Francisco Avila Cobos
The analysis of the transcriptome has significantly contributed to our understanding of the processes involved in disease and development. Moreover, the introduction of RNA-sequencing (RNA-seq) has revolutionized the field of molecular biology, revealing that up to 75% of the human genome is actively transcribed.
The majority of this transcriptome consists of long non-coding RNAs (lncRNAs) but reconstructing accurate transcript models for these lncRNAs is a major challenge when processing RNA-seq data. Furthermore, the heterogeneous nature of samples and tissues under investigation has been largely neglected. Gene expression analyses of bulk tissues often ignore cell type composition as an important confounding factor, resulting in a loss of signal from lowly abundant cell types. Hence, multiple computational approaches have been developed to infer the abundance of different cell types and/or cell type-specific expression profiles in heterogeneous samples (=computational deconvolution).
My research focuses on:
1) mining of microarray and next-generation-sequencing data (RNA-seq, ChIP-seq, CAGE-seq, etc.) and its integration in bioinformatics tools to evaluate the reliability of novel transcript annotation;
2) establishment of pipelines for drug target identification using network inference from transcriptomics data;
3) mathematical approaches used in computational deconvolution and the effect of data processing and different confounding factors on the accuracy of the deconvolution results.
BSc Biotechnology (University of León. Spain)
MSc Bioinformatics and Computational Biology (University College Cork. Republic of Ireland)
- Computational deconvolution of transcriptomics data from mixed cell populations. Avila Cobos, F, Vandesompele, J, Mestdagh, P and De Preter, K. Bioinformatics (2018).
- Zipper plot: visualizing transcriptional activity of genomic regions. Avila Cobos F, Anckaert J, Volders P, Everaert C, Rombaut D, Vandesompele J, De Preter K & Mestdagh P. BMC Bioinformatics (2017)
- MicroRNA profiling reveals a role for microRNA-218-5p in the pathogenesis of chronic obstructive pulmonary disease. Conickx, G, Mestdagh, P, Avila Cobos, F, Verhamme, F, Maes, T, Vanaudenaerde, BM, Seys, L et al. American Journal of Respiratory and Critical Care Medicine (2017).
- microRNA profiling in lung tissue and bronchoalveolar lavage of cigarette smoke-exposed mice and in COPD patients: a translational approach. Conickx, G*, Avila Cobos, F*, van den Berge, M, Faiz, A, Timens, W, Hiemstra, P, Joos, G, Brusselle, G, Mestdagh, P and Bracke, K. Scientic Reports (2017).
- Asthma inflammatory phenotypes show differential microRNA expression in sputum. Maes, T, Avila Cobos, F, Schleich, F, Sorbello, V, Henket, M, De Preter, K, Bracke, K et al. Journal of Allergy and Clinical Immunology (2016).
- Long noncoding RNA signatures define oncogenic subtypes in T-cell acute lymphoblastic leukemia. Wallaert, A, Durinck, K, Van Loocke, W, Van de Walle, I, Matthijssens, F, Volders, PJ, Avila Cobos, F et al. Leukemia (2016)