SlideShare a Scribd company logo
Introduction to Apollo

A webinar for the American chestnut & 

Chinese chestnut 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
27 August, 2015
OUTLINE

Web	
  Apollo	
  Collabora've	
  Cura'on	
  and	
  	
  
Interac've	
  Analysis	
  of	
  Genomes	
  
2OUTLINE
•  MANUAL	
  ANNOTATION	
  
necessary,	
  collabora've	
  
	
  
•  APOLLO	
  
empowering	
  collabora've	
  cura'on	
  
	
  
•  EXAMPLE	
  
demonstra'ons	
  
3
Genome Sequencing Project
Introduction
Assembly
Automated
Annotation
Manual
annotation
Using Web Apollo
Merge:
automated +
manual
Genome-wide & gene-
specific comparative
analyses
QC
QC
Synthesis &
dissemination.
Experimental design, sample
collection preparation.
QC
Sequencing
QC
QC
REVIEW ON YOUR OWN

for manual annotation
To	
  remember…	
  Biological	
  concepts	
  to	
  be=er	
  
understand	
  manual	
  annota'on	
  
4FOOD FOR THOUGHT
•  GLOSSARY	
  
from	
  con$g	
  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	
  
5
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
6CURATING 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	
  
7CURATING 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	
  regula$on!	
  
	
  
	
  
“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/
8CURATING 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.
9CURATING GENOMES
What is a gene?
“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
10CURATING 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.	
  
11CURATING 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	
  
12
"Gene structure" by Daycd- Wikimedia Commons
CURATING GENOMES
TRANSCRIPTION & TRANSLATION

now in your mind
13
Text for figures goes here
CURATING 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
CURATING GENOMES

overview
1  Predic=on	
  of	
  Gene	
  Models	
  
	
  
	
  
2  Annota=on	
  of	
  gene	
  models	
  
	
  
	
  
3  Manual	
  annota=on	
  
CURATING GENOMES 14
15Gene 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.	
  
16Gene 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
17
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	
  
18
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	
  ini$o	
  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
19
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 IS NOT PERFECT 

automated annotation remains an imperfect art
Unlike	
  the	
  more	
  highly	
  polished	
  genomes	
  of	
  earlier	
  projects,	
  today’s	
  
genomes	
  usually	
  have:	
  
•  more	
  frequent	
  assembly	
  errors,	
  which	
  lead	
  to	
  annota'on	
  of	
  
genes	
  across	
  mul'ple	
  scaffolds	
  
•  lower	
  coverage	
  
No one is perfect, least of all automated annotation. 20
Image: www.BroadInstitute.org
MANUAL ANNOTATION

working concept
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. 21
cDNAs,	
  HMM	
  domain	
  searches,	
  RNAseq,	
  
genes	
  from	
  other	
  species.	
  
22
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 23
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 24
Researchers	
  oEen	
  turn	
  to	
  colleagues	
  for	
  second	
  
opinions	
  and	
  insight	
  from	
  those	
  with	
  exper$se	
  in	
  
par$cular	
  areas	
  (e.g.,	
  domains,	
  families).	
  
APOLLO

collaborative genome annotation editing tool
25
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 26
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”.	
  
27
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 28
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
30 | 30	
GENERAL PROCESS OF CURATION

main steps to remember
31
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 33
Annotations Organism Users Groups AdminTracks
Reference
Sequence
EDITS & EXPORTS

annotation details, exon boundaries, data export
HIGHLIGHTED IMPROVEMENTS 34
1 2
Annotations
1
2
HIGHLIGHTED IMPROVEMENTS 35
Reference
Sequences
3
FASTA	
  
GFF3	
  
EDITS & EXPORTS

annotation details, exon boundaries, data export
3
36 | 36	
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.
37 | 37	
USER NAVIGATION
Becoming Acquainted with Web Apollo.
•  Annotation right-click menu
38	
Annota'ons,	
  annota'on	
  edits,	
  and	
  History:	
  stored	
  in	
  a	
  centralized	
  database.	
  
38	
USER NAVIGATION
Becoming Acquainted with Web Apollo.
39	
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.	
  
39	
USER NAVIGATION
Becoming Acquainted with Web Apollo.
40	
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.	
  
40	
USER NAVIGATION
Becoming Acquainted with Web Apollo.
41 | 41	
USER NAVIGATION
Becoming Acquainted with Web Apollo.
•  ‘Zoom	
  to	
  base	
  level’	
  op'on	
  reveals	
  the	
  DNA	
  Track.	
  
42 | 42	
USER NAVIGATION
Becoming Acquainted with Web Apollo.
•  Color	
  exons	
  by	
  CDS	
  from	
  the	
  ‘View’	
  menu.	
  
43 |
Zoom	
  in/out	
  with	
  keyboard:	
  
shiv	
  +	
  arrow	
  keys	
  up/down	
  
43	
USER NAVIGATION
Becoming Acquainted with Web Apollo.
•  Toggle	
  reference	
  DNA	
  sequence	
  and	
  transla=on	
  frames	
  in	
  forward	
  
strand.	
  Toggle	
  models	
  in	
  either	
  direc'on.	
  
Annota'ng	
  
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.	
  	
  
	
  
46 | 46	
ANNOTATING SIMPLE CASES
Becoming Acquainted with Web Apollo. SIMPLE CASES
47 |
•  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	
  aver	
  each	
  edit.	
  
47	
ADDING EXONS
Becoming Acquainted with Web Apollo. SIMPLE CASES
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	
  un$l	
  it	
  becomes	
  a	
  black	
  arrow	
  then	
  click	
  
and	
  drag	
  the	
  edge	
  of	
  the	
  exon	
  to	
  the	
  new	
  coordinate	
  posi'on	
  that	
  includes	
  the	
  UTR.	
  	
  
48 |
To	
  add	
  a	
  new	
  spliced	
  UTR	
  to	
  an	
  exis'ng	
  annota'on	
  
follow	
  the	
  procedure	
  for	
  adding	
  an	
  exon.	
  
48	
ADDING UTRs
Becoming Acquainted with Web Apollo. SIMPLE CASES
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.	
  	
  
	
  
49 | 49	
CHECK EXON INTEGRITY
Becoming Acquainted with Web Apollo. SIMPLE CASES
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.	
  
	
  
50 |
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.	
  
50	
EXON STRUCTURE INTEGRITY
Becoming Acquainted with Web Apollo. SIMPLE CASES
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	
  
51	
ORFs AND SPLICE SITES
Becoming Acquainted with Web Apollo. SIMPLE CASES
52 |
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	
  
52	
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.	
  	
  
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).	
  	
  
53 | 53	
‘START’ AND ‘STOP’ SITES
Becoming Acquainted with Web Apollo. SIMPLE CASES
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	
  shiv-­‐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.	
  
55 | 55	
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.	
  	
  
56 | 56	
COMPLEX CASES
split a gene prediction
Becoming Acquainted with Web Apollo. COMPLEX CASES
DNA	
  Track	
  
‘User-­‐created	
  Annota=ons’	
  Track	
  
57	
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.	
  
58	
COMPLEX CASES
correcting selenocysteine containing proteins
Becoming Acquainted with Web Apollo. COMPLEX CASES
59	
COMPLEX CASES
correcting selenocysteine containing proteins
Becoming Acquainted with Web Apollo. COMPLEX CASES
1.  Apollo	
  allows	
  annotators	
  to	
  make	
  single	
  base	
  modifica'ons	
  or	
  frameshivs	
  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.	
  
