Phylogenomics and Comparative Genomics

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Credits
6
Types
Compulsory
Requirements
This subject has not requirements
Department
UB;UAB
The aim of this course is to understand the main concepts and methodologies in phylogenomics and comparative genomics. It provides a rigorous training on the use of phylogenetic methods to infer evolutionary history and diversification mechanisms using high-throughput sequencing data. Furthermore, showcases the main comparative approaches to ascertain the evolution of genes and complete genomes, covering genome wide analyses of gene gains, losses, duplications and gene order conservation. The course places special emphasis on developing practical experience in state-of-the-art software through case studies grounded in current and future applications of phylogenomics and comparative genomics.

Teachers

Person in charge

  • Jesús Lozano Fernández ( )

Others

  • Marta Álvarez Presas ( )
  • Miguel Angel Arnedo Lombarte ( )

Weekly hours

Theory
2
Problems
2
Laboratory
0
Guided learning
0
Autonomous learning
6

Learning Outcomes

Learning Outcomes

Knowledge

  • K1 - Recognize the basic principles of biology, from cellular to organism scale, and how these are related to current knowledge in the fields of bioinformatics, data analysis, and machine learning; thus achieving an interdisciplinary vision with special emphasis on biomedical applications.
  • K2 - Identify mathematical models and statistical and computational methods that allow for solving problems in the fields of molecular biology, genomics, medical research, and population genetics.
  • K3 - Identify the mathematical foundations, computational theories, algorithmic schemes and information organization principles applicable to the modeling of biological systems and to the efficient solution of bioinformatics problems through the design of computational tools.
  • K7 - Analyze the sources of scientific information, valid and reliable, to justify the state of the art of a bioinformatics problem and to be able to address its resolution.

Skills

  • S1 - Integrate omics and clinical data to gain a greater understanding and a better analysis of biological phenomena.
  • S2 - Computationally analyze DNA, RNA and protein sequences, including comparative genome analyses, using computation, mathematics and statistics as basic tools of bioinformatics.
  • S3 - Solve problems in the fields of molecular biology, genomics, medical research and population genetics by applying statistical and computational methods and mathematical models.
  • S5 - Disseminate information, ideas, problems and solutions from bioinformatics and computational biology to a general audience.
  • S7 - Implement programming methods and data analysis based on the development of working hypotheses within the area of study.
  • S8 - Make decisions, and defend them with arguments, in the resolution of problems in the areas of biology, as well as, within the appropriate fields, health sciences, computer sciences and experimental sciences.

Competences

  • C2 - Identify the complexity of the economic and social phenomena typical of the welfare society and relate welfare to globalization, sustainability and climate change in order to use technique, technology, economy and sustainability in a balanced and compatible way.
  • C3 - Communicate orally and in writing with others in the English language about learning, thinking and decision making outcomes.
  • C4 - Work as a member of an interdisciplinary team, either as an additional member or performing managerial tasks, in order to contribute to the development of projects (including business or research) with pragmatism and a sense of responsibility and ethical principles, assuming commitments taking into account the available resources.

Objectives

  1. Inferring phylogenies using genome-scale data
    Related competences: K1, K2, K7, S2, S3,
  2. Acquisition of the specific knowledge of statistical inference and modelling in phylogenetics
    Related competences: K2, K3, S1, S3, S7, S8, C4,
  3. Use comparative genomics tools for solving biological problems
    Related competences: K1, S2, S3, S5, C2, C3, C4,

Contents

  1. Genes and their functions
    Origin of genes, duplication, losses and evolution. Gene structure and expression. Relationships between sequence, structure, and function and their evolution. Homology based functional inference. Protein domains and domain shuffling.
  2. Phylogenetic analyses
    Conceptual framework. Parsimony. Maximum Likelihood. Bayesian. Nodal support. Species and gene family tree reconstruction. Inference of gene duplication and other evolutionary events.
  3. Comparative sequence analyses
    Homology, Paralogy and Orthology. Methods for predicting orthology and paralogy: clustering-based and phylogeny-based. Gene families. Gene duplication, neo- and sub-functionalization. Gene family expansions and contractions. Adaptation and genome evolution.
  4. Phylogenomics
    Genome-wide phylogenetic analysis (phylome). Species tree reconstruction. Gene tree vs species tree. Non-vertical processes of evolution, horizontal gene transfer. Whole genome duplication. Timetrees and ancestral-state reconstruction
  5. Modelling molecular substitutions
    Model selection. Topological evaluation and incongruence. Inference in practice
  6. Genome comparisons
    Genome alignments and detection of conserved regions. Recent availability of chromosome-scale genomes and annotations thanks to global efforts (EBP, ERGA, CBP,...). Conserved motif discovery. Genome re-arrangements. Synteny analysis. Prediction of function from conserved gene order. Presence absence patterns. Convergent evolution. Gene tree comparison. Co-evolution between genes.
  7. Gene expression and functional analyses
    Genomics-based methods to assess gene expression. genome-wide functional annotation. Long-non-coding RNAs. Efforts in model and non-model species. Diversity of life and the tree of life. Variation of genome size and organization. Extreme genome expansions and reductions.

Activities

Activity Evaluation act


Theoretical expository lectures


Objectives: 1 2 3
Contents:
Theory
26h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
30h



Mid-term exam



Theory
2h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h

Final exam



Theory
2h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h

Teaching methodology

Lectures will be mainly of expository type. There will be also practical sessions using a wide range of phylogenetics and comparative genomics softwares, a small research project developed in group, and a group seminar covering a recent comparative genomics publication.

Evaluation methodology

Laboratory practices and seminars are mandatory. The course assessment is as follows:

60% consists of a 2 partial theoretico-practical exams taken at mid term (20%) and final term (40%).
15% corresponds to regular individual practical assignments
15% corresponds to a research project done in teams
10% corresponds to a seminar presentation in teams

Recuperation Information
Only the students that after the evaluation have a grade equal or greater than 3,5 can perform the re-evaluation exam. The re-evaluation exam will substitute the theoretico-practical part (60%).

Bibliography

Basic: