Bioinformatics is the only subject on the Biochemistry and Molecular Biology degree in which the general principles of Bioinformatics are specifically studied; however, this is a rapidly growing area and bioinformatics is widely used as a tool in other areas.



The great technological advances in the field of Molecular Biology have generated a huge amount of experimental data, and have led to the birth of new areas of knowledge, such as genomics, proteomics, transcriptomics, lipidomics, glycomics, metabolomics, and interactomics. Therefore, computers, software and algorithms need to be used to store, manage, and analyze all this information. Bioinformatics can be defined as the scientific field that uses computational methods to answer biological questions.



Basically, Bioinformatics covers three types of activities:

1.- The creation of databases capable of storing and managing large amounts of biological data. Preferably, the databases should be accessible through the Internet and have an intuitive design that facilitates their use.



2.- The development of algorithms that allow to model, visualize, extract and establish relationships between biological data (for example: methods to compare sequences or patterns of gene expression).



3.- The development and implementation of intuitive and easy-to-use computer tools that allow the selection, organization and analysis of biological data and facilitate the interpretation of the information.



The main objectives of the course are:

1.- Familiarize students with the resources available in the main bioinformatics portals available online (NCBI, SIB, EBI) so that they are able to extract all the information they may need quickly and efficiently.

2.- Provide students with solid knowledge related to the most widely used databases and tools in Bioinformatics.

3.- Train students capable of interpreting the information obtained with criteria to determine its relevance and biological meaning.



The subject uses various training resources that are carried out in teams, which facilitate autonomous learning, stimulate interest in the subject, and encourage critical thinking and reasoning.



In-depth knowledge of computer science is not required to take this subject. However, it is recommended that students have taken the compulsory subjects of Genetics and Proteomics, Protein Structure and Engineering (the 2nd year), and Methods in Molecular Biology (first semester of the third year), to understand the type of biological data with which they are going to work, as well as the methods by which they are obtained. The contents of this subject are of interest to advance in different optional subjects taught in the last year, such as Extension of Molecular Biology, Systems Biology, Genomics, or Molecular Evolution.



The subject is very useful for the professional career of any Biosciences graduate. The bioinformatician profile is now in high demand, both in public research centers and in private companies because – in addition to being essential for managing the large amounts of data now common in research projects –in silico experiments (requiring the use of computers) are possible and allow progress in research with considerable savings in time and money.

The skills acquired from the course are:

General competences:

T5 - Strengthen the skills to apply the knowledge acquired to the professional world

T17. Develop the ability to quantitatively analyze biological processes.

T20. Analyze and properly interpret data and experimental results of the area



Specific competences:

MO4.6 - Extract information from bibliographic sources and biological databases, and analyze it with bioinformatics tools



Cross-cutting skills:

T1 - Develop the capacity for analysis, synthesis and critical reasoning in the application of the scientific method

T2 - Develop autonomous learning and adaptation to new situations

T3 - Transmit ideas and communicate them to a professional and non-professional audience, encouraging the use of foreign languages, especially English

T4 - Collaborate and work in multidisciplinary and multicultural teams respecting gender equality



At the end of the subject, the specific and measurable learning outcomes that will be assessed are:

1.- Students manage the various molecular databases both to enter data and to extract information quickly and efficiently.

2.- Students analyze protein or nucleic acid sequences to extract the maximum amount of information possible.

3.- Students understand how sequences are compared to establish homology relationships and to identify patterns, motifs, and conserved domains.

4.- Students use prediction tools (structural or functional) and critically evaluate the results obtained.

6. Students know the bases of the analysis of data obtained from Next Generation Sequencing projects and other omics.

7. Students plan and carry out simple in silico research projects as a team and critically interpret and evaluate the results obtained from a biological point of view.

8.- Students communicate fundamental aspects of their professional activity to other professionals in their area, or similar areas, and to a non-specialized public.

1.- THEORETICAL CONTENT

Thirty sessions (50 minutes each) will be devoted to explaining the following topics:



PART I - INTRODUCTION

Topic 1.- Introduction. Definition and applications of Bioinformatics.

