Reliable scaling of position weight matrices for binding strength comparisons between transcription factors
Metadatos
Mostrar el registro completo del ítemEditorial
BMC
Materia
Transcription factor Position weight matrix (Position-Specific Scoring Matrix) Binding site strength Inteligencia artificial Artificial intelligence
Fecha
2015-04-20Referencia bibliográfica
Ma, X... [et al.]. Reliable scaling of position weight matrices for binding strength comparisons between transcription factors. BMC Bioinformatics 16, 265 (2015). [https://doi.org/10.1186/s12859-015-0666-1]
Patrocinador
Chinese Scholarship Council (CSC) Scholarship; Marshall Scholarship; DGICT, Madrid TIN2013-41990-R; Royal Society of LondonResumen
Background: Scoring DNA sequences against PositionWeight Matrices (PWMs) is a widely adopted method to identify
putative transcription factor binding sites. While common bioinformatics tools produce scores that can reflect the binding
strength between a specific transcription factor and the DNA, these scores are not directly comparable between
different transcription factors. Other methods, including p-value associated approaches (Touzet H, Varré J-S. Efficient
and accurate p-value computation for position weight matrices. Algorithms Mol Biol. 2007;2(1510.1186):1748–7188),
provide more rigorous ways to identify potential binding sites, but their results are difficult to interpret in terms of
binding energy, which is essential for the modeling of transcription factor binding dynamics and enhancer activities.
Results: Here, we provide two different ways to find the scaling parameter λ that allows us to infer binding energy
from a PWM score. The first approach uses a PWM and background genomic sequence as input to estimate λ for a
specific transcription factor, which we applied to show that λ distributions for different transcription factor families
correspond with their DNA binding properties. Our second method can reliably convert λ between different PWMs of
the same transcription factor, which allows us to directly compare PWMs that were generated by different approaches.
Conclusion: These two approaches provide computationally efficient ways to scale PWM scores and estimate the
strength of transcription factor binding sites in quantitative studies of binding dynamics. Their results are consistent
with each other and previous reports in most of cases.