Assigning roles to DNA regulatory motifs using comparative genomics

Abstract

Motivation: Transcription factors (TFs) are crucial during the lifetime of the cell. Their functional roles are defined by the genes they regulate. Uncovering these roles not only sheds light on the TF at hand but puts it into the context of the complete regulatory network.Results: Here, we present an alignment- and threshold-free comparative genomics approach for assigning functional roles to DNA regulatory motifs. We incorporate our approach into the Gomo algorithm, a computational tool for detecting associations between a user-specified DNA regulatory motif [expressed as a position weight matrix (PWM)] and Gene Ontology (GO) terms. Incorporating multiple species into the analysis significantly improves Gomo's ability to identify GO terms associated with the regulatory targets of TFs. Including three comparative species in the process of predicting TF roles in Saccharomyces cerevisiae and Homo sapiens increases the number of significant predictions by 75 and 200%, respectively. The predicted GO terms are also more specific, yielding deeper biological insight into the role of the TF. Adjusting motif (binding) affinity scores for individual sequence composition proves to be essential for avoiding false positive associations. We describe a novel DNA sequence-scoring algorithm that compensates a thermodynamic measure of DNA-binding affinity for individual sequence base composition. Gomo's prediction accuracy proves to be relatively insensitive to how promoters are defined. Because Gomo uses a threshold-free form of gene set analysis, there are no free parameters to tune. Biologists can investigate the potential roles of DNA regulatory motifs of interest using Gomo via the web (http://meme.nbcr.net).Contact: t.bailey@uq.edu.auSupplementary information: Supplementary data are available at Bioinformatics online.

A global role for KLF1 in erythropoiesis revealed by ChIP-seq in primary erythroid cells

“…Using an independently derived CACC motif avoids the problem of circularity that could arise if we instead employed a CACC motif derived using de novo motif discovery on our peak sequences. We measured average motif affinity, using the program AMA (Buske et al 2010), rather than simply counting the number of sequences that had at least one CACC motif occurrence at a specified score threshold. By calculating average motif affinity, we more accurately model the thermodynamic interaction of the transcription factor with the DNA.…”

“…Average motif affinity (AMA) (Buske et al 2010) was used to estimate the fraction of regions around peaks that have a nonrandomly strong KLF1 affinity as predicted by a CACC box PWM.…”

Simultaneous Pathway Activity Inference and Gene Expression Analysis Using RNA Sequencing

“…All reads were pooled to identify regions of open chromatin using MACS (Zhang et al, 2008) and then assigned to the closest gene under 100 Kb away using Homer software (Heinz et al, 2010). The average motif affinity (AMA) scores (Buske et al, 2010) were calculated for each gene by concatenating the associated regulatory sequence and using the JASPAR core mammalian motifs (Mathelier et al, 2014). Motif enrichment was determined by applying a t-test comparing AMA scores for genes within the cluster to scores of genes in other clusters (McLeay and Bailey, 2010).…”

Identification of coupling DNA motif pairs on long-range chromatin interactions in human K562 cells

“…Therefore, we have run GOMO (with its default setting) to calculate the gene ontology enrichment for each of those DNA motifs (Buske et al, 2010). Briefly, GOMO scans all the human promoters using each DNA motif and determine if any of them is significantly associated with genes linked to one or more Gene Ontology (GO) terms which are valuable to our understanding on the functions of each DNA motif.…”