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Genome annotation & comparative genomics An appreciation for: ▶ An overview of some techniques and methods are used for comparative genomics ▶ An understanding of genome annotation methods, particularly the advantages and disadvantages of the different methods: ▶ Sequence analysis (ORF finding) ▶ Comparative sequence analysis ▶ Experimental methods (RNAseq & mass-spectroscopy)
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- 1. GENE315: Genome annotation & comparative genomics Paul Gardner March 7, 2023
- 2. Objectives for lecture 04 ▶ An appreciation for: ▶ An overview of some techniques and methods are used for comparative genomics ▶ An understanding of genome annotation methods, particularly the advantages and disadvantages of the different methods: ▶ Sequence analysis (ORF finding) ▶ Comparative sequence analysis ▶ Experimental methods (RNAseq & mass-spectroscopy)
- 3. Different comparative genomics work flows DNA Sequencing Genome Assembly Genome Annotation Compare Genomes & Genes An Individual RNA Sequencing Map to Genome Genome Annotation Quantify Gene Expression An Individual A population DNA Sequencing Map to Genome Call Variation Compare Populations An Environment DNA Sequencing Genome Assembly Map to Taxonomy Compare Environments A genome sequencing project A RNA sequencing project A population genomics project A metagenome sequencing project Answer & Ask Questions Idea
- 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?
- 7. The data tsunami ▶ Thanks to new sequencing technologies ▶ Biologists no longer spend years acquiring data. ▶ The bottle-neck for research is now in the analysis phase of research. ▶ Biologists with good mathematical and statistical skills and mathematicians, statisticians and computer scientists 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 U 5' 0.0 1.0 2.0 A C GU CC A GA5 A GA U CAGG U A10 CA GU CU G A
- 8. We can annotate genomes with sequence analysis... ▶ Genes can leave a statistical signal in the genome... ▶ Example 1: in A+T rich genomes, genes can be discovered by looking for high G+C regions ▶ Example 2: identify promotors, ribosome binding sites, open-reading frames (ORFs), terminators ▶ In eukaryotes CpG islands, splicing signals and poly-A tails may be incorporated Figure from: http://zerocool.is-a-geek.net/?p=630
- 9. ORF reminder ▶ ORF (open reading frame): a stretch of codons that begins with a start codon (usually AUG) and ends at a stop codon (usually UAA, UAG or UGA). ▶ So, 3/64 codons are expected to be stops by chance... ▶ The probability of observing a stop in a sequence of length n is 1 − (61 64)n AUGAAACGCAUUAGCACCACCAUUACCACCACCAUCACCAUUACCACAGGUAACGGUGCGGGCUGA
- 10. Sequence analysis: strengths and weaknesses ▶ ORF prediction: Prodigal (Bacterial/Archaeal), MAKER (Eukaryotic prediction) ▶ Statistical model of ORF lengths, codon use & RBS sequences ▶ Strengths: ▶ very fast & cheap ▶ No prior knowledge about the genome is required (e.g. gene sequences, etc.) ▶ Weaknesses: ▶ doesn’t account for splicing – less effective in eukaryotes. ▶ false positives (see AntiFam) ▶ misses short peptides (e.g. toxin-antitoxin systems) ▶ No ncRNAs, pseudogenes, recoding & frame-shift elements
- 12. The null hypothesis is important for science! How do we know what is the truth? critical value H0 null (negative control) HA alternative (positive control) α (FPR) β (FNR) Power=1 − β (Sensitivity) 1 − α (Specificity) (TN) (TP) (FP) (FN) effect size
- 13. ORF length distributions An experiment: annotate a native and a shuffled bacterial genome. Note, that many predicted short ORFs are likely to be false! ORF lengths: native vs shuffled bacterial genome ORF Length (nts) Frequency 1 10 100 1000 10000 10 50 100 250 500 1000 2500 5000 K12: All ORFs K12 shuffled: All ORFs Probability of a stop codon 0.0 0.2 0.4 0.6 0.8 1.0
- 14. Another annotation strategy is to use homology... ▶ Based on the principle that 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. Roary) ▶ Useful for closely related species where one is well annotated ▶ Example 2: Align two or more related genomes, look for conserved regions, patterns of variation can be indicative of function (e.g. RNAz & RNAcode) ▶ coding sequences are enriched in synonymous mutations and INDELs of size 3, 6, 9, ... ▶ ncRNA sequences may conserve basepairs, resulting in covariation between alignment columns ▶ these methods require accurate and deep genome alignments
