Genome Curation using Apollo

1. Curating genes and genomes  Apollo: a collaborative tool for genome curation Monica Munoz-Torres, PhD | @monimunozto  Berkeley Bioinformatics Open-Source Projects (BBOP)  Lawrence Berkeley National Laboratory |   University of California Berkeley | U.S. Department of Energy   BioInfoGenomicsWkshopv2 | Reed College, Portland, Oregon | 10 October, 2015

2. OUTLINE  Web Apollo Collabora've Cura'on and Interac've Analysis of Genomes 2OUTLINE •  Today we will discover how to extract the most valuable informa'on about a genome through cura'on eﬀorts.

3. APOLLO DEVELOPMENT APOLLO DEVELOPERS 3 h* p://G e nom e Ar c hite c t. or g / Nathan Dunn Eric Yao JBrowse, UC Berkeley Christine Elsik’s Lab, University of Missouri Suzi Lewis Principal Investigator BBOP Moni Munoz-Torres Stephen Ficklin GenSAS, Washington State University Colin DieshDeepak Unni

4. 4 BY THE END OF THIS TALK  you will  v Be@er understand genome cura'on in the context of annota'on: assembled genome à automated annota=on à manual annota=on v Become familiar with the environment and func'onality of the Apollo genome annota'on edi'ng tool. v Learn to iden'fy homologs of known genes of interest in a newly sequenced genome. v Learn about corrobora'ng and modifying automa'cally annotated gene models using available evidence in Apollo. What to expect

5. Anatomy of a genome sequencing project

6. 6 Genome Sequencing Project Anatomy of a genome sequencing project Experimental design, sampling. Comparative analyses Consensus Gene Set Manual Annotation Automated Annotation Sequencing Assembly Synthesis & dissemination.

7. CURATING GENOMES  steps involved 1  Genera=on of Gene Models calling ORFs, one or more rounds of gene predic'on, etc. 2  Annota=on of gene models Describing func'on, expression pa@erns, metabolic network memberships. 3  Manual annota=on CURATING GENOMES 7

8. GENOME ANNOTATION  objectives and uses Curating Genomes 8 The gene set of an organism informs a variety of studies: •  Gene number, GC%, TE composi'on, repe''ve regions. •  Func'onal assignments. •  Molecular evolu'on, sequence conserva'on. •  Gene families. •  Metabolic pathways. •  What makes an organism what it is? What makes a bee a “bee”? Marbach et al. 2011. Nature Methods | Shutterstock.com | Alexander Wild

9. First, a bio-‐refresher

10. WHAT WE NEED TO KNOW  for manual annotation To remember… Biological concepts to be@er understand manual annota'on 10FOOD FOR THOUGHT •  GLOSSARY from con1g to splice site •  CENTRAL DOGMA in molecular biology •  WHAT IS A GENE? deﬁning your goal •  TRANSCRIPTION mRNA in detail •  TRANSLATION and other deﬁni'ons •  GENOME CURATION steps involved

11. 11CURATING GENOMES WHAT WE KNOW  in very general terms

12. 12CURATING GENOMES WHAT WE KNOW  in very general terms http://www.wisegeek.com/ 5’ 3’ 5’ 3’

13. 13CURATING GENOMES CENTRAL “DOGMA”  of molecular biology v  DNA can be copied to DNA (DNA replica'on), v  DNA informa'on can be copied into mRNA (transcrip'on), and v  Proteins can be synthesized using the informa'on in mRNA as a template (transla'on). http://www.wisegeek.com/

14. 14BIO-REFRESHER What is a gene? v  The deﬁni'on of a gene paints a very complex picture of molecular ac'vity and it is a con'nuously evolving concept. •  From the Sequence Ontology (SO): “A gene is a locatable region of genomic sequence, corresponding to a unit of inheritance, which is associated with regulatory regions, transcribed regions and/or other func'onal sequence regions”. “Evolving Concept” at h@p://goo.gl/LpsajQ

15. 15BIO-REFRESHER What is a gene? v  In your life'me, the Encyclopedia of DNA Elements (ENCODE) project updated this concept yet again. Long transcripts & dispersed regula1on! “A gene is a DNA segment that contributes to phenotype/func'on. In the absence of demonstrated func'on, a gene may be characterized by sequence, transcrip'on or homology.” https://www.encodeproject.org/

16. 16BIO-REFRESHER What is a gene?  let’s think computationally! v  Think of the genome as an operating system for a living being •  Considering that the nucleo'des of the genome are put together into a code that is executed through the process of transcription and translation… •  … think of genes as subroutines that are repe''vely called in the process of transcription Gerstein et al., 2007. Genome Res.

17. 17BIO-REFRESHER What is a gene?  considerations v  Also consider : •  A gene is a genomic sequence (DNA or RNA) directly encoding func'onal product molecules, either RNA or protein. •  If several func'onal products share overlapping regions, we take the union of all overlapping genomics sequences coding for them. •  This union must be coherent – i.e., processed separately for ﬁnal protein and RNA products – but does not require that all products necessarily share a common subsequence. Gerstein et al., 2007. Genome Res.

