A Gene Prediction Based
DNA Sequence Analysis Course

Genome Bioinformatics Research Laboratory

GRIB (IMIM-UPF-CRG)

1. Introduction: gene specification and gene structure in eukaryotes

2. Searching for known patterns in DNA Sequences
   1. Consensus sequences, regular expressions
   2. Position specific scoring matrices
   3. Inhomogeneous markov models
   4. PRACTICAL 1. Characterization of promoter regions

3. Inferring unknown patterns from functionally related sequences
   1. Pattern discovery
   2. Heuristics and iterative algorithms
   3. Expectation Maximization algorithms

4. Searching for content in DNA Sequences
   1. Codon bias and the periodic nature of coding DNA
   2. Markov models of coding sequences
   3. PRACTICAL 2. Finding coding regions in DNA sequences

5. Gene prediction
   1. The problem.
   2. Scoring of exons.
   3. The "exon chaining" approach for gene prediction (geneid, grail, fgenes,...)
   4. Hidden Markov Models for gene prediction (genscan, hmmgene, genie, ...)
   5. PRACTICAL 3. Gene Prediction and reannotation

6. Accuracy of gene prediction methods

7. Sequence similarity based gene prediction
   1. EST based gene prediction methods (est2genome, spidey)
   2. Protein based gene prediction methods (genomescan)
   3. Splice Aligment methods (genewise, procrustes)
   4. PRACTICAL 4. Sequence similarity and
      annotation of genomic sequences (UNIX users oriented)
   5. PRACTICAL 4b. Sequence similarity and
      annotation of genomic sequences (Windows users oriented)

8. Comparative gene prediction methods
   1. Overview of the methods
   2. Pair Hidden Markov Models (slam,...)
   3. Comparative gene prediction in human and mouse: sgp and twinscan.

9. Gene Annotation of Genomes
   1. Automatic annotation pipelines and genome browsers
   2. PRACTICAL 5. Automatic Annotation of genomic sequences.

10. The complexity of eukaryotic gene structure
    1. Promoter regions
    2. Alternative splicing
    3. Non-canonical splicing
    4. Alternative translation