Apollo Introduction for i5K Groups 2015-10-07

Introduction to Apollo 
A webinar for the i5K Research Community
Monica Munoz-Torres, PhD | @monimunozto 
Berkeley Bioinformatics Open-Source Projects (BBOP) 
Lawrence Berkeley National Laboratory |  
University of California Berkeley | U.S. Department of Energy
7 October, 2015

OUTLINE 
Web
Apollo
Collabora've
Cura'on
and

Interac've
Analysis
of
Genomes

2OUTLINE
•  Today
we
will
discover

how
to
extract
the

most
valuable
gene

informa'on
from

genome
cura'on

eﬀorts.

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 Lab,
University of Missouri
Suzi Lewis
Principal Investigator
BBOP

Moni Munoz-Torres
Stephen Ficklin
GenSAS,
Washington State University
Colin DieshDeepak Unni

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.

Introduction

5
Genome Sequencing Project
Introduction
Experimental design, sampling.
Comparative analyses
Consensus
Gene Set
Manual
Annotation
Automated
Annotation
Sequencing Assembly
Synthesis &
dissemination.

CURATING GENOMES 
overview
1  Predic=on
of
Gene
Models

2  Annota=on
of
gene
models

3  Manual
annota=on

CURATING GENOMES 6

GENOME ANNOTATION 
requires precision and depth
Anotando Genomas 7
An
organism’s
gene
set
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

Let’s
refresh
our
memory

REVIEW ON YOUR OWN 
for manual annotation
To
remember…
Biological
concepts
to
be@er

understand
manual
annota'on

9FOOD 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

10CURATING GENOMES
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

11CURATING GENOMES
What is a gene?
v  In
our
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
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/

12CURATING GENOMES
What is a gene? 
considerations
v  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.

13BIO-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?

14CURATING GENOMES
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.

15CURATING GENOMES
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

16
"Gene structure" by Daycd- Wikimedia Commons
CURATING GENOMES
TRANSCRIPTION & TRANSLATION 
now in your mind

17CURATING GENOMES
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

18Gene 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.

19Gene 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

20
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

21
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

22
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

ANNOTATION 
an imperfect art
No one is perfect, least of all automated annotation. 23
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 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. 24
cDNAs,
HMM
domain
searches,
RNAseq,

genes
from
other
species.

25
MANUAL ANNOTATION 
objectives
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

BUT, MANUAL CURATION 
does not always scale
Researchers
on
their
own;

may
or
may
not
publicize

results;
may
be
a
dead-‐end

with
very
few
people
ever

aware
of
these
results.

Elsik
et
al.
2006.
Genome
Res.
16(11):1329-‐33.

MANUAL ANNOTATION 26
Too
many
sequences
and
not
enough
hands.

A
small
group
of
highly

trained
experts
(e.g.
GO).

1
Museum

A
few
very
good
biologists,
a

few
very
good
bioinforma'cians

camping
together
for
intense
but

short
periods
of
'me.

Jamboree
2

Co*age
3

GENOME ANNOTATION 
an inherently collaborative task
APOLLO 27
Researchers
oHen
turn
to
colleagues
for
second

opinions
and
insight
from
those
with
exper1se
in

par1cular
areas
(e.g.,
domains,
families).

APOLLO 
collaborative genome annotation editing tool
28
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 ARCHITECTURE 
simpler, more flexible
APOLLO 29
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 for
selected organism
Single Data Store
PostgreSQL, MySQL,
MongoDB, ElasticSearch
Apollo v2.0

We
train
and
support
hundreds
of
geographically
dispersed
scien'sts
from

diverse
research
communi'es
to
conduct
manual
annota'ons,
to
recover

coding
sequences
in
agreement
with
all
available
biological
evidence
using

Apollo.

v  Gate
keeping
and
monitoring.

v  Tutorials,
training
workshops,
and
“geneborees”.

30
DISPERSED COMMUNITIES
collaborative manual annotation efforts
APOLLO

LESSONS LEARNED 
What
we
have
learned:

•  Collabora've
work
dis'lls
invaluable
knowledge

•  We
must
enforce
strict
rules
and
formats

•  We
must
evolve
with
the
data

•  A
li@le
training
goes
a
long
way

•  NGS
poses
addi'onal
challenges

LESSONS LEARNED 31

Apollo

h@p://genomearchitect.org/web_apollo_user_guide

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.

