Important notice
The course guide is provisional.
The PDF version of the course guide may take a few days to become available in the DDD.
Programming in Bioinformatics
Code: 42401Credits: 6
| Degree programme | Type | Course |
|---|---|---|
| Bioinformatics | OB | 1 |
Contact lecturer
- Name :
- Elisa Ruth Heymann Pignolo
- Email :
- elisa.heymann@uab.cat
Teaching staff
- Lidia Garrido Sanz
Group languages
You can consult this information at the end of the document.
Prerequisites
For the general development of the course, it is recommended to have a B2 level, or equivalent, of the English language.
For this module, it is recommended to have basic notions of computer usage in Linux (i.e., knowledge of common user tools and file manipulation).
Objectives
General objectives of this module are the application of the core tools and basic techniques for development in this area of knowledge. Provide skills to successfully assume the adaptation to changing technologies and new paradigms emerging in this interdisciplinary field.
Learning outcomes
- CA01 (Programming efficiently and accurately to solve bioinformatic problems of varying complexity.) Programming efficiently and accurately to solve bioinformatic problems of varying complexity.
- CA02 (Propose bioinformatics solutions to complex problems by participating in multidisciplinary teams.) Propose bioinformatics solutions to complex problems by participating in multidisciplinary teams.
- KA01 (Describe the fundamental concepts of programming, algorithmics, and information organisation that are applied in bioinformatics.) Describe the fundamental concepts of programming, algorithmics, and information organisation that are applied in bioinformatics.
- KA02 (Identify the main algorithmic schemes and their application cases in bioinformatics.) Identify the main algorithmic schemes and their application cases in bioinformatics.
- KA03 (Explain the fundamentals and procedures of the methods of access, analysis and data mining in bioinformatics, highlighting their usefulness for the efficient management and interpretation of large volumes of biological sequences.) Explain the fundamentals and procedures of the methods of access, analysis and data mining in bioinformatics, highlighting their usefulness for the efficient management and interpretation of large volumes of biological sequences.
- SA01 (Solve problems of a biological nature by applying programming concepts and the use of specialised bioinformatics libraries (such as BioPython or Bioconductor), guaranteeing the reliability of the code and the biological interpretation of the results.) Solve problems of a biological nature by applying programming concepts and the use of specialised bioinformatics libraries (such as BioPython or Bioconductor), guaranteeing the reliability of the code and the biological interpretation of the results.
- SA02 (Apply the main algorithmic schemes and some of their variants in the resolution of bioinformatic problems.) Apply the main algorithmic schemes and some of their variants in the resolution of bioinformatic problems.
- SA03 (Integrate access to large biological databases with local infrastructures and cloud systems, combining the necessary algorithms.) Integrate access to large biological databases with local infrastructures and cloud systems, combining the necessary algorithms.
Contents
1. Linux (commands and shell scripting)
Basic commands, user management, software management, and file system
Text processing tools and data manipulation
Redirections, pipes, and filters
Shell scripting in Bash
2. Programming languages
Introduction to R
Introduction to programming in Python in Bioinformatics
Variables, expressions, data types, operators, programming constructs, and contexts
Code reutilization: functions, modules, and subroutines
Recursive programming
Input/Output
Code debugging
3. Data structures and data processing
Basic data structures (including strings, lists, tuple, sets, and dictionaries)
Nested data structures and objects
Trees and graphs
Modelling and representing bioinformatics data
Basic bioinformatics data formats (including FASTQ, SAM, VCF)
Regular expressions
4. Bioinformatics libraries and tools
Data visualisation tools
Introduction to Biopython
Introduction to NumPy and Pandas
Learning activities and methodology
| Title | Hours | ECTS | Learning outcomes |
|---|---|---|---|
| Classroom work | 20 | 0.8 | |
| Problem solving (in class) | 14 | 0.56 | |
| Regular general work on the deliverables definition and materials given | 83 | 3.32 | |
| Work in the computing lab | 12 | 0.48 | |
| Performing lab work from recommending reading | 15 | 0.6 |
The methodology combines classroom work, supervised in-class problem solving, unsupervised work in the computing lab, homework from recommended readings and independent study student. For the delivery of the students work the virtual platform will be used.
Restricted Use of AI: For this course, the use of Artificial Intelligence (AI) technologies is permitted in a limited manner. For lab exercises and deliveries, students may use AI tools for general questions, but they are NOT allowed to submit code generated by these tools. In the lab exercises reports, students must clearly identify the assistance received from these tools, specify which tools were used, and show the prompts used. A lack of transparency in the use of AI will be considered a breach of academic integrity and may result in partial or total penalties on the activity grade, or more serious sanctions.
