Web Apollo Tutorial for the i5K copepod research community.
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The University of California hosted a webinar introducing Web Apollo, a collaborative genomic annotation editing tool aimed at the Eurytemora affinis research community. Participants learned about manual annotation processes, community-based curation, and how to effectively utilize Web Apollo for gene editing and genomic analysis. The platform enhances collaboration among researchers by facilitating intuitive editing and visualization of genomic data.
Web Apollo Tutorial for the i5K copepod research community.
- 2. An introduction to Web Apollo. A webinar for the Eurytemora affinis research community. Monica Munoz-Torres, PhD | @monimunozto Berkeley Bioinformatics Open-Source Projects (BBOP) Genomics Division, Lawrence Berkeley National Laboratory 29 August, 2014 UNIVERSITY OF CALIFORNIA
- 3. Outline 1. What is Web Apollo?: • Definition & working concept. 2. Our Experience With Community Based Curation. 3. The Manual Annotation Process. 4. Becoming acquainted with Web Apollo. 5. Example. An introduction to Web Apollo. A webinar for the Eurytemora affinis research community. Outline 3
- 4. During this webinar you will: • Learn to identify homologs of known genes of interest in your newly sequenced genome. • Become familiar with the environment and functionality of the Web Apollo genome annotation editing tool. Footer 4
- 5. What is Web Apollo? • Web Apollo is a web-based, collaborative genomic annotation editing platform. We need annotation editing tools to modify and refine the precise location and structure of the genome elements that predictive algorithms cannot yet resolve automatically. 1. What is Web Apollo? 5 Find more about Web Apollo at http://GenomeArchitect.org and Genome Biol 14:R93. (2013).
- 6. Brief history of Apollo*: Biologists could finally visualize computational analyses and experimental evidence from genomic features and build manually-curated consensus gene structures. Apollo became a very popular, open source tool (insects, fish, mammals, birds, etc.). a. Desktop: one person at a time editing a specific region, annotations saved in local files; slowed down collaboration. b. Java Web Start: users saved annotations directly to a centralized database; potential issues with stale annotation data remained. 1. What is Web Apollo? 6 *
- 7. Web Apollo • Browser-based tool integrated with JBrowse. • Two new tracks: “Annotation” and “DNA Sequence” • Allows for intuitive annotation creation and editing, with gestures and pull-down menus to create and modify transcripts and exons structures, insert comments (CV, freeform text), etc. • Customizable look & feel. • Edits in one client are instantly pushed to all other clients: Collaborative! 1. What is Web Apollo? 7
- 8. Working Concept In the context of gene manual annotation, curation tries to find the best examples and/or eliminate most errors. To conduct manual annotation efforts: Gather and evaluate all available evidence using quality-control metrics to corroborate or modify automated annotation predictions. Perform sequence similarity searches (phylogenetic framework) and use literature and public databases to: • Predict functional assignments from experimental data. • Distinguish orthologs from paralogs, and classify gene membership in families and networks. Automated gene models Evidence: cDNAs, HMM domain searches, alignments with assemblies or genes from other species. Manual annotation & curation 2. In our experience. 8
- 9. Dispersed, community-based gene manual annotation efforts. We continuously train and support hundreds of geographically dispersed scientists from many research communities, to perform biologically supported manual annotations using Web Apollo. – Gate keepers and monitoring. – Written tutorials. – Training workshops and geneborees. – Personalized user support. 2. In our experience. 9
- 10. What we have learned. Harvesting expertise from dispersed researchers who assigned functions to predicted and curated peptides we have developed more interactive and responsive tools, as well as better visualization, editing, and analysis capabilities. 2. In our experience. 10 http://people.csail.mit.edu/fredo/PUBLI/Drawing/
- 11. Collaborative Efforts Improved Automated Annotations In many cases, automated annotations have been improved (e.g: Apis mellifera. Elsik et al. BMC Genomics 2014, 15:86). Also, learned of the challenges of newer sequencing technologies, e.g.: – Frameshifts and indel errors – Split genes across scaffolds – Highly repetitive sequences To face these challenges, we train annotators in recovering coding sequences in agreement with all available biological evidence. 2. In our experience. 11
- 12. It is helpful to work together. Scientific community efforts bring together domain-specific and natural history expertise that would otherwise remain disconnected. Breaking down large amounts of data into manageable portions and mobilizing groups of researchers to extract the most accurate representation of the biology from all available data distills invaluable knowledge from genome analysis. 2. In our experience. 12
- 13. Understanding the evolution of sociality Comparing the genomes of 7 species of ants contributed to a better understanding of the evolution and organization of insect societies at the molecular level. Insights drawn mainly from six core aspects of ant biology: 1. Alternative morphological castes 2. Division of labor 3. Chemical Communication 4. Alternative social organization 5. Social immunity 6. Mutualism 13 Libbrecht et al. 2012. Genome Biology 2013, 14:212 Groups of communities continue to guide our efforts. Atta cephalotes (above) and Harpegnathos saltator. ©alexanderwild.com 2. In our experience.
