1.
Introduction to Apollo
Collaborative genome annotation editing
A webinar for the i5K Research Community – Calanoida (copepod)
Monica Munoz-Torres | @monimunozto
Berkeley Bioinformatics Open-Source Projects (BBOP)
Environmental Genomics & Systems Biology Division, Lawrence Berkeley National Laboratory
i5k Pilot Project Species Calls | 17 October, 2016
http://GenomeArchitect.org

2.
Outline
• Today you will discover
effective ways to extract
valuable information
about a genome through
curation efforts.

3.
After this talk you will...
• Better understand ‘curation’ in the context of genome annotation:
assembled genome à automated annotation à manual annotation
• Become familiar with Apollo’s environment and functionality.
• Learn to identify homologs of known genes of interest in your newly
sequenced genome.
• Learn how to corroborate and modify automatically annotated gene
models using all available evidence in Apollo.

4.
Experimental design, sampling.
Comparative analyses
Official / Merged
Gene Set
Manual
Annotation
Automated
Annotation
Sequencing
Assembly
Synthesis &
dissemination.
This is our focus.

5.
We must care about curation
Marbach et al. 2011. Nature Methods | Shutterstock.com | Alexander Wild
The gene set of an organism informs a variety of studies:
• Characterization: Gene number, GC%, TEs, repeats.
• Functional assignments.
• Molecular evolution, sequence conservation.
• Gene families.
• Metabolic pathways.
• What makes an organism what it is?
What makes a bee a “bee”?

6.
Genome Curation
Identifies elements that best
represent the underlying biology
and eliminates elements that
reflect systemic errors of
automated analyses.
Assigns function through
comparative analysis of similar
genome elements from closely
related species using literature,
databases, and experimental
data.
Apollo
Gene Ontology
Resources

7.
A few things to remember
when conducting manual annotation
7BIO-REFRESHER
• KEEP A GLOSSARY HANDY
from contig to splice site
• WHAT IS A GENE?
defining your goal
• TRANSCRIPTION
mRNA in detail
• TRANSLATION
reading frames, etc.
• GENOME CURATION
steps involved

8.
The gene: a “moving target”
“The gene is a union
of genomic
sequences encoding
a coherent set of
potentially
overlapping
functional products.”
Gerstein et al., 2007. Genome Res

9.
9
"Gene structure" by Daycd- Wikimedia Commons
BIO-REFRESHER
mRNA
• Although of brief existence, understanding mRNAs is crucial,
as they will become the center of your work.

10.
10BIO-REFRESHER
Reading frames
v In eukaryotes, only one reading frame per section of DNA is biologically
relevant at a time: it has the potential 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

11.
11BIO-REFRESHER
Splice sites
v The spliceosome catalyzes the removal of introns and the ligation of
flanking exons.
v Splicing signals (from the point of view of an intron):
• One splice signal (site) on the 5’ end: usually GT (less common: GC)
• And a 3’ end splice site: usually AG
• Canonical splice sites look like this: …]5’-GT/AG-3’[…

12.
12BIO-REFRESHER
Exons and Introns
v Introns can interrupt the reading frame of a gene by inserting a sequence
between two consecutive codons
v Between the first and second nucleotide of a codon
v Or between the second and third nucleotide of a codon
"Exon and Intron classes”. Licensed under Fair use via Wikipedia

13.
Prediction & Annotation

14.
14GENE PREDICTION & ANNOTATION
PREDICTION & ANNOTATION
v Identification and annotation of genome features:
• primarily focuses on protein-coding genes.
• also identifies RNAs (tRNA, rRNA, long and small non-coding
RNAs (ncRNA)), regulatory motifs, repetitive elements, etc.
• happens in 2 phases:
1. Computation phase
2. Annotation phase

15.
15GENE PREDICTION & ANNOTATION
COMPUTATION PHASE
a. Experimental data are aligned to the genome: expressed sequence tags,
RNA-sequencing reads, proteins (also from other species).
a. Gene predictions are generated:
- ab initio: based on nucleotide sequence and composition
e.g. Augustus, GENSCAN, geneid, fgenesh, etc.
- evidence-driven: identifying also domains and motifs
e.g. SGP2, JAMg, fgenesh++, etc.
Result: the single most likely coding sequence, no UTRs, no isoforms.
Yandell & Ence. Nature Rev 2012 doi:10.1038/nrg3174

