genome curation using apollo

Curating genes and genomesApollo: 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

OUTLINE

Web Apollo Collabora've Cura'on and Interac've Analysis of Genomes

2 OUTLINE

•  Today we will discover how to extract the most valuable informa'on about a genome through cura'on efforts.

APOLLO DEVELOPMENT

APOLLO DEVELOPERS 3

h*p : / /GenomeA r c h i t e c t . o r 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 Diesh Deepak Unni

4

BY THE END OF THIS TALKyou 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

Anatomy of a genome sequencing project

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.

CURATING GENOMESsteps 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

GENOME ANNOTATIONobjectives 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

First, a bio-‐refresher

WHAT WE NEED TO KNOWfor manual annotation

To remember… Biological concepts to be@er understand manual annota'on

10 FOOD 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 CURATING GENOMES

WHAT WE KNOWin very general terms

12 CURATING GENOMES

WHAT WE KNOWin very general terms

http://www.wisegeek.com/

5’

3’

5’

3’

13 CURATING 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 BIO-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 BIO-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 BIO-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 BIO-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 BIO-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 BIO-REFRESHER

TRANSLATIONreading 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

"Reading Frame" by Hornung Ákos - Wikimedia Commons

BIO-REFRESHER


21

"ORF" by Thatsonginc - Wikimedia Commons

BIO-REFRESHER


22 BIO-REFRESHER

TRANSLATIONreading 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

"Gene structure" by Daycd- Wikimedia Commons

BIO-REFRESHER

TRANSLATIONnow in your mind

•  Although of brief existence, understanding mRNAs is crucial, as they will become the center of your work.

24 BIO-REFRESHER

TRANSLATIONreading 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

"Protein synthesis" by Kelvinsong - Wikimedia Commons

CURATING GENOMES

TRANSLATIONin detail

26 BIO-REFRESHER

HICCUPSin 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.

Predic'on & Annota'on

28 Gene 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 Gene Prediction

GENE PREDICTIONmethods 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

Nucleic Acids 2003 vol. 31 no. 13 3738-3741

Gene Prediction

GENE PREDICTIONmethods 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

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

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

ANNOTATIONan 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

MANUAL ANNOTATIONimproving 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

BIOCURATIONstructural 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

GENOME ANNOTATIONan 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!

APOLLOcollaborative 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

APOLLO ARCHITECTUREsimple, 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

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

TRAINING CURATORSa 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

Apollo

42

APOLLOannotation 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

Let’s play!

Instructions 44 | 44

APOLLO ON THE WEBinstructions

Username: [email protected]

Password: usernumber

Email Password Server Begin at [email protected] userone 1 1 [email protected] usertwo 2 1 [email protected] userthree 3 1 [email protected] userfour 4 1 [email protected] userfive 5 1 [email protected] usersix 1 7 [email protected] userseven 2 7 [email protected] usereight 3 7 [email protected] usernine 4 7 [email protected] userten 5 7 [email protected] usereleven 1 1 [email protected] usertwelve 2 1 [email protected] userthirteen 3 1 [email protected] userfourteen 4 1 [email protected] userfijeen 5 1 [email protected] usersixteen 1 7 [email protected] userseventeen 2 7 user.eigh@[email protected] usereighteen 3 7 [email protected] usernineteen 4 7 [email protected] usertwenty 5 7 [email protected] usertwentyone 1 1 [email protected] usertwentytwo 2 1 [email protected] usertwentythree 3 1 [email protected] usertwentyfour 4 1 [email protected] usertwentyfive 5 1 [email protected] usertwentysix 1 7 [email protected] usertwentyseven 2 7 [email protected] usertwentyeight 3 7 [email protected] 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

Cura'ng with Apollo

Becoming Acquainted with Web Apollo 46 | 46

GENERAL PROCESS OF CURATIONmain 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.

USER NAVIGATIONremovable side dock

HIGHLIGHTED IMPROVEMENTS 47

Annotations Organism Users Groups Admin Tracks Reference Sequence

EDITS & EXPORTSannotation details, exon boundaries, data export


1 2

Annotations

1

2


Reference Sequences

3

FASTA

GFF3

EDITS & EXPORTSannotation details, exon boundaries, data export

3

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

USER NAVIGATION

Becoming Acquainted with Web Apollo.

