Molecular Bioinformatics
A.Y. 2023/2024
Learning objectives
The development of several high-throughput analytical approaches in molecular biology has revolutionized genomics. In particular, Next Generation Sequencing has wide applications in many functional genomics settings. This course will introduce a range of these applications, focusing on the nature of data generated, its strengths and limitations as well as computational and statistical approaches used to analyze genomic and transcriptomic datasets in various contexts.
Expected learning outcomes
At the end of the course, students will acquire:
- A knowledge of the scope of bioinformatics in genomics and functional genomics.
- A detailed appreciation of the nature of Next Generation Sequencing data from different platforms, their characteristics, advantages and weaknesses.
- An understanding of fundamental aspects of experimental design in genomics and transcriptomics.
- An understanding of data quality checking and filtering approaches.
- An appreciation of theoretical considerations underlying data analytical approaches in genomics and transcriptomics (genome assembly and annotation, variant detection, gene annotation, quantitative analysis of gene expression, analysis of small non-coding RNAs).
- The ability to critically interpret results of genome wide studies.
- Experience in the evaluation and synthesis of results of genomics experiments through the preparation and presentation of a scientific poster.
- A knowledge of the scope of bioinformatics in genomics and functional genomics.
- A detailed appreciation of the nature of Next Generation Sequencing data from different platforms, their characteristics, advantages and weaknesses.
- An understanding of fundamental aspects of experimental design in genomics and transcriptomics.
- An understanding of data quality checking and filtering approaches.
- An appreciation of theoretical considerations underlying data analytical approaches in genomics and transcriptomics (genome assembly and annotation, variant detection, gene annotation, quantitative analysis of gene expression, analysis of small non-coding RNAs).
- The ability to critically interpret results of genome wide studies.
- Experience in the evaluation and synthesis of results of genomics experiments through the preparation and presentation of a scientific poster.
Lesson period: First semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course can be attended as a single course.
Course syllabus and organization
Single session
Responsible
Lesson period
First semester
no modification to standard teaching methodologies are forseen
Course syllabus
Over the last 15-20 years, a series of high throughput analytical methods have revolutionized biological and biomedical research. Among these "big data" approaches, "Next Generation Sequencing" (NGS) methods have proved to be particularly powerful and transversal in their application. The course will focus on the strengths and limitations of NGS for various applications, as well as the nature of the data produced and their computational processing, as well as the integration of various types of "big data" in current biological and biomedical research.
1) Historical perspectives and the role of bioinformatics in genomics
2) NGS technologies, read lengths, error profiles, base quality scores, data formats, data quality control.
3) Preparation of sequencing libraries, coverage biases and the impact of PCR, targeted resequencing, indexed libraries.
4) Variant discovery and structural variation between genomes
5) Di novo genome assembly
6) Annotation of genes, transcripts and alternative splicing
7) Quantitative transcriptomics
8) Analysis of small non-coding RNAs
9) Analysis of ChIP-Seq data
10) In silico promoter analysis
11) Innovative applications of NGS in genomics
12) Poster presentation sessions
13) Integration of "Big Data" resources
1) Historical perspectives and the role of bioinformatics in genomics
2) NGS technologies, read lengths, error profiles, base quality scores, data formats, data quality control.
3) Preparation of sequencing libraries, coverage biases and the impact of PCR, targeted resequencing, indexed libraries.
4) Variant discovery and structural variation between genomes
5) Di novo genome assembly
6) Annotation of genes, transcripts and alternative splicing
7) Quantitative transcriptomics
8) Analysis of small non-coding RNAs
9) Analysis of ChIP-Seq data
10) In silico promoter analysis
11) Innovative applications of NGS in genomics
12) Poster presentation sessions
13) Integration of "Big Data" resources
Prerequisites for admission
Solid understanding of the basics of molecular Biology (DNA replication, transcription, translation)
Teaching methods
Poster presentations by groups of students will take place during synchronous programmed lecture hours.
Teaching Resources
Teaching materials remain unchanged
Next-Generation Sequencing Data Analysis By Xinkun Wang ISBN 9781482217889
Essential Bioinformatics By Jin Xiong ISBN 978-0521600828
Scientific articles will be selected and provided by the teacher via the course Ariel and Teams websites.
Next-Generation Sequencing Data Analysis By Xinkun Wang ISBN 9781482217889
Essential Bioinformatics By Jin Xiong ISBN 978-0521600828
Scientific articles will be selected and provided by the teacher via the course Ariel and Teams websites.
Assessment methods and Criteria
Written exams will be carried out in person with the possibility of taking the exam online (l) for those who are currently studying outside Italy
The exam aims to:
- ascertain whether the course objectives in terms of knowledge and understanding have been met;
- ascertain the ability to apply knowledge through the discussion of the topics covered during classes;
- verify the appropriate use of scientific language relating to the field of genetics and the ability to present the topics in a clear and logical way, with the necessary links to the content of other courses of the degree course.
Evaluation will be through written exams and through verbal presentation of posters during class sessions (online).
The evaluation consists of two parts of equal importance.
1) A written exam (around 15 multiple choice questions and 3 or 4 open questions to which short answers must be provided. Multiple choice and open questions carry the same total weight (each 25% of the FINAL EVALUATION).
2) The preparation and brief presentation to the class of a scientific poster, summarizing a relevant paper. These posters are produced by groups of three students and the choice of paper can be guided by the teacher (50% of the FINAL EVALUATION).
The exam aims to:
- ascertain whether the course objectives in terms of knowledge and understanding have been met;
- ascertain the ability to apply knowledge through the discussion of the topics covered during classes;
- verify the appropriate use of scientific language relating to the field of genetics and the ability to present the topics in a clear and logical way, with the necessary links to the content of other courses of the degree course.
Evaluation will be through written exams and through verbal presentation of posters during class sessions (online).
The evaluation consists of two parts of equal importance.
1) A written exam (around 15 multiple choice questions and 3 or 4 open questions to which short answers must be provided. Multiple choice and open questions carry the same total weight (each 25% of the FINAL EVALUATION).
2) The preparation and brief presentation to the class of a scientific poster, summarizing a relevant paper. These posters are produced by groups of three students and the choice of paper can be guided by the teacher (50% of the FINAL EVALUATION).
BIO/11 - MOLECULAR BIOLOGY - University credits: 6
Lectures: 48 hours
Professor:
Horner David Stephen
Educational website(s)
Professor(s)
Reception:
Thursday 14.00 - 17.00
Via Celoria 26, Tower B, 2nd floor