Quantitative analysis demonstrates most transcription factors require only simple models of specificity

Zhao, Yue; Stormo, Gary D.

doi:10.1038/nbt.1893

Cited by 171 publications

(229 citation statements)

References 11 publications

Supporting

Mentioning

219

Contrasting

Order By: Relevance

“…In fact, the authors invoked a model where TFs could use alternative modes of binding, each represented by independent PWMs, to explain the PBM data. We developed a nonlinear regression method, BEEML-PBM, to estimate PWM parameters from PBM data (Zhao and Stormo 2011). Using BEEML-PBM, we showed that, contrary to the conclusions of Badis et al (2009), the PWM model provides a good quantitative model of specificity for most of the TFs in their study.…”

Section: Resultsmentioning

confidence: 92%

“…All models were parameterized using the BEEML-PBM algorithm, which has been described in detail previously (Zhao and Stormo 2011). Briefly, BEEML-PBM employs nonlinear regression to obtain the parameters of the model, E !…”

Section: Parameterization Of Tf Specificity Modelmentioning

confidence: 99%

“…The squared correlation coefficient between the observed 8mer intensities for the two arrays is referred to as the replicate reproducibility. In previous work we considered models whose performance was at least 90% of the replicate reproducibility to provide very good fits to the data (Zhao and Stormo 2011). But it should be noted that it is possible, in fact common, for the model performance to exceed the replicate reproducibility as we showed in the supplemental materials of that article.…”

Section: Evaluation Of Model Performance On Pbm Datamentioning

confidence: 99%

“…Despite the restrictions imposed by this strong independence assumption, the PWM model has been successfully used to identify TF binding sites (TFBS) in sets of coexpressed genes (Stormo and Hartzell 1989;Roth et al 1998;Tavazoie et al 1999;Bussemaker et al 2001) as well as model TF binding site evolution (Doniger and Fay 2007;Mustonen et al 2008;Bradley et al 2010). Quantitative analysis of high-throughput binding data has also shown that PWMs are a good quantitative model for most TFs (Zhao et al 2009;Zhao and Stormo 2011).…”

mentioning

confidence: 99%

“…We use the binding energy estimate by maximum likelihood for PBM (BEEML-PBM) (Zhao and Stormo 2011) program to parameterize specificity models of varying complexity and find that improvements from incorporating interactions between positions are usually small, although there are some significant exceptions. Moreover, we find that interactions between neighboring bases are stronger than interactions between nonneighboring bases.…”

mentioning

confidence: 99%

See 4 more Smart Citations

Improved Models for Transcription Factor Binding Site Identification Using Nonindependent Interactions

et al. 2012

Self Cite

View full text Add to dashboard Cite

Identifying transcription factor (TF) binding sites is essential for understanding regulatory networks. The specificity of most TFs is currently modeled using position weight matrices (PWMs) that assume the positions within a binding site contribute independently to binding affinity for any site. Extensive, high-throughput quantitative binding assays let us examine, for the first time, the independence assumption for many TFs. We find that the specificity of most TFs is well fit with the simple PWM model, but in some cases more complex models are required. We introduce a binding energy model (BEM) that can include energy parameters for nonindependent contributions to binding affinity. We show that in most cases where a PWM is not sufficient, a BEM that includes energy parameters for adjacent dinucleotide contributions models the specificity very well. Having more accurate models of specificity greatly improves the interpretation of in vivo TF localization data, such as from chromatin immunoprecipitation followed by sequencing (ChIP-seq) experiments.

show abstract

Section: Resultsmentioning

confidence: 92%

Section: Parameterization Of Tf Specificity Modelmentioning

confidence: 99%

Section: Evaluation Of Model Performance On Pbm Datamentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

Improved Models for Transcription Factor Binding Site Identification Using Nonindependent Interactions

et al. 2012

Self Cite

View full text Add to dashboard Cite

show abstract

DNA Motif Databases and Their Uses

Stormo

2015

CP in Bioinformatics

View full text Add to dashboard Cite

Transcription factors (TFs) recognize and bind to specific DNA sequences. The specificity of a TF is usually represented as a position weight matrix (PWM). Several databases of DNA motifs exist and are used in biological research to address important biological questions. This overview describes PWMs and some of the most commonly used motif databases, as well as a few of their common applications. © 2015 by John Wiley & Sons, Inc.

show abstract

Context and number of noncanonical repeat variable diresidues impede the design of TALE proteins with improved DNA targeting

et al. 2019

View full text Add to dashboard Cite

Transcription activator‐like effector (TALE) proteins have been used extensively for targeted binding of fusion proteins to loci of interest in (epi)genome engineering. Such approaches typically utilize four canonical TALE repeat variable diresidue (RVD) types, corresponding to the identities of two key amino acids, to target each nucleotide. Alternate RVDs with improved specificity are desired. Here, we focused on seven noncanonical RVDs that have been suggested to have improved specificity for their target nucleotides. We used custom protein binding microarrays to characterize the DNA‐binding activity of 65 TALEs containing these alternate or corresponding canonical RVDs at multiple positions to ~5,000 unique DNA sequences per protein. We found that none of the noncanonical thymine‐targeting RVDs displayed stronger preference for thymine than did the canonical RVD. Of the noncanonical RVDs with putatively improved specificity for guanine, only EN and NH showed greater discrimination of guanine over adenine. This improved specificity, however, comes at a cost: more substitutions of a noncanonical RVD for a canonical RVD generally decreased the protein's DNA‐binding activity. Our results highlight the need to investigate RVD‐nucleotide specificities in multiple protein contexts and suggest that a balance between canonical and noncanonical RVDs is needed to build TALEs with improved specificity.

show abstract

Quantitative analysis demonstrates most transcription factors require only simple models of specificity

Cited by 171 publications

References 11 publications

Improved Models for Transcription Factor Binding Site Identification Using Nonindependent Interactions

Improved Models for Transcription Factor Binding Site Identification Using Nonindependent Interactions

DNA Motif Databases and Their Uses

Context and number of noncanonical repeat variable diresidues impede the design of TALE proteins with improved DNA targeting

Contact Info

Product

Resources

About