Abstract
Novel technologies and growing interest have resulted in a large increase in the amount of data available for genomics and transcriptomics studies, both in terms of volume and contents. Biology is relying more and more on computational methods to process, investigate, and extract knowledge from this huge amount of data. In this work, we present the TICA web server (available at http://www.gmql.eu/tica/), a fast and compact tool developed to support data-driven knowledge discovery in the realm of transcription factor interaction prediction. TICA leverages both the GenoMetric Query Language, a novel query tool (based on the Apache Hadoop and Spark technologies) specialized in the integration and management of heterogeneous, large genomic datasets, and a statistical method for robust detection of co-locations across interval-based data, in order to infer physically interacting transcription factors. Notably, TICA allows investigators to upload and analyze their own ChIP-seq experiments datasets, comparing them both against ENCODE data or between themselves, achieving computation time which increases linearly with respect to dataset size and density. Using ENCODE data from three well-studied cell lines as reference, we show that TICA predictions are supported by existing biological knowledge, making the web server a reliable and efficient tool for interaction screening and data-driven hypothesis generation.
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Notes
- 1.
The schema for ENCODE narrowpeak data files is defined at https://genome.ucsc.edu/FAQ/FAQformat.html#format12.
- 2.
The full description of GMQL language for the latest version (2.1 at the time of writing) can be found at http://www.bioinformatics.deib.polimi. it/geco/?try.
- 3.
ENCODE narrowpeaks are also given for ChIP-seqs targeting histone modifications. We remove them from the dataset by means of NOT clauses—omitted for brevity.
- 4.
These are nominal values for promoter and exon length, chosen for our experiments. Different investigators can use their own values for regulatory regions extension, depending on their biological assumptions.
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Acknowledgements
This work was supported by the ERC Advanced Grant GeCo (Data-Driven Genomic Computing) (Grant No. 693174) awarded to Prof. Stefano Ceri. We would like to thank members of the GeCo project for helpful insights. Prof. Limsoon Wong was supported in part by a Kwan-Im-Thong-Hood-Cho-Temple chair professorship.
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Perna, S., Canakoglu, A., Pinoli, P., Ceri, S., Wong, L. (2018). Implementing a Transcription Factor Interaction Prediction System Using the GenoMetric Query Language. In: Mamitsuka, H. (eds) Data Mining for Systems Biology. Methods in Molecular Biology, vol 1807. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-8561-6_6
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DOI: https://doi.org/10.1007/978-1-4939-8561-6_6
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