Environmental bioinformatics

Bioinformatics, as it integrates with computational biology, represents an innovative field that employs computer technology to enhance biological research. With the advent of high-performance computational tools, techniques, software, and databases, it has become possible to process substantial amounts of biological data more efficiently, exemplified by applications like computer-aided drug design (CADD). Today, environmental protection poses significant challenges, which be addressed through the most effective applications of information technology. Students will get knowledge and skills on the use of specific bioinformatic databases, in particular those for genetic, genomics and proteomic analyses. They will get expertise on how to treat and analyse DNA and protein sequences from microorganisms, plants and Eukaryotes; how to build a phylogenetic three. They will be introduced to the scientific meaning and scope of machine learning techniques applied toward a better environmental sustainability, with particular regard to water and soil.

Requires prior knowledge of Basic Biochemistry, Biology, Fundamentals of Environmental science and some chapters of Statistics.

1. Life Organisms
   * The cell
   * The genome
   * Virus, Bacteria, Archaea, Eukaryotes
   * Fungi, plants

2. Bioinformatics tools
   * Databases (Pubmed, PDB, ATLAS, cBIO)
   * DNA sequencing
   * Phylogenetics (genotypes/phenotypes, distance-matrix threes)
   * Genomic analyses (expression, regulation)
   * Structural Bioinformatics
   * Informatics for Metabolomics

3. Applications to Environmental Biology
   * Water metagenomics
   * Soil microbiome analysis
   * Environmental toxicans
   * Viro-Informatics
   * Machine Learning (ML) in environmental science

Knowledge and understanding:
• Big data management and analysis
• Environmental Data Mining
• Genomic tools for environmental analysis
• Metagenomics and microbiome analysis
• Phylogenetic Analysis in Environmental Studies
• Statistical Methods in Environmental Bioinformatics
• Introduction to ML in Environmental Bioinformatics

Students will know how to navigate and use a proper biological database, how to identify microorganisms, extract their DNA/protein sequences and analyse them through the main bioinformatic tools. They will know the basis of ML approaches to be applied for the environmental research sustainability.

Written examination (60%), seminar (10%), report (30%).

Assistant professor in the field of biochemistry and molecular biology at Laboratory for Environmental and Life Sciences at University of Nova Gorica.

1. Ljubic M, D'Ercole C, Waheed Y, de Marco A, Borišek J, De March M. Computational study of the HLTF ATPase remodeling domain suggests its activity on dsDNA and implications in damage tolerance. J Struct Biol. 2024 Dec;216(4):108149. doi: 10.1016/j.jsb.2024.108149. Epub 2024 Nov 2. PMID: 39491691.
2. Nakić M, De March M, de Marco A. A Practical Guide for the Quality Evaluation of Fluobodies/Chromobodies. Biomolecules. 2024 May 15;14(5):587. doi: 10.3390/biom14050587. PMID: 38785994; PMCID: PMC11117837.
3. De March M, D'Ercole C, Veggiani G, Oloketuyi S, Svigelj R, de Marco A. Biological Applications of Synthetic Binders Isolated from a Conceptually New Adhiron Library. Biomolecules. 2023 Oct 17;13(10):1533. doi: 10.3390/biom13101533. PMID: 37892215; PMCID: PMC10605594.
4. De March M, Hickey N, Geremia S. Analysis of the crystal structure of a parallel three-stranded coiled coil. Proteins. 2023 Sep;91(9):1254-1260. doi: 10.1002/prot.26557. Epub 2023 Jul 27. PMID: 37501532.
5. Spinello A, Lapenta F, De March M. The avidin-theophylline complex: A structural and computational study. Proteins. 2023 Oct;91(10):1437-1443. doi: 10.1002/prot.26538. Epub 2023 Jun 15. PMID: 37318226.