Molecular Bioinformatics
A.Y. 2021/2022
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 cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Responsible
Lesson period
First semester
More specific information on the delivery modes of training activities for academic year 2021/22 will be provided over the coming months, based on the evolution of the public health situation.
Written exams will be carried out in person with the possibility of taking the exam online (oral) for those who have reasonable justifications and are not able to attend in person.
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.
Written exams will be carried out in person with the possibility of taking the exam online (oral) for those who have reasonable justifications and are not able to attend in person.
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.
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
Participants should possess a sound grasp of basic concepts in molecular biology (DNA replication, transcription, translation, the genetic code, PCR).
Teaching methods
Frontal teaching with a high level of teacher interaction, supported by projected teaching materials, which will be made available on the course Ariel website. Extensive discussions will be carried out to allow development of critical thinking and to encourage constructive individual involvement in the teaching/learning process. Lecture attendance is highly encouraged. During the course the studentes are encouraged to prepare scientific posters on specific arguments under the guidance of the teacher.
Teaching Resources
Scientific articles will be selected and provided by the teacher via the course Ariel website. Consulting textbooks: Next-Generation Sequencing Data Analysis By Xinkun Wang ISBN 9781482217889 and Essential Bioinformatics By Jin Xiong ISBN 978-0521600828
Assessment methods and Criteria
The exam 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 (50%).
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.
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 (50%).
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.
Professor(s)
Reception:
Thursday 14.00 - 17.00
Via Celoria 26, Tower B, 2nd floor