	
  60 | 60	
Becoming Acquainted with Web Apollo. COMPLEX CASES
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.	
  
61 | 61	
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
Becoming Acquainted with Web Apollo.
Checklist	
  
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	
  
63 | 63	
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
Example 65
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 66
PubMed Search: 

what’s new?
Example 67
PubMed Search: what’s new?
Example 68
“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 69
•  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 70
>vgsc-­‐Segment3-­‐DomainII	
  
RVFKLAKSWPTLNLLISIMGKTVGALGNLTFVLCIIIFIFAVMGMQLFGKNYTEKVTKFKWSQDG
QMPRWNFVDFFHSFMIVFRVLCGEWIESMWDCMYVGDFSCVPFFLATVVIGNLVVSFMHR
BLAT search
Example 71
>vgsc-­‐Segment3-­‐DomainII	
  
RVFKLAKSWPTLNLLISIMGKTVGALGNLTFVLCIIIFIFAVMGMQLFGKNYTEKVTKFKWSQDG
QMPRWNFVDFFHSFMIVFRVLCGEWIESMWDCMYVGDFSCVPFFLATVVIGNLVVSFMHR
BLAT search
Example 72
Customizations: 

high-scoring segment pairs (hsp) in “BLAST+ Results” track
Example 73
Creating a new gene model: drag and drop
Example 74
•  Apollo automatically calculates ORF.
In this case, ORF includes the high-scoring segment pairs (hsp).
Available Tracks
Example 75
Get Sequence
Example 76
http://blast.ncbi.nlm.nih.gov/Blast.cgi
Also, flanking sequences (other gene models) vs. NCBI nr
Example 77
In	
  this	
  case,	
  two	
  gene	
  
models	
  upstream,	
  at	
  5’	
  
end.	
  
BLAST	
  hsps	
  
Review alignments
Example 78
HaztTmpM006234	
  
HaztTmpM006233	
  
HaztTmpM006232	
  
Hypothesis for vgsc gene model
Example 79
Editing: merge the three models
Example 80
Merge	
  by	
  dropping	
  an	
  
exon	
  or	
  gene	
  model	
  
onto	
  another.	
  
Merge	
  by	
  selec'ng	
  
two	
  exons	
  (holding	
  
down	
  “Shiv”)	
  and	
  
using	
  the	
  right	
  click	
  
menu.	
  
or…	
  
Editing: correct boundaries, delete exons
Example 81
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 82
Editing: modify boundaries
Example 83
Modify	
  intron	
  /	
  
exon	
  boundary	
  
also	
  at	
  coord.	
  
78,999.	
  
Finished model
Example 84
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 85
Comments	
  
(if	
  applicable)	
  
Demo	
  video	
  
APOLLO

demonstration
Apollo	
  demo	
  video	
  available	
  at:	
  	
  
h=ps://youtu.be/VgPtAP_fvxY	
  
DEMO 87
APOLLO DEVELOPMENT
APOLLO DEVELOPERS 88
h^ p://G e nom e Ar c hite c t. or g /	
  
Nathan Dunn
Eric Yao
JBrowse, UC Berkeley
Deepak Unni
Colin Diesh
Elsik Lab,
University of Missouri
Suzi Lewis
Principal Investigator
BBOP	
  
Moni Munoz-Torres
Stephen Ficklin
GenSAS,
Washington State University
•  Berkeley	
  Bioinforma=cs	
  Open-­‐source	
  Projects	
  (BBOP),	
  
Berkeley	
  Lab:	
  Web	
  Apollo	
  and	
  Gene	
  Ontology	
  teams.	
  
Suzanna	
  E.	
  Lewis	
  (PI).	
  
•  §	
  Chris$ne	
  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. 89
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	
  *	
  

More Related Content

What's hot

Apolo Taller en BIOS
Apolo Taller en BIOS Apolo Taller en BIOS
Apolo Taller en BIOS
Monica Munoz-Torres
 
Gene identification and discovery
Gene identification and discoveryGene identification and discovery
Gene identification and discovery
Amit Ruchi Yadav
 
B.sc biochem i bobi u 4 gene prediction
B.sc biochem i bobi u 4 gene predictionB.sc biochem i bobi u 4 gene prediction
B.sc biochem i bobi u 4 gene prediction
Rai University
 
RNA-seq differential expression analysis
RNA-seq differential expression analysisRNA-seq differential expression analysis
RNA-seq differential expression analysis
mikaelhuss
 
Catalyzing Plant Science Research with RNA-seq
Catalyzing Plant Science Research with RNA-seqCatalyzing Plant Science Research with RNA-seq
Catalyzing Plant Science Research with RNA-seq
Manjappa Ganiger
 
Gene prediction methods vijay
Gene prediction methods  vijayGene prediction methods  vijay
Gene prediction methods vijay
Vijay Hemmadi
 
Differential gene expression
Differential gene expressionDifferential gene expression
Differential gene expression
Denis C. Bauer
 
Bioalgo 2012-01-gene-prediction-sim
Bioalgo 2012-01-gene-prediction-simBioalgo 2012-01-gene-prediction-sim
Bioalgo 2012-01-gene-prediction-sim
BioinformaticsInstitute
 
Kogo 2013 RNA-seq analysis
Kogo 2013 RNA-seq analysisKogo 2013 RNA-seq analysis
Kogo 2013 RNA-seq analysis
Junsu Ko
 
Tyler functional annotation thurs 1120
Tyler functional annotation thurs 1120Tyler functional annotation thurs 1120
Tyler functional annotation thurs 1120
Sucheta Tripathy
 
Differential expression in RNA-Seq
Differential expression in RNA-SeqDifferential expression in RNA-Seq
Differential expression in RNA-Seq
cursoNGS
 
RNA-seq quality control and pre-processing
RNA-seq quality control and pre-processingRNA-seq quality control and pre-processing
RNA-seq quality control and pre-processing
mikaelhuss
 
Bioalgo 2012-01-gene-prediction-stat
Bioalgo 2012-01-gene-prediction-statBioalgo 2012-01-gene-prediction-stat
Bioalgo 2012-01-gene-prediction-stat
BioinformaticsInstitute
 
Why Transcriptome? Why RNA-Seq? ENCODE answers….
Why Transcriptome? Why RNA-Seq?  ENCODE answers….Why Transcriptome? Why RNA-Seq?  ENCODE answers….
Why Transcriptome? Why RNA-Seq? ENCODE answers….
Mohammad Hossein Banabazi
 
RNA-seq: A High-resolution View of the Transcriptome
RNA-seq: A High-resolution View of the TranscriptomeRNA-seq: A High-resolution View of the Transcriptome
RNA-seq: A High-resolution View of the Transcriptome
Sean Davis
 
Tag-based transcript sequencing: Comparison of SAGE and CAGE
Tag-based transcript sequencing: Comparison of SAGE and CAGETag-based transcript sequencing: Comparison of SAGE and CAGE
Tag-based transcript sequencing: Comparison of SAGE and CAGE
Matthias Harbers
 
RNA-seq Analysis
RNA-seq AnalysisRNA-seq Analysis
RNA-seq Analysis
COST action BM1006
 
Bioinformatics
BioinformaticsBioinformatics
Bioinformatics
Afra Fathima
 
An introduction to RNA-seq data analysis
An introduction to RNA-seq data analysisAn introduction to RNA-seq data analysis
An introduction to RNA-seq data analysis
AGRF_Ltd
 
Gene prediction strategies
Gene prediction strategies Gene prediction strategies
Gene prediction strategies
Amity university, Noida
 

What's hot (20)

