BF/0034/EN - BIOINFORMATICS
Anno Accademico 2021/2022
CARLA MARIA CALO' (Tit.)
- Primo Semestre
- Modalità d'Erogazione
- Lingua Insegnamento
|[60/71] BIOLOGIA CELLULARE E MOLECOLARE||[71/10 - Ord. 2021] Advanced cellular studies||9||84|
The course is taught in English.
The course aims to provide students with good knowledge for the analysis of human DNA variation, cellular transcriptome and three-dimensional protein structures. In details:
Module of human genome variability aims to analyze and identifying the actions of selective pressure on human genome. Particularly, at the end of the course the students are able to use software for genomic data analysis, correctly interpret results and plan genetic studies in a proper way. Moreover, students, starting from the knowledge of the mechanisms of molecular evolution, will be trained in the study of phylogenetic inference and will be able to reconstruct phylogenetic trees based on several molecular markers.
Module of transcriptomics aims to introduce the student to the analysis of the cellular transcriptome, presenting the main wet-lab and bioinformatics techniques used to study the complete set of RNA transcripts that are produced by the genome and its variation under specific circumstances. The skills acquired during the course will be applied to the analysis of gene expression in public transcriptomic data and the study of their differential modulation in relation to a condition of interest.
Module of protein modeling aims to initiate the student to a thorough study of protein structure considering both experimental and computational methods. The course will guide the students from the analysis and comparison of sequences to the 3D structure. In this regard, bioinformatic tools and computational methods will be applied. Combined with the sequence information the 3D structure, gives insights for the development of effective rational strategies for experiments such as site directed mutagenesis, studies of disease related mutations, or the structure-based design of specific inhibitors.
• KNOWLEDGE AND UNDERSTANDING
• The course introduces students to the main concepts of bioinformatics and computational chemistry applied to compare, study and predict the 3D structure of a protein allowing them to gain insight of their molecular/biomolecular structure and function.
Moreover, students should reach the capacity to discuss the theoretical basics of DNA sequence analysis, apply phylogenetic analysis to simple genomics data, and detect selective pressure traces.
Finally, the course will consider the main methods of extraction and quantitative and qualitative analysis of RNA, the characterization of RNA expression using high-throughput methods, and the main bioinformatics tools to quantify such expression and assess its statistical variation under a specific condition.
APPLYING KNOWLEDGE AND UNDERSTANDING
Students, through practical activities autonomous and piloted, will acquire knowledge and understanding in the application of the analysis of DNA variability, expression of genomic sequences of interest in RNA-seq data and its variation under specific circumstances, and computational methods and bioinformatics tools to study the structure and function of biological macromolecules.
The use of theory and case studies will enable students to acquire the skills needed for problem setting, problem solving and judgement making.
The students will reach a good level of communication skills to explain concepts and problems of the discipline properly, and an appropriate scientific language.
LEARNING KNOWLEDGE: Students will acquire learning skills to undertake further studies and to deal with complex problems. The course aims to empowering students to be independent in the use of open-source databases and software for DNA, protein modeling and transcriptome expression analyses, to apply them to the specific conditions and scientific questions encountered along their research experience.
The course is taught in English
Knowledge of genetics, molecular biology, biology, biochemistry, and organic chemistry. Basic knowledge of informatics (to use a computer, to use internet for database searches and software download)
The course is taught in English
Module of Human Genome Variability (CFU 2+1)
• Introduction to Human Evolutionary Genetics.
• Microevolution and genetic factors: non random mating, genetic drift, gene flow, mutation, natural selection.
• Genetic Database and data sharing: NCBI, The Genographic Project, the 1000 Genome Project, mitoMap.
• Making inference from genetic diversity: measures of molecular diversity, neutrality test, mismatch distribution.
• Phylogenetic inference: sequence allineament, genetic distances, genetic tree (UPGMA, Neighbor joining, Maximum Likelihood and maximum parsimony). Consense tree (Bootstrap method), Evolution Molecular Models. Coalescent approach to reconstruct population history.
• Bioinformatics tools for searching traces of selective pressure.
• Laboratory (12 hours): alignment of DNA sequences, use of principal programs for the analysis of genetic diversity, use of programs for the analysis of selective pressure (Pophuman,1000 Genomes Selection Browser), use of programs for genetic distance and genetic tree (Mega, Phylip).
