Apolo Taller en BIOS

Editando anotaciones con Apollo Un taller para la comunidad científica reunida en BIOS

Monica Munoz-Torres, PhD | @monimunozto

Berkeley Bioinformatics Open-Source Projects (BBOP)Lawrence Berkeley National Laboratory | University of California Berkeley | U.S. Department of Energy

BIOS, Manizales, Colombia | 21 Septiembre, 2015

APOLLO DEVELOPMENT

APOLLO DEVELOPERS 2

h"p : / /GenomeA r c h i t e c t . o r g /

Nathan Dunn

Eric Yao JBrowse, UC Berkeley

Christine Elsik’s Lab, University of Missouri

Suzi Lewis Principal Investigator

BBOP

Moni Munoz-Torres

Stephen Ficklin GenSAS,

Washington State University

Colin Diesh Deepak Unni

OUTLINE

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

3 OUTLINE

•  Hoy descubriremos cómo sortear obstáculos para extraer la información más valiosa en un proyectos de secuenciación & anotación de genomas.

4

BY THE END OF THIS TALKyou will

v BeAer 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

¿Cómo se traza el mapa de un genoma?

6

El mapa del genoma

Introduction

Diseño & muestreo

Análisis comparativos

Colección consenso de genes

Anotación manual

Anotación automatizada

Secuenciación Ensamblaje

Síntesis & publicación

7

El mapa del genoma

Introduction

Diseño & muestreo

Análisis comparativos

Colección consenso de genes

Anotación manual

Anotación automatizada

Secuenciación Ensamblaje

Síntesis & publicación

QC

QC

QC QC

QC QC

QC

CURATING GENOMESsteps involved

1  Genera=on of Gene Models calling ORFs, one or more rounds of gene predic(on, etc.

2  Annota=on of gene models Describing func(on, expression paAerns, metabolic network memberships.

3  Manual annota=on

CURATING GENOMES 8

ANOTACION DE GENOMASrequiere precisión y profundidad

Anotando Genomas 9

La colección de genes de cada organismo informa una variedad de análisis: •  Número de genes, % GC, composición de TEs, áreas repe((vas •  Asignar función

•  Evolución molecular, conservación de secuencias •  Familias de genes •  Caminos metabólicos •  ¿Qué hace único a cada organismo?

¿Qué hace “abeja” a una abeja?

Marbach et al. 2011. Nature Methods | Shutterstock.com | Alexander Wild

Refresquemos nuestra memoria.

REVIEW ON YOUR OWNfor manual annotation

To remember… Biological concepts to beAer understand manual annota(on

11 FOOD FOR THOUGHT

•  GLOSSARY from con1g to splice site

•  CENTRAL DOGMA

in molecular biology •  WHAT IS A GENE?

defining your goal

•  TRANSCRIPTION mRNA in detail

•  TRANSLATION

and other defini(ons

•  GENOME CURATION steps involved

12 BIO-REFRESHER

What is a gene?

v  The defini(on of a gene paints a very complex picture of molecular ac(vity and it is a con(nuously evolving concept.

•  From the Sequence Ontology (SO): “A gene is a locatable region of genomic sequence, corresponding to a unit of inheritance, which is associated with regulatory regions, transcribed regions and/or other func(onal sequence regions”. “Evolving Concept” at hAp://goo.gl/LpsajQ

13 BIO-REFRESHER

What is a gene?

v  In our life(me, the Encyclopedia of DNA Elements (ENCODE) project updated this concept yet again. Long transcripts & dispersed regula1on!

“A gene is a DNA segment that contributes phenotype/func(on. In the absence of demonstrated func(on, a gene may be characterized by sequence, transcrip(on or homology.”

https://www.encodeproject.org/

14 BIO-REFRESHER

What is a gene?let’s think computationally!

v  Think of the genome as an operating system for a living being

•  Considering that the nucleo(des of the genome are put together into a code that is executed through the process of transcription and translation…

•  … think of genes as subroutines that are repe((vely called in the process of transcription

Gerstein et al., 2007. Genome Res.

15 BIO-REFRESHER

What is a gene?considerations

v  Also consider : •  A gene is a genomic sequence (DNA or RNA) directly encoding

func(onal product molecules, either RNA or protein.

•  If several func(onal products share overlapping regions, we take the union of all overlapping genomics sequences coding for them.

•  This union must be coherent – i.e., processed separately for final protein and RNA products – but does not require that all products necessarily share a common subsequence.