18. 18BIO-REFRESHER “The gene is a union of genomic sequences encoding a coherent set of poten'ally overlapping func'onal products.” Gerstein et al., 2007. Genome Res The Gene: a moving target. What is a gene?

19. 19BIO-REFRESHER TRANSLATION  reading frame v  Reading frame is a manner of dividing the sequence of nucleo'des in mRNA (or DNA) into a set of consecu've, non-‐overlapping triplets (codons). v  Three frames can be read in the 5’ à 3’ direc'on. Given that DNA has two an'-‐parallel strands, an addi'onal three frames are possible to be read on the an'-‐sense strand. Six total possible reading frames exist. v  In eukaryotes, only one reading frame per sec'on of DNA is biologically relevant at a 'me: it has the poten'al to be transcribed into RNA and translated into protein. This is called the OPEN READING FRAME (ORF) •  ORF = Start signal + coding sequence (divisible by 3) + Stop signal v  The sec'ons of the mature mRNA transcribed with the coding sequence but not translated are called UnTranslated Regions (UTR); one at each end.

20. 20 "Reading Frame" by Hornung Ákos - Wikimedia Commons BIO-REFRESHER TRANSLATION  reading frame

21. 21 "ORF" by Thatsonginc - Wikimedia Commons BIO-REFRESHER TRANSLATION  reading frame

22. 22BIO-REFRESHER TRANSLATION  reading frame: splice sites v  The spliceosome catalyzes the removal of introns and the liga'on of ﬂanking exons. •  introns: spaces inside the gene, not part of the coding sequence •  exons: expression units (of the coding sequence) v  Splicing “signals” (from the point of view of an intron): •  There is a 5’ end splice “signal” (site): usually GT (less common: GC) •  And a 3’ end splice site: usually AG •  …]5’-‐GT/AG-‐3’[… v  It is possible to produce more than one protein (polypep'de) sequence from the same genic region, by alterna'vely bringing exons together= alterna=ve splicing. For example, the gene Dscam (Drosophila) has 38,000 alterna'vely spliced mRNAs = isoforms

23. 23 "Gene structure" by Daycd- Wikimedia Commons BIO-REFRESHER TRANSLATION  now in your mind •  Although of brief existence, understanding mRNAs is crucial, as they will become the center of your work.

24. 24BIO-REFRESHER TRANSLATION  reading frame: phase v  Introns can interrupt the reading frame of a gene by inser'ng a sequence between two consecu've codons v  Between the ﬁrst and second nucleo'de of a codon v  Or between the second and third nucleo'de of a codon "Exon and Intron classes”. Licensed under Fair use via Wikipedia

25. 25 "Protein synthesis" by Kelvinsong - Wikimedia Commons CURATING GENOMES TRANSLATION  in detail

26. 26BIO-REFRESHER HICCUPS  in transcription and translation v  The presence of premature Stop codons in the message is possible. A process called non-‐sense mediated decay checks for them and corrects them to avoid: incomplete splicing, DNA muta'ons, transcrip'on errors, and leaky scanning of ribosome – causing changes in the reading frame (frame shiYs). v  Inser'ons and dele'ons (indels) can cause frame shijs, when indel is not divisible by three (3). As a result, the pep'de can be abnormally long, or abnormally short – depending when the ﬁrst in-‐frame Stop signal is located.

27. Predic'on & Annota'on

28. 28Gene Prediction GENE PREDICTION v  The iden'ﬁca'on of structural features of the genome: •  Primarily focused on protein-‐coding genes. •  Predicts also transfer RNAs (tRNA), ribosomal RNAs (rRNA), regulatory mo'fs, long and small non-‐coding RNAs (ncRNA), repe''ve elements (masked), etc. •  Two methods for iden'ﬁca'on. •  Some are self-‐trained and some must be trained.

29. 29Gene Prediction GENE PREDICTION  methods for discovery 1) Ab ini,o: -‐ based on DNA composi'on, -‐ deals strictly with genomic sequences -‐ makes use of sta's'cal approaches to search for coding regions and typical gene signals. •  E.g. Augustus, GENSCAN, geneid, fgenesh, etc. 3’ Nat Rev Genet. 2015 Jun;16(6):321-32. doi: 10.1038/nrg3920

30. 30 Nucleic Acids 2003 vol. 31 no. 13 3738-3741 Gene Prediction GENE PREDICTION  methods for discovery (ctd) 2) Homology-‐based: -‐ evidence-‐based, -‐ ﬁnds genes using either similarity searches in the main databases or experimental data including RNAseq, expressed sequence tags (ESTs), full-‐length complementary DNAs (cDNAs), etc. •  E.g: fgenesh++, Just Annotate My genome (JAMg), SGP2