- 15. Example 1: mapping annotations between genomes... Image source: Michael Schatz
- 16. Example 2: the DNA encoding a protein has a distinct conservation pattern # 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 U C A G U C A G U C A G U C A G 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 I M 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 = 14 9.2 1 Da 131.18 119.12 204.23 131.18 181.19 121.16 HN NH2 NH H2N OH O H2N C H3 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 C H3 CH3 H2N OH O C H3 C H3 H2N OH O C H3 C H3 H2N OH O H2N H2N OH O C H3 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 C H3 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 amino acid 2nd 1st position 3rd U C Image source: http://upload.wikimedia.org/wikipedia/en/d/d6/GeneticCode21-version-2.svg
- 17. Example 2: DNA encodes non-coding RNAs Covariation can be important! G C G G A U U U A G C U C A G D D G G G A G A G C G C C A G A C U G A A . A . C U G GAG G U C C U G U G T . C G A U C C A C A G A A U U C G C A C C A Variable Loop Anticodon Loop T ΨC Loop 10 15 20 25 30 35 5 40 45 50 55 60 65 70 75 Anticodon Loop Acceptor Stem GCGGAUUUAGCUCAGDDGGGAGAGCGCCAGACUGAAYA.CUGGAGGUCCUGUGT.CGAUCCACAGAAUUCGCACCA 5’ 3’ Secondary Structure Tertiary Structure B C Primary Structure A Acceptor Stem T ΨC Loop Ψ Ψ 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
- 18. Homology-based annotation: strengths and weaknesses ▶ Example 1: map known genes onto genomes ▶ Strengths: fast, cheap, ... ▶ Weaknesses: ▶ Inaccurate for divergent species (e.g. the tuatara genome) ▶ Requires manual correction of border-line results ▶ Errors are propagated throughout the databases ▶ Example 2: aligning genomes, analyse patterns of variation ▶ 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. the tuatara genome) ▶ Requires manual correction of border-line results
- 19. Homology annotation: proteins are much easier to align than nucleotides 0 20 40 60 80 100 Conservation of Xfam families in bacterial genomes Conserved families (%) 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.
- 20. Another annotation strategy is to use RNA sequencing... ▶ Protein and ncRNA genes require a transcription step... ▶ Example: sequence RNAs from multiple tissues, developmental stages and environmental conditions Wang, Gerstein & Snyder (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nature Reviews Genetics.
- 21. RNA-seq based annotation Sorek & Cossart (2010) Prokaryotic transcriptomics: a new view on regulation, physiology and pathogenicity. Nature Reviews Genetics.
- 22. RNA-seq: strengths and weaknesses ▶ RNA-seq ▶ Strengths: ▶ Experimental support for transcribed regions ▶ Identifies untranslated regions (UTRs), ncRNAs, antisense RNAs, ... ▶ Can identify alternatively spliced and edited RNAs ▶ Weaknesses: ▶ Expensive & lots of work ▶ RNA degradation and genomic contamination ▶ Misses genes transcribed in specific developmental stages, tissues & environmental conditions E.g. lsy-6 microRNA ▶ Transcription does not prove translation ▶ Not all transcription is functional! Hundreds of transcriptome papers e.g.: Di Giorgio et al. (2020) Evidence for host-dependent RNA editing in the transcriptome of SARS-CoV-2. Science Advances. Lee et al. (2019) Diagnostic utility of transcriptome sequencing for rare Mendelian diseases. Genetics in Medicine. Kalucka et al. (2020) Single-Cell Transcriptome Atlas of Murine Endothelial Cells. Cell. Uhlen et al. (2017) A pathology atlas of the human cancer transcriptome. Science. etc.
- 23. Another annotation strategy is to use direct protein detection methods... ▶ Central dogma of molecular biology ▶ Example: Protein mass spectrometry Figure from: http://en.wikipedia.org/wiki/Protein mass spectrometry
- 24. 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 ▶ Current technology generally detects only the most abundant proteins ▶ Requires a reference protein DB ▶ How to deal with paralogues (duplicated genes)? ▶ Not all translation is functional! Hundreds of proteomics papers e.g.: Bojkova et al. (2020) Proteomics of SARS-CoV-2-infected host cells reveals therapy targets. Nature. Nusinow et al. (2020) Quantitative Proteomics of the Cancer Cell Line Encyclopedia. Cell. Messner et al. (2020) Ultra-High-Throughput Clinical Proteomics Reveals Classifiers of COVID-19 Infection. Cell Systems. Cheung et al. (2020) Defining the carrier proteome limit for single-cell proteomics. Nature Methods. etc.
