BIOL335: How to annotate a genome
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This document discusses various methods for annotating genomes after sequencing and assembly. Sequence analysis approaches like identifying open reading frames can rapidly and inexpensively find some genes, but have weaknesses like false positives and missing short genes. More accurate methods are needed to find non-coding RNAs, pseudogenes, and other elements. As sequencing technologies generate more data, the bottleneck has shifted to analysis, requiring skills in both biology and mathematics. The document provides an example sequence to annotate and poses questions about fast, cheap and accurate annotation methods.
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BIOL335: How to annotate a genome
- 1. Genome annotation Paul Gardner March 3, 2015 Paul Gardner Genome annotation
- 2. Medical genomics Vicky Cameron & Anna Pilbrow at Otago are identifying genetic variation and genes associated with an increased risk of heart disease. Mike Stratton at the Sanger Institute is hunting for genetic variation that is associated with an increased risk of cancer. Rob Knight at UC Boulder is sequencing the microbes that live on us. Finding associations between our health and microbial communities. See Rob’s TEDTalk. Paul Gardner Genome annotation
- 3. Agricultural genomics Graeme Attwood at AgResearch is trying to stop cows & sheep from emitting greenhouse gases by studying their gut microbes. He has sequenced two methanogenic Archaeal genomes of Methanobrevibacter sp. Honour McCann at Massey University is trying to determine how Pseudomonas syringae pv. actinidiae (PSA) is killing kiwifruit. Rebecca Ganley at SCION is investigating how Phytophthora Taxon Agathis (PTA) is causing kauri die-back disease and killing kauri trees. Paul Gardner Genome annotation
- 4. Academic interest genomics Tom Gilbert at the University of Copenhagen is sequencing bird and giant squid genomes. Elizabeth Murchison is sequencing tasmanian devils (and their transmissible cancers). See Liz’s TEDTalk. Neil Gemmel at Otago University is sequencing the tuatara genome. Paul Gardner Genome annotation
- 6. Discussion How should these researchers annotate their genomes (after they have sequenced and assembled them)? What are the fast and cheap methods? What are the most accurate methods? Paul Gardner Genome annotation
- 7. The data tsunami Thanks to new sequencing technologies (recall Ant’s teeny-tiny little sequencer). Biologists no longer spend years acquiring data. The bottle-neck for research is now in the analysis phase of research. Biologists with good mathematics skills and mathematicians with an interest in biology are in high demand. Gather data Analyze-Classify Hypotheses- Predictions Experiment GCGAGCAGACGCA CCGAACAGACACA GUGAGCAGGCGCC CCGAGCAGUCAUA ACACUGAGACGCA GCGAGCGU-AACG R A A A A R C Y Y R R G Y U U U U U U U5' 0.0 1.0 2.0 A C GU CC A GA5 A GA U CAGG U A10 CA GU CU G A Paul Gardner Genome annotation
- 8. We can use sequence analysis... Genes leave a statistical signal in the genome... Example: identify promotors, ribosome binding sites, open-reading frames (ORFs), terminators In eukaryotes CpG islands, splicing signals and poly-A tails may be incorporated How reliable are these approaches? What are the main weaknesses & strengths? Figure from: http://zerocool.is-a-geek.net/?p=630 Paul Gardner Genome annotation
- 9. Sequence analysis: strengths and weaknesses ORF prediction: Prodigal, GLIMMER Strengths: very fast cheap Weaknesses: false positives (see AntiFam) misses short peptides (e.g. toxins-antitoxin systems) No ncRNAs, pseudogenes, recoding elements, ... Paul Gardner Genome annotation
- 11. We can use homology... Evolution tends to preserve functional genomic regions... Example 1: Use an existing set of genes from related species and map these onto your genome (e.g. RATT) Example 2: Align two or more related genomes, look for conserved regions, patterns of variation can be indicative of function (e.g. QRNA, RNAz & RNAcode) How reliable are these approaches? What are the main weaknesses & strengths? Paul Gardner Genome annotation
