- About BIORISE
Dr Anastasis Oulas is a postdoctoral researcher in Bioinformatics Group at The Cyprus Institute of Neurology and Genetics. He was drawn by the rapidly evolving field of computational analysis of biological data from the very start of his academic career. Having completed a BSc in Molecular Genetics at Sussex University (UK), his interests turned to computing and the implementation of mathematical algorithms for deciphering the enormous amount of information retrieved from large-scale biological experiments. He enriched his knowledge within this field by completing a MSc in Computational Genetics and Bioinformatics at Imperial College (UK). He then moved to the Foundation of Research and Technology, Hellas (FORTH) in Crete, Greece, where he conducted research that led to the attainment of his PhD in Computational Prediction of Gene Classifiers and miRNAs in Cancer. After holding two postdoctoral positions at the Bioinformatics laboratory of the Division of Medicine and the RNA laboratory, both affiliated with the University of Crete, he took up a position as a senior postdoctoral fellow at the Institute of Marine Biology, Biotechnology and Aquaculture (IMBBC), Hellenic Centre for Marine Research (HCMR).
Early research interests focused on the improvement of classification algorithms for the successful analysis of microarray expression data obtained from cases with varying pathological conditions or different grades of cancer. He implemented machine-learning algorithms, such as Support Vector Machines and Artificial Neural Networks using programming languages such as Java. The aim of such applications is to aid in the diagnosis of complex genetic diseases such as cancer, by providing supplementary information to compliment classical clinical, histopathological and existing genetic information available today.
Dr Oulas also pursued research in the field of microRNAs (miRNA). His work in this field entailed the computational prediction of novel human miRNA genes residing within cancer associated genomic regions (CAGRs). Methods used include probabilistic Hidden Markov Models to construct a model from known miRNAs, which was concurrently utilized to scan CAGRs for the prediction of novel miRNAs. The prediction tool (SSCprofiler) is available as a graphic user interface as well as a web service.
Other work along this line involves the improvement of miRNA target prediction tools. More specifically he developed a novel computational methodology (Targetprofiler) for prediction of miRNA gene targets based on Profile Hidden Markov Models. Target predictions, using this improved methodology, have been performed for novel miRNA gene candidates predicted by SSCprofiler and a biological relevant target has been experimentally verified.
With the advent of Next Generation Sequencing (NGS) and the increasing need for analysis of data derived from these technologies, Dr Oulas shifted his focus to analysis tools used specifically for the analysis of NGS data. These include tools and pipelines for whole exome and genome sequencing data. He is involved in numerous ongoing projects involving germline variation detection and annotation, mainly focussing in the classification of variants of unknown or uncertain significance (VUS) in association with specific diseases. Additional NGS expertise includes tools for RNA-Seq data analysis for projects involving i) N. Benthamiana transcriptomics in collaboration with Plant RNA laboratory at IMBB-FORTH as well as ii) RNA-Seq data analysis projects for aquaculture cultivated fish species, Sea Bass and Sea Bream, in collaboration with researchers in IMBBC-HCMR.
He has also been involved in research related to the development as well as the application of bioinformatics tools and software for the specific analysis of bacterial diversity using genomic and metagenomic data. He has been making use of already implemented pipelines as well as modifying and setting up new procedures and methodologies for the analysis of NGS data specifically to address bacterial diversity. He is making use of 454 as well as Illumina sequencing data from 16S ribosomal genes as well as full shotgun metagenomics sequencing to investigate microbial biodiversity in marine environments.