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GMOD 2014 MAKER Lecture
- 1. MAKER The Genome Annotation Pipeline GMOD Summer Course May 19, 2014 Barry Moore/Carson Holt Yandell Lab University of Utah
- 2. MAKER • The Annotation Problem • How MAKER Works • Why Choose MAKER • Working with MAKER
- 3. What are Annotations? FunctionalStructural Function cAMP-dependent and sulfonylurea-sensitive anion transporter. Key gatekeeper influencing intracellular cholesterol transport. Subcellular location Membrane; Multi-pass membrane protein Ref.13 Ref.14. Domain Multifunctional polypeptide with two homologous halves, each containing a hydrophobic membrane-anchoring domain and an ATP binding cassette (ABC) domain.
- 4. Genomes Online Database http://www.genomesonline.org/ 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 1998 2000 2002 2004 2006 2008 2010 2012 Genomes Year Genome Project Status Incomplete Complete
- 8. Next Gen Genome Annotation 2013- 14 • Coelacanth • Pine • Sacred Lotus • Conus ballatus • Pigeon • King Cobra • Hymenopterids • Fusarium cirinatum • Cardiocondyla obscurior • Burmese Python • Sarcocystis neurona • Spotted Gar • Apple magot fly
- 9. The ‘NextGen’ Genome Project Lab/Small Group Funding Short-read Genome Sequencing RNASeq Data Genome/Transcriptome Assembly Gene Annotation Genome Database / Blast Server Manual curation New assembly Reannotate/Merge annotations
- 10. • The Annotation Problem • How MAKER Works • Why Choose MAKER • Working with MAKER MAKER
- 11. The Source of Annotations RNA and Protein Evidence Accurate Gene Annotations Ab Initio Computational Evidence
- 12. Annotating the Genome – Apollo View current evidence gene annotations genome assembly http://apollo.berkeleybop.org/
- 13. Identify and mask repetitive elements current evidence genome assembly http://www.repeatmasker.org
- 14. Generate ab initio gene predictions ab initio predictionsSNAP GeneMark Augustus current evidence genome assembly http://korflab.ucdavis.edu/
- 15. Align RNA and protein evidence ab initio predictions protein - BLASTX EST - BLASTN altEST - TBLASTX current evidence genome assembly http://blast.ncbi.nlm.nih.gov
- 16. Polish BLAST alignments with Exonerate ab initio predictions polished protein polished EST current evidence genome assembly http://www.ebi.ac.uk/~guy/exonerate/
- 17. current evidence Pass gene-finders evidence-based ‘hints’ ab initio predictions Hint-based SNAP Hint-based Augustus genome assembly
- 18. current evidence Identify gene model most consistent with evidence ab initio predictions* Hint-based SNAP Hint-based Augustus genome assembly
- 19. current evidence Revise further if necessary; create new annotation ab initio predictions genome assembly
- 20. Compute support for each portion of gene model Eilbeck et al BMC Bioinformatics 2009 genome assembly
- 21. Compute support for each portion of gene model Cantarel BL et al., Genome Res 2008 genome assembly
- 24. MAKER2 Workflow
- 26. Paralellization Efficiency Holt C, Yandell M. BMC Bioinformatics. 2011 12:491. 30 GB Pine genome annotated in 37 hrs on 6,000 CPUs at the TACC
- 27. • The Annotation Problem • How MAKER Works • Why Choose MAKER • Working with MAKER MAKER
- 28. MAKER The Genome Annotation Pipeline GMOD Summer Course May 19, 2014 Barry Moore/Carson Holt Yandell Lab University of Utah
- 29. MAKER2 Use Cases 1. De novo annotation providing quality metrics 2. Merging multiple annotation sets 3. Re-annotation with new evidence 4. Mapping annotations forward to a new assembly 5. Generating GMOD Compliant Output 1. Gbrowse/JBrowse 2. Apollo 3. Tripal
- 30. Sensitivity, Specificity, Accuracy As a Measure of Annotation Quality Gold Standard Genes
- 31. SN SP AC 1.0 1.0 100% Gold Standard Genes Perfect Accuracy Sensitivity, Specificity, Accuracy As a Measure of Annotation Quality
- 32. SN SP AC 1.0 1.0 100% 1.0 0.5 80% Gold Standard Genes Perfect Accuracy Poor Specificity Sensitivity, Specificity, Accuracy As a Measure of Annotation Quality
- 33. SN SP AC 1.0 1.0 100% 1.0 0.5 80% 0.5 1.0 80% Gold Standard Genes Perfect Accuracy Poor Specificity Poor Sensitivity Sensitivity, Specificity, Accuracy As a Measure of Annotation Quality
- 34. SN SP AC 1.0 1.0 100% 1.0 0.5 80% 0.5 1.0 80% 0.5 0.5 50% Gold Standard Genes Perfect Accuracy Poor Specificity Poor Sensitivity Poor Specificity and Sensitivity Sensitivity, Specificity, Accuracy As a Measure of Annotation Quality Guigó R et al. Genome Biol. 2006
- 35. MAKER vs. Predictors Holt C, Yandell M. BMC Bioinformatics. 2011
- 36. MAKER vs. Predictors (the wrong HMM...) Holt C, Yandell M. BMC Bioinformatics. 2011 12:491.