- 12. The QRNA approach... Rivas et al. (2001) Computational identification of noncoding RNAs in E. coli by comparative genomics. Current Biology. Paul Gardner Genome annotation
- 13. DNA encodes Protein # STOCKHOLM 1.0 #33 unique RNA sequences, 1 peptide sequence #=GR PR1 G..A..D..V..T..H..P..P..A..G..D.. #=GR PR3 GlyAlaAspValThrHisProProAlaGlyAsp platypus GGAGCAGACGTCACTCACCCCCCAGCCGGAGAT opossum GGAGCAGATGTTACTCACCCTCCTGCTGGAGAT sloth GGAGCAGACGTCACACACCCTCCCGCGGGGGAT armadillo GGAGCAGACGTCACGCACCCTCCGGCAGGGGAT tenrec GGGGCCGACGTCACGCACCCCCCTGCGGGCGAT elephant GGAGCGGATGTCACACACCCGCCTGCGGGGGAT shrew GGCGCAGATGTCACGCATCCTCCAGCAGGGGAC hedgehog GGAGCAGATGTCACACACCCCCCAGCAGGAGAT megabat GGAGCAGATGTCACACACCCTCCTGCAGGAGAT microbat GGAGCAGATGTCACCCACCCCCCTGCAGGGGAC dog GGAGCGGATGTCACACACCCCCCAGCCGGGGAC cat GGAGCCGATGTCACGCACCCCCCAGCAGGGGAT horse GGAGCGGATGTCACACACCCTCCGGCAGGGGAT pika GGAGCAGATGTCACTCACCCTCCAGCTGGGGAT rabbit GGTGCAGATGTCACACACCCCCCAGCTGGAGAT squirrel GGAGCAGATGTCACTCACCCTCCAGCGGGAGAT guinea_pig GGAGCAGATGTCACACACCCACCAGCGGGAGAT mouse GGAGCAGATGTCACTCATCCGCCTGCTGGGGAC rat GGAGCAGATGTCACTCATCCACCTGCTGGGGAT kangaroo_rat GGAGCAGATGTTACACACCCTCCAGCAGGGGAT tree_shrew GGCGCAGACGTCACGCACCCCCCGGCCGGGGAT human GGAGCGGATGTCACACACCCCCCAGCAGGGGAT tarsier GGTGCTGATGTCACACACCCCCCTGCAGGGGAT marmoset GGAGCAGATGTCACACACCCACCAGCAGGGGAT zebrafinch GGAGCAGATGTCACTCACCCTCCCGCCGGGGAT green_anole GGGGCAGACGTCACTCACCCGCCAGCCGGGGAC xenopus GGAGCAGATGTTACACACCCACCTGCTGGTGAT pufferfish GGTGCGGATGTTACTCATCCTCCTGCTGGTGAT fugu GGGGCTGATGTTACTCACCCTCCAGCTGGTGAT stickleback GGTGCAGACGTCACACATCCTCCAGCGGGTGAT medaka GGTGCCGATGTCACTCATCCTCCTGCCGGGGAC zebrafish GGGGCAGATGTTACACACCCGCCGGCTGGTGAT lamprey GGTGCCGATGTGACACACCCTCCAGCGGGAGAC // G A A A A A G G G G C C C C U U U U UC AG UCA G U C A G U C A G U C A G U C A G UC AGUCAGUCAGUC AG U C A G U C A G U C A G U C AG U C AG UCAG P S U nG nG oG oG oG G P P P P P nM nM M M nM nM nM Phenylalanine Phe Leucine Leu Leucine Leu Proline Pro Histidine His Glutamine Gln Isoleucine Ile Methionine Met Threonine Thr Asparagine Asn Lysine Lys Arginine Arg Arginine Arg Valine Val Alanine Ala Glutamic acid Glu Aspartic acid Asp Glycine Gly Serine Ser Serine Ser Tyrosine Tyr Cysteine Cys Tryptophan Trp Stops Stop E G F L S S Y C W L P H R R Q IM T N K V A D 89.09 75.07 174.20 174.20 146.19 165.19 133.11 117.15 147.13 146.15 155.16 115.13 105.09 105.09 131.18 132.12 MW =149.21Da 131.18 119.12 204.23 131.18 181.19 121.16 HN NH2 NH H2N OH O H2N CH3 OH O H2N O H2N OH O O HO H2N OH O HS H2N OH O H2N O NH2 OH O O OH H2N OH O H2N OH O NH H2N OH O N CH3 CH3 H2N OH O CH3 CH3 H2N OH O CH3 CH3 H2N OH O H2N H2N OH O CH3 S H2N OH O H2N OH O NH OH O H2N HO OH O H2N HO OH O H2N HO CH3 OH O NH H2N OH O HO H2N OH O H2N CH3 CH3 OH O Basic Acidic Polar Nonpolar (hydrophobic) S - M - P - U - nM - oG - nG - Sumo Methyl Phospho Ubiquitin N-Methyl O-glycosyl N-glycosyl Modification aminoacid 2nd1st position 3rd U C Image source: http://upload.wikimedia.org/wikipedia/en/d/d6/GeneticCode21-version-2.svgPaul Gardner Genome annotation
- 14. DNA encodes RNA G C G G A U UU A GCUC AGD D G G G A G A G C G C C A GA C U G A A . A . C U G GAGG U C C U G U G T . C G A UC CACAG A A U U C G C A C CA Variable LoopAnticodon Loop T ΨC Loop 10 15 20 25 30 355 40 45 50 55 60 65 70 75 Anticodon Loop Acceptor Stem GCGGAUUUAGCUCAGDDGGGAGAGCGCCAGACUGAAYA.CUGGAGGUCCUGUGT.CGAUCCACAGAAUUCGCACCA5’ 3’ Secondary Structure Tertiary StructureB C Primary StructureA Acceptor Stem T ΨC Loop ΨΨ Ψ Ψ Y 65 60 55 40 10 20 15 5 70 75 25 30 35 45 50 D Loop 3’ 5’ 5’ 3’ D Loop Paul Gardner Genome annotation
