformatics 21(10), 2554–2555 (2005).
  • Marioni JC, Thorne NP, Tavaré S. BioHMM: a heterogeneous hidden Markov model for segmenting array CGH data. Bioinformatics 22(9), 1144–1146 (2006).
  • Huang J, Wei W, Chen J et al. CARAT: a novel method for allelic detection of DNA copy number changes using high density oligonucleotide arrays. BMC Bioinformatics 7, 83 (2006).
  • Lingjaerde OC, Baumbusch LO, Liestøl K, Glad IK, Børresen-Dale AL. CGH-Explorer: a program for analysis of array-CGH data. Bioinformatics 21(6), 821–822 (2005).
  • Autio R, Hautaniemi S, Kauraniemi P et al. CGH-Plotter: MATLAB toolbox for CGH-data analysis. Bioinformatics 19(13), 1714–1715 (2003).
  • van de Wiel MA, Kim KI, Vosse SJ, van Wieringen WN, Wilting SM, Ylstra B. CGHcall: calling aberrations for array CGH tumor profiles. Bioinformatics 23(7), 892–894 (2007).
  • Broët P, Richardson S. Detection of gene copy number changes in CGH microarrays using a spatially correlated mixture model. Bioinformatics 22(8), 911–918 (2006).
  • Picard F, Robin S, Lavielle M, Vaisse C, Daudin JJ. A statistical approach for array CGH data analysis. BMC Bioinformatics 6, 27 (2005).
  • Myers CL, Dunham MJ, Kung SY, Troyanskaya OG. Accurate detection of aneuploidies in array CGH and gene expression microarray data. Bioinformatics 20(18), 3533–3543 (2004).
  • Wang P, Kim Y, Pollack J, Narasimhan B, Tibshirani R. A method for calling gains and losses in array CGH data. Biostatistics 6(1), 45–58 (2005).
  • Shah SP, Xuan X, DeLeeuw RJ et al. Integrating copy number polymorphisms into array CGH analysis using a robust HMM. Bioinformatics 22(14), 431–439 (2006).
  • Clevert DA, Mitterecker A, Mayr A et al. cn.FARMS: a latent variable model to detect copy number variations in microarray data with a low false discovery rate. Nucleic Acids Res. 39(12), e79 (2011).
  • Fiegler H, Redon R, Andrews D et al. Accurate and reliable high-throughput detection of copy number variation in the human genome. Genome Res. 16(12), 1566–1574 (2006).
  • Yavas G, Koyutürk M, Ozsoyoglu M, Gould MP, LaFramboise T. An optimization framework for unsupervised identification of rare copy number variation from SNP array data. Genome Biol. 10(10), 119 (2009).
  • Oba S, Tomioka N, Ohira M, Ishii S. Combfit: a normalization method for array CGH data. IPSJ Digital Courier 2, 716–725 (2006).
  • Yin XL, Li J. Detecting copy number variations from array CGH data based on a conditional random field model. J. Bioinform. Comput. Biol. 8(2), 295–314 (2010).
  • Zhao X, Li C, Paez JG et al. An integrated view of copy number and allelic alterations in the cancer genome using single nucleotide polymorphism arrays. Cancer Res. 64(9), 3060–3071 (2004).
  • Olshen AB, Venkatraman ES, Lucito R, Wigler M. Circular binary segmentation for the analysis of array-based DNA copy number data. Biostatistics 5(4), 557–572 (2004).
  • Yu T, Ye H, Sun W et al. A forward-backward fragment assembling algorithm for the identification of genomic amplification and deletion breakpoints using high-density single nucleotide polymorphism (SNP) array. BMC Bioinformatics 8, 145 (2007).
  • Pique-Regi R, Monso-Varona J, Ortega A, Seeger RC, Triche TJ, Asgharzadeh S. Sparse representation and Bayesian detection of genome copy number alterations from microarray data. Bioinformatics 24(3), 309–318 (2008).
