Open Collections

Improving the detection of transcription factor binding regions

University of British Columbia

The identification of non-coding regulatory elements in the genome has been the focus of much experimental and computational effort. However, both experimental data, such as ChIP-seq, and computational methods of transcription factor (TF) binding predictions suffer from a degree of non-specificity. ChIP-seq experiments report regions that don’t contain the expected canonical motif for the ChIPped TF, which may arise from indirect binding or a non-TF-specific mechanism. Computational predictions based on sequence-level information alone are plagued by false positives. This thesis explores computational approaches to improve both the interpretation of large-scale TF binding data, and the detection of TF binding regions. In Chapters 2 and 3 we observe that experimentally defined regulatory regions of the human genome are a mixture of sub-groups reflecting distinct properties. On average a third of a ChIP-seq dataset does not contain the targeted TF’s motif, and within this subset up to 45% of the ChIP-seq peaks are unexpectedly enriched for a small class of non-targeted TFs’ motifs. Many of these regions are not specific to a TF but are ChIPped by multiple diverse TFs across multiple cell types. These recurring regions tend to be the lower scoring peaks of a dataset, are less likely to reproduce between experimental replicates, and tend to associate with cohesin and polycomb protein occupied positions in the genome. The regulatory regions with a greater specificity for a TF do not share these properties. Based on these observations we suggest a TF ‘loading-zone’ model to account for the presence of the aforementioned recurrent regions in ChIP-seq data. In Chapter 4 we further explore the regulatory region subgroups with a biophysical simulator of TF occupancy (tfOS). Within tfOS we have incorporated TF-DNA interaction energies, TF search mechanics, cooperative TF interactions, and sequence accessibility data into the model. Simulations with tfOS across sequences reveal distinct features associated with recurrent and non-recurrent regions described in Chapter 3. The research presented has improved our understanding and interpretation of large-scale TF binding data and advanced our understanding of TF regulatory regions, leading to improved annotation and interpretation of the human genome.

Text; Dataset

2014-12-12

Attribution-NonCommercial-NoDerivs 2.5 Canada

http://hdl.handle.net/2429/51482

Bioinformatics

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/2.5/ca/