16.
16GENE PREDICTION & ANNOTATION
ANNOTATION PHASE
Experimental data (evidence) and predictions are synthetized into gene
annotations.
Result: gene models that generally include UTRs, isoforms, evidence trails.
Yandell & Ence. Nature Rev 2012 doi:10.1038/nrg3174
5’ UTR 3’ UTR

17.
17
In some cases algorithms and metrics used to generate
consensus sets may actually reduce the accuracy of the gene’s
representation.
CONSENSUS GENE SETS
Gene models may be organized into sets using:
v combiners for automatic integration of predicted sets
e.g: GLEAN, EvidenceModeler
or
v tools packaged into pipelines
e.g: MAKER, PASA, Gnomon, Ensembl, etc.
GENE PREDICTION & ANNOTATION

18.
ANNOTATION
needs some refinement
No one is perfect, least of all automated annotation. 18
New technologies bring new challenges:
• Assembly errors can cause fragmented
annotations
• Limited coverage makes precise
identification a difficult task

19.
MANUAL ANNOTATION
improving predictions
Precise elucidation of biological features
encoded in the genome requires careful
examination and review.
Schiex et al. Nucleic Acids 2003 (31) 13: 3738-3741
Automated Predictions
Experimental Evidence
Manual Annotation – to the rescue. 19
cDNAs, HMM domain searches, RNAseq,
genes from other species.

20.
GENOME CURATION
an inherently collaborative task
GENE PREDICTION & ANNOTATION 20
So many sequences, not enough hands.
Apis mellifera | Alexander Wild | www.alexanderwild.com

21.
We have provided continuous training and support for hundreds of
geographically dispersed scientists to conduct manual annotations
efforts in order to recover coding sequences in agreement with all
available biological evidence.
21
Collaboration is key!
APOLLO
• Collaborative work distills invaluable knowledge.
• A little training goes a long way!
Wet lab scientists can easily learn to maximize the
generation of accurate, biologically supported gene models.

22.
Apollo

23.
APOLLO: versatile genome annotation editing
• Apollo is a web-based genome annotation editor, integrated with JBrowse
• Supports real time collaboration & generates analysis-ready data
USER-CREATED ANNOTATIONS
EVIDENCE TRACKS
ANNOTATOR PANEL

24.
BECOMING ACQUAINTED WITH APOLLO
General process of curation
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 existing 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.

25.
Apollo- version at i5K Workspace@NAL
4. Becoming Acquainted with Web Apollo.
25
The Sequence Selection Window

26.
Sort
Apollo- version at i5K Workspace@NAL
“Old Track Select Page”
4. Becoming Acquainted with Web Apollo.
26

27.
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),
- combination track
- sequence search track
Query the genome using
BLAT.
Navigation and zoom.
Search for a gene
model or a scaffold.
Get coordinates and “rubber
band” selection for zooming.
Login
User-created
annotations.
New
annotator
panel.
Evidence
Tracks
Stage and
cell-type
specific
transcription
data.
http://genomearchitect.org/web_apollo_user_guide

28.
28 | BECOMING ACQUAINTED WITH 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 Annotations’ area.
• Hovering over annotation in progress brings up an information pop-up.
• Creating a new annotation

29.
Adding a gene model

30.
Adding a gene model

31.
Adding a gene model

32.
Editing functionality

33.
Editing functionality
Example: Adding an exon supported by experimental data
• RNAseq reads show evidence in support of a transcribed product that was not predicted.
• Add exon by dragging up one of the RNAseq reads.

34.
Editing functionality
Example: Adjusting exon boundaries supported by experimental data

35.
Curating with Apollo

36.
36 |
USER NAVIGATION
BECOMING ACQUAINTED WITH APOLLO
• ‘Zoom to base level’ reveals the DNA Track.

