Genome annotation and search for homologs

1. Genome annotation and search for homologs

2. Genome of the week • Discuss the diversity and features of selected microbial genomes. • Link to the paper describing the genome on the MMG433 website.

3. Bacillus subtilis • Gram-positive soil bacterium • Genetically tractable, well-studied • Developmental pathways (sporulation, genetic competence) • Industrial and agricultural importance • 4.2 Mb genome (sequence completed 1997)

4. B. subtilis genome features • 4,106 protein coding genes • 10 rRNA operons • Nearly 50% of the genome consists of paralogous genes. • 77 ABC transporter binding proteins • 10 phage like regions - horizontal transfer. Low GC regions in the genome. • 18 sigma factors - initiate transcription. • 34 two-component regulatory systems.

5. Sequencing of genomes • Hierarchical or contig based sequencing • Clone smaller seqments of the genome. • Labor intensive, slow • Not needed for sequencing microbial genomes • Shotgun method • Randomly clone and sequence 1.5-2 kb fragments of DNA. 5-10 fold coverage. • Computationally intensive.

6. Sequence assembly • Focus of this week’s lab exercise • Algorithms to align and edit multiple sequences • Phrap and Consed • Sequencher (commercial) for lab.

7. Finding functional features in a microbial genome. • Genes • rRNA operons, tRNAs - programs available • Origin of replication - oriC -near dnaA gene • Promoters • Transcription terminators • Horizontially transferred DNA • GC content

8. Gene finding • Easy relative to eukaryotic genomes • No introns • 80-90% of DNA encodes genes. 5% in eukaryotes. • Find open reading frames (ORF scanning). • Find start codons (mostly ATG, not always) to stop codons. Smallest ORFs - usually 300 nt in length. • Additional features. Good Shine-Dalgarno sequence (ribosome binding site). AGGAGG. Not essential. • Similarity matches to genes in other genomes. • Effective way of searching for ORFs.

9. Gene finding programs • Genefinder, Grail, Glimmer (TIGR), etc. • ORF finder from NCBI • Will use in a future lab exercise and in the final annotation project

10. Annotating genes • How to assign preliminary functions to genes. • Automated programs. • Similarity searches • BLAST and PSI-BLAST • COGs, Pfam, CDD, other databases • Only 50-75% of genes will have a predicted function. Some have no known homologs in any other genome. • Functional characterization (individual genes) • Gene knockouts • Overexpression

11. In most cases computer annotation will only be able to predict function - NOT assign function. • The biological function of many genes have not been determined, even in model systems. • As genomic characterization of gene function continues - more and more computer generated annotations will be correct.

12. Molecular function - activity of a protein at the molecular level. • Examples would be ATPase, metal binding, converting glucose-6-phosphate to fructose-6-phosphate. • Biological function - cellular role of the protein. • Examples would be translation initiation, adapting to environmental changes, glycolysis.

13. Homologs, orthologs, and paralogs. • Homologous genes are genes that share a common evolutionary ancestor. • Orthologs are genes found in different organisms that arose from a common ancestor • Paralogs are genes found in the same organism that arose from a common ancestor. Duplication could have occurred in the species or earlier.

14. Using BLAST to predict gene function. • BLAST predicted protein sequence against the non-redundant database. • Determine best hits • Automated annotation programs will often assign the best hit function to the gene being searched. • Must manually confirm automated annotations.

15. Assessment of BLAST output • What is the level of identity and similarity of the best hits? • More identity - more likely the proteins may have similar functions. • Does the area of similarity occur over the entire protein? Or just part of the protein? (fig. 2.19) • Often you will find hits to only part of your protein. A GTP-binding domain for example. • Have any of the best hits been characterized experimentally? • With so many microbial genomes sequenced chances are you will have to search extensively to find a hit that has been characterized experimentally.

16. Databases used in protein function analysis. • COGs - Cluster of orthologous groups - proteins that are best hits against each other when comparing two genomes. • Pfam - Protein families -more likely to identify conserved domains rather than full-length proteins • TIGRfam - strives to find equivalogs - “proteins that are conserved with respect to FUNCTION since their last common ancestor”

17. Databases used in protein function analysis. • SMART - Simple Modular Architecture Research Tool. • PROSITE - Protein motifs • CDD - Conserved domain database - linked to BLAST -Pfam, SMART, COGs. • InterPro - A database that brings together many of the above databases so that you can search them all at once.

18. Bottom line on databases • Are useful tools in assigning possible functions. • Be careful about annotations • example -proteins in the same COG can be orthologs that have evolved different functions. • Many annotations are not backed up by experimental data. • Some databases are automated - have not been checked for accuracy.

19. Examples YqeH and DnaA

20. Protein function • Molecular function • YqeH - GTPase • DnaA - ATPase, DNA binding • Biological function • YqeH - Unknown • DnaA -DNA replication initiation