31. 31 GENE ANNOTATION Integra'on of data from computa'onal & experimental evidence with data from predic'on tools, to generate a reliable set of structural annota=ons. Involves: 1) ab ini1o predic'ons 2) assessment of biological evidence to drive the gene predic'on process 3) synthesis of these results to produce a set of consensus gene models Gene Annotation

32. 32 In some cases algorithms and metrics used to generate consensus sets may actually reduce the accuracy of the gene’s representa'on. GENE ANNOTATION Gene models may be organized into “sets” using: v  automa'c integra'on of predicted sets (combiners); e.g: GLEAN, EvidenceModeler or v  tools packaged into pipelines; e.g: MAKER, PASA, Gnomon, Ensembl, etc. Gene Annotation

33. ANNOTATION  an imperfect art No one is perfect, least of all automated annotation. 33 New technology brings new challenges: •  Assembly errors can cause fragmented annota'ons •  Limited coverage makes precise iden'ﬁca'on a diﬃcult task Image: www.BroadInstitute.org

34. MANUAL ANNOTATION  improving predictions Precise elucida=on of biological features encoded in the genome requires careful examina=on and review. Schiex et al. Nucleic Acids 2003 (31) 13: 3738-‐3741 Automated Predictions Experimental Evidence Manual Annotation – to the rescue. 34 cDNAs, HMM domain searches, RNAseq, genes from other species.

35. 35 BIOCURATION  structural and functional adjustments Iden=ﬁes elements that best represent the underlying biology and eliminates elements that reﬂect systemic errors of automated analyses. Assigns func=on through compara've analysis of similar genome elements from closely related species using literature, databases, and experimental data. MANUAL ANNOTATION h@p://GeneOntology.org 1 2

36. GENOME ANNOTATION  an inherently collaborative task APOLLO 36 Researchers oDen turn to colleagues for second opinions and insight from those with exper1se in par1cular areas (e.g., domains, families). So many sequences, but not enough hands!

37. APOLLO  collaborative genome annotation editing tool 37 v  Web based, integrated with JBrowse. v  Supports real 'me collabora'on! v  Automa'c genera'on of ready-‐made computable data. v  Supports annota'on of genes, pseudogenes, tRNAs, snRNAs, snoRNAs, ncRNAs, miRNAs, TEs, and repeats. v  Intui've annota'on, gestures, and pull-‐down menus to create and edit transcripts and exons structures, insert comments (CV, freeform text), associate GO terms, etc. APOLLO h@p://GenomeArchitect.org

38. APOLLO ARCHITECTURE  simple, flexible ARCHITECTURE 38 Web-‐based client + annota'on-‐edi'ng engine + server-‐side data service REST / JSON Websockets Annotation Engine (Server) Shiro LDAP OAuth JBrowse Data Organism 2 Annotations Security Preferences Organisms Tracks BAM BED VCF GFF3 BigWig Annotators Google Web Toolkit (GWT) / Bootstrap JBrowse DOJO / jQuery JBrowse Data Organism 1 Load genomic evidence per selected organism Single Data Store PostgreSQL, MySQL, MongoDB, ElasticSearch Apollo v2.0

39. We con'nuously train and support hundreds of geographically dispersed scien'sts from diverse research communi'es in conduc'ng manual annota'ons eﬀorts to recover coding sequences in agreement with all available biological evidence using Apollo. 39 LESSONS LEARNED APOLLO What we have learned: •  Collabora've work dis'lls invaluable knowledge •  We must enforce strict rules and formats •  We must evolve with the data •  NGS poses addi'onal challenges

40. 40 TRAINING CURATORS  a little training goes a long way! Provided with adequate tools, wet lab scien'sts make excep'onal curators who can easily learn to maximize the genera'on of accurate, biologically supported gene models. APOLLO

41. Apollo

42. 42 APOLLO  annotation editing environment BECOMING ACQUAINTED WITH APOLLO Color by CDS frame, toggle strands, set color scheme and highlights. Upload evidence files (GFF3, BAM, BigWig), add combina=on and sequence search tracks. Query the genome using BLAT. Naviga'on and zoom. Search for a gene model or a scaffold. Get coordinates and “rubber band” selec'on for zooming. Login User-‐created annota'ons. Annotator panel. Evidence Tracks Stage and cell-‐type specific transcrip'on data. h@p://genomearchitect.org/web_apollo_user_guide