Apolo Taller en BIOS
Apolo Taller en BIOS Apolo Taller en BIOS
Apolo Taller en BIOS
 
Gene identification and discovery
Gene identification and discoveryGene identification and discovery
Gene identification and discovery
 
B.sc biochem i bobi u 4 gene prediction
B.sc biochem i bobi u 4 gene predictionB.sc biochem i bobi u 4 gene prediction
B.sc biochem i bobi u 4 gene prediction
 
RNA-seq differential expression analysis
RNA-seq differential expression analysisRNA-seq differential expression analysis
RNA-seq differential expression analysis
 
Catalyzing Plant Science Research with RNA-seq
Catalyzing Plant Science Research with RNA-seqCatalyzing Plant Science Research with RNA-seq
Catalyzing Plant Science Research with RNA-seq
 
Gene prediction methods vijay
Gene prediction methods  vijayGene prediction methods  vijay
Gene prediction methods vijay
 
Differential gene expression
Differential gene expressionDifferential gene expression
Differential gene expression
 
Bioalgo 2012-01-gene-prediction-sim
Bioalgo 2012-01-gene-prediction-simBioalgo 2012-01-gene-prediction-sim
Bioalgo 2012-01-gene-prediction-sim
 
Kogo 2013 RNA-seq analysis
Kogo 2013 RNA-seq analysisKogo 2013 RNA-seq analysis
Kogo 2013 RNA-seq analysis
 
Tyler functional annotation thurs 1120
Tyler functional annotation thurs 1120Tyler functional annotation thurs 1120
Tyler functional annotation thurs 1120
 
Differential expression in RNA-Seq
Differential expression in RNA-SeqDifferential expression in RNA-Seq
Differential expression in RNA-Seq
 
RNA-seq quality control and pre-processing
RNA-seq quality control and pre-processingRNA-seq quality control and pre-processing
RNA-seq quality control and pre-processing
 
Bioalgo 2012-01-gene-prediction-stat
Bioalgo 2012-01-gene-prediction-statBioalgo 2012-01-gene-prediction-stat
Bioalgo 2012-01-gene-prediction-stat
 
Why Transcriptome? Why RNA-Seq? ENCODE answers….
Why Transcriptome? Why RNA-Seq?  ENCODE answers….Why Transcriptome? Why RNA-Seq?  ENCODE answers….
Why Transcriptome? Why RNA-Seq? ENCODE answers….
 
RNA-seq: A High-resolution View of the Transcriptome
RNA-seq: A High-resolution View of the TranscriptomeRNA-seq: A High-resolution View of the Transcriptome
RNA-seq: A High-resolution View of the Transcriptome
 
Tag-based transcript sequencing: Comparison of SAGE and CAGE
Tag-based transcript sequencing: Comparison of SAGE and CAGETag-based transcript sequencing: Comparison of SAGE and CAGE
Tag-based transcript sequencing: Comparison of SAGE and CAGE
 
RNA-seq Analysis
RNA-seq AnalysisRNA-seq Analysis
RNA-seq Analysis
 
Bioinformatics
BioinformaticsBioinformatics
Bioinformatics
 
An introduction to RNA-seq data analysis
An introduction to RNA-seq data analysisAn introduction to RNA-seq data analysis
An introduction to RNA-seq data analysis
 
Gene prediction strategies
Gene prediction strategies Gene prediction strategies
Gene prediction strategies
 

Viewers also liked

Apollo: Scalable & collaborative curation of genomes - Biocuration 2015
Apollo: Scalable & collaborative curation of genomes - Biocuration 2015Apollo: Scalable & collaborative curation of genomes - Biocuration 2015
Apollo: Scalable & collaborative curation of genomes - Biocuration 2015
Monica Munoz-Torres
 
PAINT Family PTHR13451-MUS81
PAINT Family PTHR13451-MUS81PAINT Family PTHR13451-MUS81
PAINT Family PTHR13451-MUS81
Monica Munoz-Torres
 
Introduction to Apollo - i5k Research Community – Calanoida (copepod)
Introduction to Apollo - i5k Research Community – Calanoida (copepod)Introduction to Apollo - i5k Research Community – Calanoida (copepod)
Introduction to Apollo - i5k Research Community – Calanoida (copepod)
Monica Munoz-Torres
 
Apollo Workshop at KSU 2015
Apollo Workshop at KSU 2015Apollo Workshop at KSU 2015
Apollo Workshop at KSU 2015
Monica Munoz-Torres
 
Curation Introduction - Apollo Workshop
Curation Introduction - Apollo WorkshopCuration Introduction - Apollo Workshop
Curation Introduction - Apollo Workshop
Monica Munoz-Torres
 
CONSORCIO ONTOLOGÍA DE GENES: herramientas para anotación funcional
CONSORCIO ONTOLOGÍA DE GENES: herramientas para anotación funcionalCONSORCIO ONTOLOGÍA DE GENES: herramientas para anotación funcional
CONSORCIO ONTOLOGÍA DE GENES: herramientas para anotación funcional
Monica Munoz-Torres
 
Editing Functionality - Apollo Workshop
Editing Functionality - Apollo WorkshopEditing Functionality - Apollo Workshop
Editing Functionality - Apollo Workshop
Monica Munoz-Torres
 

Viewers also liked (7)

Apollo: Scalable & collaborative curation of genomes - Biocuration 2015
Apollo: Scalable & collaborative curation of genomes - Biocuration 2015Apollo: Scalable & collaborative curation of genomes - Biocuration 2015
Apollo: Scalable & collaborative curation of genomes - Biocuration 2015
 
PAINT Family PTHR13451-MUS81
PAINT Family PTHR13451-MUS81PAINT Family PTHR13451-MUS81
PAINT Family PTHR13451-MUS81
 
Introduction to Apollo - i5k Research Community – Calanoida (copepod)
Introduction to Apollo - i5k Research Community – Calanoida (copepod)Introduction to Apollo - i5k Research Community – Calanoida (copepod)
Introduction to Apollo - i5k Research Community – Calanoida (copepod)
 
Apollo Workshop at KSU 2015
Apollo Workshop at KSU 2015Apollo Workshop at KSU 2015
Apollo Workshop at KSU 2015
 
Curation Introduction - Apollo Workshop
Curation Introduction - Apollo WorkshopCuration Introduction - Apollo Workshop
Curation Introduction - Apollo Workshop
 
CONSORCIO ONTOLOGÍA DE GENES: herramientas para anotación funcional
CONSORCIO ONTOLOGÍA DE GENES: herramientas para anotación funcionalCONSORCIO ONTOLOGÍA DE GENES: herramientas para anotación funcional
CONSORCIO ONTOLOGÍA DE GENES: herramientas para anotación funcional
 
Editing Functionality - Apollo Workshop
Editing Functionality - Apollo WorkshopEditing Functionality - Apollo Workshop
Editing Functionality - Apollo Workshop
 

Similar to Apollo Introduction for the Chestnut Research Community

Bioinformatics
BioinformaticsBioinformatics
Bioinformatics
Nawfal Aldujaily
 
Structural annotation................pptx
Structural annotation................pptxStructural annotation................pptx
Structural annotation................pptx
Cherry
 
Apollo Collaborative genome annotation editing
Apollo Collaborative genome annotation editing Apollo Collaborative genome annotation editing
Apollo Collaborative genome annotation editing
Monica Munoz-Torres
 