- 14. A little training goes a long way! With the right tools, wet lab scientists make exceptional curators who can easily learn to maximize the generation of accurate, biologically supported gene models. 2. In our experience. 14
- 15. Manual Annotation How do we get there? 15 Assembly Manual annotation Experimental validation Automated Annotation In a genome sequencing project… 3. How do we get there?
- 16. Gene Prediction Identification of protein-coding genes, tRNAs, rRNAs, regulatory motifs, repetitive elements (masked), etc. - Ab initio (DNA composition): Augustus, GENSCAN, geneid, fgenesh - Homology-based: E.g: SGP2, fgenesh++ 16 Nucleic Acids 2003 vol. 31 no. 13 3738-3741 3. How do we get there?
- 17. Gene Annotation Integration of data from prediction tools to generate a consensus set of predictions or gene models. • Models may be organized using: - automatic integration of predicted sets; e.g: GLEAN - packaging necessary tools into pipeline; e.g: MAKER • All available biological evidence (e.g. transcriptomes) further informs the annotation process. In some cases algorithms and metrics used to generate consensus sets may actually reduce the accuracy of the gene’s representation; in such cases it is usually better to use an ab initio model to create a new annotation. 3. How do we get there? 17
- 18. Manual Genome Annotation • Identifies elements that best represent the underlying biology. • Eliminates elements that reflect the systemic errors of automated genome analyses. • Determines functional roles through comparative analysis of well-studied, phylogenetically similar genome elements using literature, databases, and the researcher’s experience. 3. How do we get there? 18
- 19. Curation Process: is Necessary 1. A computationally predicted consensus gene set is generated using multiple lines of evidence. 2. Manual annotation takes place. 3. Ideally consensus computational predictions will be integrated with manual annotations to produce an updated Official Gene Set (OGS). Otherwise, “incorrect and incomplete genome annotations will poison every experiment that uses them”. - M. Yandell. 3. How do we get there? 19
- 20. The Collaborative Curation Process at i5K 1) A computationally predicted consensus gene set has been generated using multiple lines of evidence; e.g. Consensus Gene EAFF_v0.5.3-Models. 2) i5K Projects will integrate consensus computational predictions with manual annotations to produce an updated Official Gene Set (OGS): » If it’s not on either track, it won’t make the OGS! » If it’s there and it shouldn’t, it will still make the OGS! 3. How do we get there? 20
- 21. Consensus set: reference and start point • In some cases algorithms and metrics used to generate consensus sets may actually reduce the accuracy of the gene’s representation; e.g. use Augustus model instead to create a new annotation. • Isoforms: drag original and alternatively spliced form to ‘User-created Annotations’ area. • If an annotation needs to be removed from the consensus set, drag it to the ‘User-created Annotations’ area and label as ‘Delete’ on Information Editor. • Overlapping interests? Collaborate to reach agreement. • Follow guidelines for i5K Pilot Species Projects as shown at http://goo.gl/LRu1VY 3. How do we get there? 21
- 22. Web Apollo
- 23. Web Apollo The Sequence Selection Window Sort 4. Becoming Acquainted with Web Apollo. 23
- 24. Navigation tools: pan and zoom Search box: go to a scaffold or a gene model. Grey bar of coordinates indicates location. You can also select here in order to zoom to a sub-region. ‘View’: change color by CDS, toggle strands, set highlight. ‘File’: Upload your own evidence: GFF3, BAM, BigWig, VCF*. Add combination and sequence search tracks. ‘Tools’: Use BLAT to query the genome with a protein or DNA sequence. Available Tracks ‘User-created Annotations’ Track Evidence Tracks Area Login Web Apollo Graphical User Interface (GUI) for editing annotations 4. Becoming Acquainted with Web Apollo.