Gerstein et al., 2007. Genome Res.

16 BIO-REFRESHER

“El gen es la unión de secuencias genómicas que codifican una

colección coherente de productos

funcionales que

pueden o no superponerse.”

Gerstein et al., 2007. Genome Res

El Gen: un blanco en movimiento.

¿QUÉ ES UN GEN?

17 BIO-REFRESHER

TRANSLATIONreading frame

v  Reading frame is a manner of dividing the sequence of nucleo(des in mRNA (or DNA) into a set of consecu(ve, non-‐overlapping triplets (codons).

v  Three frames can be read in the 5’ à 3’ direc(on. Given that DNA has two an(-‐parallel strands, an addi(onal three frames are possible to be read on the an(-‐sense strand. Six total possible reading frames exist.

v  In eukaryotes, only one reading frame per sec(on of DNA is biologically relevant at a (me: it has the poten(al to be transcribed into RNA and translated into protein. This is called the OPEN READING FRAME (ORF) •  ORF = Start signal + coding sequence (divisible by 3) + Stop signal

v  The sec(ons of the mature mRNA transcribed with the coding sequence but not translated are called UnTranslated Regions (UTR); one at each end.

18

"Reading Frame" by Hornung Ákos - Wikimedia Commons

BIO-REFRESHER


19

"ORF" by Thatsonginc - Wikimedia Commons

BIO-REFRESHER


20 BIO-REFRESHER

TRANSLATIONreading frame: splice sites

v  The spliceosome catalyzes the removal of introns and the liga(on of flanking exons. •  introns: spaces inside the gene, not part of the coding sequence •  exons: expression units (of the coding sequence)

v  Splicing “signals” (from the point of view of an intron): •  There is a 5’ end splice “signal” (site): usually GT (less common: GC) •  And a 3’ end splice site: usually AG •  …]5’-‐GT/AG-‐3’[…

v  It is possible to produce more than one protein (polypep(de) sequence from the same genic region, by alterna(vely bringing exons together= alterna=ve splicing. For example, the gene Dscam (Drosophila) has 38,000 alterna(vely spliced mRNAs = isoforms

21

"Gene structure" by Daycd- Wikimedia Commons

BIO-REFRESHER

TRANSLATIONnow in your mind

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

22

Text for figures goes here

BIO-REFRESHER

TRANSLATIONreading frame: phase

v  Introns can interrupt the reading frame of a gene by inser(ng a sequence between two consecu(ve codons

v  Between the first and second nucleo(de of a codon

v  Or between the second and third nucleo(de of a codon

"Exon and Intron classes”. Licensed under Fair use via Wikipedia

23

"Protein synthesis" by Kelvinsong - Wikimedia Commons

CURATING GENOMES

TRANSLATIONin detail

24 BIO-REFRESHER

HICCUPSin transcription and translation

v  The presence of premature Stop codons in the message is possible. A process called non-‐sense mediated decay checks for them and corrects them to avoid: incomplete splicing, DNA muta(ons, transcrip(on errors, and leaky scanning of ribosome – causing changes in the reading frame (frame shiYs).

v  Inser(ons and dele(ons (indels) can cause frame shios, when indel is not divisible by three (3). As a result, the pep(de can be abnormally long, or abnormally short – depending when the first in-‐frame Stop signal is located.

Predicción & Anotación

26 Gene Prediction

GENE PREDICTION

v  The iden(fica(on of structural features of the genome:

•  Primarily focused on protein-‐coding genes. •  Predicts also transfer RNAs (tRNA), ribosomal RNAs (rRNA),

regulatory mo(fs, long and small non-‐coding RNAs (ncRNA), repe((ve elements (masked), etc.

•  Two methods for iden(fica(on. •  Some are self-‐trained and some must be trained.

27 Gene Prediction

GENE PREDICTIONmethods for discovery

1) Ab ini,o: -‐ based on DNA composi(on, -‐ deals strictly with genomic sequences -‐ makes use of sta(s(cal approaches to search for coding regions and typical gene signals. •  E.g. Augustus, GENSCAN,

geneid, fgenesh, etc.

3’

Nat Rev Genet. 2015 Jun;16(6):321-32. doi: 10.1038/nrg3920

28

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

Gene Prediction

GENE PREDICTIONmethods for discovery (ctd)

2) Homology-‐based: -‐ evidence-‐based, -‐ finds genes using either similarity searches in the main databases or experimental data including RNAseq, expressed sequence tags (ESTs), full-‐length complementary DNAs (cDNAs), etc.