Functional genomics
Functional genomicsFunctional genomics
Functional genomics
saswat tripathy
 
genomeannotation-160822182432.pdf
genomeannotation-160822182432.pdfgenomeannotation-160822182432.pdf
genomeannotation-160822182432.pdf
VidyasriDharmalingam1
 
Genome annotation
Genome annotationGenome annotation
Genome annotation
Shifa Ansari
 
Bioinformatics.Practical Notebook
Bioinformatics.Practical NotebookBioinformatics.Practical Notebook
Bioinformatics.Practical Notebook
Naima Tahsin
 
31931 31941
31931 3194131931 31941
31931 31941
Amit Gupta
 
Gene prediction and expression
Gene prediction and expressionGene prediction and expression
Gene prediction and expression
ishi tandon
 
CROP GENOME SEQUENCING
CROP GENOME SEQUENCINGCROP GENOME SEQUENCING
CROP GENOME SEQUENCING
SABYASACHISAHU10
 
Whole genome sequencing of bacteria & analysis
Whole genome sequencing of bacteria & analysisWhole genome sequencing of bacteria & analysis
Whole genome sequencing of bacteria & analysis
drelamuruganvet
 
Lecture bioinformatics Part2.next generation
Lecture bioinformatics Part2.next generationLecture bioinformatics Part2.next generation
Lecture bioinformatics Part2.next generation
MohamedHasan816582
 
EiB Seminar from Antoni Miñarro, Ph.D
EiB Seminar from Antoni Miñarro, Ph.DEiB Seminar from Antoni Miñarro, Ph.D
EiB Seminar from Antoni Miñarro, Ph.D
Statistics and Bioinformatics (EiB-UB)
 
BTC 506 Gene Identification using Bioinformatic Tools-230302130331.pptx
BTC 506 Gene Identification using Bioinformatic Tools-230302130331.pptxBTC 506 Gene Identification using Bioinformatic Tools-230302130331.pptx
BTC 506 Gene Identification using Bioinformatic Tools-230302130331.pptx
ChijiokeNsofor
 
Genomiclibrary 151004020241-lva1-app6891
Genomiclibrary 151004020241-lva1-app6891Genomiclibrary 151004020241-lva1-app6891
Genomiclibrary 151004020241-lva1-app6891
saurabh verma
 
Impact_of_gene_length_on_DEG
Impact_of_gene_length_on_DEGImpact_of_gene_length_on_DEG
Impact_of_gene_length_on_DEG
Long Pei
 
Assembly and gene_prediction
Assembly and gene_predictionAssembly and gene_prediction
Assembly and gene_prediction
Bas van Breukelen
 
Finding genes
Finding genesFinding genes
Finding genes
Sabahat Ali
 
SAGE- Serial Analysis of Gene Expression
SAGE- Serial Analysis of Gene ExpressionSAGE- Serial Analysis of Gene Expression
SAGE- Serial Analysis of Gene Expression
Aashish Patel
 
Marker devt. workshop 27022012
Marker devt. workshop 27022012Marker devt. workshop 27022012
Marker devt. workshop 27022012
Koppolu Ravi
 

Similar to Apollo Introduction for the Chestnut Research Community (20)

Bioinformatics
BioinformaticsBioinformatics
Bioinformatics
 
Structural annotation................pptx
Structural annotation................pptxStructural annotation................pptx
Structural annotation................pptx
 
Apollo Collaborative genome annotation editing
Apollo Collaborative genome annotation editing Apollo Collaborative genome annotation editing
Apollo Collaborative genome annotation editing
 
Functional genomics
Functional genomicsFunctional genomics
Functional genomics
 
genomeannotation-160822182432.pdf
genomeannotation-160822182432.pdfgenomeannotation-160822182432.pdf
genomeannotation-160822182432.pdf
 
Genome annotation
Genome annotationGenome annotation
Genome annotation
 
Bioinformatics.Practical Notebook
Bioinformatics.Practical NotebookBioinformatics.Practical Notebook
Bioinformatics.Practical Notebook
 
31931 31941
31931 3194131931 31941
31931 31941
 
Gene prediction and expression
Gene prediction and expressionGene prediction and expression
Gene prediction and expression
 
CROP GENOME SEQUENCING
CROP GENOME SEQUENCINGCROP GENOME SEQUENCING
CROP GENOME SEQUENCING
 
Whole genome sequencing of bacteria & analysis
Whole genome sequencing of bacteria & analysisWhole genome sequencing of bacteria & analysis
Whole genome sequencing of bacteria & analysis
 
Lecture bioinformatics Part2.next generation
Lecture bioinformatics Part2.next generationLecture bioinformatics Part2.next generation
Lecture bioinformatics Part2.next generation
 
EiB Seminar from Antoni Miñarro, Ph.D
EiB Seminar from Antoni Miñarro, Ph.DEiB Seminar from Antoni Miñarro, Ph.D
EiB Seminar from Antoni Miñarro, Ph.D
 
BTC 506 Gene Identification using Bioinformatic Tools-230302130331.pptx
BTC 506 Gene Identification using Bioinformatic Tools-230302130331.pptxBTC 506 Gene Identification using Bioinformatic Tools-230302130331.pptx
BTC 506 Gene Identification using Bioinformatic Tools-230302130331.pptx
 
Genomiclibrary 151004020241-lva1-app6891
Genomiclibrary 151004020241-lva1-app6891Genomiclibrary 151004020241-lva1-app6891
Genomiclibrary 151004020241-lva1-app6891
 
Impact_of_gene_length_on_DEG
Impact_of_gene_length_on_DEGImpact_of_gene_length_on_DEG
Impact_of_gene_length_on_DEG
 
Assembly and gene_prediction
Assembly and gene_predictionAssembly and gene_prediction
Assembly and gene_prediction
 
Finding genes
Finding genesFinding genes
Finding genes
 
SAGE- Serial Analysis of Gene Expression
SAGE- Serial Analysis of Gene ExpressionSAGE- Serial Analysis of Gene Expression
SAGE- Serial Analysis of Gene Expression
 
Marker devt. workshop 27022012
Marker devt. workshop 27022012Marker devt. workshop 27022012
Marker devt. workshop 27022012
 

More from Monica Munoz-Torres

Apollo Workshop AGS2017 Editing functionality
Apollo Workshop AGS2017 Editing functionalityApollo Workshop AGS2017 Editing functionality
Apollo Workshop AGS2017 Editing functionality
Monica Munoz-Torres
 
Apollo Workshop AGS2017 Introduction
Apollo Workshop AGS2017 IntroductionApollo Workshop AGS2017 Introduction
Apollo Workshop AGS2017 Introduction
Monica Munoz-Torres
 
Apollo Exercises Kansas State University 2015
Apollo Exercises Kansas State University 2015Apollo Exercises Kansas State University 2015
Apollo Exercises Kansas State University 2015
Monica Munoz-Torres
 
Apollo annotation guidelines for i5k projects Diaphorina citri
Apollo annotation guidelines for i5k projects Diaphorina citriApollo annotation guidelines for i5k projects Diaphorina citri
Apollo annotation guidelines for i5k projects Diaphorina citri
Monica Munoz-Torres
 
JBrowse & Apollo Overview - for AGR
JBrowse & Apollo Overview - for AGRJBrowse & Apollo Overview - for AGR
JBrowse & Apollo Overview - for AGR
Monica Munoz-Torres
 
Apollo Genome Annotation Editor: Latest Updates, Including New Galaxy Integra...
Apollo Genome Annotation Editor: Latest Updates, Including New Galaxy Integra...Apollo Genome Annotation Editor: Latest Updates, Including New Galaxy Integra...
Apollo Genome Annotation Editor: Latest Updates, Including New Galaxy Integra...
Monica Munoz-Torres
 
