Prediction of CRMs in Gene Regulatory Sequences using INSECT 2.0: The Thyroid hormone case in Xenopus laevis.

GONZALES, NAHUEL; GALETTO, CAROLINA; IZAGUIRRE, MF; CASCO, VH; KOILE, DANIEL; ROHR, CRISTIAN; PEREZ CASTRO, CAROLINA; YANKILEVICH, PATRICIO; PARRA, GONZALO

Buenos Aires

Congreso; The fourth International Society for Computational Biology Latin America Bioinformatics Conference (ISCB-LA), jointly organized with A2B2C; 2016

Prediction of CRMs in Gene Regulatory Sequences using INSECT 2.0: The Thyroid hormone case in Xenopus laevisGonzalez N1, Galetto CD1, Izaguirre MF1, Casco VH1, Koile Daniel2, Rohr CO2, Perez-Castro C2, Yankilevich P2, Parra RG3.(1) Engineering School, National University of Entre Rios (2) Biomedical Research Institute of Buenos Aires (IBioBA) ? CONICET. (3) Max Planck Institute for Biophysical Chemistry, Göttingen, GermanyBackground:Transcriptional regulation occurs through the concerted action of several transcription factors (TFs) that cooperatively bind to Cis-Regulatory Modules (CRMs). CRMs usually contain a variable number of TFs binding sites (TFBSs). INSECT 2.0 is a web-server to identify potential CRMs at gene sequences. By combining several strategies, it provides a flexible CRMs analysis, applying different search schemes and restriction parameters. Thyroid hormone (T3) is essential for normal development and functioning of vertebrates. Its effects are mainly mediated through transcriptional regulation by T3 receptors on T3 response elements (TRE). INSECT 2.0 was used in order to predict potential T3 CRMs in Xenopus laevis (Xl).Methods:TREs were modeled as bi-TFBSs CRMs, spaced exactly by 4 nucleotides (nt). Adjusting several parameters, TREs hits were predicted with INSECT 2.0 in control genes. A threshold of 80% maximum affinity and 4nt spacing was shown to be the best combination for the benchmarking. This was used to study genes that are believed to be important for Xl during metamorphosis and were contrasted with experimental results in the wet lab. All genomic sequences were retrieved from the XENBASE database.Results:We were able to predict promising CRMs at many Xl candidate genes that are now being experimentally tested. INSECT 2.0 not only provides valuable predictions with a grammatical modeling of CRMs to minimize false positives, the information generated using this tool can be a valuable guide for designing experiments and building hypothesis, and its implementation is sufficiently user-friendly to allow highly complex analysis to non-specialized users.