High Performance Computing and Big Data Analytics
Code: 43917 ECTS Credits: 12| Degree | Type | Year |
|---|---|---|
| Bioinformatics | OT | 0 |
Contact
- Name:
- Miquel Àngel Senar Rosell
- Email:
- miquelangel.senar@uab.cat
Teachers
- Oscar Lao Grueso
- Emanuele Raineri
Teaching groups languages
You can view this information at the end of this document.
Prerequisites
To carry out this module is necessary to have passed previously both compulsory modules: Programming in Bioinformatics and Core Bioinformatics.
It is recommended you have a Level B2 of English or equivalent.
Objectives and Contextualisation
This module aims to provide students with the necessary knowledge and skills (1) to implement performance engineering approaches into modern computing platforms and (2) to perform statistical analyses of Big Data.
Competences
- Communicate research results clearly and effectively in English.
- Design and apply scientific methodology in resolving problems.
- Possess and understand knowledge that provides a basis or opportunity for originality in the development and/or application of ideas, often in a research context.
- Propose biocomputing solutions for problems deriving from omic research.
- Propose innovative and creative solutions in the field of study
- Use and manage bibliographical information and computer resources in the area of study
- Use operating systems, programs and tools in common use in biocomputing and be able to manage high performance computing platforms, programming languages and biocomputing analysis.
Learning Outcomes
- Apply advanced statistical methods (automatic learning, graph theory) to model and analyse bioinformatics problems involving massive biological data.
- Communicate research results clearly and effectively in English.
- Describe and apply clustering techniques and common classification algorithms.
- Describe the operation, characteristics and limitations of the techniques, tools and methodologies to describe, analyze and interpret the amount of data produced by high-throughput technologies.
- Design and apply scientific methodology in resolving problems.
- Generate efficient parallel computing algorithms and applications for CID.
- Know and handle open-source tools for parallel, distributed and scalable analysis through automatic learning.
- Know the principles of massive data storage and management.
- Know the principles of process parallelisation.
- Learn new ways to model, store, recover and analyse abstract data types (graphs).
- Learning to handle new platforms computing platforms, paradigms, and design applications that require massive computing and data handling.
- Possess and understand knowledge that provides a basis or opportunity for originality in the development and/or application of ideas, often in a research context.
- Propose innovative and creative solutions in the field of study
- Provide parallel solutions to specific bioinformatic problems.
- Train, evaluate and validate predictive models.
- Use and manage bibliographical information and computer resources in the area of study
Content
Modern Computer Architecture
- Cluster systems
- System Middleware and Programming Frameworks
Advanced Programming Models
- Advanced shell scripting
- Using system tools for bioinformatics analysis
- High Performance Computing with Python
Big Data Analytics
- Theory and tools of advanced statistics in Big Data analytics (dimensionality reduction, variable selection and Spark)
- Machine learning theory and algorithms. Applications in Bioinformatics
- Predictive modelling: data mining, model evaluation and validation
- Data classification: naïve Bayes and decision trees learning
- Association rule learning
- Clustering analysis: k-means algorithm
- Graph Theory for Big Data
Activities and Methodology
| Title | Hours | ECTS | Learning Outcomes |
|---|---|---|---|
| Type: Directed | |||
| Solving problems in class and work in the biocomputing lab | 32 | 1.28 | 1, 15, 11, 10, 9, 8, 7, 4, 3, 6, 14, 12 |
| Theoretical classes | 38 | 1.52 | 1, 15, 11, 10, 9, 8, 7, 4, 3, 5, 6, 14, 13, 12, 16 |
| Type: Autonomous | |||
| Regular study | 226 | 9.04 | 1, 15, 11, 10, 9, 8, 7, 4, 3, 5, 6, 13, 16 |
By following a problem-oriented approach, students will get insight about efficient computational algorithms, methods and platforms and the statistical methods to be applied to challenging bioinformatics problems dealing with Big Data.
In this course, the use of Artificial Intelligence (AI) technologies is permitted as an integral part of the assignment development, provided that the final result reflects a significant contribution from the student in personal analysis and reflection. The student must clearly identify which parts were generated using this technology, specify the tools used, and include a critical reflection on how they influenced the process and final outcome of the assignment. Lack of transparency in the use of AI will be considered a breach of academic honesty and may result in a penalty on the assignment grade or greater sanctions in serious cases.
Annotation: Within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.
Assessment
Continous Assessment Activities
| Title | Weighting | Hours | ECTS | Learning Outcomes |
|---|---|---|---|---|
| Individual theoretical and practical tests | 30% | 4 | 0.16 | 1, 15, 11, 10, 2, 9, 8, 7, 4, 3, 6, 14, 13, 12 |
| Works done and presented by the student (student's portfolio) | 70% | 0 | 0 | 1, 15, 11, 10, 2, 9, 8, 7, 4, 3, 5, 6, 14, 13, 12, 16 |
The evaluation system is organized in two main activities. There will be, in addition, a retake exam. The details of the activities are:
Main evaluation activities
- Student's portfolio (60%): works done and presented by the student all along the course. None of the individual assessment activities will account for more than 50% of the final mark.
- Individual theoretical and practical test (40%): for each of the main modules of the subject, an individual evaluation mechanism will be established through an oral or written test.
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.
Not valuable
The student will be graded as "Not Valuable" if the weight of the evaluation is less than 67% of the final score.
Unique assessment
This subject/module does not provide for the single assessment system.
Bibliography
Updated bibliography will be recommended in each session of this module by the professor, and links will be made available on the Student's Area of the MSc Bioinformatics official website
Software
Linux + SLURM and other tools from Linux enviroments
Python and other tools from its ecosystem
R and other tools from its ecosystem
Groups and Languages
Please note that this information is provisional until 30 November 2025. You can check it through this link. To consult the language you will need to enter the CODE of the subject.
| Name | Group | Language | Semester | Turn |
|---|---|---|---|---|
| (PLABm) Practical laboratories (master) | 1 | English | first semester | morning-mixed |
| (TEm) Theory (master) | 1 | English | first semester | morning-mixed |