Gene Ontology Consortium: Website & COmmunity
Gene Ontology Consortium: Website & COmmunityGene Ontology Consortium: Website & COmmunity
Gene Ontology Consortium: Website & COmmunity
Monica Munoz-Torres
 
Essential Requirements for Community Annotation Tools
Essential Requirements for Community Annotation ToolsEssential Requirements for Community Annotation Tools
Essential Requirements for Community Annotation Tools
Monica Munoz-Torres
 
Data Visualization And Annotation Workshop at Biocuration 2015
Data Visualization And Annotation Workshop at Biocuration 2015Data Visualization And Annotation Workshop at Biocuration 2015
Data Visualization And Annotation Workshop at Biocuration 2015
Monica Munoz-Torres
 
Apollo: developers call 2015-02-05
Apollo: developers call 2015-02-05Apollo: developers call 2015-02-05
Apollo: developers call 2015-02-05
Monica Munoz-Torres
 
Apollo and i5K: Collaborative Curation and Interactive Analysis of Genomes
Apollo and i5K: Collaborative Curation and Interactive Analysis of GenomesApollo and i5K: Collaborative Curation and Interactive Analysis of Genomes
Apollo and i5K: Collaborative Curation and Interactive Analysis of Genomes
Monica Munoz-Torres
 
Web Apollo Workshop University of Exeter
Web Apollo Workshop University of ExeterWeb Apollo Workshop University of Exeter
Web Apollo Workshop University of Exeter
Monica Munoz-Torres
 
Munoz torres web-apollo-workshop_exeter-2014_ss
Munoz torres web-apollo-workshop_exeter-2014_ssMunoz torres web-apollo-workshop_exeter-2014_ss
Munoz torres web-apollo-workshop_exeter-2014_ss
Monica Munoz-Torres
 

More from Monica Munoz-Torres (13)

Apollo Workshop AGS2017 Editing functionality
Apollo Workshop AGS2017 Editing functionalityApollo Workshop AGS2017 Editing functionality
Apollo Workshop AGS2017 Editing functionality
 
Apollo Workshop AGS2017 Introduction
Apollo Workshop AGS2017 IntroductionApollo Workshop AGS2017 Introduction
Apollo Workshop AGS2017 Introduction
 
Apollo Exercises Kansas State University 2015
Apollo Exercises Kansas State University 2015Apollo Exercises Kansas State University 2015
Apollo Exercises Kansas State University 2015
 
Apollo annotation guidelines for i5k projects Diaphorina citri
Apollo annotation guidelines for i5k projects Diaphorina citriApollo annotation guidelines for i5k projects Diaphorina citri
Apollo annotation guidelines for i5k projects Diaphorina citri
 
JBrowse & Apollo Overview - for AGR
JBrowse & Apollo Overview - for AGRJBrowse & Apollo Overview - for AGR
JBrowse & Apollo Overview - for AGR
 
Apollo Genome Annotation Editor: Latest Updates, Including New Galaxy Integra...
Apollo Genome Annotation Editor: Latest Updates, Including New Galaxy Integra...Apollo Genome Annotation Editor: Latest Updates, Including New Galaxy Integra...
Apollo Genome Annotation Editor: Latest Updates, Including New Galaxy Integra...
 
Gene Ontology Consortium: Website & COmmunity
Gene Ontology Consortium: Website & COmmunityGene Ontology Consortium: Website & COmmunity
Gene Ontology Consortium: Website & COmmunity
 
Essential Requirements for Community Annotation Tools
Essential Requirements for Community Annotation ToolsEssential Requirements for Community Annotation Tools
Essential Requirements for Community Annotation Tools
 
Data Visualization And Annotation Workshop at Biocuration 2015
Data Visualization And Annotation Workshop at Biocuration 2015Data Visualization And Annotation Workshop at Biocuration 2015
Data Visualization And Annotation Workshop at Biocuration 2015
 
Apollo: developers call 2015-02-05
Apollo: developers call 2015-02-05Apollo: developers call 2015-02-05
Apollo: developers call 2015-02-05
 
Apollo and i5K: Collaborative Curation and Interactive Analysis of Genomes
Apollo and i5K: Collaborative Curation and Interactive Analysis of GenomesApollo and i5K: Collaborative Curation and Interactive Analysis of Genomes
Apollo and i5K: Collaborative Curation and Interactive Analysis of Genomes
 
Web Apollo Workshop University of Exeter
Web Apollo Workshop University of ExeterWeb Apollo Workshop University of Exeter
Web Apollo Workshop University of Exeter
 
Munoz torres web-apollo-workshop_exeter-2014_ss
Munoz torres web-apollo-workshop_exeter-2014_ssMunoz torres web-apollo-workshop_exeter-2014_ss
Munoz torres web-apollo-workshop_exeter-2014_ss
 

Recently uploaded

Probing the northern Kaapvaal craton root with mantle-derived xenocrysts from...
Probing the northern Kaapvaal craton root with mantle-derived xenocrysts from...Probing the northern Kaapvaal craton root with mantle-derived xenocrysts from...
Probing the northern Kaapvaal craton root with mantle-derived xenocrysts from...
James AH Campbell
 
degree Certificate of Aston University
degree Certificate of Aston Universitydegree Certificate of Aston University
degree Certificate of Aston University
ebgyz
 
Transmission Spectroscopy of the Habitable Zone Exoplanet LHS 1140 b with JWS...
Transmission Spectroscopy of the Habitable Zone Exoplanet LHS 1140 b with JWS...Transmission Spectroscopy of the Habitable Zone Exoplanet LHS 1140 b with JWS...
Transmission Spectroscopy of the Habitable Zone Exoplanet LHS 1140 b with JWS...
Sérgio Sacani
 
Computer aided biopharmaceutical characterization
Computer aided biopharmaceutical characterizationComputer aided biopharmaceutical characterization
Computer aided biopharmaceutical characterization
souravpaul769171
 
The Dynamical Origins of the Dark Comets and a Proposed Evolutionary Track
The Dynamical Origins of the Dark Comets and a Proposed Evolutionary TrackThe Dynamical Origins of the Dark Comets and a Proposed Evolutionary Track
The Dynamical Origins of the Dark Comets and a Proposed Evolutionary Track
Sérgio Sacani
 
A slightly oblate dark matter halo revealed by a retrograde precessing Galact...
A slightly oblate dark matter halo revealed by a retrograde precessing Galact...A slightly oblate dark matter halo revealed by a retrograde precessing Galact...
A slightly oblate dark matter halo revealed by a retrograde precessing Galact...
Sérgio Sacani
 
Properties of virus(Ultrastructure and types of virus)
Properties of virus(Ultrastructure and types of virus)Properties of virus(Ultrastructure and types of virus)
Properties of virus(Ultrastructure and types of virus)
saloniswain225
 
SCIENCEgfvhvhvkjkbbjjbbjvhvhvhvjkvjvjvjj.pptx
SCIENCEgfvhvhvkjkbbjjbbjvhvhvhvjkvjvjvjj.pptxSCIENCEgfvhvhvkjkbbjjbbjvhvhvhvjkvjvjvjj.pptx
SCIENCEgfvhvhvkjkbbjjbbjvhvhvhvjkvjvjvjj.pptx
WALTONMARBRUCAL
 
