BIOINFORMATICA

General teaching training objectives in terms of expected learning outcomes.

• Knowledge and understanding: The course aims to give the knowledge and basic and advanced skills to the analysis of bioinformatics data.
• Applying knowledge and understanding: the student will acquire knowledge about the models and algorithms for analyzing bioinformatics data.
• Making judgments: Through concrete examples and case studies, the student will be able to independently develop solutions to specific problems related to bioinformatics data analysis.
• Communication skills: the student will acquire the necessary communication skills and expressive appropriateness in the use of technical language in the general area of ​​bioinformatics and computational biology.
• Learning skills: The course aims to provide students with the necessary theoretical and practical methods to deal independently and solve new problems that may arise during a work activity. For this purpose, different topics will be covered in class by involving students in the search for possible solutions to real problems, using benchmarks available in the literature.

Lectures.

Should teaching be carried out in mixed mode or remotely, it may be necessary to introduce changes with respect to previous statements, in line with the programme planned and outlined in the syllabus.

• Prerequisites of Biology
  • Cells, genomes and evolution
  • The genome and the genes
  • Transcription
  • Translation
  • Coding and non-coding RNA
• Prerequisites of probability and statistics for bioinformatics
• R Programming language
• Pairwise and Multiple Alignment
• Biological databases
• Transcriptome analysis tools (Microarrays, Next-generation Sequencing) and Biomarkers
• DNA sequencing and RNAseq pipeline
• R package Bioconductor and differential analysis in R
• Tools for Mining of biological networks (Graph Matching, Network Biomarkers, Pathway Analysis, Network Alignment)

• Anna Tramontano “Bioinformatica” Zanichelli
• Krane, Raymer. “Fondamenti di Bioinformatica” Pearson
• Jambeck, Gibas “Developing Bioinformatics Computer Skills” O'Reilly
• Pascarella-Paiardini “Bioinformatica” Zanichelli
• Lewin “Il Gene” – Edizione Compatta-Zanichelli
• The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Trevor Hastie,Robert Tibshirani, Jerome Friedman, Springer