  • Sun W, Wright FA, Tang Z et al. Integrated study of copy number states and genotype calls using high-density SNP arrays. Nucleic Acids Res. 37(16), 5365–5377 (2009).
  • Hupé P, Stransky N, Thiery JP, Radvanyi F, Barillot E. Analysis of array CGH data: from signal ratio to gain and loss of DNA regions. Bioinformatics 20(18), 3413–3422 (2004).
  • Ben-Yaacov E, Eldar YC. A fast and flexible method for the segmentation of aCGH data. Bioinformatics 24(16), i139–i145 (2008).
  • Day N, Hemmaplardh A, Thurman RE, Stamatoyannopoulos JA, Noble WS. Unsupervised segmentation of continuous genomic data. Bioinformatics 23(11), 1424–1426 (2007).
  • Teo SM, Pawitan Y, Kumar V et al. Multi-platform segmentation for joint detection of copy number variants. Bioinformatics 27(11), 1555–1561 (2011).
  • Laframboise T, Harrington D, Weir BA. PLASQ: a generalized linear model-based procedure to determine allelic dosage in cancer cells from SNP array data. Biostatistics 8(2), 323–336 (2007).
  • Rueda OM, Díaz-Uriarte R. Flexible and accurate detection of genomic copy-number changes from aCGH. PLoS Comput. Biol. 3(6), e122 (2007).
  • Magi A, Benelli M, Marseglia G, Nannetti G, Scordo MR, Torricelli F. A shifting level model algorithm that identifies aberrations in array-CGH data. Biostatistics 11(2), 265–280 (2010).
  • Huang J, Salim A, Lei K, O’Sullivan K, Pawitan Y. Classification of array CGH data using smoothed logistic regression model. Stat. Med. 28(30), 3798–3810 (2009).
  • Andersson R, Bruder CE, Piotrowski A et al. A segmental maximum a posteriori approach to genome-wide copy number profiling. Bioinformatics 24(6), 751–758 (2008).
  • Huang J, Gusnanto A, O’Sullivan K, Staaf J, Borg A, Pawitan Y. Robust smooth segmentation approach for array CGH data analysis. Bioinformatics 23(18), 2463–2469 (2007).
  • Assié G, LaFramboise T, Platzer P, Bertherat J, Stratakis CA, Eng C. SNP arrays in heterogeneous tissue: highly accurate collection of both germline and somatic genetic information from unpaired single tumor samples. Am. J. Hum. Genet. 82(4), 903–915 (2008).
  • Price TS, Regan R, Mott R et al. SW-ARRAY: a dynamic programming solution for the identification of copy-number changes in genomic DNA using array comparative genome hybridization data. Nucleic Acids Res. 33(11), 3455–3464 (2005).
  • Scharpf RB, Parmigiani G, Pevsner J, Ruczinski I. Hidden Markov models for the assessment of chromosomal alterations using high-throughput SNP arrays. Ann. Appl. Stat. 2(2), 687–713 (2008).
  • Hsu L, Self SG, Grove D et al. Denoising array-based comparative genomic hybridization data using wavelets. Biostatistics 6(2), 211–226 (2005).
  • Frankenberger C, Wu X, Harmon J et al. WebaCGH: an interactive online tool for the analysis and display of array comparative genomic hybridisation data. Appl. Bioinformatics 5(2), 125–130 (2006).
  • Van Vooren S, Thienpont B, Menten B et al. Mapping biomedical concepts onto the human genome by mining literature on chromosomal aberrations. Nucleic Acids Res. 35(8), 2533–2543 (2007).
  • Hristovski D, Peterlin B, Mitchell JA, Humphrey SM. Using literature-based discovery to identify disease candidate genes. Int. J. Med. Inform. 74(2–4), 289–298 (2005).
  • Hutz JE, Kraja AT, McLeod HL, Province MA. CANDID: a flexible method for prioritizing candidate genes for complex human traits. Genet. Epidemiol. 32(8), 779–790 (2008).