- 25. Weird gene interlude: Inside-out genes ▶ SNHG1 (a.k.a. UHG) is transcribed like a normal mRNA ▶ Spliced ▶ Exons have no open reading frame, and are quickly degraded ▶ The introns are highly conserved, stable trancripts, are snoRNAs. SnoRNAs are important for maturing rRNA. Scale chr11: DNase Clusters Multiz Align 1 kb hg38 62,853,000 62,853,500 62,854,000 62,854,500 62,855,000 62,855,500 GENCODE v29 Comprehensive Transcript Set (only Basic displayed by default) C/D and H/ACA Box snoRNAs, scaRNAs, and microRNAs from snoRNABase and miRBase H3K27Ac Mark (Often Found Near Regulatory Elements) on 7 cell lines from ENCODE DNase I Hypersensitivity Peak Clusters from ENCODE (95 cell types) 100 vertebrates Basewise Conservation by PhyloP Vertebrate Multiz Alignment & Conservation (100 Species) SNHG1 SNHG1 SNHG1 SNHG1 SNHG1 SNHG1 SNHG1 SNHG1 SNHG1 SNHG1 SNHG1 SNORD22 SNHG1 SNORD30 RF00099 SNORD28 SNORD27 SNORD26 SNORD25 U22 U31 U30 U29 U28 U27 U26 U25 Layered H3K27Ac 100 _ 0 _ Cons 100 Verts 4.88 _ -4.5 _ 0 - Tycowski KT, Shu M & Steitz JA (1996) A mammalian gene with introns instead of exons generating stable RNA products. Nature.
- 26. Multi-omics: How cool is this?! Blevins et al. (2019) Extensive post-transcriptional buffering of gene expression in the response to severe oxidative stress in baker’s yeast. Scientific Reports.
- 27. Combining evidence ▶ Robust science does not rely upon a single methodology, or dataset... ▶ Certainty in annotations comes from combining multiple lines of evidence ▶ COLLECT COLLAGES OF EVIDENCE... Giglio et al. (2019) ECO, the Evidence & Conclusion Ontology: community standard for evidence information. Nucleic Acids Research.
- 28. The main points ▶ An overview of comparative genomics ▶ An understanding of computational and experimental strategies for annotating genomes ▶ Computational: ORF prediction, mapping from existing genome annotations, pattern hunting in alignments ▶ Experimental: RNA-seq and Mass-spec (many other emerging tools too) ▶ An understanding of the advantages and limitations of different genome annotation methods
- 29. Self-evaluation exercises ▶ Genome annotation is a fundamental problem in bioinformatics and genomics. Describe four methods for annotating genes encoded in genome sequences. What are their main disadvantages and advantages? ▶ You’ve been given the task of annotating the Giant Wētā genome. A colleague has recently spent a year annotating the related African King Cricket genome. Outline a fast and cheap method for annotating your Wētā genome. ▶ Define the term “Open reading frame” (ORF). Describe the relationship between and ORF and a gene. Describe how an ORF can be located in an uncharacterised sequence.
- 30. Self-evaluation exercises ▶ You have run an ORF finder and a non-coding RNA annotation tool on the above genomic sequence. The results show an overlap between a predicted ORF and a ribosomal RNA. Which annotation is likely to be correct? Justify your answer. ▶ Consider the below alignment. Based upon the patterns of sequence variation predict whether the sequences encoded in this genomic region are protein coding or non-coding. Justify your answer. species1 GGTAAGCTGGCGCGTCAGTTTGAGCAGCAG...GGT species2 GGTAAACTGGCGCGCCAGTTTGAGCAGCAG...GGT species3 GGCAAACTCGCCCGCCAGTTGGAACACCATcagGGG
- 31. Further reading ▶ Reviews: ▶ Zerbino, Frankish & Flicek (2020) Progress, Challenges, and Surprises in Annotating the Human Genome. Annual Review of Genomics and Human Genetics. ▶ RNA-seq ▶ Wang, Gerstein & Snyder (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nature Reviews Genetics. ▶ Proteomics ▶ Nesvizhskii (2014) Proteogenomics: concepts, applications and computational strategies. Nature Methods.
- 32. Questions relating to my lectures can be asked & viewed here: https://docs.google.com/document/d/1PQd dp7C 0cXA8SwUv- qrkTOj8c8fUAt-U Z5dg2yc8/edit?usp=sharing
- 33. Homework: How to make a sequence alignment? ▶ Play: http://phylo.cs.mcgill.ca
- 35. The End Matanaka, Waikouiti, 14 Nov 2020.