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Combining transcription factor binding affinities with open chromatin data for accurate gene expression prediction

The binding and contribution of transcription factors (TF) to cell specific gene expression is often deduced from open-chromatin measurements to avoid cost and labour intensive TF ChIP-seq assays.It is important to develop reliable and fast computational methods for accurate TF binding prediction in open-chromatin regions (OCRs). Here, we report a novel segmentation-based method, TEPIC, to predict TF binding by combining sets of OCRs with position weight matrices.TEPIC can be applied to various open-chromatin data, e.g. DNaseI-seq and NOMe-seq, using either peaks or footprints as input.In addition to open-chromatin data, also Histone-Marks (HMs) can be used in TEPIC to identify candidate TF binding sites.TEPIC computes TF affinities and uses open-chromatin/HM signal intensity as quantitative measures of TF binding strength.Using machine learning techniques, we show that incorporating low affinity binding sites improves our ability to explain gene expression variability compared to the standard presence/absence classification of binding sites.Further, we show that both footprints and peaks capture essential TF binding events and lead to a good prediction performance.In our application, gene-based scores computed by TEPIC with one open-chromatin assay nearly reach the quality of several TF ChIP-seq datasets.Finally, we show that these scores correctly predict known transcriptional regulators as illustrated by the application to novel DNaseI-seq and NOMe-seq data for primary human hepatocytes and CD4+ T-cells, respectively.

Click on a Dataset ID in the table below to learn more, and to find out who to contact about access to these data

Dataset ID Description Technology Samples
EGAD00001002735 Illumina HiSeq 2000 Illumina HiSeq 2500 10
Publications Citations
Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction.
Nucleic Acids Res 45: 2017 54-66
54
On the problem of confounders in modeling gene expression.
Bioinformatics 35: 2019 711-719
5