Bioinformatics Unit

The Bioinformatics unit  carries out its own theoretic research as well as it supports the other groups by computerized and statistic methods. It develops its main scientific work in the fields of systems biology and bioinformatics by high-performance computing techniques and tools. In this context, the life cycle of any scientific activity follows that of the wet-lab experiment, which each of them is linked to, in order to enhance cooperation and the overall quality of the outcomes.

 

The unit studies biological networks and interactive systems by:

  1. Framing and modeling the biological events through formal and standard languages;
  2. Analyzing structural, static and dynamic properties (topology inspection, Importance ranking, logic properties verification, pedigree analysis);
  3. Simulating complex macro-events (Stochastic, deterministic and multi-scale simulation);
  4. Planning and scheduling experimental workflows;
  5. Efficiently handling and managing biological data.

 

The unit also deals with:

  1. NGS data analysis (mapping, targeted resequencing, exon counting, small InDels, SNPs, CNVs and fusion splicing finding);
  2. Microarray data analysis (pre-processing, segmentation and CNVs finding, allele-specific genotyping);
  3. Analysis of the potential risks of the genetic mutations according to protein structure changes and to ancestral conservation evidences.

Most activities encompass the implementation of high performance software packages or routines.

 

Algorithms and Software

  1. Ocean is a plug-in-based framework designed and implemented to analyze biological models through HPC techniques.
  2. Ranker is an Ocean’s plug-in designed to rank webs’ (groups of) vertices according to well-grounded centrality/importance measures.
  3. Sweeper is an Ocean’s plug-in thought to: (i) manage parameters of biological models, (ii) simulate the sweeped models on multicore-cluster architectures and (iii) collect and statistically analyze results.
  4. spPeAn is a standalone software package designed to simulate the budding yeast cell-cycle model in a stochastic environment and in a maximally parallel manner. It performs pedigree analysis of a mother cell up to her n-th generation. Consistency and viability checks are included in order to detect abnormal duplication and dead cells.

 

  1. Microsoft Research (COSBI), Italy
  2. Centre for Integrative Biology (CIBIO), Italy
  3. University of Calabria, DEIS, Italy
  4. Ca’ Foscari University of Venezia, Italy
  5. Magna Græcia University of Catanzaro, Italy
  6. INRIA Rennes Bretagne Atlantique, France
  7. INRIA École normale supérieure – France
  8. Virginia Polytechnic Institute and State University Blacksburg, VA, USA
  9. Masaryk University, CZ
  • Tommaso Mazza, PhD
  • Stefano Castellana, PhD
  • Caterina Fusilli, PhD
  • NVIDIA – Professor partnership
  • Microsoft – Research grant

Team Leader

Name Dr. Tommaso Mazza, PhD
Division Istituto CSS Mendel
Contacts t.mazza@css-mendel.it

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