Bragg Brentano Alignment for D4 with LynxEye Rev3.pptx
Bragg Brentano Alignment for D4 with LynxEye Rev3.pptxBragg Brentano Alignment for D4 with LynxEye Rev3.pptx
Bragg Brentano Alignment for D4 with LynxEye Rev3.pptx
Lisandro Cunci
 
Deploying DAPHNE Computational Intelligence on EuroHPC Vega for Benchmarking ...
Deploying DAPHNE Computational Intelligence on EuroHPC Vega for Benchmarking ...Deploying DAPHNE Computational Intelligence on EuroHPC Vega for Benchmarking ...
Deploying DAPHNE Computational Intelligence on EuroHPC Vega for Benchmarking ...
University of Maribor
 
Phytoremediation: Harnessing Nature's Power with Phytoremediation
Phytoremediation: Harnessing Nature's Power with PhytoremediationPhytoremediation: Harnessing Nature's Power with Phytoremediation
Phytoremediation: Harnessing Nature's Power with Phytoremediation
Gurjant Singh
 
Gasification and Pyrolyssis of plastic Waste under a Circular Economy perpective
Gasification and Pyrolyssis of plastic Waste under a Circular Economy perpectiveGasification and Pyrolyssis of plastic Waste under a Circular Economy perpective
Gasification and Pyrolyssis of plastic Waste under a Circular Economy perpective
Recupera
 
[1] Data Mining - Concepts and Techniques (3rd Ed).pdf
[1] Data Mining - Concepts and Techniques (3rd Ed).pdf[1] Data Mining - Concepts and Techniques (3rd Ed).pdf
[1] Data Mining - Concepts and Techniques (3rd Ed).pdf
PANDURANGLAWATE1
 
THE ESSENCE OF CHANGE CHAPTER ,energy,conversion,life is easy,laws of physics
THE ESSENCE OF CHANGE CHAPTER ,energy,conversion,life is easy,laws of physicsTHE ESSENCE OF CHANGE CHAPTER ,energy,conversion,life is easy,laws of physics
THE ESSENCE OF CHANGE CHAPTER ,energy,conversion,life is easy,laws of physics
Dr. sreeremya S
 
smallintestinedisorders-causessymptoms-240626051934-b669b27d.pptx
smallintestinedisorders-causessymptoms-240626051934-b669b27d.pptxsmallintestinedisorders-causessymptoms-240626051934-b669b27d.pptx
smallintestinedisorders-causessymptoms-240626051934-b669b27d.pptx
muralinath2
 
El Nuevo Cohete Ariane de la Agencia Espacial Europea-6_Media-Kit_english.pdf
El Nuevo Cohete Ariane de la Agencia Espacial Europea-6_Media-Kit_english.pdfEl Nuevo Cohete Ariane de la Agencia Espacial Europea-6_Media-Kit_english.pdf
El Nuevo Cohete Ariane de la Agencia Espacial Europea-6_Media-Kit_english.pdf
Champs Elysee Roldan
 
Modelling, Simulation, and Computer-aided Design in Computational, Evolutiona...
Modelling, Simulation, and Computer-aided Design in Computational, Evolutiona...Modelling, Simulation, and Computer-aided Design in Computational, Evolutiona...
Modelling, Simulation, and Computer-aided Design in Computational, Evolutiona...
University of Maribor
 
Lunar Mobility Drivers and Needs - Artemis
Lunar Mobility Drivers and Needs - ArtemisLunar Mobility Drivers and Needs - Artemis
Lunar Mobility Drivers and Needs - Artemis
Sérgio Sacani
 
20240710 ACMJ Diagrams Set 3.docx . Apache, Csharp, Mysql, Javascript stack a...
20240710 ACMJ Diagrams Set 3.docx . Apache, Csharp, Mysql, Javascript stack a...20240710 ACMJ Diagrams Set 3.docx . Apache, Csharp, Mysql, Javascript stack a...
20240710 ACMJ Diagrams Set 3.docx . Apache, Csharp, Mysql, Javascript stack a...
Sharon Liu
 
Liver & Gall Bladder 23098463278654387654328765439875.pptx
Liver & Gall Bladder 23098463278654387654328765439875.pptxLiver & Gall Bladder 23098463278654387654328765439875.pptx
Liver & Gall Bladder 23098463278654387654328765439875.pptx
muralinath2
 

Recently uploaded (20)

Probing the northern Kaapvaal craton root with mantle-derived xenocrysts from...
Probing the northern Kaapvaal craton root with mantle-derived xenocrysts from...Probing the northern Kaapvaal craton root with mantle-derived xenocrysts from...
Probing the northern Kaapvaal craton root with mantle-derived xenocrysts from...
 
degree Certificate of Aston University
degree Certificate of Aston Universitydegree Certificate of Aston University
degree Certificate of Aston University
 
Transmission Spectroscopy of the Habitable Zone Exoplanet LHS 1140 b with JWS...
Transmission Spectroscopy of the Habitable Zone Exoplanet LHS 1140 b with JWS...Transmission Spectroscopy of the Habitable Zone Exoplanet LHS 1140 b with JWS...
Transmission Spectroscopy of the Habitable Zone Exoplanet LHS 1140 b with JWS...
 
Computer aided biopharmaceutical characterization
Computer aided biopharmaceutical characterizationComputer aided biopharmaceutical characterization
Computer aided biopharmaceutical characterization
 
The Dynamical Origins of the Dark Comets and a Proposed Evolutionary Track
The Dynamical Origins of the Dark Comets and a Proposed Evolutionary TrackThe Dynamical Origins of the Dark Comets and a Proposed Evolutionary Track
The Dynamical Origins of the Dark Comets and a Proposed Evolutionary Track
 
A slightly oblate dark matter halo revealed by a retrograde precessing Galact...
A slightly oblate dark matter halo revealed by a retrograde precessing Galact...A slightly oblate dark matter halo revealed by a retrograde precessing Galact...
A slightly oblate dark matter halo revealed by a retrograde precessing Galact...
 
Properties of virus(Ultrastructure and types of virus)
Properties of virus(Ultrastructure and types of virus)Properties of virus(Ultrastructure and types of virus)
Properties of virus(Ultrastructure and types of virus)
 
SCIENCEgfvhvhvkjkbbjjbbjvhvhvhvjkvjvjvjj.pptx
SCIENCEgfvhvhvkjkbbjjbbjvhvhvhvjkvjvjvjj.pptxSCIENCEgfvhvhvkjkbbjjbbjvhvhvhvjkvjvjvjj.pptx
SCIENCEgfvhvhvkjkbbjjbbjvhvhvhvjkvjvjvjj.pptx
 
Bragg Brentano Alignment for D4 with LynxEye Rev3.pptx
Bragg Brentano Alignment for D4 with LynxEye Rev3.pptxBragg Brentano Alignment for D4 with LynxEye Rev3.pptx
Bragg Brentano Alignment for D4 with LynxEye Rev3.pptx
 
Deploying DAPHNE Computational Intelligence on EuroHPC Vega for Benchmarking ...
Deploying DAPHNE Computational Intelligence on EuroHPC Vega for Benchmarking ...Deploying DAPHNE Computational Intelligence on EuroHPC Vega for Benchmarking ...
Deploying DAPHNE Computational Intelligence on EuroHPC Vega for Benchmarking ...
 
