Información del Plan Docente

1. Información Básica

1.1. Objetivos de la asignatura

To know the theoretical basis of different  tools for statistical modeling of biological data. To know when they can
be applied and   the types of problems that  can be  solved by each technique.
 
To know how to apply statistical tools to the analysis of biological data using adequate software and to program
basic analysis.
 
To know how to interpret the results of statistical analysis.
 
To know the bioinformatic tools for the study of genomes, genes and proteins with relevant applications in
Biotechnology and Biomedicine.
 
To  be trained in the use of basic programming techniques applied to Biology.

These approaches and objectives are aligned with the following Sustainable Development Goals (SDGs) of the United Nations 2030 Agenda (https://www.un.org/sustainabledevelopment/es/), in such a way that the acquisition of the learning outcomes of the module provides training and competence to contribute to some extent to their achievement: (4) Quality education, (5) Gender equality, (8) Decent work and economic growth, (9) Industry, innovation and infrastructure, (10) Reducing inequality, (17) Partnerships for the goals.

1.2. Contexto y sentido de la asignatura en la titulación

This course introduces some fundamental concepts  of statistical modelling for biological data and algorithms of computational biology and bioinformatics, from a practical point of view.

1.3. Recomendaciones para cursar la asignatura

Students are expected to be familiar with  basic statistics and molecular biology, and should have basic programming skills, particularly in R. More precisely, they  should have taken courses on  probability and statistical inference, and they should be familiar with the use of random variables, probability distributions, point estimation,  hypothesis testing and basic statistical models.

2. Competencias y resultados de aprendizaje

2.1. Competencias

Relevant probabilistic models and results for the analysis of biological data.

Statistical inference methods for  problems in Biotechnology.

Construction and validation of predictive and classification models (supervised and  unsupervised methods).

Programming in R for analysing and plotting biological data.

Main data formats in bioinformatics.

Main knowledge for  sequence alignment algorithms.

Main approaches for  comparing and predicting regulatory sequences and structures in proteins.

Main concepts of RNA sequencing.

2.2. Resultados de aprendizaje

The student should demonstrate the following skills:

1:  To be able to select the appropriate statistical tool or technique to model different types of biological data and to implement the analysis and modelling of biological data using R.

2:  To  be able to use fundamental computational tools for the study of genomes, genes and proteins, and their applications
in biotechnology and biomedicine.

3: To perform simple programming tasks in the context of biological data.

2.3. Importancia de los resultados de aprendizaje

The skills developed while accomplishing these goals are valuable to understand the  statistical methods and
bioinformatics procedures in the literature, and  often used during the course of research projects in this area.

3. Evaluación

3.1. Tipo de pruebas y su valor sobre la nota final y criterios de evaluación para cada prueba

A. Solving of problems and practical cases, both individually and in team work. Students must submit a report at
the end of each chapter following the guidelines and presentation format.  These types of tasks are framed
within the concept of continuous evaluation, which will allow monitoring of the learning process.

 B. Written quizes or exams which can include both theoretical and applied questions discussed throughout the
course.

After each chapter or session, students are expected to return in time a report including different  individual or
team work tasks which will be marked by the lecturer in order to track  the progress.

Students are expected to participate in class and might optionally be given a final exam regarding any aspect of
the contents of the course.

4. Metodología, actividades de aprendizaje, programa y recursos

4.1. Presentación metodológica general

The methodology followed in this course is oriented towards the achievement of the learning objectives.  Several teaching and learning tasks are implemented, such as theory sessions and computer lab sessions. Theoretical and practical issues will be often combined in the same sessions,  so that lectures will take place in a computer room.

Students are expected to participate actively in the class throughout the semester.

Classroom materials will be available via Moodle. These include a repository of the lecture notes used in class, the course syllabus, datasets as well as other course-specific learning materials, including a discussion forum.

Further information regarding the course will be provided on the first day of class.

4.2. Actividades de aprendizaje

This is a 6 ECTS course organized as follows:

- Theory sessions. Lecture notes and   examples will be available for the students.

- Laboratory sessions.  They aim  to carry out exercises  to solve  problems that  appear in biological  studies using adequate software. The code, and their corresponding solutions will be available for the students. Some of the exercises will be solvedin class by the teacher.  Students are provided in advance with task guidelines for each session.

- Assignments.  The lecturer will also assign unsolved exercises, which the students will submit to be assessed.

- Autonomous work.

4.3. Programa

The course will address the following topics:
 
Section 1. Biostatistics:
 
  Probabilistic results for  statistical  inference. Central limit theorem and others.

  Statistical Inference. Parametric and non parametric tests.

  Multiple testing and error control.

  Bayesian methods.

  Supervised methods: Prediction models (linear regression models, ANOVA, generalised linear models). Model validation
and goodness of fit measures (AIC, BIC, MSE, ROC curves and others).

  Unsupervised methods: Classification techniques and dimension reduction techniques.

  Markov chains and hidden models.

 
Section 2. Bioinformatics
 
 Computational tools for data processing in bioinformatics: reading, manipulation and writing of files

Common formats in bioinformatics: nucleic acid and protein sequences and their alignments (FASTA), molecular structures (PDB) and phylogenetic trees (Newick).

Algorithms of the dynamic programming for local and global alignments. Search for similar sequences in local databases by means of alignments. Multiple alignments of DNA and protein sequences.
Alignments of protein structures and calculation of RMSD.

Design, development and fundamentals of analysis of RNAseq experiments. Gene functional and structural annotation.

4.4. Planificación de las actividades de aprendizaje y calendario de fechas clave

The course is taught throughout the second semester, from February to June:
   - The first 30h  correspond to Biostatistics
   - The following 30h  correspond to Bioinformatics
 
Further information concerning the timetable, classroom, office hours, assessment dates and other details regarding this
course will be provided on the first day of class or please refer to the "Facultad de Ciencias" website and the department
website ( https://ciencias.unizar.es )

4.5. Bibliografía y recursos recomendados

http://psfunizar10.unizar.es/br13/egAsignaturas.php?codigo=68460