Figure 1

Method overview. We used genome-wide data including (a) binding of transcription factors in ChIP on chip experiments [16], (b) annotations of computationally inferred upstream sequence motifs [22] and (c) gene expression time profiles for different synchronization methods [2]. (d) The cis-regulatory information in a and b is combined by finding statistically associated combinations of transcription factors and motifs (i.e. cis-regulatory descriptors). (e) Periodic expression in each experiment is inferred from the temporal expression profiles. (f) Finally, machine learning is applied to model periodic expression. The model consists of rules associating cis-regulatory descriptors with patterns of periodic expression.