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Genomics

Code: 42399
Credits: 12
2026/2027
Degree programme Type Course
Bioinformatics OP 1

Contact lecturer

Name :
Antoni Barbadilla Prados
Email :
antonio.barbadilla@uab.cat

Teaching staff

Olga Dolgova Konjushenko
Marta Coronado Zamora
Jaime Martinez Urtaza
Oscar Lao Grueso
Juan Ramón Gonzalez Ruiz
Sònia Casillas Viladerrams

Teaching staff (external to UAB)

Miguel Pérez-Enciso
Simon Heath

Group languages

You can consult this information at the end of the document.

Prerequisites

To carry out this module is necessary to have passed previously both compulsory modules: Programming in Bioinformatics and Core Bioinformatics. Basic notions in genetics are also needed.

It is recommended you have a Level B2 of English or equivalent.

Objectives

The technological capacity to generate massive genomic and multiomics data grows at a relentless pace without a parallel growth of the bioinformatics expertise to deal with the integration of molecular data.

The purpose of this module is to provide the knowledge and technical skills which are required to successfully meet the current challenges of genomic and multiomics analyses, with special focus on transcriptomics, epigenomics and metagenomics. The AI advances in the present Genomics Science is also dealed with.

Learning outcomes

  • CA06 (Integrate genomic analysis methodologies in the development of interdisciplinary research projects.) Integrate genomic analysis methodologies in the development of interdisciplinary research projects.
  • CA07 (Incorporate the gender and sex perspective in the analysis and design of bioinformatics solutions.) Incorporate the gender and sex perspective in the analysis and design of bioinformatics solutions.
  • KA07 (Explain the main methodologies, techniques and tools commonly used in the processes of sequencing, assembly and annotation of genomes.) Explain the main methodologies, techniques and tools commonly used in the processes of sequencing, assembly and annotation of genomes.
  • KA08 (Define aspects related to the organisation, evolution, expression and variation of genomes.) Define aspects related to the organisation, evolution, expression and variation of genomes.
  • KA09 (Explain the information contained in genomic databases and de novo genomes, and their relevance for the study of genomic diversity and complexity.) Explain the information contained in genomic databases and de novo genomes, and their relevance for the study of genomic diversity and complexity.
  • SA08 (Use bioinformatics tools to assemble, annotate, and explore genomes using genomic databases and browsers.) Use bioinformatics tools to assemble, annotate, and explore genomes using genomic databases and browsers.
  • SA09 (Apply statistical analysis techniques in genomic variation and GWAS studies.) Apply statistical analysis techniques in genomic variation and GWAS studies.
  • SA10 (Operate with transcriptomic and epigenomic data from high-performance platforms using programming languages.) Operate with transcriptomic and epigenomic data from high-performance platforms using programming languages.

Contents

Lesson 1. Introduction: Genomes and Omics Data

Lesson 2. Making Sense of Genome Data

2.1 Genome Assembly

2.2 Genome Annotation

2.3 Functional Analysis

Lesson 3. Genome Visualization

Lesson 4. Genome Variation

4.1 Theory

4.2 Data

Lesson 5. Association Studies and GWA

Lesson 6. Transcriptomics

6.1 Microarrays

6.2 RNAseq

Lesson 7. Systems Genetics: Integrating Omics Data

Lesson 8. Artificial Intelligence and Machine Learning in Genomics

Lesson 9. Epigenomics

Lesson 10. Metagenomics

Student Seminar Session

Module Assessment Exam

Closing Lecture

Learning activities and methodology

Title Hours ECTS Learning outcomes
Theoretical-Practical classes 37 1.48 CA06, CA07, KA07, KA08, KA09, SA08, SA09, SA10
Solving problems in class and work in the computing lab 28 1.12 CA06, KA07, KA08, KA09, SA08, SA09, SA10
Seminars 4 0.16 CA06, CA07, KA07, KA08, KA09, SA08, SA09, SA10
Performing individual and team works 120 4.8 CA06, CA07, KA07, KA08, KA09, SA08, SA09, SA10
Regular study 107 4.28 CA06, CA07, KA07, KA08, KA09, SA08, SA09, SA10

The methodology combines master classes, solving practical problems and real cases, work in the computing lab, performing individual and team works, readings and discussing papers related to the thematic blocks. As ICT resource we will use the virtual teaching platform of the master.

