Building accurate sequence-to-affinity models from high-throughput in vitro protein-DNA binding data using FeatureREDUCE

Transcription is the process by which the information contained within DNA is copied to a short-lived molecule called RNA so that it can be transported to other areas of the cell for various purposes. Transcription factors are key components in this process. These proteins recognise and gather at specific sequences of DNA near genes, and then assist the enzymes that copy the information in the gene into a molecule of RNA. This means that transcription factors essentially control which genes are expressed, and when and where these genes are expressed. Recent technological advances have made it possible to identify where transcription factors can bind within DNA sequences. Yet, while a lot of data has been generated in this area, the computational tools needed to make sense of it have not kept pace. Now, Riley et al. have developed software called FeatureREDUCE that will allow researchers to build computer predictions of how strongly a transcription factor will interact with specific short sections of DNA sequence. The software can be applied to experimental data collected in so-called ‘protein binding microarray’ experiments. FeatureREDUCE can also be used to investigate questions in the field of transcription factor research that had previously remained unanswered. First, to what level of detail can data obtained from recent technological advances be understood? Second, can transcription factors bind to DNA in more than one way, and can data from protein binding microarrays be used to uncover this? Riley et al. show that FeatureREDUCE can produce accurate and interpretable clues about the biology behind how transcription factors recognize DNA sequences. This includes how mutations as small as a change to single DNA letter can affect recognition. The next step will be to use the software to make sense of the existing volume of experimental data regarding protein-DNA interactions and data that will be generated in future experiments.