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. 2017 Jul 5;7:4660. doi: 10.1038/s41598-017-04929-6

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© The Author(s) 2017

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Comparison of our method to peak callers with respect to potential sources of bias. Potentially influencing factors for our predictions (red bars) where compared against F-Seq (blue bars), MACS (orange bars) and Hotspot (green bars). Neither, the chromosomal location nor gene type nor the GC content of misclassified genes distinguishes predictions from observations. However, by predicting accessibility based on gene expression (bottom right histogram), we significantly reduce the number of misclassified genes in regions expressed above 0.1 FPKM (6% compared to 41.6%, 38.6% and 37.2%). Due to the deterministic prediction, genes with no expression are overrepresented in the range of 0 to 0.1 FPKM (first bar, 38% out of 94%).