BACKGROUND: Array-based detection of copy number variations (CNVs) is widely used for identifying disease-specific genetic variations. However, the accuracy of CNV detection is not sufficient and results differ depending on the detection programs used and their parameters. In this study, we evaluated five widely used CNV detection programs, Birdsuite (mainly consisting of the Birdseye and Canary modules), Birdseye (part of Birdsuite), PennCNV, CGHseg, and DNAcopy from the viewpoint of performance on the Affymetrix platform using HapMap data and other experimental data. Furthermore, we identified CNVs of 180 healthy Japanese individuals using parameters that showed the best performance in the HapMap data and investigated their characteristics. RESULTS: The results indicate that Hidden Markov model-based programs PennCNV and Birdseye (part of Birdsuite), or Birdsuite show better detection performance than other programs when the high reproducibility rates of the same individuals and the low Mendelian inconsistencies are considered. Furthermore, when rates of overlap with other experimental results were taken into account, Birdsuite showed the best performance from the view point of sensitivity but was expected to include many false negatives and some false positives. The results of 180 healthy Japanese demonstrate that the ratio containing repeat sequences, not only segmental repeats but also long interspersed nuclear element (LINE) sequences both in the start and end regions of the CNVs, is higher in CNVs that are commonly detected among multiple individuals than that in randomly selected regions, and the conservation score based on primates is lower in these regions than in randomly selected regions. Similar tendencies were observed in HapMap data and other experimental data. CONCLUSIONS: Our results suggest that not only segmental repeats but also interspersed repeats, especially LINE sequences, are deeply involved in CNVs, particularly in common CNV formations.The detected CNVs are stored in the CNV repository database newly constructed by the "Japanese integrated database project" for sharing data among researchers. http://gwas.lifesciencedb.jp/cgi-bin/cnvdb/cnv_top.cgi.
BACKGROUND: Array-based detection of copy number variations (CNVs) is widely used for identifying disease-specific genetic variations. However, the accuracy of CNV detection is not sufficient and results differ depending on the detection programs used and their parameters. In this study, we evaluated five widely used CNV detection programs, Birdsuite (mainly consisting of the Birdseye and Canary modules), Birdseye (part of Birdsuite), PennCNV, CGHseg, and DNAcopy from the viewpoint of performance on the Affymetrix platform using HapMap data and other experimental data. Furthermore, we identified CNVs of 180 healthy Japanese individuals using parameters that showed the best performance in the HapMap data and investigated their characteristics. RESULTS: The results indicate that Hidden Markov model-based programs PennCNV and Birdseye (part of Birdsuite), or Birdsuite show better detection performance than other programs when the high reproducibility rates of the same individuals and the low Mendelian inconsistencies are considered. Furthermore, when rates of overlap with other experimental results were taken into account, Birdsuite showed the best performance from the view point of sensitivity but was expected to include many false negatives and some false positives. The results of 180 healthy Japanese demonstrate that the ratio containing repeat sequences, not only segmental repeats but also long interspersed nuclear element (LINE) sequences both in the start and end regions of the CNVs, is higher in CNVs that are commonly detected among multiple individuals than that in randomly selected regions, and the conservation score based on primates is lower in these regions than in randomly selected regions. Similar tendencies were observed in HapMap data and other experimental data. CONCLUSIONS: Our results suggest that not only segmental repeats but also interspersed repeats, especially LINE sequences, are deeply involved in CNVs, particularly in common CNV formations.The detected CNVs are stored in the CNV repository database newly constructed by the "Japanese integrated database project" for sharing data among researchers. http://gwas.lifesciencedb.jp/cgi-bin/cnvdb/cnv_top.cgi.