- 37. Annotation Edit Distance Gold Standard GenesGold Standard Evidence Protein Alignments EST Alignments mRNASeq Eilbeck et al BMC Bioinformatics 2009
- 38. Annotation Edit Distance SN SP AED 1.0 1.0 0.0 1.0 0.5 0.2 0.5 1.0 0.2 0.5 0.5 0.5 Gold Standard Evidence Perfect Accuracy Poor Specificity Poor Sensitivity Poor Specificity and Sensitivity Eilbeck et al BMC Bioinformatics 2009
- 39. AED as a Measure of Genome Wide Annotation Quality Eilbeck et al BMC Bioinformatics 2009
- 40. TAIR Star Rating System http://www.arabidopsis.org/
- 41. AED Agrees well with the TAIR star system Evidence: mRNA-seq (17 experiments), ESTs, full length cDNAs, Swiss-Prot (minus Arabidopsis) 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.25 0.5 0.75 1 CumulativeFractionofAnnotations AED ***** (7,880) **** (12,654) *** (2,087) ** (2,188) * (1,788) (604)
- 42. Holt C, Yandell M. BMC Bioinformatics. 2011 AED as a Measure of Annotation Quality
- 43. MAKER Annotations Match the Evidence Well 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.25 0.5 0.75 1 CumulativeFractionofAnnotations AED TAIR10 rep transcripts (27,206) MAKER de novo (25,956) MAKER update of TAIR10 (26,885) 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.25 0.5 0.75 1 CumulativeFractionofAnnotations AED chr10 rep transcripts (2,688) MAKER de novo (3,056) MAKER update of v3 (2,661) A. thaliana Z. mays Campbell et al, 2013 submitted
- 44. Protein Domain Content As a Measure of Annotation Quality Holt C, Yandell M. BMC Bioinformatics. 2011
- 45. MAKER vs. Predictors Holt C, Yandell M. BMC Bioinformatics. 2011
- 46. • The Annotation Problem • How MAKER Works • Why Choose MAKER • Working with MAKER MAKER
- 49. MAKER Installation • Automated query/answer based installation script. • Installs Perl prerequisites. • Installs necessary executables – RepeatMasker (RepBase) – BLAST+ – Exonerate – SNAP • Even installs MWAS and MPICH2
- 50. MAKER Runtime Features • Fill out a config file with input data and parameters • Parallelize: – Running with MPI – Simply start multiple instances in the same directory. • Re-run MAKER in the same directory and it won't redo completed work. • Restart aborted jobs without losing any work.
- 51. Accessory Scripts Over 30 accessory scripts: •cegma2zff •chado2gff3 •cufflinks2gff3 •gff3_2_gtf •gff3_preds2models •gff3_to_eval_gtf •maker2chado •maker2jbrowse •maker2zff •tophat2gff3 •compare •evaluator •gff3_merge •fasta_merge •fasta_tool •fix_fasta •genemark_gtf2gff3 •ipr_update_gff •iprscan2gff3iprscan_batch •iprscan_wrap •maker_functional •maker_functional_fasta •maker_functional_gff •maker_map_ids •map2assembly •map_data_ids •map_fasta_ids •map_gff_ids •split_fasta
- 52. • The Annotation Problem • How MAKER Works • Why Choose MAKER • Working with MAKER MAKER
- 55. Acknowledgements • Mark Yandell – Carson Holt – Mike Campbell – Daniel Ence – Steven Flygare – Zev Kronenberg – Qing Li – Marc Singleton – Bretty Kennedy – Brandi Cantarel – Hadi Islam – Shawn Reynearson – Nicole Ruiz – Keith Simmons – Bret Heale • Alejandro Alvarado – Eric Ross • Jason Stajich • Sophia Robb • Kevin Childs • Shin-Han Shui • Ning Jiang • Yanni Sun