Becoming Acquainted with Web Apollo
33 | 33
GENERAL PROCESS OF CURATION 
main steps to remember

34
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.

Let’s
play
with
Apollo.

REMOVABLE SIDE DOCK 
with customizable tabs
HIGHLIGHTED IMPROVEMENTS 36
Annotations Organism Users Groups AdminTracks
Reference
Sequence

EDITS & EXPORTS 
annotation details, exon boundaries, data export
1 2
Annotations
1
2

Reference
Sequences
3
FASTA

GFF3

EDITS & EXPORTS 
annotation details, exon boundaries, data export
3

39 | 39
Becoming Acquainted with Web Apollo.
USER NAVIGATION
Annotator

panel.

•  Choose appropriate evidence tracks from list 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.

40 | 40
USER NAVIGATION
•  Annotation right-click menu

41
Annota'ons,
annota'on
edits,
and
History:
stored
in
a
centralized
database.

41
USER NAVIGATION

42
The
Annota'on
Informa=on
Editor

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.

42
USER NAVIGATION

43
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.

43
USER NAVIGATION

44 | 44
USER NAVIGATION
•  ‘Zoom
to
base
level’
op'on
reveals
the
DNA
Track.

45 | 45
USER NAVIGATION
•  Color
exons
by
CDS
from
the
‘View’
menu.

46 |
Zoom
in/out
with
keyboard:

shix
+
arrow
keys
up/down

46
USER NAVIGATION
•  Toggle
reference
DNA
sequence
and
transla=on
frames
in
forward

strand.
Toggle
models
in
either
direc'on.

“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.

49 | 49
ANNOTATING SIMPLE CASES
Becoming Acquainted with Web Apollo. SIMPLE CASES

50 |
•  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
axer
each
edit.

50
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.

51 |
To
add
a
new
spliced
UTR
to
an
exis'ng
annota'on

follow
the
procedure
for
adding
an
exon.

51
ADDING UTRs

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.

52 | 52
CHECK EXON INTEGRITY

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.

53 |
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.

53
EXON STRUCTURE INTEGRITY

Flags
non-‐canonical

splice
sites.

Selec'on
of
features
and
sub-‐
features

Edge-‐matching

Evidence
Tracks
Area

‘User-‐created
Annota'ons’
Track

Apollo’s
edi'ng
logic
(brain):

§  selects
longest
ORF
as
CDS

§  ﬂags
non-‐canonical
splice
sites

54
ORFs AND SPLICE SITES

55 |
Exon/intron
junc'on
possible
error

Original
model

Curated
model

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

55
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.

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).

56 | 56
‘START’ AND ‘STOP’ SITES

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
shix-‐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.
Add
comments

to
record
that
this
annota'on
is
the
result
of
a
merge.

58 | 58
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.

59 | 59
COMPLEX CASES
split a gene prediction

DNA
Track

‘User-‐created
Annota=ons’
Track

60
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.

61
COMPLEX CASES
correcting selenocysteine containing proteins

62
COMPLEX CASES
correcting selenocysteine containing proteins

1.  Apollo
allows
annotators
to
make
single
base
modifica'ons
or
frameshixs
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.

63 | 63
COMPLEX CASES
correcting frameshifts, single-base errors, and selenocysteines

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.

64 | 64
Always
Remember:
Web
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

1.  Can
you
add
UTRs
(e.g.:
via
RNA-‐Seq)?

2.  Check
exon
structures

3.  Check
splice
sites:
most
splice
sites
display
these

residues
…]5’-‐GT/AG-‐3’[…

4.  Check
‘Start’
and
‘Stop’
sites

5.  Check
the
predicted
protein
product(s)

–  Align
it
against
relevant
genes/gene
family.

–  blastp
against
NCBI’s
RefSeq
or
nr

6.  If
the
protein
product
s'll
does
not
look
correct

then
check:

–  Are
there
gaps
in
the
genome?

–  Merge
of
2
gene
predic'ons
on
the
same

scaffold

–  Merge
of
2
gene
predic'ons
from
different

scaffolds

–  Split
a
gene
predic'on

–  Frameshigs

–  error
in
the
genome
assembly?

–  Selenocysteines,
single-‐base
errors,
etc

66 | 66
7.  Finalize
annota'on
by
adding:

–  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.

–  Whether
your
model
replaces
one
or
more
models

from
the
official
gene
set
(so
it
can
be
deleted).

–  The
kinds
of
changes
you
made
to
the
gene
model

of
interest,
if
any.

