MOTIVATION: ChIP-chip has been widely used for various genome-wide biological investigations. Given the small number of replicates (typically two to three) per biological sample, methods of analysis that control the variance are desirable but in short supply. We propose a double error shrinkage (DES) method by using moving average statistics based on local-pooled error estimates which effectively control both heterogeneous error variances and correlation structures of an extremely large number of individual probes on tiling arrays. RESULTS: Applying DES to ChIP-chip tiling array study for discovering genome-wide protein-binding sites, we identified 8400 target regions that include highly likely TFIID binding sites. About 33% of these were well matched with the known transcription starting sites on the DBTSS library, while many other newly identified sites have a high chance to be real binding sites based on a high positive predictive value of DES. We also showed the superior performance of DES compared with other commonly used methods for detecting actual protein binding sites.
MOTIVATION: ChIP-chip has been widely used for various genome-wide biological investigations. Given the small number of replicates (typically two to three) per biological sample, methods of analysis that control the variance are desirable but in short supply. We propose a double error shrinkage (DES) method by using moving average statistics based on local-pooled error estimates which effectively control both heterogeneous error variances and correlation structures of an extremely large number of individual probes on tiling arrays. RESULTS: Applying DES to ChIP-chip tiling array study for discovering genome-wide protein-binding sites, we identified 8400 target regions that include highly likely TFIID binding sites. About 33% of these were well matched with the known transcription starting sites on the DBTSS library, while many other newly identified sites have a high chance to be real binding sites based on a high positive predictive value of DES. We also showed the superior performance of DES compared with other commonly used methods for detecting actual protein binding sites.
Authors: B Ren; F Robert; J J Wyrick; O Aparicio; E G Jennings; I Simon; J Zeitlinger; J Schreiber; N Hannett; E Kanin; T L Volkert; C J Wilson; S P Bell; R A Young Journal: Science Date: 2000-12-22 Impact factor: 47.728
Authors: Nitin Jain; Jayant Thatte; Thomas Braciale; Klaus Ley; Michael O'Connell; Jae K Lee Journal: Bioinformatics Date: 2003-10-12 Impact factor: 6.937
Authors: Simon Cawley; Stefan Bekiranov; Huck H Ng; Philipp Kapranov; Edward A Sekinger; Dione Kampa; Antonio Piccolboni; Victor Sementchenko; Jill Cheng; Alan J Williams; Raymond Wheeler; Brant Wong; Jorg Drenkow; Mark Yamanaka; Sandeep Patel; Shane Brubaker; Hari Tammana; Gregg Helt; Kevin Struhl; Thomas R Gingeras Journal: Cell Date: 2004-02-20 Impact factor: 41.582
Authors: Tae Hoon Kim; Leah O Barrera; Ming Zheng; Chunxu Qu; Michael A Singer; Todd A Richmond; Yingnian Wu; Roland D Green; Bing Ren Journal: Nature Date: 2005-06-29 Impact factor: 49.962
Authors: Christopher T Harbison; D Benjamin Gordon; Tong Ihn Lee; Nicola J Rinaldi; Kenzie D Macisaac; Timothy W Danford; Nancy M Hannett; Jean-Bosco Tagne; David B Reynolds; Jane Yoo; Ezra G Jennings; Julia Zeitlinger; Dmitry K Pokholok; Manolis Kellis; P Alex Rolfe; Ken T Takusagawa; Eric S Lander; David K Gifford; Ernest Fraenkel; Richard A Young Journal: Nature Date: 2004-09-02 Impact factor: 49.962
Authors: Ling V Sun; Liang Chen; Frauke Greil; Nicolas Negre; Tong-Ruei Li; Giacomo Cavalli; Hongyu Zhao; Bas Van Steensel; Kevin P White Journal: Proc Natl Acad Sci U S A Date: 2003-07-22 Impact factor: 11.205