EMBEDDED SYSTEMS FOR E-HEALTH

International Teaching EMBEDDED SYSTEMS FOR E-HEALTH

0622900032
DIPARTIMENTO DI INGEGNERIA DELL'INFORMAZIONE ED ELETTRICA E MATEMATICA APPLICATA
EQF7
DIGITAL HEALTH AND BIOINFORMATIC ENGINEERING
2021/2022

OBBLIGATORIO
YEAR OF COURSE 1
YEAR OF DIDACTIC SYSTEM 2018
SECONDO SEMESTRE
CFUHOURSACTIVITY
324LESSONS
324LAB
Objectives
The course provides the basic knowledge for the analysis of data produced by Next Generation Sequencing platforms and the construction of pipelines for the analysis of such data.

Knowledge and understanding
Bioinformatics databases, structure, methods of access and consultation. Main problems for the analysis of omics data such as the alignment of sequences, the search for genes, mutations and variants, the correlation between genes, genome assembly. Characteristics of the main tools and platforms available on the market.

Applying knowledge and understanding
Access the main bioinformatics databases and use them in specific applications. Use the main tools and platforms for data analysis. Create pipelines for the analysis of different types of omics data such as DNA, RNA, mRNA, miRNA, referring to different species.
Prerequisites
In order to achieve the objectives of the course even not formally requested it is strongly recommended, that students have followed the courses of [0622900007] Elements Of Biology and [0622900008] elements of medical genetics and genomics.
Contents
COURSE INTRODUCTION AND BIOINFORMATICS PROBLEMS (LECTURE / PRACTICE / LABORATORY HOURS 2/0/0)

INTRO TO R PROGRAMMING (LECTURE / PRACTICE / LABORATORY HOURS 2/4/0)

INDEXING TECHNIQUES FOR READ ALIGNMENT (LECTURE / PRACTICE / LABORATORY HOURS 6/0/0)

BIOLOGICAL AND BIOINFORMATICS DATABASES AND RESOURCES (LECTURE / PRACTICE / LABORATORY HOURS 2/4/0)

NEXT GENERATION SEQUENCING TECHNOLOGIES AND APPLICATIONS (LECTURE / PRACTICE / LABORATORY HOURS 4/10/0)

GENOME ASSEMBLY ALGORITHMS AND GRAPHS (LECTURE / PRACTICE / LABORATORY HOURS 4/2/0)

BIOINFORMATICS APPLICATION (LECTURE / PRACTICE / LABORATORY HOURS 0/0/8)

TOTAL LECTURE / PRACTICE / LABORATORY HOURS 20/20/8
Teaching Methods
THE COURSE (48H OF LECTURES, EXERCISES AND LABORATORY ACTIVITIES) IS CHARACTERIZED BY A DYNAMIC SETTING, THAT INCLUDES THE ANALYSIS OF STUDY CASES WITH THE ACTIVE PARTICIPATION OF THE STUDENTS WHO WILL PERFORM SPECIFIC INSIGHTS ON THE USE OF NGS TECHNOLOGIES AND GENOME ANALYSIS TOOLS AND FRAMEWORKS DURING THE IMPLEMENTATION OF THE PROJECT WORK. IN PARTICULAR, THE TEACHING ACTIVITIES WILL INCLUDE LECTURES (20H), EXERCISES (20H) AND LABORATORY (8H) WORKING GROUPS FOR THE DEVELOPMENT OF THE PROJECT. FOR THE DEVELOPMENT OF THE PROJECT WORK STUDENTS WILL APPLY THEIR KNOWLEDGE IN ORDER TO, INDEPENDENTLY, CHOOSE THE MOST APPROPRIATE TECHNOLOGIES (FRAMEWORKS, TOOLS, ETC.) TO SOLVE SPECIFIC PROBLEMS IN THE SELECTED APPLICATION DOMAINS. THE EDUCATIONAL ACTIVITIES WILL BE SUPPORTED BY THE USE OF THE DIEM E-LEARNING PLATFORM (HTTP://ELEARNING.DIEM.UNISA.IT) TO FACILITATE AND STIMULATE DISCUSSION AND DEBATE AMONG STUDENTS AS WELL AS FOR THE NOTIFICATION AND DISTRIBUTION OF TEACHING MATERIALS.
Verification of learning
THE FINAL EXAM IS DESIGNED TO ASSESS THE OVERALL KNOWLEDGE AND UNDERSTANDING OF THE CONCEPTS PRESENTED IN THE COURSE, THE ABILITY TO APPLY THAT KNOWLEDGE TO DEVELOP SPECIFIC APPLICATIONS AS WELL AS THE ABILITY TO COMMUNICATE AND PRESENT THE WORK CARRIED OUT (COMMUNICATION SKILLS). THE EXAMINATION CONSISTS OF A PRACTICAL PART AND AN ORAL EXAM (INTERVIEW). THE PRACTICAL PART CONSISTS OF THE DEVELOPMENT OF A PROJECT WORK TO BE CARRIED OUT IN GROUPS (2-4 STUDENTS) ON THE PROPOSED BAC. THE ORAL EXAM CONSISTS OF THE PRESENTATION OF WHAT HAS BEEN ACHIEVED DURING THE DEVELOPMENT OF THE PROJECT WORK. EACH GROUP MEMBERS EXPOSE ITS OWN CONTRIBUTION FOR THE REALIZATION OF THE PROJECT TOGETHER WITH A DISCUSSION OF THE BIOINFORMATICS TOOLS AND FRAMEWORK USED AND THE ACHIEVED RESULTS.
IN THE FINAL EVALUATION, EXPRESSED WITH A MARK RANGE OF 30/30, THE PRACTICAL PART WILL WEIGH 65% AND THE ORAL EXAM FOR 35%. “HONOURS” (30/30 CUM LAUDE) WILL BE AWARDED TO STUDENTS WHO DEMONSTRATE A FULL MASTERY OF ALL THE MAIN METHODOLOGICAL AND TECHNOLOGICAL ASPECTS ADDRESSED IN THE COURSE AND HOW THEY CAN BE USED FOR THE CREATION OF APPLICATIONS AND SOLUTIONS IN DIFFERENT APPLICATION DOMAINS TOGETHER WITH THE IMPLICATIONS DERIVED FROM THEIR USE.
Texts
COURSE BOOKS
COMPUTATIONAL METHODS FOR NEXT GENERATION SEQUENCING DATA ANALYSIS (MANDOIU I AND ZELIKOVSKY A) (2016)
BIOINFORMATICS ALGORITHMS - AN ACTIVE LEARNING APPROACH (3RD EDITION - 2018) PHILLIP COMPEAU & PAVEL PEVZNER.
SUGGESTED BOOKS AND LEARNING MATERIAL
HTTPS://EN.WIKIBOOKS.ORG/WIKI/NEXT_GENERATION_SEQUENCING_%28NGS%29
NEXT-GENERATION SEQUENCING DATA ANALYSIS (XINKUN WANG) (2014)

BIOINFORMATICS: A PRACTICAL HANDBOOK OF NEXT GENERATION SEQUENCING AND ITS APPLICATIONS BY LLOYD LOW, MARTTI TAMMI 2017

SUPPLEMENTARY TEACHING MATERIAL WILL BE AVAILABLE ON THE UNIVERSITY E-LEARNING PLATFORM (HTTP://ELEARNING.UNISA.IT) ACCESSIBLE TO STUDENTS USING THEIR OWN UNIVERSITY CREDENTIALS.
More Information
The course is held in English.
  BETA VERSION Data source ESSE3