Functional Genomic (Bioinformatic Approach)

Code:

BIOL4012

Acronym:

BIOL4012

Keywords
Classification	Keyword
OFICIAL	Biology

Instance: 2024/2025 - 2S

Active?	Yes
Responsible unit:	Department of Biology
Course/CS Responsible:	Master in Functional Biology and Biotechnology of Plants

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
M:BFBP	12	Plano de Estudos M:BFBP_2015_2016	1	-	6	42	162

Teaching language

Suitable for English-speaking students

Objectives

Principal aims:
To develop an integrated view of the structure and function of genomes, transcriptomes and proteomes and to know the modern methodologies associated with functional genomics, including sequencing, annotation, in silico analysis of functional genomics databases, transcriptomics and main principles of bioinformatics.

Specific aims: 
- Compare the genes and gene families among model plants like Arabidopsis thaliana and species of agronomic interest; understand comparative genomic resources.
- To use the main in silico/bioinformatics tools for characterizing genes and proteins in plants.
- Analyze RNA-seq gene expression data towards the characterization of differentially expressed genes.
- Use protein data interpretation and analysis tools. Characterize proteins in terms of biochemical parameters, prediction of subcellular localization and post-translational modifications, prediction of 3D structure.

- Know fundamental principles of bioinformatics analysis; experiment on the use of command lines in a LINUX environment.

 

Learning outcomes and competences

After completing the UC, the student should be able to:

- Understand the importance and dynamics of genome annotation;

- Manipulate DNA sequences;

- Discover the use of various databases associated with gene expression analysis, co-expression networks and functional networks, GO term enrichment, protein characterization, comparative genomics, automated assignment of gene families, phylogenies.

- Understand the main methodological steps associated with RNA-seq transcriptomics;

- Be familiarized with the concepts associated with advanced bioinformatics and have a first contact with the LINUX environment.

 

Working method

Presencial

Program

Analysis of genes and and genome annotation using databases on platforms such as Ensembl Plants, Phytozome, PLAZA, NCBI. Identification of open reading frames (ORF Finder).

Sequence similarity search (BLAST at NCBI). Multiple sequence alignment (e.g. ClustalW). Search and manipulation of DNA sequences using BLAST/MEGA.

Analysis of gene expression in different organs or tissues and also under abiotic and biotic stress conditions, based on gene expression atlases available in silico (e.g. BAR). Analysis of co-expression networks (e.g. ATTED-II) and functional networks (e.g. STRING). GO enrichment analysis of lists of genes of interest (e.g. PANTHER).

Characterization of proteins based on biochemical parameters, prediction of subcellular localization and targeting signals for cellular compartments, prediction of post-translational modifications, prediction of functional domains and three-dimensional structure. Protein analysis tools (e.g. UniProtKB, KEGG).

Comparative genomics: analyzes of automated assignment of gene families, genome synteny and automatically generated phylogenetic trees (e.g. Ensembl Plants, Plaza, Phylogenes). Alignment tools and manual generation of phylogenetic trees.

Review of the main concepts of NGS and transcriptomic analysis by RNA-Seq. Tutorials and pipelines for data analysis. RNASeq bioinformatics exercise: analysis of read counts (read counts, normalization), exploratory data analysis (PCA, MDS, Clustering), analysis of differential gene expression (methods, MA and volcano plots), analysis of lists of differentially expressed genes.

Overview of important concepts in bioinformatics: LINUX environment; terminal vs GUI; commands (one-liners) vs scripts; main programming languages (Pearl, Python, Java); infrastructures for cloud computing. Bioinformatics exercise in LINUX: guidance on the LINUX command line, dealing with fasta files.

Mandatory literature

Bob B. Buchanan; Biochemistry & molecular biology of plants. ISBN: 0-943088-39-9

Complementary Bibliography

K. Peter C. Vollhardt and Neil E. Schore; Genetics: From Genes to Genomes, Pearson, 2017. ISBN: 10-1259700909
Philip Meneely; Genetic Analysis: genes, genomes and networks in Eukarytes , Oxford, 2020. ISBN: 13: 978-0198809906

Teaching methods and learning activities

Teaching methodologies include:

- Expository classes using powerpoint presentations, with student participation being encouraged through a discussion component.

- Interactive and problem-solving classes or exercises, involving students in a more active process.

- Analysis of case studies that illustrate the main challenges and solutions found in themes of the curricular unit.

keywords

Natural sciences > Biological sciences

Evaluation Type

Distributed evaluation with final exam

Assessment Components

designation	Weight (%)
Exame	60,00
Apresentação/discussão de um trabalho científico	40,00
Total:	100,00

Amount of time allocated to each course unit

designation	Time (hours)
Estudo autónomo	70,00
Frequência das aulas	42,00
Apresentação/discussão de um trabalho científico	50,00
Total:	162,00

Eligibility for exams

- Attendance in 75% of the classes.

- Carrying out all components of the evaluation process.

- Minimum score of 6 points (0 to 20) at the Exam component.

Calculation formula of final grade

1. Oral presentation (40%): in silico analysis of a gene of interest, using the set of functional inference tools taught during classes.


2. Exam (60%): exam on content taught at the UC, including the solving of practical exercises.

Classification improvement

Observations

Coordenator – Herlander Azevedo

Jury – Herlander Azevedo, Isabel Amorim