Authors: Laura Dumas; Young H Kim; Anis Karimpour-Fard; Michael Cox; Janet Hopkins; Jonathan R Pollack; James M Sikela Journal: Genome Res Date: 2007-07-31 Impact factor: 9.043
Authors: George H Perry; Amir Ben-Dor; Anya Tsalenko; Nick Sampas; Laia Rodriguez-Revenga; Charles W Tran; Alicia Scheffer; Israel Steinfeld; Peter Tsang; N Alice Yamada; Han Soo Park; Jong-Il Kim; Jeong-Sun Seo; Zohar Yakhini; Stephen Laderman; Laurakay Bruhn; Charles Lee Journal: Am J Hum Genet Date: 2008-01-24 Impact factor: 11.025
Authors: Kai Wang; Mingyao Li; Dexter Hadley; Rui Liu; Joseph Glessner; Struan F A Grant; Hakon Hakonarson; Maja Bucan Journal: Genome Res Date: 2007-10-05 Impact factor: 9.043
Authors: Tamim H Shaikh; Xiaowu Gai; Juan C Perin; Joseph T Glessner; Hongbo Xie; Kevin Murphy; Ryan O'Hara; Tracy Casalunovo; Laura K Conlin; Monica D'Arcy; Edward C Frackelton; Elizabeth A Geiger; Chad Haldeman-Englert; Marcin Imielinski; Cecilia E Kim; Livija Medne; Kiran Annaiah; Jonathan P Bradfield; Elvira Dabaghyan; Andrew Eckert; Chioma C Onyiah; Svetlana Ostapenko; F George Otieno; Erin Santa; Julie L Shaner; Robert Skraban; Ryan M Smith; Josephine Elia; Elizabeth Goldmuntz; Nancy B Spinner; Elaine H Zackai; Rosetta M Chiavacci; Robert Grundmeier; Eric F Rappaport; Struan F A Grant; Peter S White; Hakon Hakonarson Journal: Genome Res Date: 2009-07-10 Impact factor: 9.043
Authors: J M Friedman; Agnes Baross; Allen D Delaney; Adrian Ally; Laura Arbour; Linlea Armstrong; Jennifer Asano; Dione K Bailey; Sarah Barber; Patricia Birch; Mabel Brown-John; Manqiu Cao; Susanna Chan; David L Charest; Noushin Farnoud; Nicole Fernandes; Stephane Flibotte; Anne Go; William T Gibson; Robert A Holt; Steven J M Jones; Giulia C Kennedy; Martin Krzywinski; Sylvie Langlois; Haiyan I Li; Barbara C McGillivray; Tarun Nayar; Trevor J Pugh; Evica Rajcan-Separovic; Jacqueline E Schein; Angelique Schnerch; Asim Siddiqui; Margot I Van Allen; Gary Wilson; Siu-Li Yong; Farah Zahir; Patrice Eydoux; Marco A Marra Journal: Am J Hum Genet Date: 2006-07-25 Impact factor: 11.025
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Authors: George H Perry; Fengtang Yang; Tomas Marques-Bonet; Carly Murphy; Tomas Fitzgerald; Arthur S Lee; Courtney Hyland; Anne C Stone; Matthew E Hurles; Chris Tyler-Smith; Evan E Eichler; Nigel P Carter; Charles Lee; Richard Redon Journal: Genome Res Date: 2008-09-04 Impact factor: 9.043
Authors: Jeffrey M Kidd; Gregory M Cooper; William F Donahue; Hillary S Hayden; Nick Sampas; Tina Graves; Nancy Hansen; Brian Teague; Can Alkan; Francesca Antonacci; Eric Haugen; Troy Zerr; N Alice Yamada; Peter Tsang; Tera L Newman; Eray Tüzün; Ze Cheng; Heather M Ebling; Nadeem Tusneem; Robert David; Will Gillett; Karen A Phelps; Molly Weaver; David Saranga; Adrianne Brand; Wei Tao; Erik Gustafson; Kevin McKernan; Lin Chen; Maika Malig; Joshua D Smith; Joshua M Korn; Steven A McCarroll; David A Altshuler; Daniel A Peiffer; Michael Dorschner; John Stamatoyannopoulos; David Schwartz; Deborah A Nickerson; James C Mullikin; Richard K Wilson; Laurakay Bruhn; Maynard V Olson; Rajinder Kaul; Douglas R Smith; Evan E Eichler Journal: Nature Date: 2008-05-01 Impact factor: 49.962
Authors: Stefano Colella; Christopher Yau; Jennifer M Taylor; Ghazala Mirza; Helen Butler; Penny Clouston; Anne S Bassett; Anneke Seller; Christopher C Holmes; Jiannis Ragoussis Journal: Nucleic Acids Res Date: 2007-03-06 Impact factor: 16.971
Authors: A Gurgul; I Jasielczuk; T Szmatoła; K Pawlina; T Ząbek; K Żukowski; M Bugno-Poniewierska Journal: Genetica Date: 2015-02-04 Impact factor: 1.082
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