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Transcription factors (TFs) influence gene expression by binding to specific cis-acting elements in a genomic sequence. Thus, accurate models for describing the binding properties of TFs are essential in modeling transcription. From a set of known transcription factor binding sites (TFBSs) for a given TF, the binding preference is generally represented in the form of a position weight matrix (PWM) (also called position-specific scoring matrix) derived from a position frequency matrix (PFM). A PFM is essentially an occurrence table, summarizing the number of each nucleotide observed at each position of a set of aligned TFBSs. Compared with simpler models like consensus sequences, PWMs allow for an additive probabilistic description of binding preferences.