A ChIC solution for ChIP-seq quality assessment
Livi, C. M.; Tagliaferri, I.; Pal, K.; Sebestyen, E.; Lucini, F.; Bianchi, A.; Valsoni, S.; Lanzuolo, C.; Ferrari, F.
Show abstract
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.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Robustness and applicability of functional genomics tools on scRNA-seq data 96%
- COCOA: Coordinate covariation analysis of epigenetic heterogeneity 95%
- Simultaneous smoothing and detection of topological units of genome organization from sparse chromatin contact count matrices with matrix factorization 95%
Similar papers in this journal
Similar papers in this journal
- BART3D: Inferring transcriptional regulators associated with differential chromatin interactions from Hi-C data 95%
- EpiSAFARI: Sensitive detection of valleys in epigenetic signals for enhancing annotations of functional elements 95%
- diffONT: predicting methylation-specific PCR biomarkers based on nanopore sequencing data for clinical application 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.