37.
37 |
USER NAVIGATION
BECOMING ACQUAINTED WITH APOLLO
• Color exons by CDS from the ‘View’ menu.

38.
38 |
Zoom in/out with keyboard:
shift + arrow keys up/down
USER NAVIGATION
BECOMING ACQUAINTED WITH APOLLO
• Toggle reference DNA sequence and translation frames in forward
strand. Toggle models in either direction.

39.
annotating simple cases

40.
“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 prediction.
- evidence that extends beyond the predicted model is assumed
to be non-coding sequence.
The following are simple modifications.
ANNOTATING SIMPLE CASES
BECOMING ACQUAINTED WITH APOLLO SIMPLE CASES

41.
• A confirmation 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 after each edit.
ADDING EXONS
BECOMING ACQUAINTED WITH APOLLO SIMPLE CASES

42.
If transcript alignment data are available & extend beyond your original annotation,
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 until it becomes a black arrow then click
and drag the edge of the exon to the new coordinate position that includes the UTR.
ADDING UTRs
To add a new spliced UTR to an existing
annotation also follow the procedure for adding an exon.
BECOMING ACQUAINTED WITH APOLLO SIMPLE CASES

43.
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 annotation to
select ‘Set 3’ end’ or ‘Set 5’ end’ as
appropriate.
In some cases all the data may disagree with the annotation, in
other cases some data support the annotation and some of the
data support one or more alternative transcripts. Try to annotate
as many alternative transcripts as are well supported by the data.
MATCHING EXON BOUNDARY TO EVIDENCE
BECOMING ACQUAINTED WITH APOLLO SIMPLE CASES

44.
1. Two exons from different tracks sharing the same start/end coordinates
display a red bar to indicate matching edges.
2. Selecting the whole annotation or one exon at a time, use this edge-
matching function and scroll along the length of the annotation,
verifying exon boundaries against available data.
Use square [ ] brackets to scroll from exon to exon.
User curly { } brackets to scroll from annotation to annotation.
3. Check if cDNA / RNAseq reads lack one or more of the annotated exons
or include additional exons.
CHECKING EXON INTEGRITY
BECOMING ACQUAINTED WITH APOLLO SIMPLE CASES

45.
Non-canonical splice sites flags. Double click: selection of
feature and sub-features
Evidence Tracks Area
‘User-created Annotations’ Track
Edge-matching
Apollo’s editing logic (brain):
§ selects longest ORF as CDS
§ flags non-canonical splice sites
ORFs AND SPLICE SITES
BECOMING ACQUAINTED WITH APOLLO SIMPLE CASES

46.
Non-canonical splices are indicated by
an orange circle with a white
exclamation 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
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 a
comment.
Exon/intron splice site error warning
Curated model
BECOMING ACQUAINTED WITH APOLLO SIMPLE CASES

47.
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 selecting
an in-frame ‘Start’ codon further up or
downstream, depending on evidence (proteins,
RNAseq).
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).
‘Start’ AND ‘Stop’ SITES
BECOMING ACQUAINTED WITH APOLLO SIMPLE CASES

48.
annotating complex cases

49.
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 Annotations’ area shift-click to select an intron from each gene model and
right click to select the ‘Merge’ option from the menu.
2. Drag supporting evidence tracks over the candidate models to corroborate overlap, or
review edge matching and coverage across models.
3. Check the resulting translation by querying a protein database e.g. UniProt, NCBI nr. Add
comments to record that this annotation is the result of a merge.
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 APOLLO COMPLEX CASES

50.
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 entire length
- Transcript data may support a split, but first verify whether they are
alternative transcripts.
COMPLEX CASES
split a gene prediction
BECOMING ACQUAINTED WITH APOLLO COMPLEX CASES

51.
DNA Track
‘User-created Annotations’ Track
COMPLEX CASES
annotate frameshifts and correct single-base errors
Always remember: when annotating 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.
BECOMING ACQUAINTED WITH APOLLO COMPLEX CASES