Phytoremediation: Harnessing Nature's Power with Phytoremediation
Phytoremediation: Harnessing Nature's Power with PhytoremediationPhytoremediation: Harnessing Nature's Power with Phytoremediation
Phytoremediation: Harnessing Nature's Power with Phytoremediation
 
Gasification and Pyrolyssis of plastic Waste under a Circular Economy perpective
Gasification and Pyrolyssis of plastic Waste under a Circular Economy perpectiveGasification and Pyrolyssis of plastic Waste under a Circular Economy perpective
Gasification and Pyrolyssis of plastic Waste under a Circular Economy perpective
 
[1] Data Mining - Concepts and Techniques (3rd Ed).pdf
[1] Data Mining - Concepts and Techniques (3rd Ed).pdf[1] Data Mining - Concepts and Techniques (3rd Ed).pdf
[1] Data Mining - Concepts and Techniques (3rd Ed).pdf
 
THE ESSENCE OF CHANGE CHAPTER ,energy,conversion,life is easy,laws of physics
THE ESSENCE OF CHANGE CHAPTER ,energy,conversion,life is easy,laws of physicsTHE ESSENCE OF CHANGE CHAPTER ,energy,conversion,life is easy,laws of physics
THE ESSENCE OF CHANGE CHAPTER ,energy,conversion,life is easy,laws of physics
 
smallintestinedisorders-causessymptoms-240626051934-b669b27d.pptx
smallintestinedisorders-causessymptoms-240626051934-b669b27d.pptxsmallintestinedisorders-causessymptoms-240626051934-b669b27d.pptx
smallintestinedisorders-causessymptoms-240626051934-b669b27d.pptx
 
El Nuevo Cohete Ariane de la Agencia Espacial Europea-6_Media-Kit_english.pdf
El Nuevo Cohete Ariane de la Agencia Espacial Europea-6_Media-Kit_english.pdfEl Nuevo Cohete Ariane de la Agencia Espacial Europea-6_Media-Kit_english.pdf
El Nuevo Cohete Ariane de la Agencia Espacial Europea-6_Media-Kit_english.pdf
 
Modelling, Simulation, and Computer-aided Design in Computational, Evolutiona...
Modelling, Simulation, and Computer-aided Design in Computational, Evolutiona...Modelling, Simulation, and Computer-aided Design in Computational, Evolutiona...
Modelling, Simulation, and Computer-aided Design in Computational, Evolutiona...
 
Lunar Mobility Drivers and Needs - Artemis
Lunar Mobility Drivers and Needs - ArtemisLunar Mobility Drivers and Needs - Artemis
Lunar Mobility Drivers and Needs - Artemis
 
20240710 ACMJ Diagrams Set 3.docx . Apache, Csharp, Mysql, Javascript stack a...
20240710 ACMJ Diagrams Set 3.docx . Apache, Csharp, Mysql, Javascript stack a...20240710 ACMJ Diagrams Set 3.docx . Apache, Csharp, Mysql, Javascript stack a...
20240710 ACMJ Diagrams Set 3.docx . Apache, Csharp, Mysql, Javascript stack a...
 
Liver & Gall Bladder 23098463278654387654328765439875.pptx
Liver & Gall Bladder 23098463278654387654328765439875.pptxLiver & Gall Bladder 23098463278654387654328765439875.pptx
Liver & Gall Bladder 23098463278654387654328765439875.pptx
 

