Syllabus Information

1.1. Aims of the course

To learn how to select appropriate statistical frameworks to model some types of biological data.

To be acquainted with some primary computational tools for the study of genomes, genes and proteins.

To learn general-purpose scripting languages that can be applied to statistical and predictive analyses in molecular biology, genomics and biotechnology.

1.2. Context and importance of this course in the degree

By using a practical approach, this course introduces some fundamental concepts and algorithms of computational biology and bioinformatics. 

1.3. Recommendations to take this course

Students are expected to be familiar with statistics and molecular biology, and should have basic programming skills.

2.1. Competences

Relevant probabilistic models for the analysis of biological data.

Statistical inference methods for typical problems.

Construction and validation of predictive and classification models.

Main data formats in bioinformatics.

Key sequence and structure alignment algorithms.

Main approaches for computing phylogenetic trees.

Use of locally installed tools: BLAST+, BWA, HMMER, CLUSTAL-OMEGA, MAMMOTH, MODELTEST, PHYML.

R programming for analysing and plotting biological data.

Handling large data files with Perl one-liners.

2.2. Learning goals

In order to pass, the student should demonstrate the following skills:
1: He/she is able to select the appropriate statistical framework to model some types of biological data.
2: He/she is knowledgeable about fundamental computational tools for the study of genomes, genes and proteins, and their applications in biotechnology and biomedicine.
3: He/she can perform simple programming tasks in the context of biological data

2.3. Importance of learning goals

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

3.1. Assessment tasks (description of tasks, marking system and assessment criteria)

Practical cases (50%): After each session, students are expected to return in time a report which will be marked by the responsible lecturer in order to track individual progress and work.

Class attendance (50%): Students are expected to participate and might optionally be given a final exam with short questions regarding any aspect of the contents of the course.

4.1. Methodological overview

The methodology followed in this course is oriented towards achievement of the learning objectives. Lectures will take place in a computer room so that students can carry out their practical work in sessions of 4 hours each. Learning material will be distributed in English in the form of presentations and/or Markdown documents that can be displayed with Rstudio or any Internet browser. Theoretical and practical contents will be combined in the same session.

4.2. Learning tasks

Students will review the literature, produce source code to solve practical cases and discuss their solutions with the rest of the class.

4.3. Syllabus

The course will address the following topics:

Topic 1. Introduction to statistical methods and programming in the R language.

Topic 2. Review of some distributions of probability.

Topic 3. Classical inference and Bayesian methods.

Topic 4. Classification and prediction.

Topic 5. Markov chains and hidden models.

Topic 6. Model validation, false positives and negatives. Receiver Operating Characteristic curves.

Topic 7. Multiple testing and error control. Limitations of Bonferroni, alternatives.

Topic 8. Introduction to processing large data files in the terminal with Perl one-liners.

Topic 9. Bioinformatics file formats: FASTA, FASTQ, PDB/PDBML, Newick.

Topic 10. Dynamic programming algorithms for local and global alignments.

Topic 11. Sequence similarity searches in local databases.

Topic 12. Multiple sequence alignment of nucleotide and protein sequences.

Topic 13. Alignment and superposition of protein structures. Scores.

Topic 14. Phylogenetic trees from distance matrices and evolution models.

4.4. Course planning and calendar

The course is taught throughout the second semester, from February to June.
The first 20h are taught JL Ojeda Cabrera [jojeda@unizar.es].
The following 20h by B Contreras Moreira [bcontreras@eead.csic.es].

4.5. Bibliography and recommended resources

R programming:
Gentleman,R. 2009. R Programming for Bioinformatics. CRC Press.

Sequence analysis:
Durbin,R., Eddy, S.R., Krogh,A., Mitchison,G. (1999) Biological Sequence Analysis. Probabilistic Models of Proteins and Nucleic Acids. Cambridge University Press.
Mäkinen, V., D. Belazzougui, F. Cunial, and A.I. Tomescu. 2015. Genome-Scale Algorithm Design: Biological Sequence Analysis in the Era of High-Throughput Sequencing. Cambridge University Press.

RStudio:
https://www.rstudio.com

Structural Bioinformatics:
http://eead-csic-compbio.github.io/bioinformatica_estructural

Perl:
http://eead.csic.es/compbio/material/bioinfoPerl