43. Let’s play!

44. Instructions 44 | 44 APOLLO ON THE WEB  instructions Username: user.number@example.com Password: usernumber Email Password Server Begin at user.one@example.com userone 1 1 user.two@example.com usertwo 2 1 user.three@example.com userthree 3 1 user.four@example.com userfour 4 1 user.five@example.com userfive 5 1 user.six@example.com usersix 1 7 user.seven@example.com userseven 2 7 user.eight@example.com usereight 3 7 user.nine@example.com usernine 4 7 user.ten@example.com userten 5 7 user.eleven@example.com usereleven 1 1 user.twelve@example.com usertwelve 2 1 user.thirteen@example.com userthirteen 3 1 user.fourteen@example.com userfourteen 4 1 user.fijeen@example.com userfijeen 5 1 user.sixteen@example.com usersixteen 1 7 user.seventeen@example.com userseventeen 2 7 user.eigh@een@example.com usereighteen 3 7 user.nineteen@example.com usernineteen 4 7 user.twenty@example.com usertwenty 5 7 user.twentyone@example.com usertwentyone 1 1 user.twentytwo@example.com usertwentytwo 2 1 user.twentythree@example.com usertwentythree 3 1 user.twentyfour@example.com usertwentyfour 4 1 user.twentyfive@example.com usertwentyfive 5 1 user.twentysix@example.com usertwentysix 1 7 user.twentyseven@example.com usertwentyseven 2 7 user.twentyeight@example.com usertwentyeight 3 7 user.twentynine@example.com usertwentynine 4 7 Server URL 1 h@p://52.26.7.239:8080/apollo/annotator/index 2 h@p://52.89.205.105:8080/apollo/annotator/index 3 h@p://52.89.230.210:8080/apollo/annotator/index 4 h@p://52.89.149.42:8080/apollo/annotator/index 5 h@p://52.89.233.118:8080/apollo/annotator/index

45. Cura'ng with Apollo

46. Becoming Acquainted with Web Apollo 46 | 46 GENERAL PROCESS OF CURATION  main steps to remember 1.  Select or find a region of interest, e.g. scaffold. 2.  Select appropriate evidence tracks to review the gene model. 3.  Determine whether a feature in an exis'ng evidence track will provide a reasonable gene model to start working. 4.  If necessary, adjust the gene model. 5.  Check your edited gene model for integrity and accuracy by comparing it with available homologs. 6.  Comment and finish.

47. USER NAVIGATION  removable side dock HIGHLIGHTED IMPROVEMENTS 47 Annotations Organism Users Groups AdminTracks Reference Sequence

48. EDITS & EXPORTS  annotation details, exon boundaries, data export HIGHLIGHTED IMPROVEMENTS 48 1 2 Annotations 1 2

49. HIGHLIGHTED IMPROVEMENTS 49 Reference Sequences 3 FASTA GFF3 EDITS & EXPORTS  annotation details, exon boundaries, data export 3

50. 50 | 50 Becoming Acquainted with Web Apollo. USER NAVIGATION Annotator panel. •  Choose appropriate evidence from list of “Tracks” on annotator panel. •  Select & drag elements from evidence track into the ‘User-‐created Annota1ons’ area. •  Hovering over annota'on in progress brings up an informa'on pop-‐up. •  Crea'ng a new annota'on

51. 51 | 51 USER NAVIGATION Becoming Acquainted with Web Apollo. •  Annota'on right-‐click menu

52. 52 | 52 USER NAVIGATION Becoming Acquainted with Web Apollo. •  ‘Zoom to base level’ op'on reveals the DNA Track.

53. 53 | 53 USER NAVIGATION Becoming Acquainted with Web Apollo. •  Color exons by CDS from the ‘View’ menu.

54. 54 | Zoom in/out with keyboard: shij + arrow keys up/down 54 USER NAVIGATION Becoming Acquainted with Web Apollo. •  Toggle reference DNA sequence and transla=on frames in forward strand. Toggle models in either direc'on.

55. Annota'on

56. simple cases

57. “Simple case”: -‐ the predicted gene model is correct or nearly correct, and -‐ this model is supported by evidence that completely or mostly agrees with the predic'on. -‐ evidence that extends beyond the predicted model is assumed to be non-‐coding sequence. The following are simple modiﬁca'ons. 57 | 57 ANNOTATING SIMPLE CASES Becoming Acquainted with Web Apollo. SIMPLE CASES

58. 58 | •  A conﬁrma'on box will warn you if the receiving transcript is not on the same strand as the feature where the new exon originated. •  Check ‘Start’ and ‘Stop’ signals ajer each edit. 58 ADDING EXONS Becoming Acquainted with Web Apollo. SIMPLE CASES

59. If transcript alignment data are available and extend beyond your original annota'on, you may extend or add UTRs. 1.  Right click at the exon edge and ‘Zoom to base level’. 2.  Place the cursor over the edge of the exon un1l it becomes a black arrow then click and drag the edge of the exon to the new coordinate posi'on that includes the UTR. 59 | 59 ADDING UTRs Becoming Acquainted with Web Apollo. SIMPLE CASES To add a new spliced UTR to an exis'ng annota'on follow the procedure for adding an exon.