52.
COMPLEX CASES
correcting selenocysteine containing proteins
BECOMING ACQUAINTED WITH APOLLO COMPLEX CASES

53.
COMPLEX CASES
correcting selenocysteine containing proteins
BECOMING ACQUAINTED WITH APOLLO COMPLEX CASES

54.
1. Apollo allows annotators to make single base modifications or frameshifts that are reflected in
the sequence and structure of any transcripts overlapping the modification. These
manipulations do NOT change the underlying genomic sequence.
2. If you determine that you need to make one of these changes, zoom in to the nucleotide level
and right click over a single nucleotide on the genomic sequence to access a menu that
provides options for creating insertions, deletions or substitutions.
3. The ‘Create Genomic Insertion’ feature will require you to enter the necessary string of
nucleotide residues that will be inserted to the right of the cursor’s current location. The
‘Create Genomic Deletion’ option will require you to enter the length of the deletion, starting
with the nucleotide where the cursor is positioned. The ‘Create Genomic Substitution’ feature
asks for the string of nucleotide residues that will replace the ones on the DNA track.
4. Once you have entered the modifications, Apollo will recalculate the corrected transcript and
protein sequences, which will appear when you use the right-click menu ‘Get Sequence’
option. Since the underlying genomic sequence is reflected in all annotations that include the
modified region you should alert the curators of your organisms database using the
‘Comments’ section 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’ option from
the menu.
COMPLEX CASES
annotating frameshifts and correcting single-base errors & selenocysteines
BECOMING ACQUAINTED WITH APOLLO COMPLEX CASES

55.
55 |
USER NAVIGATION
BECOMING ACQUAINTED WITH APOLLO
• Information Editor

56.
The Annotation Information Editor
USER NAVIGATION
BECOMING ACQUAINTED WITH APOLLO

57.
The Annotation Information Editor
• Add PubMed IDs
• Include GO terms as appropriate
from any of the three ontologies
• Write comments stating how you
have validated each model.
USER NAVIGATION
BECOMING ACQUAINTED WITH APOLLO

58.
58 |
USER NAVIGATION
BECOMING ACQUAINTED WITH APOLLO
• Keeping track of each edit

59.
Annotations, annotation edits, and History: stored in a centralized database.
USER NAVIGATION
BECOMING ACQUAINTED WITH APOLLO

60.
Follow the checklist until you are happy with the annotation!
And remember to…
– comment to validate your annotation, even if you made no changes to an
existing 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.
60 |
Always Remember: Apollo curation is a community effort so please
use comments to communicate the reasons for your
annotation. Your comments will be visible to everyone.
COMPLETING THE ANNOTATION
BECOMING ACQUAINTED WITH APOLLO

61.
Checklist

62.
• 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.
• Additional functionality may be necessary:
– merging 2 gene predictions - same scaffold
– ‘merging’ 2 gene predictions - different
scaffolds
– splitting a gene prediction
– annotating frameshifts
– annotating selenocysteines, correcting
single-base and other assembly errors, etc.
62 |
• Add:
– Important project information 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 functional assignments, e.g.
via BLAST, RNA-Seq data, literature searches,
etc.
CHECKLIST
for accuracy and integrity
MANUAL ANNOTATION CHECKLIST

63.
Genome curation with i5k

64.
64i5K Workspace@NAL
The collaborative curation process at i5k
1. A computationally predicted consensus gene set has been generated
using multiple lines of evidence; e.g. HVIT_v0.5.3-Models
1. i5K Projects will integrate consensus computational predictions with
manual annotations to produce an updated Official Gene Set (OGS):
Warning!
• 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!

65.
The ‘Replace Models’ rules
BECOMING ACQUAINTED WITH APOLLO http://tinyurl.com/apollo-i5k-replace

66.
66i5K Workspace@NAL
3. In some cases algorithms and metrics used to generate consensus sets
may actually reduce the accuracy of the gene’s representation. Use your
judgment, try choosing a different model to begin the annotation.
4. Isoforms: drag original and alternatively spliced form to ‘User-created
Annotations’ area.
5. If an annotation needs to be removed from the consensus set, drag it to
the ‘User-created Annotations’ area and label as ‘Delete’ on the
Information Editor.
6. Overlapping interests? Collaborate to reach agreement.
7. Follow guidelines for i5K Pilot Species Projects, at http://goo.gl/LRu1VY
The collaborative curation process at i5k

