Strategies for analyzing highly enriched IP-chip datasets
<p>Abstract</p> <p>Background</p> <p>Chromatin immunoprecipitation on tiling arrays (ChIP-chip) has been employed to examine features such as protein binding and histone modifications on a genome-wide scale in a variety of cell types. Array data from the latter studies...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2009-09-01
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| Series: | BMC Bioinformatics |
| Online Access: | http://www.biomedcentral.com/1471-2105/10/305 |
| _version_ | 1811316000459587584 |
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| author | Tavaré Simon Aparicio Oscar M Viggiani Christopher J Knott Simon RV |
| author_facet | Tavaré Simon Aparicio Oscar M Viggiani Christopher J Knott Simon RV |
| author_sort | Tavaré Simon |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>Chromatin immunoprecipitation on tiling arrays (ChIP-chip) has been employed to examine features such as protein binding and histone modifications on a genome-wide scale in a variety of cell types. Array data from the latter studies typically have a high proportion of enriched probes whose signals vary considerably (due to heterogeneity in the cell population), and this makes their normalization and downstream analysis difficult.</p> <p>Results</p> <p>Here we present strategies for analyzing such experiments, focusing our discussion on the analysis of Bromodeoxyruridine (BrdU) immunoprecipitation on tiling array (BrdU-IP-chip) datasets. BrdU-IP-chip experiments map large, recently replicated genomic regions and have similar characteristics to histone modification/location data. To prepare such data for downstream analysis we employ a dynamic programming algorithm that identifies a set of putative unenriched probes, which we use for both within-array and between-array normalization. We also introduce a second dynamic programming algorithm that incorporates <it>a priori </it>knowledge to identify and quantify positive signals in these datasets.</p> <p>Conclusion</p> <p>Highly enriched IP-chip datasets are often difficult to analyze with traditional array normalization and analysis strategies. Here we present and test a set of analytical tools for their normalization and quantification that allows for accurate identification and analysis of enriched regions.</p> |
| first_indexed | 2024-04-13T11:41:13Z |
| format | Article |
| id | doaj.art-f2ce362df73a4186aa8d474a68c0abd3 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-04-13T11:41:13Z |
| publishDate | 2009-09-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-f2ce362df73a4186aa8d474a68c0abd32022-12-22T02:48:19ZengBMCBMC Bioinformatics1471-21052009-09-0110130510.1186/1471-2105-10-305Strategies for analyzing highly enriched IP-chip datasetsTavaré SimonAparicio Oscar MViggiani Christopher JKnott Simon RV<p>Abstract</p> <p>Background</p> <p>Chromatin immunoprecipitation on tiling arrays (ChIP-chip) has been employed to examine features such as protein binding and histone modifications on a genome-wide scale in a variety of cell types. Array data from the latter studies typically have a high proportion of enriched probes whose signals vary considerably (due to heterogeneity in the cell population), and this makes their normalization and downstream analysis difficult.</p> <p>Results</p> <p>Here we present strategies for analyzing such experiments, focusing our discussion on the analysis of Bromodeoxyruridine (BrdU) immunoprecipitation on tiling array (BrdU-IP-chip) datasets. BrdU-IP-chip experiments map large, recently replicated genomic regions and have similar characteristics to histone modification/location data. To prepare such data for downstream analysis we employ a dynamic programming algorithm that identifies a set of putative unenriched probes, which we use for both within-array and between-array normalization. We also introduce a second dynamic programming algorithm that incorporates <it>a priori </it>knowledge to identify and quantify positive signals in these datasets.</p> <p>Conclusion</p> <p>Highly enriched IP-chip datasets are often difficult to analyze with traditional array normalization and analysis strategies. Here we present and test a set of analytical tools for their normalization and quantification that allows for accurate identification and analysis of enriched regions.</p>http://www.biomedcentral.com/1471-2105/10/305 |
| spellingShingle | Tavaré Simon Aparicio Oscar M Viggiani Christopher J Knott Simon RV Strategies for analyzing highly enriched IP-chip datasets BMC Bioinformatics |
| title | Strategies for analyzing highly enriched IP-chip datasets |
| title_full | Strategies for analyzing highly enriched IP-chip datasets |
| title_fullStr | Strategies for analyzing highly enriched IP-chip datasets |
| title_full_unstemmed | Strategies for analyzing highly enriched IP-chip datasets |
| title_short | Strategies for analyzing highly enriched IP-chip datasets |
| title_sort | strategies for analyzing highly enriched ip chip datasets |
| url | http://www.biomedcentral.com/1471-2105/10/305 |
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