60. To modify an exon boundary and match data in the evidence tracks: select both the oﬀending exon and the feature with the expected boundary, then right click on the annota'on to select ‘Set 3’ end’ or ‘Set 5’ end’ as appropriate. 60 | In some cases all the data may disagree with the annota'on, in other cases some data support the annota'on and some of the data support one or more alterna've transcripts. Try to annotate as many alterna've transcripts as are well supported by the data. 60 MATCHING EXON BOUNDARY TO EVIDENCE Becoming Acquainted with Web Apollo. SIMPLE CASES

61. 1.  Zoom in to clearly resolve each exon as a dis'nct rectangle. 2.  Two exons from diﬀerent tracks sharing the same start and/or end coordinates will display a red bar to indicate matching edges. 3.  Selec'ng the whole annota'on or one exon at a 'me, use this ‘edge-‐ matching’ func'on and scroll along the length of the annota'on, verifying exon boundaries against available data. Use square [ ] brackets to scroll from exon to exon. 4.  Check if cDNA / RNAseq reads lack one or more of the annotated exons or include addi'onal exons. 61 | 61 CHECKING EXON INTEGRITY Becoming Acquainted with Web Apollo. SIMPLE CASES

62. Non-‐canonical splice sites ﬂags. Double click: selec'on of feature and sub-‐features Evidence Tracks Area ‘User-‐created Annota1ons’ Track Edge-‐matching Apollo’s edi'ng logic (brain): §  selects longest ORF as CDS §  ﬂags non-‐canonical splice sites 62 ORFs AND SPLICE SITES Becoming Acquainted with Web Apollo. SIMPLE CASES

63. 63 | Non-‐canonical splices are indicated by an orange circle with a white exclama'on point inside, placed over the edge of the oﬀending exon. Canonical splice sites: 3’-‐…exon]GA / TG[exon…-‐5’ 5’-‐…exon]GT / AG[exon…-‐3’ reverse strand, not reverse-‐complemented: forward strand 63 SPLICE SITES Becoming Acquainted with Web Apollo. SIMPLE CASES Zoom to review non-‐canonical splice site warnings. Although these may not always have to be corrected (e.g GC donor), they should be ﬂagged with the appropriate comment. Exon/intron splice site error warning Curated model

64. Web Apollo calculates the longest possible open reading frame (ORF) that includes canonical ‘Start’ and ‘Stop’ signals within the predicted exons. If ‘Start’ appears to be incorrect, modify it by selec'ng an in-‐frame ‘Start’ codon further up or downstream, depending on evidence (protein database, addi'onal evidence tracks). It may be present outside the predicted gene model, within a region supported by another evidence track. In very rare cases, the actual ‘Start’ codon may be non-‐canonical (non-‐ATG). 64 | 64 ‘START’ AND ‘STOP’ SITES Becoming Acquainted with Web Apollo. SIMPLE CASES

65. complex cases

66. Evidence may support joining two or more diﬀerent gene models. Warning: protein alignments may have incorrect splice sites and lack non-‐conserved regions! 1.  In ‘User-‐created Annota,ons’ area shij-‐click to select an intron from each gene model and right click to select the ‘Merge’ op'on from the menu. 2.  Drag suppor'ng evidence tracks over the candidate models to corroborate overlap, or review edge matching and coverage across models. 3.  Check the resul'ng transla'on by querying a protein database e.g. UniProt, NCBI nr. Add comments to record that this annota'on is the result of a merge. 66 | 66 Red lines around exons: ‘edge-‐matching’ allows annotators to conﬁrm whether the evidence is in agreement without examining each exon at the base level. COMPLEX CASES merge two gene predictions on the same scaffold Becoming Acquainted with Web Apollo. COMPLEX CASES

67. One or more splits may be recommended when: -‐ different segments of the predicted protein align to two or more different gene families -‐ predicted protein doesn’t align to known proteins over its en're length Transcript data may support a split, but first verify whether they are alterna've transcripts. 67 | 67 COMPLEX CASES split a gene prediction Becoming Acquainted with Web Apollo. COMPLEX CASES

68. DNA Track ‘User-‐created Annota=ons’ Track 68 COMPLEX CASES correcting frameshifts and single-base errors Becoming Acquainted with Web Apollo. COMPLEX CASES Always remember: when annota'ng gene models using Apollo, you are looking at a ‘frozen’ version of the genome assembly and you will not be able to modify the assembly itself.

69. 69 COMPLEX CASES correcting selenocysteine containing proteins Becoming Acquainted with Web Apollo. COMPLEX CASES

70. 70 COMPLEX CASES correcting selenocysteine containing proteins Becoming Acquainted with Web Apollo. COMPLEX CASES