Topic 2.- Biological sequences. Information in nucleic acids and proteins. Mathematical models of biological sequences. Statistical analysis of sequences.



PART II - DATABASES AND SEQUENCE ANNOTATION

Topic 3.- Introduction to databases. Sequence annotation. Sequence formats.

Topic 4.- Annotation of nucleotide sequences. Location of coding sequences. Location of regulatory elements.

Topic 5.- Primary databases of nucleic acids: GenBank-ENA-DDBJ. Record structure. Features table. Search strategies.

Topic 6.- Annotation of protein sequences. Determination of its physical-chemical parameters. Protease breakpoints. Sites of post-translational modification. Signal sequences. Domains.

Topic 7.- Primary protein databases: UNIPROT-KB. Record structure. Features table. Search strategies.



PART III - SEQUENCE ANALYSIS

Topic 8.- Comparison of sequences. Homologous sequences (orthologous, paralogous, xenologous). Alignment types. Scoring systems. Substitution matrices (PAM, BLOSUM). Penalties.

Topic 9.- Alignment of two sequences. The Brute Force algorithm. Point matrices (dot plots). Dynamic programming algorithms. Heuristic algorithms.

Topic 10.- The NCBI BLAST tool. Program variants. Analysis of the results.

Unit 11.- Multiple sequence alignment (MSA). Dynamic programming algorithms. Heuristic algorithms. Alignment editing.

Topic 12.- Analysis of conserved motifs: motifs, patterns, rules, fingerprints, blocks, profiles, hidden Markov models. Secondary databases.

Topic 13.- Phylogenetic analysis.



PART IV – ANALYSIS OF NGS DATA AND OMIC APPROXIMATIONS

Topic 14.- Analysis of DNA sequences. Genomics.

Topic 15.- Analysis of gene expression. Transcriptomics.

Topic 16.- Proteomics.

Topic 17.- Introduction to bioinformatic analysis at the level of families and routes.



2.- COMPUTER PRACTICAL CLASSES

5 sessions (4 hours each) will be devoted to carrying out practical exercises:

1.- Primary databases of nucleotide sequences (GenBank)

2.- Primary databases of protein sequences (Uniprot-KB)

3.- Alignment of two sequences

4.- Multiple alignment of sequences

5.- Omic studies





3.- CLASSROOM PRACTICAL CLASSES

Five classes (50 minutes each) will be devoted to solving various types of problems:

1.- Sequence analysis

2.- Hidden Markov Motives (HMM)

3.- Sequence alignment using dynamic programming algorithms

4.- Position-specific scoring matrices (PSSM)



4.- SEMINARS

Five classes of 50 minutes each will be dedicated to the presentation by the students of a topic related to the content of the subject that is prepared in groups. Participation and debate will be encouraged. The teacher will act as moderator.



Possible topics for seminars:

Hidden Markov Motifs

Alignment scoring matrices

Alignment Penalty Systems

Dynamic programming algorithms

Primary databases

Localization of coding sequences

Analysis of conserved motifs

NGS data analysis

Neural Networks and Artificial Intelligence

Theoretical classes (30 in-person hours and 45 remote hours)

They are taught in the classroom and are based on the teaching material made available to the student on the eGela platform. These are basically presentations with the most relevant subject contents.

Classroom practical classes (5 in-person hours and 7.5 remote hours)

They are taught in the classroom and consist of solving problems related to the subject syllabus.



Computer practical classes (20 in-person hours and 30 remote hours)

They are taught in the computer room and consist of using the resources offered by the Internet to work in teams to complete a series of practical exercises related to the subject's agenda. The methodology used for this section consists of project-based learning.

Seminars (5 in-person hours and 7.5 remote hours)

They are taught in the classroom. For each seminar, the students must prepare the proposed topic using the documentation. Participation and debate among students will be encouraged. The teacher will act as moderator.



Students can make use of the tutorial classes to clarify any doubts they may have.

