Omics sciences (2016/2017)

Course code
4S003719
Credits
12
Coordinator
Alessandra Maria Bossi
Teaching is organised as follows:
Unit Credits Academic sector Period Academic staff
MODULO II 6 CHIM/01-ANALYTICAL CHEMISTRY See the unit page See the unit page
MODULO I 6 AGR/07-AGRICULTURAL GENETICS See the unit page See the unit page

Learning outcomes

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MM: MODULO II
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Furnish base theoretical and practical concepts for a proteomic study.
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MM: MODULO I
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------------------------ MM: Metabolomica teoria ------------------------ The main objective of the course is to introduce the metabolomics analysis as an instrument to investigate the biological complexity of cells/tissues/organs/organisms from the point of view of the small molecules, that are the intermediate and end products of the metabolism. The metabolomics approach will be described as pipeline, a chain of connected steps from the experiment design up to the analysis and the data modeling. ------------------------ MM: Trascrittomica laboratorio ------------------------ The practical part of this Course aims to provide knowledge for proper planning, application and interpretation of a transcriptomic analysis. More in detail students will have the opportunity to design the experimental plan for a trascriptomic analysis, face the wet lab sample preparation procedure, specifically forthe RNASeq approach, and will perform the bioinformatic data analysis and interpretation. ------------------------ MM: Trascrittomica teoria ------------------------ The theory part of this course has as aim to explain what a transcriptomic analsys is and for which purpose it can be applied. Furthermore it aims to provide knowledge about the different approaches and technique which can be applied for a transcriptomic analysis.

Syllabus

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MM: MODULO II
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------------------------ MM: Metabolomica laboratorio ------------------------ -the experimental design; -metabolite extraction and preparation for LC-MS; -data formats and conversion; -chromatogram analysis and metabolite annotation; -data preprocessing; the effect of the technique of data normalization; -data processing (with MZmine): m/z feature extraction, chromatogram deconvolution and alignement; production of the Feature Quantification Matrix; -data analysis through multivariate analysis (PCA, PLS-DA, OPLS-DA) and univariate analysis. ------------------------ MM: Proteomica laboratorio ------------------------ 1. Introduction. 2. Sample preparation. 3. Gel-Based Proteomics. 4. Gel-Free Proteomics. 5. Saple pre-fractionations and analysis of low abundant proteins. 6. Proteins and peptides mass spettrometry and mass/mass spectrometry. 7. Protein identification methods and Protein Database Search. 8. Quantitative Proteomics. 9. Examples of proteomics applied to food. ------------------------ MM: Proteomica teoria ------------------------ 1. Introduction. 2. Sample preparation. 3. Gel-Based Proteomics. 4. Gel-Free Proteomics. 5. Saple pre-fractionations and analysis of low abundant proteins. 6. Proteins and peptides mass spettrometry and mass/mass spectrometry. 7. Protein identification methods and Protein Database Search. 8. Quantitative Proteomics. 9. Examples of proteomics applied to food.
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MM: MODULO I
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------------------------ MM: Metabolomica teoria ------------------------ ------------------------ MM: Trascrittomica laboratorio ------------------------ Experimental design for transcriptomic analysis Sampling and RNA extraction from treated and untreated samples Evaluation of RNA quality (Bioanalyser) Preparation of a library for RNASeq analysis using the TruSeq Illumina protocol: mRNA purification, fragmentation, random primer cDNA synthesis, cDNA purification, second strand synthesis, fill in reaction, 3’ Adenilation, adapters ligation with sequencing index, purification of cDNA ligated, PCR enrichment, final purification Evaluation of library quality Bioinformatics analysis of reads and identification of differentially expressed (FASTQC, sequences trimming, sequences alligneament and statistical analsysi for identification of DEG, exercices with small dataset) Comparison to microarray dataset Data mining (Venn diagrams, gene ontology, enrichment analysis, clustering) ------------------------ MM: Trascrittomica teoria ------------------------ Transcriptome composition and control. Why to study a transcriptome. Methods for RNA extraction and RNA quality assessment for transcriptomics, basics in reverse transcription and methods for single gene expression analysis (summary) Genome wide expression analysis methods: basics and experimental design PCR based transcriptomic analysis (Differential display, cDNA AFLP) Hybridization based transcriptomic analysis (macroarray, microarray production, different microarray based oligo platforms, microarray design and controls, different protocols for probe preparation and labelling, hybridization protocols, image acquisition, procedure for data analysis, data mining) Sequencing based transcriptomic analysis (EST libraries, SAGE, MPSS, RNA Seq, Procedure for RNASeq library preparation, library quality assessment, library sequencing, procedure for RNASeq data analysis , data mining, examples of application)

Assessment methods and criteria

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MM: MODULO II
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------------------------ MM: Metabolomica laboratorio ------------------------ ------------------------ MM: Proteomica laboratorio ------------------------ Written 2 hours. ------------------------ MM: Proteomica teoria ------------------------ Written 2 hours
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MM: MODULO I
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------------------------ MM: Metabolomica teoria ------------------------ ------------------------ MM: Trascrittomica laboratorio ------------------------ Oral examination ------------------------ MM: Trascrittomica teoria ------------------------ Oral examination