- 15. Homology-based annotation: strengths and weaknesses Example 1: map known genes onto genomes Strengths: fast, cheap, ... Weaknesses: Inaccurate for divergent species (e.g. Graeme’s Methanobrevibacter or GEBA genomes) Requires manual correction of border-line results Errors are propagated throughout the databases Example 2: aligning genomes Strengths: “cheap” if genomes already exist fast for small genomes evolutionary support for all discoveries Weaknesses: Requires lots of powerful computers for large genomes Inaccurate for divergent species (e.g. Neil’s tuatara or Graeme’s Methanobrevibacter) Requires manual correction of border-line results Paul Gardner Genome annotation
- 16. Homology annotation: nucleotides are difficult to align 0 20 40 60 80 100 Conservation of Xfam families in bacterial genomes Conservedfamilies(%) Freq. RNA−seq species 0 10 Pfam (N=6671) Rfam (N=331) 0.0 0.1 0.2 0.3 0.4 0.5 0.6 Phylogenetic distance Lindgreen et al. (2014) Robust identification of noncoding RNA from transcriptomes requires phylogenetically-informed sampling. PLOS Computational Biology. Paul Gardner Genome annotation
- 17. We can use RNA detection methods... Remember the central dogma of molecular biology Example: sequence RNAs from multiple tissues, developmental stages and environmental conditions How reliable is this approach? What are the main weaknesses & strengths? Wang, Gerstein & Snyder (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nature Reviews Genetics. Paul Gardner Genome annotation
- 18. RNA-seq: strengths and weaknesses RNA-seq Strengths: Experimental support for transcribed regions Identifies untranslated regions (UTRs), ncRNAs, antisense RNAs, ... Identifies alternatively spliced and edited RNAs Weaknesses: Expensive & lots of work RNA degradation and genomic contamination Transcription does not prove translation Will miss genes transcribed in specific developmental stages, tissues & environmental conditions E.g. lsy-6 microRNA Paul Gardner Genome annotation
- 19. We can use protein detection methods... Central dogma of molecular biology Example: Protein mass spectrometry How reliable is this approach? What are the main weaknesses & strengths? Figure from: http://en.wikipedia.org/wiki/Protein mass spectrometry Paul Gardner Genome annotation
- 20. Protein mass spectrometry: strengths and weaknesses Protein mass spectrometry Strengths: Experimental support for translated regions Identifies alternative isoforms and post-translational modifications (Ezkurdia et al. 2012) Weaknesses: Expensive & lots of work Misses genes transcribed in specific developmental stages, tissues & environmental conditions Currently technology generally only detects the most abundant proteins Ezkurdia et al. (2012) Comparative proteomics reveals a significant bias toward alternative protein isoforms with conserved structure and function. Mol Biol Evol. Paul Gardner Genome annotation
- 21. How cool is this?! Schwanh¨ausser et al. (2011) Global quantification of mammalian gene expression control. Nature Paul Gardner Genome annotation
- 22. This is also kinda neat... Lu et al. (2007) Absolute protein expression profiling estimates the relative contributions of transcriptional and translational regulation. Nature Biotechnology Paul Gardner Genome annotation
- 23. Relevant reading Reviews: Stein L (2001) Genome annotation: from sequence to biology. Nature Reviews Genetics. Reed JL et al. (2006) Towards multidimensional genome annotation. Nature Reviews Genetics. ORF finding: Delcher AL et al. (2007) Identifying bacterial genes and endosymbiont DNA with Glimmer. Bioinformatics. Hyatt D et al. (2010) Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics. RNA-seq (Ant’s lectures) Wang, Gerstein & Snyder (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nature Reviews Genetics. Proteomics (Sarah’s lectures) Ezkurdia et al. (2012) Comparative proteomics reveals a significant bias toward alternative protein isoforms with conserved structure and function. Mol Biol Evol. Paul Gardner Genome annotation
- 24. Homework: How to make a sequence alignment? Play: http://phylo.cs.mcgill.ca or even better, play Ribo: http://ribo.cs.mcgill.ca/ Paul Gardner Genome annotation
- 25. The End Paul Gardner Genome annotation