ENCODE Spike-In, Yale Group
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Guoneng Zhong
PROVIDER: E-GEOD-10004 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
Publications
Systematic evaluation of variability in ChIP-chip experiments using predefined DNA targets.
Johnson David S DS Li Wei W Gordon D Benjamin DB Bhattacharjee Arindam A Curry Bo B Ghosh Jayati J Brizuela Leonardo L Carroll Jason S JS Brown Myles M Flicek Paul P Koch Christoph M CM Dunham Ian I Bieda Mark M Xu Xiaoqin X Farnham Peggy J PJ Kapranov Philipp P Nix David A DA Gingeras Thomas R TR Zhang Xinmin X Holster Heather H Jiang Nan N Green Roland D RD Song Jun S JS McCuine Scott A SA Anton Elizabeth E Nguyen Loan L Trinklein Nathan D ND Ye Zhen Z Ching Keith K Hawkins David D Ren Bing B Scacheri Peter C PC Rozowsky Joel J Karpikov Alexander A Euskirchen Ghia G Weissman Sherman S Gerstein Mark M Snyder Michael M Yang Annie A Moqtaderi Zarmik Z Hirsch Heather H Shulha Hennady P HP Fu Yutao Y Weng Zhiping Z Struhl Kevin K Myers Richard M RM Lieb Jason D JD Liu X Shirley XS
Genome research 20080207 3
The most widely used method for detecting genome-wide protein-DNA interactions is chromatin immunoprecipitation on tiling microarrays, commonly known as ChIP-chip. Here, we conducted the first objective analysis of tiling array platforms, amplification procedures, and signal detection algorithms in a simulated ChIP-chip experiment. Mixtures of human genomic DNA and "spike-ins" comprised of nearly 100 human sequences at various concentrations were hybridized to four tiling array platforms by eigh ...[more]
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Project description:The most widely used method for detecting genome-wide protein-DNA interactions is chromatin immunoprecipitation on tiling microarrays, commonly known as ChIP-chip. Here, we conducted the first objective analysis of tiling array platforms, amplification procedures, and signal detection algorithms in a simulated ChIP-chip experiment. Mixtures of human genomic DNA and spike-ins comprised of nearly 100 human sequences at various concentrations were hybridized to four tiling array platforms by eight independent groups. Blind to the number of spike-ins, their locations, and the range of concentrations, each group made predictions of the spike-in locations. We found that microarray platform choice is not the primary determinant of overall performance. In fact, variation in performance between labs, protocols and algorithms within the same array platform was greater than the variation in performance between array platforms. However, each array platform had unique performance characteristics that varied with tiling resolution and the number of replicates, which have implications for cost versus detection power. Long oligonucleotide arrays were slightly more sensitive at detecting very low enrichment. On all platforms, simple sequence repeats and genome redundancy tended to result in false positives. LM-PCR and WGA, the most popular sample amplification techniques, reproduced relative enrichment levels with high fidelity. Performance among signal detection algorithms was heavily dependent on array platform. The spike-in DNA samples and the data presented here provide a stable benchmark against which future ChIP platforms, protocol improvements, and analysis methods can be evaluated. This SuperSeries is composed of the following subset Series: GSE9732: Spike-in Experiment for ChIP-chip Simulation GSE9842: Systematic evaluation of variability in simulated ChIP-chip experiments GSE9848: Systematic evaluation of variability in simulated ChIP-chip experiments Kevin_Encode GSE9849: Systematic evaluation of variability in simulated ChIP-chip experiments Myles_Encode GSE10004: ENCODE Spike-In, Yale Group GSE10076: ENCODE spikein, amplified DNA samples, NimbleGen arrays GSE10090: ENCODE spikein, nonamplified DNA samples, NimbleGen arrays GSE10112: Systematic evaluation of variability in simulated ChIP-chip experiments Keywords: SuperSeries For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf Refer to individual Series
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