| Autor: |
Federica Lucini, Koustav Pal, Endre Sebestyén, Ilario Tagliaferri, Andrea Bianchi, Chiara Lanzuolo, Carmen Maria Livi, Francesco Ferrari, Sara Valsoni |
| Rok vydání: |
2020 |
| Předmět: |
|
| DOI: |
10.1101/2020.05.19.103887 |
| Popis: |
Despite the widespread adoption of the ChIP-seq technique, there is still no consensus on quality assessment procedures. Quantitative metrics previously proposed in literature are not always effective in discriminating the success or failure of an experiment, thus hampering objectivity and reproducibility of quality control. Here we introduce ChIC, a new framework for ChIP-seq data quality assessment that overcomes the limitations of previous solutions. ChIC is the first method for ChIP-seq quality control directly considering the enrichment profile shape, thus achieving good performances on ChIP targets yielding sharp and broad peaks alike. We integrate a comprehensive set of quality control metrics into one single score reliably summarizing the sample quality. The ChIC score is based on a machine learning classifier trained on a compendium with thousands of ChIP-seq profiles, which can also be used as a reference for easier evaluation of new datasets. ChIC is implemented as a user-friendly R/Bioconductor package. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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