–  Any
appropriate
func'onal
assignments
of
interest

to
the
community
(e.g.
via
BLAST,
RNA-‐Seq
data,

literature
searches,
etc.)

THE CHECKLIST
for accuracy and integrity
MANUAL ANNOTATION CHECKLIST

Example
Example 68
A
public
Apollo
Demo
using
the
Honey
Bee
genome
is
available
at

h@p://genomearchitect.org/WebApolloDemo

-‐
Demonstra'on
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 69

PubMed Search:  
what’s new?
Example 70

PubMed Search: what’s new?
Example 71
“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 for our gene of
interest?
Example 72
•  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 73
>vgsc-‐Segment3-‐DomainII

RVFKLAKSWPTLNLLISIMGKTVGALGNLTFVLCIIIFIFAVMGMQLFGKNYTEKVTKFKWSQDG
QMPRWNFVDFFHSFMIVFRVLCGEWIESMWDCMYVGDFSCVPFFLATVVIGNLVVSFMHR

BLAT search
Example 74
>vgsc-‐Segment3-‐DomainII

RVFKLAKSWPTLNLLISIMGKTVGALGNLTFVLCIIIFIFAVMGMQLFGKNYTEKVTKFKWSQDG
QMPRWNFVDFFHSFMIVFRVLCGEWIESMWDCMYVGDFSCVPFFLATVVIGNLVVSFMHR

Customizations:  
high-scoring segment pairs (hsp) in “BLAST+ Results” track
Example 76

Creating a new gene model: drag and drop
Example 77
•  Apollo automatically calculates ORF.
In this case, ORF includes the high-scoring segment pairs (hsp).

Get Sequence
Example 79
http://blast.ncbi.nlm.nih.gov/Blast.cgi

Also, flanking sequences (other gene models) vs. NCBI nr
Example 80
In
this
case,
two
gene

models
upstream,
at
5’

end.

BLAST
hsps

Review alignments
Example 81
HaztTmpM006234

HaztTmpM006233

HaztTmpM006232

Hypothesis for vgsc gene model
Example 82

Editing: merge the three models
Example 83
Merge
by
dropping
an

exon
or
gene
model

onto
another.

Merge
by
selec'ng

two
exons
(holding

down
“Shix”)
and

using
the
right
click

menu.

or…

Editing: correct boundaries, delete exons
Example 84
Modify
exon
/
intron

boundary:

-‐  Drag
the
end
of
the

exon
to
the
nearest

canonical
splice
site.

-‐  Use
right-‐click
menu.

Delete
ﬁrst
exon
from

HaztTmpM006233

Editing: set translation start
Example 85

Editing: modify boundaries
Example 86
Modify
intron
/

exon
boundary

also
at
coord.

78,999.

Finished model
Example 87
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

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 88
Comments

(if
applicable)

APOLLO 
demonstration
DEMO 90
Apollo
demo
video
available
at:

h@ps://youtu.be/VgPtAP_fvxY

•  Berkeley
Bioinforma=cs
Open-‐source
Projects
(BBOP),

Berkeley
Lab:
Web
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.

•  Apollo
is
supported
by
NIH
grants
5R01GM080203
from

NIGMS,
and
5R01HG004483
from
NHGRI;
by
Contract

No.
60-‐8260-‐4-‐005
from
the
Na'onal
Agricultural
Library

(NAL)
at
the
United
States
Department
of
Agriculture

(USDA);
and
by
the
Director,
Oﬃce
of
Science,
Oﬃce
of

Basic
Energy
Sciences,
of
the
U.S.
Department
of
Energy

under
Contract
No.
DE-‐AC02-‐05CH11231

• 

•  For
your
a*en=on,
thank
you!

Thank you. 91
Web
Apollo

Nathan
Dunn

Colin
Diesh
§

Deepak
Unni
§

Gene
Ontology

Chris
Mungall

Seth
Carbon

Heiko
Dietze

BBOP

Web
Apollo:
h@p://GenomeArchitect.org

i5K:
h@p://arthropodgenomes.org/wiki/i5K

GO:
h@p://GeneOntology.org

Thanks!

NAL
at
USDA

Monica
Poelchau

Christopher
Childers

Gary
Moore

HGSC
at
BCM

fringy
Richards

Dan
Hughes

Kim
Worley

JBrowse

Eric
Yao
*

Apollo Introduction for i5K Groups 2015-10-07

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