71. 1.  Apollo allows annotators to make single base modifica'ons or frameshijs that are reflected in the sequence and structure of any transcripts overlapping the modifica'on. These manipula'ons do NOT change the underlying genomic sequence. 2.  If you determine that you need to make one of these changes, zoom in to the nucleo'de level and right click over a single nucleo'de on the genomic sequence to access a menu that provides op'ons for crea'ng inser'ons, dele'ons or subs'tu'ons. 3.  The ‘Create Genomic Inser=on’ feature will require you to enter the necessary string of nucleo'de residues that will be inserted to the right of the cursor’s current loca'on. The ‘Create Genomic Dele=on’ op'on will require you to enter the length of the dele'on, star'ng with the nucleo'de where the cursor is posi'oned. The ‘Create Genomic Subs=tu=on’ feature asks for the string of nucleo'de residues that will replace the ones on the DNA track. 4.  Once you have entered the modifica'ons, Apollo will recalculate the corrected transcript and protein sequences, which will appear when you use the right-‐click menu ‘Get Sequence’ op'on. Since the underlying genomic sequence is reflected in all annota'ons that include the modified region you should alert the curators of your organisms database using the ‘Comments’ sec'on to report the CDS edits. 5.  In special cases such as selenocysteine containing proteins (read-‐throughs), right-‐click over the offending/premature ‘Stop’ signal and choose the ‘Set readthrough stop codon’ op'on from the menu. 71 | 71 Becoming Acquainted with Web Apollo. COMPLEX CASES COMPLEX CASES correcting frameshifts, single-base errors, and selenocysteines

72. 72 | 72 USER NAVIGATION Becoming Acquainted with Web Apollo. •  Annotation right-click menu

73. 73 Annota'ons, annota'on edits, and History: stored in a centralized database. 73 USER NAVIGATION Becoming Acquainted with Web Apollo.

74. Follow the checklist un'l you are happy with the annota'on! And remember to… –  comment to validate your annota'on, even if you made no changes to an exis'ng model. Think of comments as your vote of conﬁdence. –  or add a comment to inform the community of unresolved issues you think this model may have. 74 | 74 Always Remember: Apollo cura'on is a community eﬀort so please use comments to communicate the reasons for your annota'on. Your comments will be visible to everyone. COMPLETING THE ANNOTATION Becoming Acquainted with Apollo.

75. 75 | 75 USER NAVIGATION Becoming Acquainted with Web Apollo. •  Annotation right-click menu

76. 76 The Annota'on Informa=on Editor 76 USER NAVIGATION Becoming Acquainted with Web Apollo. DBXRefs are database crossed references: if you have reason to believe that this gene is linked to a gene in a public database (including your own), then add it here.

77. 77 The Annota'on Informa=on Editor •  Add PubMed IDs •  Include GO terms as appropriate from any of the three ontologies •  Write comments sta'ng how you have validated each model. 77 USER NAVIGATION Becoming Acquainted with Web Apollo.

78. Checklist

79. •  Check ‘Start’ and ‘Stop’ sites. •  Check splice sites: most splice sites display these residues …]5’-‐GT/AG-‐3’[… •  Check if you can annotate UTRs, for example using RNA-‐Seq data: –  Align it against relevant genes/gene family –  blastp against NCBI’s RefSeq or nr •  Check for gaps in the genome. •  Addi'onal func'onality may be necessary: –  Merging 2 gene predic'ons on the same scaffold –  Merging 2 gene predic'ons from different scaffolds –  Spligng a gene predic'on –  Correc'ng frameshiYs and other errors in the genome assembly –  Annotate selenocysteines, correct single-‐ base errors, etc. 79 | 79 •  Add: –  Important project informa'on in the form of comments –  IDs from public databases e.g. GenBank (via DBXRef), gene symbol(s), common name(s), synonyms, top BLAST hits, orthologs with species names, and everything else you can think of, because you are the expert. –  Comments about the kinds of changes you made to the gene model of interest, if any. –  Any appropriate func'onal assignments, e.g. via BLAST, RNA-‐Seq data, literature searches, etc. THE CHECKLIST for accuracy and integrity MANUAL ANNOTATION CHECKLIST

80. Example

81. Example Example 81 A public Apollo Demo using the Honey Bee genome is available at h@p://genomearchitect.org/WebApolloDemo -‐ Cura'on example using the Hyalella azteca genome (amphipod crustacean).

82. What do we know about this genome? •  Currently publicly available data at NCBI: •  >37,000 nucleo'de seqsà scaffolds, mitochondrial genes •  300 amino acid seqsà mitochondrion •  53 ESTs •  0 conserved domains iden'fied •  0 “gene” entries submi@ed •  Data at i5K Workspace@NAL (annota'on hosted at USDA) -‐ 10,832 scaffolds: 23,288 transcripts: 12,906 proteins Example 82

83. PubMed Search:   what’s new? Example 83

84. PubMed Search: what’s new? Example 84 “Ten popula'ons (3 cultures, 7 from California water bodies) differed by at least 550-‐fold in sensi=vity to pyrethroids.” “By sequencing the primary pyrethroid target site, the voltage-‐gated sodium channel (vgsc), we show that point muta'ons and their spread in natural popula'ons were responsible for differences in pyrethroid sensi'vity.” “The finding that a non-‐target aqua'c species has acquired resistance to pes'cides used only on terrestrial pests is troubling evidence of the impact of chronic pes=cide transport from land-‐based applica'ons into aqua'c systems.”

