Abstract
BackgroundTranscriptional regulation is primarily mediated by the binding of factors to non-coding regions in DNA. Identification of these binding regions enhances understanding of tissue formation and potentially facilitates the development of gene therapies. However, successful identification of binding regions is made difficult by the lack of a universal biological code for their characterisation.ResultsWe extend an alignment-based method, changept, and identify clusters of biological significance, through ontology and de novo motif analysis. Further, we apply a Bayesian method to estimate and combine binary classifiers on the clusters we identify to produce a better performing composite.ConclusionsThe analysis we describe provides a computational method for identification of conserved binding sites in the human genome and facilitates an alternative interrogation of combinations of existing data sets with alignment data.
Highlights
Transcriptional regulation is primarily mediated by the binding of factors to non-coding regions in Deoxyriboucleic acid (DNA)
We find that combinations that use markers based on chromatin immuno-precipitation followed by sequencing (ChIP-Seq) and DNase Hypersensitivity (DHS) outperform those that do not, possibly indicating the presence of enhancers within these clusters
The motifs are presented for the relevant clusters, along with predicted Transcription factor binding site (TFBS), as we further investigate the biological significance of these enrichments
Summary
Transcriptional regulation is primarily mediated by the binding of factors to non-coding regions in DNA. Gene expression is largely mediated through the binding of proteins to non-coding regions in deoxyriboucleic acid (DNA) This is achieved by directly stabilising or blocking the binding of riboucleic acid (RNA) polymerase, or by interacting with other proteins and co-factors capable of influencing transcription [1]. Maderazo et al BMC Genomics (2022) 23:78 regulatory domain of their target genes is likely variable per gene Varying genomic factors such as sequence specificity, chromatin accessibility, protein-protein interactions, epigenetic modifications to the DNA and histone structure contribute to TF binding to their target sites [11,12,13]. Factors such as these can vary through the life-cycle of a cell, allowing for the expression of different profiles required of a cell to proceed down a particular developmental pathway or to carry out specific functions
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