Large-Scale Quality Analysis of Published ChIP-seq Data
ChIP-seq has become the primary method for identifying in vivo protein–DNA interactions on a genome-wide scale, with nearly 800 publications involving the technique appearing in PubMed as of December 2012. Individually and in aggregate, these data are an important and information-rich resource. Howe...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | en_US |
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Genetics Society of America
2014
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| Online Access: | http://hdl.handle.net/1721.1/87581 |
| _version_ | 1826213299891470336 |
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| author | Kundaje, Anshul Marinov, Georgi K. Park, Peter J. Wold, Barbara J. |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Kundaje, Anshul Marinov, Georgi K. Park, Peter J. Wold, Barbara J. |
| author_sort | Kundaje, Anshul |
| collection | MIT |
| description | ChIP-seq has become the primary method for identifying in vivo protein–DNA interactions on a genome-wide scale, with nearly 800 publications involving the technique appearing in PubMed as of December 2012. Individually and in aggregate, these data are an important and information-rich resource. However, uncertainties about data quality confound their use by the wider research community. Recently, the Encyclopedia of DNA Elements (ENCODE) project developed and applied metrics to objectively measure ChIP-seq data quality. The ENCODE quality analysis was useful for flagging datasets for closer inspection, eliminating or replacing poor data, and for driving changes in experimental pipelines. There had been no similarly systematic quality analysis of the large and disparate body of published ChIP-seq profiles. Here, we report a uniform analysis of vertebrate transcription factor ChIP-seq datasets in the Gene Expression Omnibus (GEO) repository as of April 1, 2012. The majority (55%) of datasets scored as being highly successful, but a substantial minority (20%) were of apparently poor quality, and another ∼25% were of intermediate quality. We discuss how different uses of ChIP-seq data are affected by specific aspects of data quality, and we highlight exceptional instances for which the metric values should not be taken at face value. Unexpectedly, we discovered that a significant subset of control datasets (i.e., no immunoprecipitation and mock immunoprecipitation samples) display an enrichment structure similar to successful ChIP-seq data. This can, in turn, affect peak calling and data interpretation. Published datasets identified here as high-quality comprise a large group that users can draw on for large-scale integrated analysis. In the future, ChIP-seq quality assessment similar to that used here could guide experimentalists at early stages in a study, provide useful input in the publication process, and be used to stratify ChIP-seq data for different community-wide uses. |
| first_indexed | 2024-09-23T15:46:52Z |
| format | Article |
| id | mit-1721.1/87581 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T15:46:52Z |
| publishDate | 2014 |
| publisher | Genetics Society of America |
| record_format | dspace |
| spelling | mit-1721.1/875812022-09-29T16:05:59Z Large-Scale Quality Analysis of Published ChIP-seq Data Kundaje, Anshul Marinov, Georgi K. Park, Peter J. Wold, Barbara J. Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Kundaje, Anshul ChIP-seq has become the primary method for identifying in vivo protein–DNA interactions on a genome-wide scale, with nearly 800 publications involving the technique appearing in PubMed as of December 2012. Individually and in aggregate, these data are an important and information-rich resource. However, uncertainties about data quality confound their use by the wider research community. Recently, the Encyclopedia of DNA Elements (ENCODE) project developed and applied metrics to objectively measure ChIP-seq data quality. The ENCODE quality analysis was useful for flagging datasets for closer inspection, eliminating or replacing poor data, and for driving changes in experimental pipelines. There had been no similarly systematic quality analysis of the large and disparate body of published ChIP-seq profiles. Here, we report a uniform analysis of vertebrate transcription factor ChIP-seq datasets in the Gene Expression Omnibus (GEO) repository as of April 1, 2012. The majority (55%) of datasets scored as being highly successful, but a substantial minority (20%) were of apparently poor quality, and another ∼25% were of intermediate quality. We discuss how different uses of ChIP-seq data are affected by specific aspects of data quality, and we highlight exceptional instances for which the metric values should not be taken at face value. Unexpectedly, we discovered that a significant subset of control datasets (i.e., no immunoprecipitation and mock immunoprecipitation samples) display an enrichment structure similar to successful ChIP-seq data. This can, in turn, affect peak calling and data interpretation. Published datasets identified here as high-quality comprise a large group that users can draw on for large-scale integrated analysis. In the future, ChIP-seq quality assessment similar to that used here could guide experimentalists at early stages in a study, provide useful input in the publication process, and be used to stratify ChIP-seq data for different community-wide uses. 2014-05-30T14:50:19Z 2014-05-30T14:50:19Z 2013-12 2013-09 Article http://purl.org/eprint/type/JournalArticle 2160-1836 http://hdl.handle.net/1721.1/87581 Marinov, G. K., A. Kundaje, P. J. Park, and B. J. Wold. “Large-Scale Quality Analysis of Published ChIP-Seq Data.” G3: Genes-Genomes-Genetics 4, no. 2 (March 13, 2014): 209–223. en_US http://dx.doi.org/10.1534/g3.113.008680 G3: Genes-Genomes-Genetics Creative Commons Attribution http://creativecommons.org/licenses/by/3.0/ application/pdf Genetics Society of America Genetics Society of America |
| spellingShingle | Kundaje, Anshul Marinov, Georgi K. Park, Peter J. Wold, Barbara J. Large-Scale Quality Analysis of Published ChIP-seq Data |
| title | Large-Scale Quality Analysis of Published ChIP-seq Data |
| title_full | Large-Scale Quality Analysis of Published ChIP-seq Data |
| title_fullStr | Large-Scale Quality Analysis of Published ChIP-seq Data |
| title_full_unstemmed | Large-Scale Quality Analysis of Published ChIP-seq Data |
| title_short | Large-Scale Quality Analysis of Published ChIP-seq Data |
| title_sort | large scale quality analysis of published chip seq data |
| url | http://hdl.handle.net/1721.1/87581 |
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