- 25. Flags non-canonical splice sites. Selection of features and sub-features Edge-matching ‘User-created Annotations’ Track Evidence Tracks Area The editing logic in the server: selects longest ORF as CDS flags non-canonical splice sites Web Apollo 4. Becoming Acquainted with Web Apollo. 25
- 26. Web Apollo DNA Track ‘User-created Annotations’ Track 4. Becoming Acquainted with Web Apollo. There are two new kinds of tracks for: annotation editing sequence alteration editing
- 27. Web Apollo Annotations, annotation edits, and History: stored in a centralized database. 4. Becoming Acquainted with Web Apollo.
- 28. Web Apollo 4. Becoming Acquainted with Web Apollo. 28 • DBXRefs • PubMed IDs • GO terms • Comments The Information Editor
- 29. Additional Functionality In addition to protein-coding gene annotation that you know and love. • Non-coding genes: ncRNAs, miRNAs, repeat regions, and TEs • Sequence alterations (less coverage = more fragmentation) • Visualization of stage and cell-type specific transcription data as coverage plots, heat maps, and alignments 4. Becoming Acquainted with Web Apollo.
- 30. How to begin curating To find the gene region you wish to annotate, you may use: a) a protein sequence from another species b) a sequence from a similar gene c) on your own, you aligned your gene models or transcriptomic data to the genome. d) you used high quality proteins and/or gene family alignments (multi or single species) and are able to identify conserved domains. Option 1 – You have a sequence but don’t know where it is in this genome: • Use BLAT in Web Apollo window, or BLAST at NAL’s i5k BLAST server, available at: http://i5k.nal.usda.gov/blastn • Alternatively, use any other tool; for example Geneious. Option 2 – The genome has already been annotated with your sequences and you have a gene identifier that has been indexed in Web Apollo. • That is, you know where to look, so type the ID in the Search box of Web Apollo. • Web Apollo autocompletes using a case-insensitive search anchored on the left-hand side of the word. For example “HaGR” will show all “hagr” objects (up to 30). • Choose one of the genes and click “Go”. • You can do that with Domains, Alignments or Gene names provided to you (if they have been indexed). Option 3 – Find genes based on functional ontology terms or network membership identifiers.
- 31. General Process of Curation 1. Select the chromosomal region of interest, e.g. scaffold. 2. Select appropriate evidence tracks. 3. Determine whether a feature in an existing evidence track will provide a reasonable gene model to start working. - If yes: select and drag the feature to the ‘User-created Annotations’ area, creating an initial gene model. If necessary use editing functions to adjust the gene model. - Nothing available to you? Let’s have a talk. 4. Check your edited gene model for integrity and accuracy by comparing it with available homologs. 4. Becoming Acquainted with Web Apollo 31 | Always remember: when annotating gene models using Web Apollo, you are looking at a ‘frozen’ version of the genome assembly and you will not be able to modify the assembly itself.