Module of transcriptomics (CFU 2+1)
• Introduction to the concepts of transcriptome and transcriptomics
• Main methods for the extraction and quantitative/qualitative analysis of RNA in a sample
• High throughput techniques for RNA expression analysis
• Common open-source reference databases for gene expression studies
• RNA-seq data analysis: quality control, mapping, read count and expression quantification
• Bioinformatics techniques for differential expression analyses
• Transcript prediction and visualization in the genome context
Module of protein modeling (CFU 2+1)
• Introduction to protein structure and the folding mechanism
• Uniprot and other useful biological DBs
• Protein Data Bank and overview of experimental methods to obtain the 3D structure model of a macromolecule
• File formats for small- and macro-molecules
• PDB models analysis
• 3D visualization tools
• Sequence alignments
• 3D alignments
• Methods for secondary structure prediction
• Methods for 3D structure prediction
• Validation and analysis of models
• Overview of structure-based computational methods
• Introduction to System Biology
The course is taught in English.
Teaching will be organized in frontal classes, integrated and "augmented" with online strategies, in order to guarantee its use in an innovative and inclusive way.
Seminars, practical experience in the bioinformatics laboratory
The teaching method includes classroom lectures with oral presentation, structured as follows:
• introduction aimed at providing an overview of what will later be discussed;
• development, which presents in detail the contents highlight the connection between ideas and key points.
• conclusion, or summary, aimed at reinforcing the learning of the lesson content and reconnecting with the general goals.
• Furthermore, the issues discussed in class will be exercised in the computer lab.
The course is taught in English
Students are required to demonstrate their level of knowledge by oral examination and an easy computer simulation where they are asked to show critical understanding of the key concepts and of the methodology’s application.
The final grade considers the following factors:
-Quality of the knowledge, skills, competences showed:
a) appropriateness, accuracy and consistency of knowledge
b) appropriateness, accuracy and consistency of skills
c) appropriateness, accuracy and consistency of expertise
a) Expressive capacity and proper use of the specific language of the discipline;
b) Logical ability also in the consequential fitting of the contents;
c) Ability to connect different subjects by finding the common points and establish a consistent overall design;
d) Ability to summarize through the use of symbolism on the matter, and including the graphic expression of ideas and concepts, for example scheme of biological processes and structures.
Ability to talk and interact with the teacher during the interview.
a) critical spirit;
b) ability to self-evaluation.
Consequently, the judgment can be:
a) Fair (from 18 to 20): the student demonstrates little knowledge acquired, superficial level, several gaps. Expressive abilities modest, but sufficient to argue the contents of the program. Poor capacity for synthesis, and lack of logical connections between subjects.
b) Moderate (from 21 to 23): the student demonstrates a discreet acquisition of knowledge but lack of depth, some gaps. Expressive abilities more than sufficient to support a coherent dialogue the exposure, acceptable mastery of the language of science, capacity of synthesis, and ability to graphic expression acceptable.
c) Good (from 24 to 26): the student demonstrates a broad knowledge, moderate depth, minimal gaps, satisfactory mastery of the expressive capabilities and significant scientific language; critical ability, good capacity for synthesis and ability to graphic expression more than acceptable.
d) Outstanding (from 27 to 29): the student demonstrates excellent knowledge of the program well depth, with marginal gaps. Remarkable powers of expression and high mastery of scientific language; remarkable dialogue capacity, good competence and relevant aptitude for logic synthesis, high capacity for synthesis and graphic expression.
e) Excellent (30): the student demonstrates an excellent knowledge of the subject with no gaps (or irrelevant) and excellent exposition skills. Excellent ability dialogical aptitude to make connections between different subjects, excellent ability to synthesize and very familiar with the expression graphics.
The praise (30/30 cum laude), is attributed to the candidates that demonstrate an excellent knowledge of the whole program, arguing the subjects correctly and fluently, absence of gaps, have excellent scientific language skills and demonstrate ability in the practice use of bioinformatics tools.
Human genomics variation: Jobling, Hollox, Hurles, Kivisild, Tyler-Smith. Human Evolutionary Genetics. Garland Science, 2014"
Protein modeling: From Protein Structure to Function with Bioinformatics -Daniel John Rigden -Springer
Transcriptomics: RNA-seq Data Analysis: A Practical Approach (by Eija Korpelainen, Chapman & Hall/CRC Computational Biology Series)