67.
Example

68.
What’s new?...
finding inspiration in PubMed.
Example 68
“Molecular analysis of bed bug populations from across the USA and Europe
found that >80% and >95% of the respective populations contained V419L
and/or L925I mutations in the voltage-gated sodium channel gene, indicating
widespread distribution of target-site-based pyrethroid resistance.”
Homalodisca vitripennis | Alexander Wild | www.alexanderwild.comHalyomorpha halys | Fondazione Edmund Mach - Italy
Now for our species of interest. . .

69.
Example
Example 69
Curation example using the Hyalella azteca
genome (amphipod crustacean).

70.
What do we know about this genome?
• Currently publicly available data at NCBI:
• >37,000 nucleotide seqsà scaffolds, mitochondrial genes
• 344 amino acid seqsà mitochondrion
• 47 ESTs
• 0 conserved domains identified
• 0 “gene” entries submitted
• Data at i5K Workspace@NAL (annotation hosted at USDA)
- 10,832 scaffolds: 23,288 transcripts: 12,906 proteins
Example 70

71.
PubMed Search:
what’s new?
Example 71

72.
PubMed Search: what’s new?
Example 72
“Ten populations differed by at least 550-fold in sensitivity to
pyrethroids.”
“Sequencing the primary pyrethroid target site, the voltage-
gated sodium channel (vgsc), shows that point mutations and
their spread in natural populations were responsible for
differences in pyrethroid sensitivity.”
“The finding that a non-target aquatic species has acquired
resistance to pesticides used only on terrestrial pests is
troubling evidence of the impact of chronic pesticide
transport from land-based applications into aquatic systems.”

73.
How many sequences are there, publicly available,
for our gene of interest?
Example 73
• Para, (voltage-gated sodium channel alpha
subunit; Nasonia vitripennis).
• NaCP60E (Sodium channel protein 60 E; D.
melanogaster).
– MF: voltage-gated cation channel activity
(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?

74.
Retrieving sequences for a
sequence similarity search.
Example 74
>vgsc-Segment3-DomainII
RVFKLAKSWPTLNLLISIMGKTVGALGNLTFVLCIIIFIFAVMGMQLFGKNYTEKVTKFKWSQD
GQMPRWNFVDFFHSFMIVFRVLCGEWIESMWDCMYVGDFSCVPFFLATVVIGNLVVSFMHR

75.
BLAT search
input
Example 75
>vgsc-Segment3-DomainII
RVFKLAKSWPTLNLLISIMGKTVGALGNLTFVLCIIIFIFAVMGMQLFGKNYTEKVTKFKWSQD
GQMPRWNFVDFFHSFMIVFRVLCGEWIESMWDCMYVGDFSCVPFFLATVVIGNLVVSFMHR

76.
BLAT search
results
Example 76
• High-scoring segment pairs (hsp)
are listed in tabulated format.
• Clicking on one line of results
sends you to those coordinates.

77.
BLAST at i5K
https://i5k.nal.usda.gov/blast
Example 77
>vgsc-Segment3-DomainII
RVFKLAKSWPTLNLLISIMGKTVGALGNLTFVLCIIIFIFAVMGMQLFGKNYTEKVTKFKWSQD
GQMPRWNFVDFFHSFMIVFRVLCGEWIESMWDCMYVGDFSCVPFFLATVVIGNLVVSFMHR

78.
BLAST at i5K
https://i5k.nal.usda.gov/blast
Example 78

79.
BLAST at i5K: hsps in “BLAST+ Results” track
Example 79

80.
Creating a new gene model: drag and drop
Example 80
• Apollo automatically calculates longest ORF.
• In this case, ORF includes the high-scoring segment pairs (hsp),
marked here in blue.
• Note that gene is transcribed from reverse strand.

