Exploring comprehensive within-motif dependence of transcription factor binding in Escherichia coli

Abstract

Modeling the binding of transcription factors helps to decipher the control logic behind transcriptional regulatory networks. Position weight matrix is commonly used to describe a binding motif but assumes statistical independence between positions. Although current approaches take within-motif dependence into account for better predictive performance, these models usually rely on prior knowledge and incorporate simple positional dependence to describe binding motifs. The inability to take complex within-motif dependence into account may result in an incomplete representation of binding motifs. In this work, we applied association rule mining techniques and constructed models to explore within-motif dependence for transcription factors in Escherichia coli. Our models can reflect transcription factor-DNA recognition where the explored dependence correlates with the binding specificity. We also propose a graphical representation of the explored within-motif dependence to illustrate the final binding configurations. Understanding the binding configurations also enables us to fine-tune or design transcription factor binding sites, and we attempt to present the configurations through exploring within-motif dependence.

CircularLogo: A lightweight web application to visualize intra-motif dependencies

“…Comparing to accomplishments in developing new statistical models, much fewer efforts have been spent on developing tools to visualize the intra-motif dependencies. Currently, tools that are capable of visualizing positional dependencies include CorreLogo and ELRM [16,17]. CorreLogo uses three-dimensional sequence logos to depict mutual information from DNA or RNA alignment via VRML and JVX output.…”

Análise de dependência entre posições de bases de sequências motivos de fatores de transcrição aplicada à comparação de modelos baseados em Position Weight Matrix e Gramática Regular Estocástica

“…Que se saiba, não existe uma diferença clara na literatura entre PWMs como classificadores de SLFT e PWMs como o modelo utilizado com fins de descoberta de motivos. Somado a isso, existe pelo menos uma evidência na literatura de que obter SLFTs que foram classificados a partir de PWMs não limitaria a identificação de dependência entre bases de um motivo (YANG;CHANG, 2015). Conforme foram comparados PWM e GRE-LAPFA apenas nesses 2047 FTs, notou-se que poderia ser informativo testar diferentes algoritmos de descoberta de motivos para evitar qualquer conclusão precipitada sobre a análise final.…”