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.
Computational Biology and Data Analysis
Code: 44784Credits: 6
| Degree programme | Type | Course |
|---|---|---|
| Plant Biology, Genomics and Biotechnology | OB | 1 |
Contact lecturer
- Name :
- David Caparrós Ruiz
- Email :
- david.caparros@uab.cat
Teaching staff
- Ivan Alejandro Reyna Llorens
- Robertas Ursache
Teaching staff (external to UAB)
- Luca Piccinini
- Nicolas Bologna
- Jae-Seong Yang
- Víctor Manuel González Miguel
Group languages
You can consult this information at the end of the document.
Prerequisites
Although there are no official prerequisites for studying this module, it is recommended to have basic knowledge in biochemistry and Molecular and Genetic Biology, preferably in the area of plants.
Objectives
Recent technological advances that combine physics, optics, chemistry, and molecular biology have led to increasingly powerful experimental methods, generating vast amounts of publicly available biological data. This includes next-generation sequencing (NGS), transcriptomics, metabolomics, phenomics, and large-scale single-cell datasets—collectively known as "omics."
At the same time, synthetic biology and gene editing technologies are enabling the design, construction, and modeling of new genetic circuits, pushing the boundaries of what we can engineer and understand in plant biology and beyond.
In this module, students will use publicly available data and computational tools to explore synthetic biology questions in silico. The focus will be on building a solid foundation in data analysis, visualization, and interpretation, with an emphasis on applying these skills to problems in modern molecular design.
Learning outcomes
- CA10 (Apply the appropriate scientific terminology to argue the results of the research and communicate their conclusions to specialised and non-specialised audiences in a clear and unambiguous way.) Apply the appropriate scientific terminology to argue the results of the research and communicate their conclusions to specialised and non-specialised audiences in a clear and unambiguous way.
- CA11 (Apply the knowledge acquired and your ability to solve problems in new or unfamiliar environments within broader (or multidisciplinary) contexts related to Plant Biology, Genomics and Biotechnology.) Apply the knowledge acquired and your ability to solve problems in new or unfamiliar environments within broader (or multidisciplinary) contexts related to Plant Biology, Genomics and Biotechnology.
- CA12 (Use new bioinformatic tools to describe predictive models of experimental omics data in the fields of Plant Biology, Genomics and Biotechnology.) Use new bioinformatic tools to describe predictive models of experimental omics data in the fields of Plant Biology, Genomics and Biotechnology.
- KA09 (Critically identify public and scientific information related to the development of computational biology in relation to the scientific and business environment.) Critically identify public and scientific information related to the development of computational biology in relation to the scientific and business environment.
- KA10 (Select study methodologies and case study examples in plant biology and genomics.) Select study methodologies and case study examples in plant biology and genomics.
- SA16 (Interpret and discover patterns in experimental data using appropriate knowledge of biostatistics.) Interpret and discover patterns in experimental data using appropriate knowledge of biostatistics.
- SA17 (Apply mathematical methods of analysis and predictive modelling by assimilating different types of experimental omics data and using an appropriate programming language.) Apply mathematical methods of analysis and predictive modelling by assimilating different types of experimental omics data and using an appropriate programming language.
- SA18 (Apply the most appropriate methods and techniques to genomics, phenomics, transcriptomic, proteomic and metabolomic analyses.) Apply the most appropriate methods and techniques to genomics, phenomics, transcriptomic, proteomic and metabolomic analyses.
- SA19 (Apply bioinformatic tools to genomic studies of plant systematics and phylogeny and interpret the results obtained from the experiments carried out.) Apply bioinformatic tools to genomic studies of plant systematics and phylogeny and interpret the results obtained from the experiments carried out.
Contents
Introduction to R programming with Tidyverse.
Biostatistic.
Synthetic Biology Tools.
Data scanning.
Genomics bioinformatics.
Learning activities and methodology
| Title | Hours | ECTS | Learning outcomes |
|---|---|---|---|
| exam preparation | 20 | 0.8 | |
| supervision in the development of practical exercises | 16 | 0.64 | |
| bibliographic studies | 30 | 1.2 | |
| lectures | 18 | 0.72 | |
| bioinformatic sessions | 15 | 0.6 | |
| autonomous studies | 40 | 1.6 |
- Interactive master class in computer classroom
- Seminars and Practice Resolution
- Elaboration of reports
- Forum participation
Assessment
Continuous assessment activities
| Title | Weight | Hours | ECTS | Learning outcomes |
|---|---|---|---|---|
| Proactive attitude, class participation, scientific rigor in discussions, etc | 40 | 6 | 0.24 | CA10, CA11, CA12, KA09, KA10, SA16, SA17, SA18, SA19 |
| exam related to the classes | 60 | 5 | 0.2 | CA10, CA11, CA12, KA09, KA10, SA16, SA17, SA18, SA19 |
Evaluation in this module will be based on continuous assessment, with the aim of encouraging student engagement and sustained effort. The assessment activities include:
- A written exam covering theoretical content discussed in the lectures.
- Analysis and discussion of practical case studies based on scientific articles and real bioinformatics datasets. This activity will require proactive participation, critical thinking, and scientific rigor. These elements will be assessed throughout the course.
In addition, the evaluation will promote a set of key learning outcomes that, are essential for acquiring core competencies in computational and synthetic biology:
- Critically identify scientific and public information related to computational biology and its scientific and industrial context.
- Select study methodologies and practical case examples in plant biology and genomics.
- Interpret and identify patterns in experimental data using appropriate biostatistical knowledge.
- Use the most suitable methods and techniques in studies of genomics, transcriptomics, phenomics, proteomics, and metabolomics.
- Use appropriate scientific terminology to argue findings and communicate conclusions clearly to both specialized and general audiences.
- Apply acquired knowledge and problem-solving skills in new or multidisciplinary contexts related to plant biology, genomics, and biotechnology.
In this subject, the use of Artificial Intelligence (AI) technologies is allowed as an integral part of the development of the work, provided that the final result reflects a significant contribution of the student in the analysis and personal reflection. The student must clearly identify which parts have been generated with this technology, specify the tools used and include a critical reflection on how these have influenced the process and the final result of the activity. The lack of transparency in the use of AI will be considered a lack of academic honesty and may lead to a penalty in the grade of the activity, or greater sanctions in serious cases.
Bibliography
Revolutionizing agriculture with synthetic biology | Nature Plants
The Big Book of Machine Learning Use Cases | Databricks
Fundamentals of Biostatistics; Rosner, B. ( 8ª Edición Agosto 2015) ISBN 9781305268920, Editorial CENGAGE
Software
These classes will be performed using the computers in the UAB computer classroom, which will have installed all required programs.
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 |
| (PAULm) Classroom practices (master) | 1 | English | first semester | morning-mixed |