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

Continuous assessment activities

Title Weight Hours ECTS Learning outcomes
Soft skills (assistence, arrival on time and active participation in class) 10% 0 0 CA06, CA07, KA07, KA08, KA09, SA08, SA09, SA10
Student's portfolio 45% 0 0 CA06, CA07, KA07, KA08, KA09, SA08, SA09, SA10
Individual theoretical and practical test 45% 4 0.16 CA06, CA07, KA07, KA08, KA09, SA08, SA09, SA10

The evaluation system is organized in three main activities. There will be, in addition, a retake exam. The details of the activities are:

Main evaluation activities

  • Student's portfolio (45%): Work done and presented by the student.
  • Individual theoretical and practical tests (45%)
  • Soft skills (10%): assistence, arrival on time and proactive participation in class.

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 soft skills cannot be recuperated.

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

Students who take advantage of the unique evaluation will take a single synthesis test in which the contents of the entire theory program of the subject will be evaluated. The test will consist of theoretical questions and problems and will take place coinciding with the same date set in the calendar for the last continuous assessment exam.

The same evaluation system will be applied as for the continuous evaluation. The grade obtained in this synthesis test will account for 40% of the final grade for the subject.

Seminars and problems (portfolio) are evaluated in the same way and dates as in the continuous assessment. The grade obtained will mean 60% of the final grade for the course.

Use of AI

In this course, the use of Artificial Intelligence (AI) technologies is permitted as an integral part of the assignment, provided that the final result reflects a significant contributionby the student to personal analysis and reflection.

Students must clearly identify which parts were generated with 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 will result in a penalty on the assignment grade, or greater penalties in serious cases.


Improper use of AI on assessment activities

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 may lead to a significant alteration of 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

Basic references

  • Archibald, J. M. 2018. Genomics: A Very Short Introduction. The Very Short Introductions series from Oxford University Press.
  • Brown, T. A. 2018. Genomes. 4r edition. Garland Science
  • Mäkinen, V.; Belazzougui, D.; Cunial, F. and Tomescu, A.I. 2023. Genome-Scale Algorithm Design: Bioinformatics in the Era of High-Throughput Sequencing. 2nd edition. Cambridge University Press.
  • Compeau, P and P. Pevzner. 2015. Bioinformatics Algorithms Volume 1 and 2. 2n edition. Active Learning Publishers LLC
  • Gibson, G. and S. V. Muse, 2009. A Primer of Genome Science. Sinauer, Massachusetts. 3rd edition.
  • Brown, T. A. 2018. Genomes. 4th edition. Taylor & Francis Inc.
  • Lesk, M. K. 2017. Introduction to Genomics. 3rd edition. Oxford Univ. Press.
  • MacLean, Dan 2023. R Bioinformatics Cookbook: Utilize R packages for bioinformatics, genomics, data science, and machine learning. Packt Publishing.
  • Makinen, V.; A. Belazzougui, F. Cunial, A.I. Tomescu. 2105. Genome-Scale Algorithm Design: Biological Sequence Analysis in the Era of High-Throughput Sequencing. Cambridge Univ Press.
  • Marshall, Christina 2019. Bioinformatics and Functional Genomics. Callisto Reference
  • Pevzner, P. and R. Shamir. 2011. Bioinformatics for Biologists. Cambridge University Press
  • Raza, K. 2024. Deep Learning in Genetics and Genomics: Volume 1: Foundations and Introductory Applications. Academic Press.
  • Samuelsson, T. 2012. Genomics and Bioinformatics: An Introduction to Programming Tools for Life Scientists. Cambridge University Press.
  • Springer Computational Biology Book Series (https://link.springer.com/series/5769/books)
  • Genomics articles from across Nature Portfolio (https://www.nature.com/subjects/genomics)
  • Bioinformatics articles from across Nature Portfolio (https://www.nature.com/subjects/bioinformatics)

Recomended Websites

  • National Human Genome Research Institute (USA) (http://www.genome.gov/)
  • Genomic careers (http://www.genome.gov/genomicCareers/video_find.cfm)
  • 1000 genomes project (http://www.internationalgenome.org/)
  • PopHuman database (http://pophuman.uab.cat)
  • PopLife database (https://poplife.pic.es/)
  • Genomeonline databases (GOLD) (https://gold.jgi.doe.gov/)
  • Genome data viewer NCBI (https://www.ncbi.nlm.nih.gov/genome/gdv/)
  • Ensembl genome browser (http://www.ensembl.org)
  • UCSC genomebrowser (http://genome.ucsc.edu/)
  • Genome size databases (http://www.genomesize.com/)
  • Bioinformatics Barcelona (https://bioinformaticsbarcelona.eu/es/)
  • Course: Gurrent topics in Genome Analysis 2016. NHGRI (http://www.genome.gov/12514288)
  • International Society for Computational Biology (https://www.iscb.org/)


Software

Software to be used through the module

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
(SEMm) Seminars (master) 1 English first semester morning-mixed