•  E.g: fgenesh++, Just Annotate My genome (JAMg), SGP2

29

GENE ANNOTATION

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

Gene Annotation

30

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

ANOTACIONun arte imperfecto

No one is perfect, least of all automated annotation. 31

Nuevas tecnologías traen nuevos retos: •  Errores en el ensamblaje pueden causar

fragmentación en las anotaciones •  Cobertura limitada dificulta la

iden(ficación con certeza

Image: www.BroadInstitute.org

ANOTACION MANUALmejorando predicciones

Schiex et al. Nucleic Acids 2003 (31) 13: 3738-‐3741

Predicciones Automatizadas

Evidencia Experimental

Manual Annotation – to the rescue. 32

cDNAs, búsquedas con HMM, RNAseq, genes de otras especies.

Entonces, es necesario refinar las predicciones de elementos biológicos

codificados en el genoma, lo que requiere una cuidadosa revisión.

33

BIOCURACIONajustes estructurales y funcionales

Iden(ficar los elementos del genoma que mejor representan la biología subyacente y eliminar los elementos que reflejan errores sistémicos de los análisis automa(zados.

Asignar funciones a través de análisis compara(vos entre elementos genómicos similares de organismos cercanamente relacionados usando literatura, bases de datos, y datos experimentales.

BIOCURACION

hAp://GeneOntology.org

1

2

MANUAL ANNOTATION 34

PERO, EN CURACIONno siempre era posible ampliar estos esfuerzos

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.

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

ANOTACIONun ejercicio en colaboración

COLABORANDO 35

Los inves1gadores usualmente buscamos las opiniones y percepciones de colegas con

experiencia en áreas específicas del conocimiento.

Por ejemplo, dominios conservados o familias de genes.

Apollouna herramienta para editar anotaciones

36

v  En la web, integrado con JBrowse.

v  ¡Permite la colaboración en (empo real!

v  Automá(camente genera datos en

formatos comunes para análisis.

v  Anotación manual de genes, pseudogenes, tRNAs,

snRNAs, snoRNAs, ncRNAs, miRNAs, TEs, y fragmentos repe((vos.

v  Funciones intui(vas y menús desplegables crean y editan estructuras

de transcritos y exones, insertan comentarios (CV, texto libre), y

términos de GO, etc.

INTRODUCING APOLLO

hAp : / /GenomeArch i tec t .o rg /

ARQUITECTURAsimple, flexible

ARCHITECTURE 37

Cliente de web + Motor de edición de anotaciones + Servicio de datos en el servidor

REST / JSON Websockets

Motor de Anotación (Servidor)

Shiro

LDAP

OAuth

Annotations

Security

Preferences

Organisms

Tracks

BAM BED VCF GFF3 BigWig

Curadores

Google Web Toolkit (GWT) / Bootstrap

JBrowse DOJO / jQuery Datos a JBrowse Organismo 1

Carga de datos con evidencia genómica para cada organismo

Servicio único de almacenamiento PostgreSQL, MySQL, MongoDB,

ElasticSearch

Apollo v2.0

Datos a JBrowse Organismo 2

CLIENTE DE WEBpanel del curador

ARCHITECTURE 38


Curadores

Google Web Toolkit (GWT) / Bootstrap

JBrowse DOJO / jQuery

Apollo v2.0

BAM BED VCF GFF3 BigWig


Usa GWT/Bootstrap en el frente para proveerle

un comportamiento versátil a la aplicación.

Panel Del Curador

¡NUEVO!

¡NUEVO!

MOTOR DE ANOTACIONlógica de edición

ARCHITECTURE 39


Shiro

LDAP

OAuth Datos a JBrowse

Organismo 2

Datos a JBrowse Organismo 1

Servicio único de almacenamiento

Apollo v2.0

Controladores Grails (J2EE servlet) llevan las solicitudes al directorio

de datos apropiado para cada organismo en JBrowse

Carga de datos con evidencia genómica para cada organismo

¡NUEVO!

Cliente de web


SERVICIO DE DATOS EN EL SERVIDORservicio único de almacenamiento

ARCHITECTURE 40

Anotaciones

Seguridad

Preferencias

Organismos

Pistas de datos Servicio único de almacenamiento PostgreSQL, MySQL, MongoDB,

ElasticSearch


Un solo servicio de almacenamiento, consultable, para guardar las anotaciones. ¡NUEVO!