Assessment
Continuous assessment activities
| Title | Weight | Hours | ECTS | Learning outcomes |
|---|---|---|---|---|
| Laboratory work | 30% | 3 | 0.12 | CA01, CA02, KA01, KA02, SA01, SA02, SA03 |
| Evaluation of work done during the module, presented by the user | 10% | 1 | 0.04 | CA01, CA02, KA01, KA02 |
| Final Exam | 60% | 2 | 0.08 | CA01, CA02, KA01, KA02, KA03, SA02 |
The methodology will combine classroom work, problem solving in the classroom, unsupervised work done in the computing lab and individual work from recommended readings. It will make use of the virtual platform and will make references to selected publications related to the thematic blocks. None of the individual assessment activities will account for more than 50% of the final mark.
Retake exam
To be eligible for the retake process, the student should have been previously evaluated in a set of activities equaling at least two thirds of the final score of the module. The teacher will inform the procedure and deadlines for the retake process. Please note that activities performed in class cannot be made up.
Not valuable
The student will be graded as \"Not Valuable\" if the weight of the evaluation is less than 67% of the final score.
This subject/module does not offer a single assessment evaluation.
Any irregularity committed during an assessment activity (academic misconduct, plagiarism, or improper use of AI, unless such use is expressly authorized in the course syllabus) that that could significantly alter the grade will result in that activity being graded as 0. If the course syllabus stipulates that obtaining a minimum mark in this assessment is an essential requirement to pass the course, or if multiple irregularities occur in the assessment activities of the same course, the final grade for the course will be 0. Furthermore, disciplinary proceedings may be initiated against any student who incurs any of these irregularities.
Bibliography
- Bell, Charles; Kindahl, Mats; Thalmann, Lars. “MySQL High Availability”. O’Reilly, 2010.
- Benson, D. A., Karsch-Mizrachi, I., Lipman, D. J., Ostell, J., Rapp, B. A. & Wheeler, D. L. (2002).
- GenBank. Nucl. Acids Res., 30(1):17-20. URL http://nar.oupjournals.org/cgi/content/abstract/30/1/17.
- Berman, H. M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T. N., Weissig, H., Shindyalov, I. N. & Bourne, P. E. (2000).The Protein Data Bank. Nucleic Acids Res., 28(1):235-242.
- Bessant, C., Shadford, I., Oakley, D. \"Building Bioinformatics Solutions with Perl, R and MySQL\", Oxford University Press, 2009
- Boeckmann, B., Bairoch, A., Apweiler, R., Blatter, M.-C., Estreicher, A., Gasteiger, E., Martin, M. J., Michoud, K., O'Donovan, C., Phan, I., Pilbout, S. & Schneider, M. .The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003. Nucleic Acids Res., 31(1):365-370.
- Christiansen, P., Wall, L., Orwant, J., \"Programming Perl\". 4th Edition, O'Reilly, 2012
- Mäkinen et al., Genome-Scale Algorithm Design: Biological Sequence Analysis in the Era of High-Throughput Sequencing. Cambridge Univ. Press, 2015.
- Matloff, N., \"The Art of R Programming\". No Starch Press Inc., 2011
- Lutz, M., \"Learning Python\", O'Reilly, 5th edition, 2013
- Siever, E., Figgins, S., \"Linux in a nutshell\" O'Reilly 2009.
- Sobell, M., \"A Practical Guide to Linux. Commands, editors and shell programming\". Prentice Hall, 2009.
- Tindall, James., Begining Perl for Bioinformatics. O´Reilly 2012.
Recommended websites
- http://mscbioinformatics.uab.cat
- https://cv.uab.cat
Search for bioinformatics and computer science topics in UAB library e-book resources:
- http://www.uab.cat/biblioteques/trobador
- http://pagines.uab.cat/bctdigital
Software
Linux (Ubuntu, Bash, linux-tools, etc)
Python 3.x
Jupyter Notebook / PyCharm
Matplotlib/Seaborn
Numpy/Pandas
Course groups and languages
The information provided is provisional until November 30. After this date, you will be able to consult the language of each group through this link. To access the information, you will need to enter the course CODE
| Type of teaching | Group | Language | Semester | Shift |
|---|---|---|---|---|
| (TEm) Theory (master) | 1 | English | first semester | morning-mixed |
| (PLABm) Practical laboratories (master) | 1 | English | first semester | morning-mixed |