Identifying Data 2018/19
Subject (*) Genomics Code 614522006
Study programme
Mestrado Universitario en Bioinformática para Ciencias da Saúde
Descriptors Cycle Period Year Type Credits
Official Master's Degree 1st four-month period
First Optional 6
Language
Spanish
Teaching method Face-to-face
Prerequisites
Department Bioloxía
Coordinador
Vila Taboada, Marta
E-mail
marta.vila.taboada@udc.es
Lecturers
Becerra Fernandez, Manuel
Cerdan Villanueva, Maria Esperanza
Vila Taboada, Marta
Vizoso Vázquez, Ángel José
E-mail
manuel.becerra@udc.es
esper.cerdan@udc.es
marta.vila.taboada@udc.es
a.vizoso@udc.es
Web
General description Denomínase xenómica ao conxunto de ciencias e técnicas dedicadas ao estudo integral do funcionamento, a evolución e a orixe dos xenomas. A xenómica usa coñecementos derivados de distintas ciencias como son: xenética, bioloxía molecular, bioquímica, informática, estatística, matemáticas, física, etc.
A diferenza da xenética clásica que a partir dun fenotipo, xeralmente mutante, busca o ou os xenes responsables de devandito fenotipo, a xenómica ten como obxectivo predicir a función dos xenes a partir da súa secuencia ou das súas interaccións con outros xenes.
As ciencias xenómicas han tido un importante auxe nos últimos anos, sobre todo grazas ás tecnoloxías avanzadas de secuenciación de ADN, aos avances en bioinformática e ás técnicas cada vez máis sofisticadas para realizar análises de xenomas completos.


Competencies
STUDY PROGRAMME COMPETENCES
TypeA Code  
  Job guided
  AJ8 CE8 - Understanding the basis of the information of the hereditary material, its transmission, analysis and evolution
  AJ9 CE9 – To understand the benefits and the problems associated with the sequencing and the use of biological sequences, as well as knowing the structures and techniques for their processing
TypeB Code  
  Job guided
  BJ1 CB6 - Own and understand knowledge that can provide a base or opportunity to be original in the development and/or application of ideas, often in a context of research
  BJ2 CB7 - Students should know how to apply the acquired knowledge and ability to problem solving in new environments or little known within broad (or multidisciplinary) contexts related to their field of study
  BJ5 CB10 - Students should possess learning skills that allow them to continue studying in a way that will largely be self-directed or autonomous.
  BJ6 CG1 -Search for and select the useful information needed to solve complex problems, driving fluently bibliographical sources for the field
  BJ7 CG2 - Maintain and extend well-founded theoretical approaches to enable the introduction and exploitation of new and advanced technologies
  BJ8 CG3 - Be able to work in a team, especially of interdisciplinary nature
TypeC Code  
  Job guided
  CJ1 CT1 - Express oneself correctly, both orally writing, in the official languages of the autonomous community
  CJ2 CT2 - Dominate the expression and understanding of oral and written form of a foreign language
  CJ3 CT3 - Use the basic tools of the information technology and communications (ICT) necessary for the exercise of their profession and lifelong learning
  CJ7 CT7 – To maintain and establish strategies for scientific updating as a criterion for professional improvement.
  CJ8 CT8 - Rating the importance that has the research, innovation and technological development in the socio-economic and cultural progress of society

Learning aims

Contents
Topic Sub-topic
Introduction: from Molecular Genetics to Genomics Molecular markers
Applications ot recombinant DNA technologies
PCR and real-time quantitative PCR
Sanger sequencing
DNA editing techniques
The Human Genome Project Approaches for whole genome sequencing
Next Generation Sequencing (NGS) Platforms
Paired-end libraries
Data files
Whole genome sequencing Mate-pair libraries
Annotation
Comparative genomics
Palaeogenomics
Metagenomics Metabarcoding
Clinical Genomics Amplicon-seq
Panel-seq
Exome-seq
Comparative genomic hybrisidation (CGH-array)
Pharmacogenomics
Single Nucleotide Polymorphisms (SNPs) Genome wide association studies (GWAS)
Digital genetic testing
Functional Genomics Transcriptome analysis: microarrays and NGS (RNA-seq, CHiP-seq)
ENCODE project
Epigenomics
Hands on Sequence alignment
Genomic databases and projects
Solving exercises with GENOMESPACE and/or GALAXY
Gene expression analysis: microchips and microarrays

Planning
Methodologies / tests Competencies Ordinary class hours Student’s personal work hours Total hours
ICT practicals B2 B5 B8 C3 21 42 63
Mixed objective/subjective test A8 A9 B2 C1 C2 C3 2 8 10
Guest lecture / keynote speech A8 A9 B1 B6 B7 C1 C2 C7 C8 21 52.5 73.5
 
Personalized attention 3.5 0 3.5
 
(*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students.

Methodologies
Methodologies Description
ICT practicals Hands on: students solve exercises using their own laptop.
Mixed objective/subjective test Assessment of the learning process. Tests may include multiple choice questions, problem solving and computer exercises. Instructors will decide whether scheduling a separate test for the computer exercises depending on the progress of the group.
Guest lecture / keynote speech Each instructor will explain the basic contents of each topic interacting as much as possible with the students.

Personalized attention
Methodologies
ICT practicals
Description
The instructors will carefully supervise the student's work during the hands-on sessions.
In the event of having officially certified "part-time" students, the instructors will take the appropriate measures so that their scores are not affected.

Assessment
Methodologies Competencies Description Qualification
Guest lecture / keynote speech A8 A9 B1 B6 B7 C1 C2 C7 C8 Students must attend at least 80% of the lecturers in order to pass the subject.
Scores will depend on the result of a multiple choice test. In addition, similar calculations to the ones worked during lectures may be required.
70
ICT practicals B2 B5 B8 C3 Students must attend at least 80% of the hands on sessions in order to pass the subject.
Scores will depend on the result of an exam: students will use their own laptop to solve a set of exercises. This exam may be scheduled not to overlap with the "theory" test.
30
 
Assessment comments

In the event of having officially certified "part-time" students, the instructors will take the appropriate measures so that their scores are not affected.


Sources of information
Basic Lesk, Arthur (2012). Introduction to Genomics. Oxford University Press
Campbell, AM & Heyer LJ (2007). Discovering Genomics, Proteomics & Bioinformatics. Pearson Benjamin Cummings

Complementary



Recommendations
Subjects that it is recommended to have taken before
Introduction to molecular biology/614522004
Genetics and molecular evolution/614522005

Subjects that are recommended to be taken simultaneously

Subjects that continue the syllabus
Fundamentals of bioinformatics/614522008

Other comments

Do not take this course unless your level of English is B1 or higher.



(*)The teaching guide is the document in which the URV publishes the information about all its courses. It is a public document and cannot be modified. Only in exceptional cases can it be revised by the competent agent or duly revised so that it is in line with current legislation.