Bioinformatics
Toward a gold standard for promoter prediction evaluation
Bioinformatics
| Hi-index | 0.00 |
Motivation: The current methods for the determination of the statistical significance of peaks and regions in NGS data require an explicit normalisation step to compensate for (global or local) imbalances in the sizes of sequenced and mapped libraries There are no canonical methods for performing such compensations, hence a number of different procedures serving this goal in different ways can be found in the literature Unfortunately, the normalisation has a significant impact on the final results Different methods yield very different numbers of detected “significant peaks” even in the simplest scenario of ChIP-Seq experiments which compare the enrichment in a single sample relative to a matching control This becomes an even more acute issue in the more general case of the comparison of multiple samples, where a number of arbitrary design choices will be required in the data analysis stage, each option resulting in possibly (significantly) different outcomes. Results: In this paper we investigate a principled statistical procedure which eliminates the need for a normalisation step We outline its basic properties, in particular the scaling upon depth of sequencing For the sake of illustration and comparison we report the results of re-analysing a ChIP-Seq experiment for transcription factor binding site detection In order to quantify the differences between outcomes we use a novel method based on the accuracy of in silico prediction by SVM-models trained on part of the genome and tested on the remainder. Availability: The supplementary material is available at [1].