- 32. Example Introductory demonstration using the Apis mellifera genome. Q&A session using the Eurytemora affinis genome at https://apollo.nal.usda.gov/euraff/selectTrack.jsp A public Honey Bee Web Apollo Demo is available at http://genomearchitect.org/WebApolloDemo Example 32
- 33. What do we know for this species? • What data are currently available? • At NCBI: • 5,570 nucleotide sequences scaffolds • 446 amino acid sequences CO-I • 0 conserved domains identified • 0 “gene” entries submitted Footer 33
- 34. PubMed Search: what’s new? Footer 34 Empirical examples of beneficial reversal of dominance: • Warfarin resistance: mutation of VKORC1 is associated with increased dietary requirement for vit. K
- 35. How many sequences for your gene of interest? And what do we know about it? • VKORC1 – vit. K epoxide reductase complex, subunit 1. • MF: quinone binding (IEA, GO:0048038), vit K epoxide reductase activity (IDA, GO:0047057). • BP: blood coagulation (IMP, GO:0007596), bone development (ISS,GO:0060348). • CC: endoplasmic reticulum membrane (TAS, GO:0005789), integral component of membrane (IEA, GO:0016021). Footer 35
- 36. BLAST at i5K https://i5k.nal.usda.gov/blast Footer 36 To Web Apollo
- 37. BLAST at i5K: hsps in “BLAST+ results” track Footer 37
- 38. Available Tracks Footer 38
- 39. Creating a new gene model: drag and drop • Web Apollo automatically calculates the longest open reading frame (ORF). In this case, the ORF includes the hsp. Footer 39
- 40. Get Sequence Footer 40 http://blast.ncbi.nlm.nih.gov/Blast.cgi
- 41. Flanking sequences (other gene models) vs. NCBI nr At 3’ end Footer 41 At 5’ end
- 42. Additional evidence in support of split Footer 42
- 43. Editing: split Footer 43
- 44. Finished model Footer 44
- 45. Information Editor • DBXRefs: NP_076869.1, H. sapiens, RefSeq • PubMed identifier: PMID:24337963 • Gene Ontology IDs: GO:0048038, GO:0047057, GO:0007596, GO:0060348, GO:0005789, GO:0016021. • Comments. • Name, Symbol. • Approve / Delete radio button. Footer 45 Comments
- 46. Arthropodcentric Thanks! AgriPest Base FlyBase Hymenoptera Genome Database VectorBase Acromyrmex echinatior Acyrthosiphon pisum Apis mellifera Atta cephalotes Bombus terrestris Camponotus floridanus Helicoverpa armigera Linepithema humile Manduca sexta Mayetiola destructor Nasonia vitripennis Pogonomyrmex barbatus Solenopsis invicta Tribolium castaneum… and you!
- 47. Thanks! • Berkeley Bioinformatics Open-source Projects (BBOP), Berkeley Lab: Web Apollo and Gene Ontology teams. Suzanna E. Lewis (PI). • Christine G. Elsik (PI). § University of Missouri. • Ian Holmes (PI). University of California, Berkeley. • Arthropod genomics community, i5K Steering Committee, Monica Poelchau at USDA/NAL, fringy Richards at HGSC-BCM, Alexie Papanicolaou at CSIRO, Oliver Niehuis at 1KITE http://www.1kite.org/, BGI, and the Honey Bee Genome Sequencing Consortium. • Web Apollo is supported by NIH grants 5R01GM080203 from NIGMS, and 5R01HG004483 from NHGRI, and by the Director, Office of Science, Office of Basic Energy Sciences, of the U.S. Department of Energy under Contract No. DE-AC02- 05CH11231. • Insect images used with permission: http://AlexanderWild.com and O. Niehuis. • For your attention, thank you! Colleagues at BBOP Web Apollo Suzanna Lewis Gregg Helt Colin Diesh § Deepak Unni § Gene Ontology Chris Mungall Seth Carbon Heiko Dietze Web Apollo: http://GenomeArchitect.org GO: http://GeneOntology.org i5K: http://arthropodgenomes.org/wiki/i5K Thank you. 47