  • Aerts S, Lambrechts D, Maity S et al. Gene prioritization through genomic data fusion. Nat. Biotechnol. 24(5), 537–544 (2006).
  • Perez-Iratxeta C, Bork P, Andrade-Navarro MA. Update of the G2D tool for prioritization of gene candidates to inherited diseases. Nucleic Acids Res. 35(Web Server issue), 212–216 (2007).
  • Hehir-Kwa JY, Wieskamp N, Webber C et al. Accurate distinction of pathogenic from benign CNVs in mental retardation. PLoS Comput. Biol. 6(4), e1000752 (2010).
  • Seelow D, Schwarz JM, Schuelke M. GeneDistiller – distilling candidate genes from linkage intervals. PLoS ONE 3(12), e3874 (2008).
  • Yu W, Wulf A, Liu T, Khoury MJ, Gwinn M. Gene Prospector: an evidence gateway for evaluating potential susceptibility genes and interacting risk factors for human diseases. BMC Bioinformatics 9, 528 (2008).
  • Köhler S, Bauer S, Horn D, Robinson PN. Walking the interactome for prioritization of candidate disease genes. Am. J. Hum. Genet. 82(4), 949–958 (2008).
  • Fontaine JF, Priller F, Barbosa-Silva A, Andrade-Navarro MA. Génie: literature-based gene prioritization at multi genomic scale. Nucleic Acids Res. 39(Web Server issue), 455–461 (2011).
  • George RA, Liu JY, Feng LL, Bryson-Richardson RJ, Fatkin D, Wouters MA. Analysis of protein sequence and interaction data for candidate disease gene prediction. Nucleic Acids Res. 34(19), e130 (2006).
  • van Driel MA, Bruggeman J, Vriend G, Brunner HG, Leunissen JA. A text-mining analysis of the human phenome. Eur. J. Hum. Genet. 14(5), 535–542 (2006).
  • Xiong Q, Qiu Y, Gu W. PGMapper: a web-based tool linking phenotype to genes. Bioinformatics 24(7), 1011–1013 (2008).
  • Radivojac P, Peng K, Clark WT et al. An integrated approach to inferring gene-disease associations in humans. Proteins 72(3), 1030–1037 (2008).
  • Cheng D, Knox C, Young N, Stothard P, Damaraju S, Wishart DS. PolySearch: a web-based text mining system for extracting relationships between human diseases, genes, mutations, drugs and metabolites. Nucleic Acids Res. 36(Web Server issue), 399–405 (2008).
  • Yoshida Y, Makita Y, Heida N et al. PosMed (Positional Medline): prioritizing genes with an artificial neural network comprising medical documents to accelerate positional cloning. Nucleic Acids Res. 37(Web Server issue), 147–152 (2009).
  • Franke L, van Bakel H, Fokkens L, de Jong ED, Egmont-Petersen M, Wijmenga C. Reconstruction of a functional human gene network, with an application for prioritizing positional candidate genes. Am. J. Hum. Genet. 78(6), 1011–1025 (2006).
  • Yue P, Melamud E, Moult J. SNPs3D: candidate gene and SNP selection for association studies. BMC Bioinformatics 7, 166 (2006).
  • Adie EA, Adams RR, Evans KL, Porteous DJ, Pickard BS. SUSPECTS: enabling fast and effective prioritization of positional candidates. Bioinformatics 22(6), 773–774 (2006).
  • Masotti D, Nardini C, Rossi S et al. TOM: enhancement and extension of a tool suite for in silico approaches to multigenic hereditary disorders. Bioinformatics 24(3), 428–429 (2008).
  • Chen J, Bardes EE, Aronow BJ, Jegga AG. ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res. 37(Web Server issue), 305–311 (2009).
  • Warde-Farley D, Donaldson SL, Comes O et al. The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res. 38(Web Server issue), 214–220 (2010).

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