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Basic Bioinformatics — IQB723

Graduate course, usually offered once a year.

Instructor
Rafael Dias Mesquita (Department of Biochemistry / Institute of Chemistry / UFRJ)
Intended audience
Graduate students with no prior background in bioinformatics.
Workload
30 hours — lecture and laboratory course (15 hours of lectures and 15 hours of hands-on practice)
Credits
2
Places
12
Prerequisites
None, though previous coursework in biochemistry and molecular biology is desirable. Each student must bring a laptop with working wireless networking.

Software installation guides

Additional software to install, without a guide: FigTree, MEGA6 and PyMOL.

Syllabus

Introduction to bioinformatics, software tools, file formats used in bioinformatics, online databases, similarity searches, conserved domains, sequence alignment, orthology and related concepts, phylogeny and phylogenetic trees, structural modelling.

Aim

To teach basic bioinformatics concepts and to train students in the use of a range of programs — almost all of them available as web services — without requiring Linux or the command line.

Programme

  1. Introduction to bioinformatics: basic concepts of a computer (hardware, operating system and programs) and of molecular biology (DNA structure, translation, reading frames, protein structure and conserved domains).
  2. Software tools:
    • Firefox: installing and using add-ons.
    • Text editors (Textpad): character encoding and line-ending conversion, line wrapping, find and replace, column selection.
    • Sequence editors (BioEdit): loading a sequence, editing, building the reverse complement, translating, and so on.
  3. Bioinformatics file formats (FASTA and GenBank): viewing and editing sequences.
    Practical: converting text files between Unix and Windows line endings; searching and editing to remove one string and replace another with the original sequence present in a FASTA-formatted protein.
  4. NCBI databases: PubMed, books, proteins (nr, Swiss-Prot, RefSeq…), conserved domains (CDD), nucleotides (nt, mRNA, ORF, genes…), taxonomy and others — access, search, display and download.
  5. Similarity searches: scoring matrices and the BLAST family of programs; position-specific scoring matrices and PSI-BLAST.
    Practical: find a gene, mRNA, ORF or protein by keyword; download the gene, cDNA and encoded protein sequences in FASTA; find 5 similar sequences from closely related organisms with BLAST restricted by taxid; search for ESTs supporting the chosen mRNA; download the similar sequences identified and any ESTs found.
  6. Conserved domains, databases and domain architecture: CDD, PFAM, BLOCKS, SMART and others — access, search, display and download.
    Practical: use the previous session's sequences to search for conserved domains in at least two databases and compare the results; look for other sequences with the same domain architecture; check the functional annotation and whether the similar sequences identified earlier are among them.
  7. Sequence alignment (ClustalW, Praline, Muscle, T-Coffee): local vs. global, pairwise vs. multiple, parameters and scoring matrices, and alignment quality assessment.
    Practical: pairwise alignment of the mRNA, ORF and best match; align the 6 mRNAs/ORFs, check quality and identify regions suitable for designing degenerate and species-specific primers; align the 6 proteins, check quality and verify whether the regions chosen for primer design are conserved at the protein level and whether the conserved domains identified earlier align well.
  8. Orthology, paralogy, homology and similarity: databases of orthologous clusters — COG, KOG, KEGG, PRO, GO, HomoloGene and others. Identifying orthologues and metabolic pathways in KEGG.
    Practical: check whether the protein belongs to a cluster of orthologues in one of these databases and whether the selected similar sequences belong to the same cluster; check whether the protein takes part in a metabolic pathway and generate a coloured map for the species of interest.
  9. Phylogeny and dendrograms: fundamentals of phylogeny, distance matrix methods, maximum parsimony and likelihood, Bayesian inference and model selection. Building rooted and unrooted trees.
    Practical: load the multiple protein alignment and build a phylogenetic tree with one of the methods covered.
  10. Homology modelling: identifying and choosing a three-dimensional protein structure, alignment and modelling.
    Practical: check whether a three-dimensional structure exists for the protein or for any of the similar sequences already identified; otherwise find the most similar sequence with a solved structure; align the structure's sequence with the others and submit homologous sequences from at least 2 different organisms for modelling.
  11. Three-dimensional alignment and model quality assessment: RMSD, assessment of loops and secondary structure, and model stabilisation.
    Practical: visualise the models created and align the reference structure with the generated models; compute the RMSD and assess the quality of the models.

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