Computational Genomics

Credits
6
Types
Compulsory
Requirements
This subject has not requirements , but it has got previous capacities
Department
UB
Mail
jabril@ub.edu
In the Computational Genomics course, different bioinformatic approaches will be used to understand the Biology of the genome sequences under study (for instance, from sequencing data on DNA, RNA, ChIP, etc.). With an eminently genomic focus, the idea is to apply computational methods to analyze the structure and function of sequences, with particular emphasis on the process of annotating functional elements at the genomic level, such genes and their regulatory regions.

Teachers

Person in charge

  • Josep Francesc Abril Ferrando (jabril@ub.edu)

Others

  • Maria Tarradas Alemany (mtarradasi@ub.edu)

Weekly hours

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

Competences

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. To acquire advanced knowledge on the Computational Genomics Field.
      Related competences: K1, K2, K3, K7,
    2. Understand the computational protocols, which parameters affect the outcomes from the bioinformatics tools used in the field, and properly interpret the results of the analyses.
      Related competences: S1, S2, S3, S5, S7, S8, C2, C3, C4,

    Contents

    1. Introduction to Computational Genomics
      Sequences and annotations; basic data formats; sequence ontology; sequence basic stats and biases; sequencing methods (from Sanger to single molecule technologies) and range of applications (DNA-seq, RNA-eq, ChIP-seq, chromatin conformation, ...); sequencing quality and coverage. Sequence repositories (NCBI, EnsEMBL/BioMart, UCSC, others).
    2. Sequence Analysis
      Alphabets and strings; sequence complexity, entropy, and information content; k-mer analysis; repetitive elements (types, detection, and masking).
    3. Sequence Assembly
      Genomes, transcriptomes, and meta-genomes assemblies; reads, contigs, scaffolds, and chromosomes; assembly algorithms, from prefix-suffix alignment to assembly graphs; de-Bruijn graphs; compression-based string matching: mapping reads over assemblies (DNA aligners); assembly assessment metrics, N50, completeness (CEGMA, BUSCO); accuracy assessment of assembler tools (GAGE, Assemblathon) and DNA aligners (RGASP).
    4. Sequence Models
      Consensus sequence. Modeling signals and content: regular expressions, position weight matrices (PWMs), Markov chains, hidden Markov models (HMMs), other models.
    5. Computational Gene Finding
      The genome landscape: signals, exons, genes, regulatory elements, chromatin marks, etc...; comparing gene-finding on prokaryota vs eukaryota; computational gene-finding approaches: ab-initio, similarity-based, homology-based, comparative genomics, NGS; dynamic programming to assemble exons; generalized hidden Markov models (GHMMs); phylogenetic models (phyloHMMs); prediction of non-cannonical features: selenoproteins, pseudogenes, non-coding RNAs, ...; GF accuracy assessment: metrics (sensitivity/specificity, ...), benchmarks (*GASP).
    6. Regulatory Elements Prediction
      Regulatory elements: regulatory programs (network complexity, space and time compartimentalization), transcription factors and transcription factor binding sites (TFBSs), promoters, enhancers; pattern matching (TranFac/Jaspar/Oreganno); pattern discovery (PEAKS, MEME); phylogenetic footprinting; NGS-approaches, decyphering epigenetic code with ChIP-seq; annotating chromatin conformation over genomic sequences.
    7. Functional Annotation
      From sequence to function: genes, transcripts, and proteins; gene ontologies (GO, KO); annotating domains: patterns (PROSITE), profile HMMs (PFAM, RFAM), homology-based approaches: BLAST searches versus NOG models; meta-genomic samples: ecological network functional components, species composition and diversity measures.
    8. Managing Annotations' Data
      Annotation pipelines: manual curation procedures (NCBI, VEGA), Maker, Galaxy, EnsEMBL; visualization paradigms: from gff2ps to circos, from command-line tools to graphical interfaces (Apollo, IGV), genome browsers (EnsEMBL, UCSC-Genome browser, GBrowse/JBrowse); distributed annotation systems; database tracks versus custom tracks.

    Activities

    Activity Evaluation act


    End Term Exam


    Objectives: 1 2
    Week: 18
    Theory
    0h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    0h




    Teaching methodology

    Conceptual materials necessary to understand the topics of the Computational Genomics field will be delivered as face-to-face lectures (theory sessions). Then, each student will play with example cases and/or protocols to apply some of the ideas exposed in the theory sessions (practical sessions), which can lead to further work outside the classroom in order to complete the requested exercises for the continued evaluation (exercise submissions to the Virtual Campus).

    Independent individual study and work efforts will be necessary, in very varying amounts depending on the profit and capability of each student, in order to absorb and extend, if needed, the essentials of the concepts provided in class.

    Teaching Resources:
    + Lecture notes: Slides will be made available before the classes from Virtual Campus.
    + Practicals: Materials for the exercises will be available from Virtual Campus.
    + Links to further resources will be accessible through the Virtual Campus.

    Evaluation methodology

    Evaluation of academic performance for this subject will be based on these two blocs:

    + Practicals (Continued Evaluation): Students must submit to the Virtual Campus several exercises that will be proposed all along the practical sessions. Details about the formatting and submission procedure will be provided on the first practical session. Students will have about a week to submit each exercise through the links provided on the Virtual Campus.The tasks must be submitted individually, they cannot be solved in groups, and each student is responsible for their submissions and uploading them within the deadline established for each one.

    + Theory (End Term Synthesis Exam): Theoretical lectures will be assessed by a synthesis exam to be realized at the end of the term on the date assigned in the calendar.

    The final grade will be obtained by adding the grades of the continuous assessment (60%) and the synthesis test (40%). To pass the course, a minimum grade of 5 out of 10 is required, once all grades have been added together.

    Only students who have failed the subject and have obtained a minimum grade of 2.5 out of 10 in the synthesis test may take the Re-Assessment test, on the date assigned to the calendar for this activity. In this case, the grade of the Re-Assessment test will replace that of the Synthesis Test.

    If the teaching staff deems it appropriate, an oral test may be given to validate the authorship of any of the evaluation tests.

    Bibliography

    Basic

    Complementary

    Previous capacities

    Prior knowledge of Unix, Perl/Python, R, and MarkDown is recommended, as well as some ground concepts on Molecular Genetics and Genomics.