Teaching Guide ANALYSIS OF GENETIC DATA 1ST YEAR, 2ND SEMESTER MASTER EN ING. BIOMÉDICA MODALITY: ON CAMPUS ACADEMIC YEAR 2020-2021 ESCUELA ...
←
→
Page content transcription
If your browser does not render page correctly, please read the page content below
Teaching Guide ANALYSIS OF GENETIC DATA 1ST YEAR, 2ND SEMESTER MASTER EN ING. BIOMÉDICA MODALITY: ON CAMPUS ACADEMIC YEAR 2020-2021 ESCUELA POLITÉCNICA SUPERIOR
Guía Docente / Curso 2020-2021
1. COURSE/SUBJECT INFORMATION
1.- SUBJECT:
Title: Analysis of Genetic Data
Code:
Academic year: 1st Semester: 2nd
Type: Optional ECTS: 5.0
Language: English Modality: On campus
Degree in which the course is taught: Biomedical Engineering M. Sc.
Faculty/School: Escuela Politécnica Superior
2.- COURSE ORGANIZATION:
Department: Tecnologías de la Información
Knowledge area: Signal and communication theory
2. LECTURES OF THE COURSE
1.- FACULTY INFORMATION:
Professor CONTACT INFORMATION
Name: Fátima Sánchez cabo
Tln. (ext):
Email: fscabo@cnic.es
Office: External
Professor CONTACT INFORMATION
Name: Carlos Torroja Fungairiñona
Tln. (ext):
Email: ctorroja@googlemail.com
Office: External
2.- TUTORIALS:
For any queries, students can contact lecturers by e-mail, phone or visit their office during the
teacher’s tutorial times published on the students’ Virtual Campus.
Also, the professor could summon the student to see to any aspect of the course or any activity part
of the evaluation of the subject.
2Guía Docente / Curso 2020-2021
3. COURSE DESCRIPTION
NGS technologies have revolutionized the acquisition of genetic data. They have also been with
spectacular success to the analysis of gene expression, chromatin Conformation, cell lineage
decisions, diagnostics, and much more. The bioinformatic analysis of the genetic data generated by
such techniques is fast becoming the bottleneck in biological research. In this course, we will learn
the main sequencing techniques, the data formats they generate, and how to handle them in
different applications
4. COMPETENCIES
1.- COMPETENCES:
Code Basic competencies
Poseer y comprender conocimientos que aporten una base u oportunidad de ser
CB6 originales en el desarrollo y/o aplicación de ideas, a menudo en un contexto de
investigación.
Que los estudiantes sean capaces de aplicar los conocimientos adquiridos y su
CB7 capacidad de resolución de problemas en entornos nuevos o poco conocidos dentro de
contextos más amplios (o multidisciplinares) relacionados con su área de estudio.
Que los estudiantes sepan comunicar sus conclusiones y los conocimientos y razones
CB9 últimas que las sustentan a públicos especializados y no especializados de un modo
claro y sin ambigüedades.
Que los estudiantes posean las habilidades de aprendizaje que les permitan continuar
CB10
estudiando de un modo que habrá de ser en gran medida autodirigido o autónomo.
Code General Competencies
CG1 Aplicar el pensamiento analítico.
Code Specific competencies
Aplicar herramientas avanzadas de la ingeniería, las matemáticas y la física en la
CE01
resolución de problemas biomédicos.
CE02 Realizar análisis estadísticos avanzados de datos biomédicos.
Diseñar sistemas de gestión de información hospitalarios, incluyendo soluciones de
CE04 eHealth y de mHealth, conociendo los estándares que permiten la interoperabilidad de
dichos sistemas.
Code Optional competencies
CO09 Comprender los procesos de adquisición de datos ómicos.
CO10 Aplicar las principales técnicas de análisis de datos ómicos.
2.- LEARNING OUTCOMES:
3Guía Docente / Curso 2020-2021
Learning outcomes
LO1. Know and understand the main techniques for genomic sequencing and alignment.
LO2. Be able to use the different formats for representation of omic information.
LO3. Be able to employ the main data sources and bioinformatics resources in use in the field.
LO4. Know and be able to apply the main computational techniques for the analysis of genetic data.
5. LEARNING ACTIVITIES
1.- DISTRIBUTION OF STUDENTS’ ASSIGNMENT
Total Hours of the Course 150
Code Name Hours
on-campus
AF-1 Lecture 34
AF-2 Exercise-problems seminar 20
AF-4 Practice 16
TOTAL Presence hours 70
Code Name Not On-
Campus
Hours
AF-5 Student self-work 80
2.- DESCRIPTION OF LEARNING ACTIVITIES (AF):
Activity Definition
AF-1 Learning activity oriented preferably to the competence of acquisition of
Lecture knowledge (competence 1 MECES) and representative of more theoretical
subjects. This activity gives priority to the transmission of knowledge by the
professor, with the previous preparation or later study from the student.
AF-2 Learning activity which highlights the participation of the student in the
Seminar reasoned interpretation of the contents and the sources of the area of study.
It is oriented preferably to the competence of the application of knowledge
(competence 2 MECES), and also to the ability of gathering, interpreting,
and judging information and relevant data (competence 3 MECES). It is
representative of mixed profile activities or subjects; theories and practices.
AF-4 Learning activity oriented preferably to the competence of application of
Practice knowledge (competence 2 MECES) and representative of subjects or
practical activities (labs, radio studies, TV studies and/or any other proper
space).
4Guía Docente / Curso 2020-2021
AF-7 Learning activity where the student develops his or her study in an
Student self-work autonomous way working with formative materials.
6. ASSESSMENT OF LEARNING
1.- ASSESMENT SYSTEM AND CRITERIA:
ORDINARY EXAM. CONTINUOUS ASSESSMENT:
The student must show a minimum level of knowledge in all the learning outcomes in the final
exam.
The student must obtain an average grade in the tests no smaller tan al 4.0 in order to have
as final grade the average with the practical sessions. The percentages associates with each
activity are:
ORDINARY EXAMINATION (continuous assessment)
Name Percentage
Tests (S1) 50%
Practical sessions (S2) 50%
Test Description of the test Approximate weight
SE-1: Written test Written assessments as essays or multiple- 50%
choice tests or true-false tests, element
matching, problem solving, etc.
SE-2: Portfolio Set of physical or digital deliverables results 50%
or parts of a project.
EXTRAORDINARY EXAMINATION
The student that not pass the ordinary examination will have the chance to carry out the extraordinary
examination. This examination will be composed of a single exam that will determine the final grade of
the course with disregard of the academic performance in the ordinary examination. In the same line
as in the first examination, the student must reach the minimum established level for each learning
outcome.
7. COURSE PROGRAMME
1.- COURSE PROGRAMME:
Theoretical program:
1. Foundations of genetics
2. Next Generation Sequencing techniques and applications: RNA-Seq, DNA-Seq, Chip-Seq.
3. Existing NGS platforms.
4. Sequence alignment
5. Commonly used file formats for the storage of genetic information: BAM, SAM, BAI, FASTA,
FASTQ…
6. Analysis of genetic data.
7. Bioinformatics resources: databases, software, resource indexes, bibliographical resources.
8. Interaction networks, Gene Regulatory Networks.
9. Legal and ethical aspects.
5Guía Docente / Curso 2020-2021
Practical program:
1. Handling and transformation of genetic data in various formats, such as BAM, SAM, BAI,
FASTA, FASTQ.
2. Analysis with standard bioinformatics tools like Bioconductor of a real genetic dataset.
8. RECOMMENDED READING
1.- ESSENTIAL BIBLIOGRAPHY:
Bioinformatics and Functional Genomics (3 rd edition). J. Pevsner, Wiley, 2015 ISBN: 9781118581780
Understanding Bioinformatics. M. Zvelebil, J.O. Baum, Garland Science, 2007, ISBN: 9780815340249
2.- ADDITIONAL BIBLIOGRAPHY:
Introduction to Bioinformatics. A. M. Lesk (Fourth edition), Oxford, 2014
3.- WEB RESOURCES:
https://hms-dbmi.github.io/qmb-2016/lectures/cc
https://explorecourses.stanford.edu/search?view=catalog&filter-oursestatus-
Active=on&page=0&catalog=&q=CS+279%3A+Computational+Biology%3A+Structure+and+Organization+of+Bi
omolecules+and+Cells&collapse=
https://bioinf.comav.upv.es/courses.html
https://github.com/quinlan-lab/applied-computational-genomics
9. ATTITUDE IN THE CLASSROOM
1.- REGULATIONS:
Any irregular act of academic integrity (no reference to cited sources, plagiarism of work or
inappropriate use of prohibited information during examinations) or signing the attendance
sheet for fellow students not present in class will result in the student not being eligible for
continuous assessment and possibly being penalized according to the University regulations.
10. EXCEPTIONAL MEASURES
Should an exceptional situation occur which prevents continuing with face-to-face teaching
under the conditions previously established to this end, the University will take appropriate
decisions and adopt the necessary measures to guarantee the acquisition of skills and
attainment of learning outcomes as established in this Course Unit Guide. This will be done in
accordance with the teaching coordination mechanisms included in the Internal Quality
Assurance System of each degree.
6You can also read