85. How many sequences are there, publicly available, for our gene of interest? Example 85 •  Para, (voltage-‐gated sodium channel alpha subunit; Nasonia vitripennis). •  NaCP60E (Sodium channel protein 60 E; D. melanogaster). –  MF: voltage-‐gated ca'on channel ac'vity (IDA, GO:0022843). –  BP: olfactory behavior (IMP, GO: 0042048), sodium ion transmembrane transport (ISS,GO:0035725). –  CC: voltage-‐gated sodium channel complex (IEA, GO:0001518). And what do we know about them?

86. Retrieving sequences for   sequence similarity searches. Example 86 >vgsc-‐Segment3-‐DomainII RVFKLAKSWPTLNLLISIMGKTVGALGNLTFVLCIIIFIFAVMGMQLFGKNYTEKVTKFKWSQDG QMPRWNFVDFFHSFMIVFRVLCGEWIESMWDCMYVGDFSCVPFFLATVVIGNLVVSFMHR

87. BLAT search    input Example 87 >vgsc-‐Segment3-‐DomainII RVFKLAKSWPTLNLLISIMGKTVGALGNLTFVLCIIIFIFAVMGMQLFGKNYTEKVTKFKWSQDG QMPRWNFVDFFHSFMIVFRVLCGEWIESMWDCMYVGDFSCVPFFLATVVIGNLVVSFMHR

88. BLAT search    results Example 88 •  High-‐scoring segment pairs (hsp) are listed in tabulated format. •  Clicking on one line of results sends you to those coordinates.

89. Creating a new gene model: drag and drop Example 89 •  Apollo automatically calculates ORF. In this case, ORF includes the high-scoring segment pairs (hsp), marked here in blue.

90. Available Tracks Example 90

91. Get Sequence Example 91 http://blast.ncbi.nlm.nih.gov/Blast.cgi

92. Also, flanking sequences (other gene models) vs. NCBI nr Example 92 In this case, two gene models upstream, at 5’ end. BLAST hsps

93. Review alignments Example 93 HaztTmpM006234 HaztTmpM006233 HaztTmpM006232

94. Hypothesis for vgsc gene model Example 94

95. Editing: merge the three models Example 95 Merge by dropping an exon or gene model onto another. Merge by selec'ng two exons (holding down “Shij”) and using the right click menu. or…

96. Result of merging the three models. Example 96

97. Editing: correct boundaries Example 97 Modify exon / intron boundary: -‐  Drag the end of the exon to the nearest canonical splice site. or -‐  Use right-‐click menu.

98. Editing: set translation start Example 98

99. Editing: delete exon Example 99 Delete ﬁrst exon from HaztTmpM006233

100. Editing: add an exon - supported by RNAseq Example 100 •  RNAseq reads show evidence in support of transcribed product, which was not predicted. •  Add exon at coordinates 97946-‐98012 by dragging up one of the RNAseq reads.

101. Editing: and adjust other exon boundary using evidence Example 101

102. Editing: adjust other boundaries supported by evidence Example 102

103. Finished model Example 103 Corroborate integrity and accuracy of the model: -‐ Start and Stop -‐ Exon structure and splice sites …]5’-‐GT/AG-‐3’[… -‐ Check the predicted protein product vs. NCBI nr, UniProt, etc.

104. Information Editor •  DBXRefs: e.g. NP_001128389.1, N. vitripennis, RefSeq •  PubMed iden'ﬁer: PMID: 24065824 •  Gene Ontology IDs: GO:0022843, GO: 0042048, GO:0035725, GO:0001518. •  Comments. •  Name, Symbol. •  Approve / Delete radio bu@on. Example 104 Comments (if applicable)

105. Demo

106. APOLLO  demonstration DEMO 106 Demo video is available at h@ps://youtu.be/VgPtAP_fvxY

107. OUTLINE  Web Apollo Collabora've Cura'on and Interac've Analysis of Genomes 107OUTLINE •  BIO-‐REFRESHER biological concepts for cura'on •  ANNOTATION automa'c predic'ons •  MANUAL ANNOTATION necessary, collabora've •  APOLLO advancing collabora've cura'on •  EXAMPLE demos •  EXERCISES

108. Exercises

109. Exercises Live Demonstra'on using the Apis mellifera genome. 110 1. Evidence in support of protein coding gene models. 1.1 Consensus Gene Sets: Oﬃcial Gene Set v3.2 Oﬃcial Gene Set v1.0 1.2 Consensus Gene Sets comparison: OGSv3.2 genes that merge OGSv1.0 and RefSeq genes OGSv3.2 genes that split OGSv1.0 and RefSeq genes 1.3 Protein Coding Gene Predic=ons Supported by Biological Evidence: NCBI Gnomon Fgenesh++ with RNASeq training data Fgenesh++ without RNASeq training data NCBI RefSeq Protein Coding Genes and Low Quality Protein Coding Genes 1.4 Ab ini,o protein coding gene predic=ons: Augustus Set 12, Augustus Set 9, Fgenesh, GeneID, N-‐SCAN, SGP2 1.5 Transcript Sequence Alignment: NCBI ESTs, Apis cerana RNA-‐Seq, Forager Bee Brain Illumina Con'gs, Nurse Bee Brain Illumina Con'gs, Forager RNA-‐Seq reads, Nurse RNA-‐Seq reads, Abdomen 454 Con'gs, Brain and Ovary 454 Con'gs, Embryo 454 Con'gs, Larvae 454 Con'gs, Mixed Antennae 454 Con'gs, Ovary 454 Con'gs Testes 454 Con'gs, Forager RNA-‐Seq HeatMap, Forager RNA-‐Seq XY Plot, Nurse RNA-‐Seq HeatMap, Nurse RNA-‐Seq XY Plot Becoming Acquainted with Web Apollo.