•  Annota'on right-‐click menu

52 | 52

USER NAVIGATION


•  ‘Zoom to base level’ op'on reveals the DNA Track.

53 | 53

USER NAVIGATION


•  Color exons by CDS from the ‘View’ menu.

54 |

Zoom in/out with keyboard: shij + arrow keys up/down

54

USER NAVIGATION


•  Toggle reference DNA sequence and transla=on frames in forward strand. Toggle models in either direc'on.

Annota'on

simple cases

“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 |

•  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


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


To add a new spliced UTR to an exis'ng annota'on follow the procedure for adding an exon.

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


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


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


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


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

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


complex cases

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

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


DNA Track

‘User-‐created Annota=ons’ Track

68

COMPLEX CASES correcting frameshifts and single-base errors


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

COMPLEX CASES correcting selenocysteine containing proteins


70

COMPLEX CASES correcting selenocysteine containing proteins


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

USER NAVIGATION


•  Annotation right-click menu

73

Annota'ons, annota'on edits, and History: stored in a centralized database.

73

USER NAVIGATION


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

USER NAVIGATION


•  Annotation right-click menu

76

The Annota'on Informa=on Editor

76

USER NAVIGATION


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

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


Checklist

•  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

Example

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).

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

PubMed Search: what’s new?

Example 83

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.”

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?

Retrieving sequences for sequence similarity searches.

Example 86

>vgsc-‐Segment3-‐DomainII RVFKLAKSWPTLNLLISIMGKTVGALGNLTFVLCIIIFIFAVMGMQLFGKNYTEKVTKFKWSQDGQMPRWNFVDFFHSFMIVFRVLCGEWIESMWDCMYVGDFSCVPFFLATVVIGNLVVSFMHR

BLAT search

input

Example 87

>vgsc-‐Segment3-‐DomainII RVFKLAKSWPTLNLLISIMGKTVGALGNLTFVLCIIIFIFAVMGMQLFGKNYTEKVTKFKWSQDGQMPRWNFVDFFHSFMIVFRVLCGEWIESMWDCMYVGDFSCVPFFLATVVIGNLVVSFMHR

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.

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.

Available Tracks

Example 90

Get Sequence

Example 91

http://blast.ncbi.nlm.nih.gov/Blast.cgi

Also, flanking sequences (other gene models) vs. NCBI nr

Example 92

In this case, two gene models upstream, at 5’ end.

BLAST hsps

Review alignments

Example 93

HaztTmpM006234

HaztTmpM006233

HaztTmpM006232

Hypothesis for vgsc gene model

Example 94

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…

Result of merging the three models.

Example 96

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.

Editing: set translation start

Example 98

Editing: delete exon

Example 99

Delete first exon from HaztTmpM006233

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.

Editing: and adjust other exon boundary using evidence

Example 101

Editing: adjust other boundaries supported by evidence

Example 102

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.

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)

APOLLOdemonstration

DEMO 106

Demo video is available at h@ps://youtu.be/VgPtAP_fvxY

OUTLINE

Web Apollo Collabora've Cura'on and Interac've Analysis of Genomes

107 OUTLINE

•  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

Exercises

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


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


Instrucciones 112 | 112

APOLLO ON THE WEBinstructions

Username: [email protected]

Password: usernumber

Email Password Server Begin at [email protected] userone 1 1 [email protected] usertwo 2 1 [email protected] userthree 3 1 [email protected] userfour 4 1 [email protected] userfive 5 1 [email protected] usersix 1 7 [email protected] userseven 2 7 [email protected] usereight 3 7 [email protected] usernine 4 7 [email protected] userten 5 7 [email protected] usereleven 1 1 [email protected] usertwelve 2 1 [email protected] userthirteen 3 1 [email protected] userfourteen 4 1 [email protected] userfijeen 5 1 [email protected] usersixteen 1 7 [email protected] userseventeen 2 7 user.eigh@[email protected] usereighteen 3 7 [email protected] usernineteen 4 7 [email protected] usertwenty 5 7 [email protected] usertwentyone 1 1 [email protected] usertwentytwo 2 1 [email protected] usertwentythree 3 1 [email protected] usertwentyfour 4 1 [email protected] usertwentyfive 5 1 [email protected] usertwentysix 1 7 [email protected] usertwentyseven 2 7 [email protected] usertwentyeight 3 7 [email protected] 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

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 *