• Continuous Assessment System
• Final Assessment System
• Tools and qualification percentages:
  • Written test to be taken (%): 25
  • Multiple-Choice Test (%): 25
  • Realization of Practical Work (exercises, cases or problems) (%): 10
  • Team projects (problem solving, project design)) (%): 30
  • Exhibition of works, readings ... (%): 10

The assessment of the subject will be mixed: continuous assessment during the semester and a final exam. The following criteria will be adopted:



1. Final exam (50%): The exam will consist of multiple-choice questions, problems and short questions. To pass the subject it is necessary to obtain a grade equal to or greater than 5 (out of 10). Otherwise, the score obtained in the other teaching modalities will be maintained for the following assessment sessions.

2. Computer practical classes (30%): attendance (missing the class penalizes) and the presentation of the corresponding exercises.

3. Classroom practical classes (10%): attendance (missing the class penalizes) and the presentation of the solved problems.

4. Seminars (10%): attendance (missing the class penalizes), the presentation of the seminar, and the active participation in class.



These criteria may be modified depending on how the program develops throughout the course. Any change will be notified to the students prior to the exam.



In any case, students will have the right to be assessed through the final assessment system, regardless of whether or not they have participated in the continuous assessment system. To that end, students must submit the waiver of continuous assessment to the teaching staff responsible for the subject, within 9 weeks from the beginning of the semester, in accordance with the academic calendar of the center.



In the case of students on both continuous and final assessments, not taking the written test will be qualified as "not presented" in the final grade for the course.



The use of books, notes, phones, electronic devices, computers, or other equipment is not allowed during assessment tests.



If any academic dishonesty or fraudulent practices are detected, the rules of the UPV/EHU will be enforced.

The assessment criteria will be the same as in the ordinary exam. In exceptional situations, the criteria will be established with the student.

The passing grades of the continuous assessment obtained by the student during the course are kept. In case of failing grades, the final assessment test will constitute 100% of the grade. Not taking the written test will be qualified as "not presented" in the final grade for the course.



The use of books, notes, phones, electronic devices, computers, or other equipment is not allowed during assessment tests.



If any academic dishonesty or fraudulent practices are detected, the rules of the UPV/EHU will be enforced.

Teaching content available on eGela

Basic bibliography

1.- Understanding bioinformatics. Marketa Zvelebil & Jeremy O. Baum. Garland Science (2008)

2.- Bioinformatics and Functional Genomics (3rd edition). Jonathan Pevsner. Wiley Blackwell (2015)

3.- Bioinformatics. Sequence and genome analysis (2nd edition). David W. Mount. CSHL Press (2004)

4.- Essential bioinformatics. Jin Xiong. Cambridge University Press (2006)

5.- Bioinformatics for dummies (2nd edition). Jean-Michel Claverie & Cedric Notredame. Wiley Publishing Inc. (2007)

6.- Introduction to Bioinformatics. Anna Tramontano. Chapman & Hall-CRC (2007)

7.- Advances in Bioinformatics. Vijai ¬Singh & Ajay¬Kumar. Springer (2021)

8.- Essentials of Bioinformatics, Volume I. Understanding Bioinformatics: Genes to Proteins. Noor¬ Ahmad¬ Shaik, Khalid ¬Rehman¬ Hakeem, Babajan¬ Banaganapalli & Ramu Elango. Spinger (2019)

In-depth bibliography

1.- Biological sequence analysis. Probabilistic models of proteins and nucleic acids. R. Durbin, S. Eddy, A. Krogh y G. Nitchison. Cambridge University Press (2006)
2.- Introduction to computational genomics. Nello Cristianini y Matthew W. Hahn. Cambridge University Press (2007)
3.- Essentials of Bioinformatics, Volume II. In Silico Life Sciences: Medicine. Noor¬ Ahmad¬ Shaik, Khalid ¬Rehman¬ Hakeem, Babajan¬ Banaganapalli & Ramu Elango. Spinger (2019)

Journals

WIREs Computational Molecular Science
Bioinformatics
PLOS Computational Biology
Briefings in Bioinformatics
Database
Nucleic Acid Research (Database issue)

Web addresses

1.- http://www.ncbi.nlm.nih.gov/
2.- http://www.ebi.ac.uk/
3.- http://www.expasy.org/
4. https://usegalaxy.org/