81.
Available Tracks
Example 81

82.
Get Sequence
Example 82
http://blast.ncbi.nlm.nih.gov/Blast.cgi

83.
Also, flanking sequences (other gene models) vs. NCBI nr
Example 83
In this case, two gene
models upstream, at 5’
end.
BLAST hsps

84.
Review alignments
Example 84
HaztTmpM006234
HaztTmpM006233
HaztTmpM006232

85.
Hypothesis for vgsc gene model
Example 85

86.
Editing: merge the three models
Example 86
Merge by dropping an
exon or gene model
onto another.
Merge by selecting
two exons (holding
down “Shift”) and
using the right click
menu.
or…

87.
Result of merging the gene models:
Example 87

88.
Editing: correct offending splice site
Example 88
Modify exon / intron
boundary:
- Drag the end of the
exon to the nearest
canonical splice site.
or
- Use right-click menu.

89.
Editing: set translation start
Example 89

90.
Editing: delete exon not supported by evidence
Example 90
Delete first exon from
HaztTmpM006233

91.
Editing: add an exon supported by RNAseq
Example 91
• 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.

92.
Editing: adjust offending splice site using evidence
Example 92

93.
Editing: adjust other boundaries supported by evidence
Example 93

94.
Finished model
Example 94
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.

95.
Information Editor
• DBXRefs: e.g. NP_001128389.1, N.
vitripennis, RefSeq
• PubMed identifier: PMID: 24065824
• Gene Ontology IDs: GO:0022843,
GO:0042048, GO:0035725,
GO:0001518.
• Comments
• Name, Symbol
• Approve / Delete radio button
Example 95
Comments
(if applicable)

96.
Go play!

97.
PUBLIC DEMO
97 |
APOLLO ON THE WEB
instructions
At i5K
1. Register for access to Apollo at the i5K Workspace@NAL at
https://i5k.nal.usda.gov/web-apollo-registration
2. Contact the coordinator for each species community to receive
more information about how to contribute. Contact info is available
on each organism’s page.

98.
PUBLIC DEMO
98 |
APOLLO ON THE WEB
instructions
Public Honey bee demo available at:
http://GenomeArchitect.org/WebApolloDemo
Username:
demo@demo.com
Password:
demo

99.
APOLLO
demonstration
PUBLIC DEMO 99
Demonstration video is available at
https://youtu.be/VgPtAP_fvxY

100.
OUTLINE
100OUTLINE
• BIO-REFRESHER
biological concepts for curation
• ANNOTATION
automatic predictions
• MANUAL ANNOTATION
necessary, collaborative
• APOLLO
advancing collaborative curation
• EXAMPLE
demos

101.
Apollo Development
Nathan Dunn
Technical Lead Eric Yao
Christine Elsik’s Lab,
University of Missouri
Suzi Lewis
Principal Investigator
BBOP
Moni Munoz-Torres
Project Manager
Deepak Unni
JBrowse. Ian Holmes’ Lab
University of California, Berkeley

102.
• Berkeley Bioinformatics Open-source Projects (BBOP),
Berkeley Lab: 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.
• Stephen Ficklin, GenSAS, Washington State University
• Apollo is supported by NIH grants 5R01GM080203
from NIGMS, and 5R01HG004483 from NHGRI. 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 attention, thank you!
Apollo
Nathan Dunn
Deepak Unni §
Gene Ontology
Chris Mungall
Seth Carbon
Heiko Dietze
BBOP
Learn more about Apollo at http://GenomeArchitect.org
Thank you!
NAL at USDA
Monica Poelchau
Mei-Ju Chen
Christopher Childers
Gary Moore
HGSC at BCM
fringy Richards
Kim Worley
JBrowse Eric Yao *

103.
Interface Updates
Annotator Panel

104.
Interface Updates
gene
mRNA

105.
Update: Transforming coordinates
Bringing exons closer together to facilitate
annotation of gene models with long introns.
1,275 bp
Concept for Apollo v2.1 – Northern Spring 2016

106.
Transforming coordinates
Assembly artifacts may cause gene models to be split
across two or more scaffolds. To facilitate annotation,
Apollo allows the generation of an artificial space where
the annotation can be completed.
Scaffold 2Scaffold 1
Genome
Assembly
. . . . . .
Scaffold n