Apollo v2.0

¡COLABOREMOS!Apollo tiene código abierto y es expandible

HIGHLIGHTED IMPROVEMENTS 41

The Genome Sequence Annotation Server (GenSAS) Annotate

Los usuarios pueden adicionar programas para permi=r sus propios procesos de trabajo.

Ejemplos: •  GenSAS: plataforma para

anotación estructural del genoma.

•  i5K: -‐ Espacio en NAL para compar(r ensamblajes y conjuntos de genes, y para anotación manual. -‐ Proyecto Piloto >40 genomas: 47 charlas, 9 posters en Simposio de Genómica de Artrópodos.

Annotate

National Agricultural Library

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”.

42

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 liAle training goes a long way •  NGS poses addi(onal challenges

LESSONS LEARNED 43

¿Cuál es la tarea del curador?

Becoming Acquainted with Web Apollo 45 | 45

GENERAL PROCESS OF CURATIONmain steps to remember

1.  Select or find a region of interest, e.g. scaffold. 2.  Select appropriate evidence tracks to review the gene model.

3.  Determine whether a feature in an exis(ng evidence track will provide a reasonable gene model to start working.

4.  If necessary, adjust the gene model.

5.  Check your edited gene model for integrity and accuracy by comparing it with available homologs.

6.   Comment and finish.

46 CURATING GENOMES

WHAT ANNOTATORS SHOULD LOOK FORannotators: that’s you!

v  Annota=ng a simple case: WHEN “The official predic(on is correct, or nearly correct, assuming that no aligned data extends beyond the gene model and if so, it is not likely to be coding sequence, and/or the gene predic(on matches what you know about the gene”: a.  Can you add UTRs? b.  Check exon structures. c.  Check splice sites: …]5’-‐GT/AG-‐3’[… d.  Check ‘start’ and ‘stop’ sites. e.  Check the predicted protein product(s). f.  If the protein product s(ll does not look correct, go on to “Annota(ng

more complex cases”.

47 CURATING GENOMES

WHAT ANNOTATORS SHOULD LOOK FORcontinued

v  Addi=onal func=onality. You may also need to learn how to: a.  Get genomic sequence b.  Merge exons c.  Add/Delete an exon d.  Create an exon de novo (within an intron or outside exis(ng

annota(ons). e.  Right/apple-‐click on a feature to get feature ID and addi(onal

informa(on f.  Looking up homolog descrip(ons going to the accession web page at

UniProt/Swissprot

48 CURATING GENOMES


v  Annota=ng more complex cases: a.  Incomplete annota(on: protein integrity checks, indicate gaps, missing 5’

sequences or missing 3’ sequences. b.  Merge of 2 gene predic(ons on same scaffold c.  Merge of 2 gene predic(ons on different scaffolds (uh-‐oh!). d.  Split of a gene predic(on e.  Frameshios, Selenocysteine, single-‐base errors, and other inconvenient

phenomena

49 CURATING GENOMES


v  Adding important project informa=on in the form of Canned and/or Customized Comments: a.  NCBI ID, RefSeq ID, gene symbol(s), common name(s), synonyms, top

BLAST hits (GenBank IDs), orthologs with species names, and anything else you can think of, because you are the expert.

b.  Type of annota(on (e.g.: whether or not the gene model was changed) c.  Data source (for example if the Fgeneshpp predicted gene was the

star(ng point for your annota(on) d.  The kinds of changes you made to the gene model, e.g.: split, merge e.  Func(onal descrip(on f.  Whether you would like for your MOD curator to check the annota(on g.  Whether part of your gene is on a different scaffold.

50

TRAINING CURATORSa little training goes a long way!

Provided with adequate tools, wet lab scien(sts make excep(onal curators who can easily learn to maximize the genera(on of accurate, biologically supported gene models.

APOLLO

Conozcamos a Apollo hAp://genomearchitect.org/web_apollo_user_guide

52

Apolloámbito de edición para anotaciones

BECOMING ACQUAINTED WITH APOLLO

Color por marco de lectura, alternar cadena, cambiar esquema de color, resaltador

Cargar evidencia experimental (GFF3, BAM, BigWig), pistas de datos de combinación y búsqueda.

Interrogar el genoma usando BLAT.

Navegación y zoom.

