Weirauch Lab

  • Current Projects

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    + Determination of transcription factor binding motifs

    In collaboration with Tim Hughes at the University of Toronto, I have developed a joint computational and experimental method for accurately determining the sequence binding preferences of transcription factors. Using this approach, we have characterized the binding preferences of thousands of transcription factors, covering most of the branches of the eukaryotic tree and the majority of transcription factor families. The availability of this resource, and its continued development, will open new opportunities for a wide variety of genomic and functional genomic analyses. Further, it provides the groundwork for future studies aimed at understanding how transcription factors interact with DNA, both in vitro and in vivo.

    + Computational prediction of transcription factor binding

    Proper control of gene expression is governed by the complex interplay of many transcription factors. One major component governing where and how a transcription factor binds to genomic DNA is its inherent sequence binding preferences. In our lab, we evaluate models of transcription factor binding preferences, with the end goal of accurate prediction of transcription factor occupancy in vivo. In addition to accurate sequence preference models, we develop methods integrating multiple sources of information, including DNA accessibility, protein interactions, DNA looping, epigenetic modifications, and gene expression.

    + Understanding gene misregulation mechanisms in human diseases

    Recent advances in genome sequencing technologies have resulted in a considerable increase in our ability to associate genomic regions with human diseases. One major result of these studies is that a substantial proportion of genetic risk is likely attributable to sequences not located within genes. Such non-coding regions frequently harbor binding sites for transcription factors, which control the degree, timing, and magnitude of gene expression. Recent studies have linked disruptions in transcription factor binding to a variety of human diseases. In our lab, we apply our advances in knowledge of transcription factor binding preferences (see project 1) and how to accurately model them (see project 2) to discover disruptions in transcription factor binding that contribute to human diseases. In particular, we focus on autoimmune diseases, with emphasis on Systemic Lupus Erythematosus (SLE, or Lupus).

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    Eukaryotic transcription factor “constellation”

    Eukaryotic transcription factor “constellation”


    This network image depicts all known and predicted eukaryotic transcription factors (TFs), encompassing the ~300 genomes that have been sequenced.   Each node represents a TF.   Links connect TFs with similar DNA binding domains.  TF node color indicates the current knowledge of its binding motif: red nodes depict TFs with known motifs; orange depict those whose motifs can be inferred (because they are linked to at least one red node); blue nodes are TFs whose motifs we are currently determining; grey nodes are unknown motifs.  Major TF families are labeled.

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    Evaluation of 20 methods for modeling transcription factor binding preferences

    Evaluation of 20 methods for modeling transcription factor binding preferences

    We evaluated a panel of methods for modeling and predicting transcription factor (TF) binding in vitro.  Each method was assessed based on its ability to accurately predict protein binding microarray (PBM) data assaying a wide range of mouse TFs.  This heatmap image depicts the final score for each method on each TF, along with the type of model employed by each method.  Results from this study suggest that simple position weight matrix (PWM)-based models are capable of accurately capturing the binding preferences of most TFs.