1. Genome Annotation: From Sequence to Biology Ashley Bateman & Andrew Tritt Genetics 677 Prof. Ahna Skop Spring 2009

2. Introduction -over 450 organisms have been completely sequenced since 1995, and many more have working drafts -361 prokaryotes, 28 archaea, 20 protists, 8 plants, 15 fungi, 26 mammals, and 21 “other” (wikipedia) List of Sequenced Organisms

3. Genome Sequencing Sanger Sequencing 454 Sanger Solexa

4. http://www.nature.com/nrmicro/journal/vaop/ncurrent/images/nrmicro1901-f3.jpg Reads ~200 bp 454 Sequencing: Sequencing by synthesis 1-fix DNA strands to beads in water-in-oil emulsion 2-DNA amplified by PCR 3-use PP i product of PCR to determine identity of added base

5. High Throughput Sanger Sequencing ~900 bp read -DNA of interest inserted into a plasmid, and sequenced using primers for plasmid

6. ~26-50 bp reads -newest sequencing technology --> cheaper and faster -small reads present problems if dealing with repetitive sequence Solexa Sequencing http://seqanswers.com/forums/showthread.php?t=21

7. The process of taking the DNA sequence produced by genome-sequencing projects, and adding layers of analysis/interpretation to understand its biological significance in a larger context Genome Annotation

9. Genome Annotation: A multistep process 3 general levels of annotation: -1 Nucleotide-level(where) -2 Protein-level(what) -3 Process-level(how)

10. Stein, 2001.

11. Nucleotide-level Annotation: Mapping - “…identify the punctuation marks…” - Identification and placement of known landmarks into the genome (genes, genetic markers, etc.) - Connects the pre-genomic literature with post-genomic research

12. Nucleotide-level Annotation: Finding Genomic Landmarks -short sequences: PCR-based genetic markers (ID with e-PCR program) -long sequences: RFLPs (ID with BLASTN, etc.)

13. Nucleotide-level Annotation: Gene Finding Prokaryotes: ID ORFs Eukaryotes: Sophisticated software needed (gene prediction) -overlapping ORFs -signal-to-noise ratio -splicing -unclear exon/intron delineations

14. -use algorithms that contain sensors to identify specific sequence features - neural networks - rule-based system - hidden Markov model -sequence similarity to known CDS -BLAST -cDNA -EST’s Ab initio gene prediction - without use of prior knowledge about similarities to other genes Gene Prediction Software

15. Hidden Markov Models EXON A: 0.2 C: 0.3 G: 0.3 T: 0.2 INTRON A: 0.25 C: 0.25 G: 0.25 T: 0.25 1.0 0.85 0.05 0.10 0.95 0.05 - a set of states with transition and emission probabilities - genes in a sequence predicted by finding most probable path Example : DNA Sequence : AGTTCGAATCGATGCTAAGACGA Possible Path : EEEEIIIIIIIIIIIIIIEEEEE Most probable path: EEEIIIIIIIIIIIIIIIIIEEE

16. Sequence Similarity -currently, most powerful tool for detecting CDS -Problems exist: -Fragmentary ESTs -Repetitive cDNA sequences -Ortholog-paralog problem -Incomplete data ab initio predictions + similarity data = more powerful model

17. Nucleotide-level Annotation: non-coding RNAs and regulatory regions -include tRNAs, rRNAs, snRNAs, nRNAs -transcription factor binding sites -largely unknown; active area of bioinformatics research

18. Nucleotide-level Annotation: non-coding RNAs and regulatory regions -red and blue boxes represent unknown positions of motifs -Gibbs Motif Sampler 1 and MEME infer models for motifs and identify motif locations within sequences 1 Lawrence et al. 1993, Thompson et al. 2007

19. Nucleotide-level Annotation: Repetitive Elements & Segmental Duplications Repetitive Elements: -account for a large proportion of genome size variation -important to (generally) exclude these from later assembly process -problematic for next-gen sequencing technologies Segmental Duplications: -paralogs exist throughout many genomes

20. Nucleotide-level Annotation: Mapping Variation -SNPs are important for population genetics and association mapping AAGTCGATGCTAGCGCTACTAGCTAGGCTCGATGTT AAGTCGATGCTAGCGCTACTAGCTAGGCTAGATGTT AAGTCGATGCTAGCCCTACTAGCTAGGCTCGATGTT AAGTCGATGCTAGCGCTACTAGCTAGGCTAGATGTT AAGTCGATGCTAGCCCTACTAGCTAGGCTTGATGTT AAGTCGATGCTAGCGCTACTAGCTAGGCTCGATGTT SNPs

21. Protein-level Annotation -Assign putative functions to proteins of an organism -Classify proteins into families: -using similarities to better-characterized proteins of other species (BLASTP) -on the basis of functional domains, motifs, and folds -Search against protein databases of functional domains (e.g. PFAM) -InterPro: integration of several protein databases -makes things much easier!

22. Process-level Annotation -linking the genome to biological processes -bench work required (e.g. microarrays, RNAi, etc.) -classification scheme required: Gene Ontology (GO) -standardized vocabulary for molecular function, biological process, and cellular component -hierarchy of terms provides flexibility for new additions

23. Process-level Annotation -hierarchical structure of GO terminology

24. Organizing Annotation Efforts Several models: - factory - museum - cottage industry - party Bioinformatics research in biomedical text mining to automate annotation process

25. Conclusion A synthesis of biology and annotation must be developed… …change is constant, databases are updated sometimes hourly… …the experimental literature of the past must be tied with the genome annotations of the future!

26. Student Question “The paper was mostly about predicting the number of genes and proteins in an organism. Why do we need to predict the number of genes and proteins in the cell? It appears that most studies identify genes based on phenotypes. For proteins, many methodologies exist for identifying protein function. I cannot see the purpose of this prediction--pardon my short sightedness. Also, has a standardized format emerged in regard to the genome files?” NCBI standardized format example