Development of bioinformatics tools for the analysis of multivariate data. Optimisation of Standard Operating Procedures. Metabolomics and genomics analysis.
Description of Research
The last few decades have witnessed an explosion of data in almost all fields, from health sciences to plant biology, to the extent that the bottleneck in research has shifted from data generation to data analysis. As a result, there is an increasing need for approaches to manage and extract useful knowledge from complex data and to develop data-based bioinformatics solutions.
The Bioinformatics Unit focuses on the development of bioinformatics tools and statistical approaches to facilitate the interpretation of data generated from high-throughput platforms, including metabolomics and genomics data. The Group’s research strategy focus is on medical sciences and plant biology, which include:
- Design of high-throughput experiments. Planning an experiment properly is very important in order to ensure that the right type of data and sufficient sample size and power are available to answer the research questions of interest as clearly and efficiently as possible.
- Developing the optimal standard operating procedures for data managing, sample collection, and processing.
- Exploratory approaches to data mining, especially those geared toward the discovery of patterns in the data for exploratory tasks using clustering and feature extraction methods. These methods are important to stratify an ensemble of samples into groups on the basis of their molecular profile.
- Classification and regression analyses of molecular profiles using machine learning algorithms. Multi-parameters models can be built to improve, for example, the diagnosis of disease using standard methods.
- Interpreting the results to gain insights into biological mechanisms through Functional analysis methods.
Cacciatore, S., Luchinat, C., Tenori, L. 2014. Knowledge discovery by accuracy maximization. Proc Natl Acad Sci USA 111, 5117-5122 PubMed link
Cacciatore, S., Tenori, L., Luchinat, C., Bennett, P.R., MacIntyre, D.A. 2016. KODAMA: an R package for knowledge discovery and data mining. Bioinformatics btw705 PubMed link
Cacciatore, S., Zadra, G., Bango, C., Penney, K.L., Tyekucheva, S., Yanes, O., Loda, M. 2017. Metabolic Profiling in Formalin-Fixed and Paraffin Embedded Prostate Cancer Tissues. Mol Cancer Res 15, 439-447 PubMed link
Meucci, S., Keilholz, U., Heim, D., Klauschen, F., Cacciatore, S. 2019. Somatic genome alterations in relation to age in lung adenocarcinoma. Int J Cancer 145, 2091-2099 PubMed link
Labbé, D.P., Zadra, G., Yang, M., Reyes, J.M., Lin, C.Y., Cacciatore, S., Ebot, E.M., Creech, A.L., Giunchi, F., Fiorentino, M., Elfandy, H., Syamala, S., Karoly, E.D., Alshalalfa, M., Erho, N., Ross, A., Schaeffer, E.M., Gibb, E.A., Takhar, M., Den, R.B., Lehrer, J., Karnes, R.J., Freedland, S.J., Davicioni, E., Spratt, D.E., Ellis, L., Jaffe, J.D., DʼAmico, A.V., Kantoff, P.W., Bradner, J.E., Mucci, L.A., Chavarro, J.E., Loda, M., Brown, M. 2019. High-fat diet fuels prostate cancer progression by rewiring the metabolome and amplifying the MYC program. Nature Comm 10, 4358 PubMed link
Semreen, M.H., Alniss, H., El-Awady, R., Cacciatore, S., Mousa, M., Almehdi, A.H., El-Huneidi, W., Zerbini, L.F., Soares, N.C. 2020. GC-MS based comparative metabolomic analysis of MCF7 and MDA-MB-231 cancer cells treated with Tamoxifen and/or Paclitaxel. J Proteomics 225, 103875 PubMed link