110. Exercises Live Demonstra'on using the Apis mellifera genome. 111 1. Evidence in support of protein coding gene models (Con=nued). 1.6 Protein homolog alignment: Acep_OGSv1.2 Aech_OGSv3.8 Cﬂo_OGSv3.3 Dmel_r5.42 Hsal_OGSv3.3 Lhum_OGSv1.2 Nvit_OGSv1.2 Nvit_OGSv2.0 Pbar_OGSv1.2 Sinv_OGSv2.2.3 Znev_OGSv2.1 Metazoa_Swissprot 2. Evidence in support of non protein coding gene models 2.1 Non-‐protein coding gene predic=ons: NCBI RefSeq Noncoding RNA NCBI RefSeq miRNA 2.2 Pseudogene predic=ons: NCBI RefSeq Pseudogene Becoming Acquainted with Web Apollo.

111. Instrucciones 112 | 112 APOLLO ON THE WEB  instructions Username: user.number@example.com Password: usernumber Email Password Server Begin at user.one@example.com userone 1 1 user.two@example.com usertwo 2 1 user.three@example.com userthree 3 1 user.four@example.com userfour 4 1 user.five@example.com userfive 5 1 user.six@example.com usersix 1 7 user.seven@example.com userseven 2 7 user.eight@example.com usereight 3 7 user.nine@example.com usernine 4 7 user.ten@example.com userten 5 7 user.eleven@example.com usereleven 1 1 user.twelve@example.com usertwelve 2 1 user.thirteen@example.com userthirteen 3 1 user.fourteen@example.com userfourteen 4 1 user.fijeen@example.com userfijeen 5 1 user.sixteen@example.com usersixteen 1 7 user.seventeen@example.com userseventeen 2 7 user.eigh@een@example.com usereighteen 3 7 user.nineteen@example.com usernineteen 4 7 user.twenty@example.com usertwenty 5 7 user.twentyone@example.com usertwentyone 1 1 user.twentytwo@example.com usertwentytwo 2 1 user.twentythree@example.com usertwentythree 3 1 user.twentyfour@example.com usertwentyfour 4 1 user.twentyfive@example.com usertwentyfive 5 1 user.twentysix@example.com usertwentysix 1 7 user.twentyseven@example.com usertwentyseven 2 7 user.twentyeight@example.com usertwentyeight 3 7 user.twentynine@example.com usertwentynine 4 7 Server URL 1 h@p://52.26.7.239:8080/apollo/annotator/index 2 h@p://52.89.205.105:8080/apollo/annotator/index 3 h@p://52.89.230.210:8080/apollo/annotator/index 4 h@p://52.89.149.42:8080/apollo/annotator/index 5 h@p://52.89.233.118:8080/apollo/annotator/index

112. Thank you. 113 •  Berkeley Bioinforma=cs Open-‐source Projects (BBOP), Berkeley Lab: Apollo and Gene Ontology teams. Suzanna E. Lewis (PI). •  § Chris1ne G. Elsik (PI). University of Missouri. •  * Ian Holmes (PI). University of California Berkeley. •  Arthropod genomics community: i5K Steering Commi@ee (esp. Sue Brown (Kansas State)), Alexie Papanicolaou (UWS), and the Honey Bee Genome Sequencing Consor'um. •  Stephen Ficklin, GenSAS, Washington State University •  Apollo is supported by NIH grants 5R01GM080203 from NIGMS, and 5R01HG004483 from NHGRI. Both projects are also supported by the Director, Oﬃce of Science, Oﬃce of Basic Energy Sciences, of the U.S. Department of Energy under Contract No. DE-‐AC02-‐05CH11231 •  •  For your a*en=on, thank you! Apollo Nathan Dunn Colin Diesh § Deepak Unni § Gene Ontology Chris Mungall Seth Carbon Heiko Dietze BBOP Apollo: h@p://GenomeArchitect.org GO: h@p://GeneOntology.org i5K: h@p://arthropodgenomes.org/wiki/i5K Thank you! NAL at USDA Monica Poelchau Christopher Childers Gary Moore HGSC at BCM fringy Richards Kim Worley JBrowse Eric Yao *

Genome Curation using Apollo

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Genome Curation using Apollo

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