Apollo Introduction for the Chestnut Research Community

  • 1. Introduction to Apollo
 A webinar for the American chestnut & 
 Chinese chestnut 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 27 August, 2015
  • 2. OUTLINE
 Web  Apollo  Collabora've  Cura'on  and     Interac've  Analysis  of  Genomes   2OUTLINE •  MANUAL  ANNOTATION   necessary,  collabora've     •  APOLLO   empowering  collabora've  cura'on     •  EXAMPLE   demonstra'ons  
  • 3. 3 Genome Sequencing Project Introduction Assembly Automated Annotation Manual annotation Using Web Apollo Merge: automated + manual Genome-wide & gene- specific comparative analyses QC QC Synthesis & dissemination. Experimental design, sample collection preparation. QC Sequencing QC QC
  • 4. REVIEW ON YOUR OWN
 for manual annotation To  remember…  Biological  concepts  to  be=er   understand  manual  annota'on   4FOOD FOR THOUGHT •  GLOSSARY   from  con$g  to  splice  site     •  CENTRAL  DOGMA   in  molecular  biology     •  WHAT  IS  A  GENE?   defining  your  goal   •  TRANSCRIPTION   mRNA  in  detail     •  TRANSLATION   and  other  defini'ons   •  GENOME  CURATION   steps  involved  
  • 5. 5 BY THE END OF THIS TALK
 you will
 v Be=er  understand  genome  cura'on  in  the  context  of  annota'on:     assembled  genome  à  automated  annota=on  à  manual  annota=on   v Become  familiar  with  the  environment  and  func'onality  of  the  Apollo   genome  annota'on  edi'ng  tool.   v Learn  to  iden'fy  homologs  of  known  genes  of  interest  in  a  newly   sequenced  genome.   v Learn  about  corrobora'ng  and  modifying  automa'cally  annotated  gene   models  using  available  evidence  in  Apollo.   Introduction
  • 6. 6CURATING GENOMES What is a gene? v  The  defini'on  of  a  gene  paints  a  very  complex  picture  of  molecular  ac'vity   and  it  is  a  con'nuously  evolving  concept.     •  From  the  Sequence  Ontology  (SO):   “A  gene  is  a  locatable  region  of  genomic  sequence,  corresponding  to  a  unit   of  inheritance,  which  is  associated  with  regulatory  regions,  transcribed   regions  and/or  other  func'onal  sequence  regions”.       “Evolving  Concept”  at  h=p://goo.gl/LpsajQ  
  • 7. 7CURATING GENOMES What is a gene? v  In  our  life'me,  the  Encyclopedia  of  DNA  Elements  (ENCODE)  project   updated  this  concept  yet  again.  Long  transcripts  &  dispersed  regula$on!       “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/
  • 8. 8CURATING 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.
  • 9. 9CURATING GENOMES What is a gene? “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
  • 10. 10CURATING 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.  
  • 11. 11CURATING 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  
  • 12. 12 "Gene structure" by Daycd- Wikimedia Commons CURATING GENOMES TRANSCRIPTION & TRANSLATION
 now in your mind
  • 13. 13 Text for figures goes here CURATING 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
  • 14. CURATING GENOMES
 overview 1  Predic=on  of  Gene  Models       2  Annota=on  of  gene  models       3  Manual  annota=on   CURATING GENOMES 14
  • 15. 15Gene 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.  
  • 16. 16Gene 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
  • 17. 17 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  
  • 18. 18 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  ini$o  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
  • 19. 19 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
  • 20. ANNOTATION IS NOT PERFECT 
 automated annotation remains an imperfect art Unlike  the  more  highly  polished  genomes  of  earlier  projects,  today’s   genomes  usually  have:   •  more  frequent  assembly  errors,  which  lead  to  annota'on  of   genes  across  mul'ple  scaffolds   •  lower  coverage   No one is perfect, least of all automated annotation. 20 Image: www.BroadInstitute.org
  • 21. MANUAL ANNOTATION
 working concept 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. 21 cDNAs,  HMM  domain  searches,  RNAseq,   genes  from  other  species.  
  • 22. 22 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  
  • 23. 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 23 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  
  • 24. GENOME ANNOTATION
 an inherently collaborative task APOLLO 24 Researchers  oEen  turn  to  colleagues  for  second   opinions  and  insight  from  those  with  exper$se  in   par$cular  areas  (e.g.,  domains,  families).  
  • 25. APOLLO
 collaborative genome annotation editing tool 25 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    
  • 26. APOLLO ARCHITECTURE
 simpler, more flexible APOLLO 26 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
  • 27. 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”.   27 DISPERSED COMMUNITIES collaborative manual annotation efforts APOLLO
  • 28. 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 28
  • 30. 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 30 | 30 GENERAL PROCESS OF CURATION
 main steps to remember
  • 31. 31 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.  
  • 32. Let’s  play  with  Apollo.  
  • 33. REMOVABLE SIDE DOCK
 with customizable tabs HIGHLIGHTED IMPROVEMENTS 33 Annotations Organism Users Groups AdminTracks Reference Sequence
  • 34. EDITS & EXPORTS
 annotation details, exon boundaries, data export HIGHLIGHTED IMPROVEMENTS 34 1 2 Annotations 1 2
  • 35. HIGHLIGHTED IMPROVEMENTS 35 Reference Sequences 3 FASTA   GFF3   EDITS & EXPORTS
 annotation details, exon boundaries, data export 3
  • 36. 36 | 36 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.
  • 37. 37 | 37 USER NAVIGATION Becoming Acquainted with Web Apollo. •  Annotation right-click menu
  • 38. 38 Annota'ons,  annota'on  edits,  and  History:  stored  in  a  centralized  database.   38 USER NAVIGATION Becoming Acquainted with Web Apollo.
  • 39. 39 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.   39 USER NAVIGATION Becoming Acquainted with Web Apollo.
  • 40. 40 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.   40 USER NAVIGATION Becoming Acquainted with Web Apollo.
  • 41. 41 | 41 USER NAVIGATION Becoming Acquainted with Web Apollo. •  ‘Zoom  to  base  level’  op'on  reveals  the  DNA  Track.  
  • 42. 42 | 42 USER NAVIGATION Becoming Acquainted with Web Apollo. •  Color  exons  by  CDS  from  the  ‘View’  menu.  
  • 43. 43 | Zoom  in/out  with  keyboard:   shiv  +  arrow  keys  up/down   43 USER NAVIGATION Becoming Acquainted with Web Apollo. •  Toggle  reference  DNA  sequence  and  transla=on  frames  in  forward   strand.  Toggle  models  in  either  direc'on.  
  • 46. “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.       46 | 46 ANNOTATING SIMPLE CASES Becoming Acquainted with Web Apollo. SIMPLE CASES
  • 47. 47 | •  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  aver  each  edit.   47 ADDING EXONS Becoming Acquainted with Web Apollo. SIMPLE CASES
  • 48. 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  un$l  it  becomes  a  black  arrow  then  click   and  drag  the  edge  of  the  exon  to  the  new  coordinate  posi'on  that  includes  the  UTR.     48 | To  add  a  new  spliced  UTR  to  an  exis'ng  annota'on   follow  the  procedure  for  adding  an  exon.   48 ADDING UTRs Becoming Acquainted with Web Apollo. SIMPLE CASES
  • 49. 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.       49 | 49 CHECK EXON INTEGRITY Becoming Acquainted with Web Apollo. SIMPLE CASES
  • 50. 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.     50 | 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.   50 EXON STRUCTURE INTEGRITY Becoming Acquainted with Web Apollo. SIMPLE CASES
  • 51. 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   51 ORFs AND SPLICE SITES Becoming Acquainted with Web Apollo. SIMPLE CASES
  • 52. 52 | 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   52 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.    
  • 53. 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).     53 | 53 ‘START’ AND ‘STOP’ SITES Becoming Acquainted with Web Apollo. SIMPLE CASES
  • 55. 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  shiv-­‐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.   55 | 55 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
  • 56. 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.     56 | 56 COMPLEX CASES split a gene prediction Becoming Acquainted with Web Apollo. COMPLEX CASES
  • 57. DNA  Track   ‘User-­‐created  Annota=ons’  Track   57 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.  
  • 58. 58 COMPLEX CASES correcting selenocysteine containing proteins Becoming Acquainted with Web Apollo. COMPLEX CASES
  • 59. 59 COMPLEX CASES correcting selenocysteine containing proteins Becoming Acquainted with Web Apollo. COMPLEX CASES
  • 60. 1.  Apollo  allows  annotators  to  make  single  base  modifica'ons  or  frameshivs  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.    60 | 60 Becoming Acquainted with Web Apollo. COMPLEX CASES COMPLEX CASES correcting frameshifts, single-base errors, and selenocysteines
  • 61. 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.   61 | 61 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 Becoming Acquainted with Web Apollo.
  • 63. 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   63 | 63 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
  • 65. Example Example 65 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).  
  • 66. 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 66
  • 67. PubMed Search: 
 what’s new? Example 67
  • 68. PubMed Search: what’s new? Example 68 “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.”  
  • 69. How many sequences for our gene of interest? Example 69 •  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?  
  • 70. Retrieving sequences for 
 sequence similarity searches. Example 70 >vgsc-­‐Segment3-­‐DomainII   RVFKLAKSWPTLNLLISIMGKTVGALGNLTFVLCIIIFIFAVMGMQLFGKNYTEKVTKFKWSQDG QMPRWNFVDFFHSFMIVFRVLCGEWIESMWDCMYVGDFSCVPFFLATVVIGNLVVSFMHR
  • 71. BLAT search Example 71 >vgsc-­‐Segment3-­‐DomainII   RVFKLAKSWPTLNLLISIMGKTVGALGNLTFVLCIIIFIFAVMGMQLFGKNYTEKVTKFKWSQDG QMPRWNFVDFFHSFMIVFRVLCGEWIESMWDCMYVGDFSCVPFFLATVVIGNLVVSFMHR
  • 73. Customizations: 
 high-scoring segment pairs (hsp) in “BLAST+ Results” track Example 73
  • 74. Creating a new gene model: drag and drop Example 74 •  Apollo automatically calculates ORF. In this case, ORF includes the high-scoring segment pairs (hsp).
  • 77. Also, flanking sequences (other gene models) vs. NCBI nr Example 77 In  this  case,  two  gene   models  upstream,  at  5’   end.   BLAST  hsps  
  • 78. Review alignments Example 78 HaztTmpM006234   HaztTmpM006233   HaztTmpM006232  
  • 79. Hypothesis for vgsc gene model Example 79
  • 80. Editing: merge the three models Example 80 Merge  by  dropping  an   exon  or  gene  model   onto  another.   Merge  by  selec'ng   two  exons  (holding   down  “Shiv”)  and   using  the  right  click   menu.   or…  
  • 81. Editing: correct boundaries, delete exons Example 81 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  
  • 82. Editing: set translation start Example 82
  • 83. Editing: modify boundaries Example 83 Modify  intron  /   exon  boundary   also  at  coord.   78,999.  
  • 84. Finished model Example 84 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  
  • 85. 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 85 Comments   (if  applicable)  
  • 87. APOLLO
 demonstration Apollo  demo  video  available  at:     h=ps://youtu.be/VgPtAP_fvxY   DEMO 87
  • 88. APOLLO DEVELOPMENT APOLLO DEVELOPERS 88 h^ p://G e nom e Ar c hite c t. or g /   Nathan Dunn Eric Yao JBrowse, UC Berkeley Deepak Unni Colin Diesh Elsik Lab, University of Missouri Suzi Lewis Principal Investigator BBOP   Moni Munoz-Torres Stephen Ficklin GenSAS, Washington State University
  • 89. •  Berkeley  Bioinforma=cs  Open-­‐source  Projects  (BBOP),   Berkeley  Lab:  Web  Apollo  and  Gene  Ontology  teams.   Suzanna  E.  Lewis  (PI).   •  §  Chris$ne  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. 89 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  *