Buscar un gen o un grupo

Obtener coordenadas, y hacer zoom con “selección elás(ca”

Login

Anotaciones creadas por el usuario Panel del

curador

Pistas de datos de evidencia

Datos transcriptómicos de estadío y (po celular específicos.

¡Ahora juguemos!

Instrucciones 54 | 54

APOLLO EN LA WEBinstrucciones

Email: [email protected]

Contraseña:

nombreapellido

Email Contraseña Servidor Empezar en [email protected] userone 1 1 [email protected] usertwo 2 1 [email protected] userthree 3 1 [email protected] userfour 4 1 [email protected] userfive 5 1 [email protected] usersix 1 7 [email protected] userseven 2 7 [email protected] usereight 3 7 [email protected] usernine 4 7 [email protected] userten 5 7 [email protected] usereleven 1 1 [email protected] usertwelve 2 1 [email protected] userthirteen 3 1 [email protected] userfourteen 4 1 [email protected] userfioeen 5 1 [email protected] usersixteen 1 7 [email protected] userseventeen 2 7 [email protected] usereighteen 3 7 [email protected] usernineteen 4 7 [email protected] usertwenty 5 7 [email protected] usertwentyone 1 1 [email protected] usertwentytwo 2 1 [email protected] usertwentythree 3 1 [email protected] usertwentyfour 4 1 [email protected] usertwentyfive 5 1 [email protected] usertwentysix 1 7 [email protected] usertwentyseven 2 7 [email protected] usertwentyeight 3 7 [email protected] usertwentynine 4 7

Servidor URL 1 hAp://54.94.132.228:8080/apollo/annotator/index 2 hAp://54.207.71.112:8080/apollo/annotator/index 3 hAp://54.207.106.136:8080/apollo/annotator/index 4 hAp://54.207.113.253:8080/apollo/annotator/index 5 hAp://54.232.217.84:8080/apollo/annotator/index

Funcionalidad y navegación.

56

Apollofuncionalidad

BECOMING ACQUAINTED WITH APOLLO

•  Agregar: –  IDs de bases de datos públicas (e.g. GenBank, usando DBXRef); símbolo(s) de cada gen, nombre(s) común(es), sinónimos, el mejor resultado de BLAST, ortólogos con el nombre de la especie.

–  Asignaciones de función apropiadas (e.g. via datos de RNA-‐Seq, búsquedas de literatura, búsquedas con HMMs, etc.)

–  Comentarios acerca de las modificaciones que se realizaron, o si ninguna fue necesaria.

–  Y otras notas que se le ocurran al biocurador.

•  Corregir si(os de ‘Inicio’ y ‘Parada’ •  Arreglar si(os de ayuste no canónicos •  Anotar UTRs (e.g.: usando RNA-‐Seq) •  Obtener & corregir predicciones de

productos de proteínas -‐ Alinearlos con genes o familias de genes relevantes. -‐ Usar blastp en RefSeq o nr de NCBI

•  Revisar la falta de datos en el ensamble •  Unir 2 predicciones de genes en el

mismo grupo •  Dividir una predicción de gen •  Corregir desplazamientos de la pauta

de lectura, y otros errores en el ensamblaje

•  Anotar selenocisteínas, errores de una sola base, etc.

REMOVABLE SIDE DOCKwith customizable tabs


Annotations Organism Users Groups Admin Tracks Reference Sequence

EDITS & EXPORTSannotation details, exon boundaries, data export


1 2

Annotations

1

2


Reference Sequences

3

FASTA

GFF3

EDITS & EXPORTSannotation details, exon boundaries, data export

3

60 | 60 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.

61 | 61

USER NAVIGATION

Becoming Acquainted with Web Apollo.

•  Annotation right-click menu

62

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

62

USER NAVIGATION


63

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.

63

USER NAVIGATION


64

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.

64

USER NAVIGATION


65 | 65

USER NAVIGATION


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

66 | 66

USER NAVIGATION


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

67 |

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

67

USER NAVIGATION


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

Anotación

casos simples

“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.

70 | 70

ANNOTATING SIMPLE CASES

Becoming Acquainted with Web Apollo. SIMPLE CASES

71 |

•  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 aoer each edit.

71

ADDING EXONS


If transcript alignment data are available and extend beyond your original annota(on, you may extend or add UTRs.

1.  Right click at the exon edge and ‘Zoom to base level’.

2.  Place the cursor over the edge of the exon un1l it becomes a black arrow then click and drag the edge of the exon to the new coordinate posi(on that includes the UTR.

72 |

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

72

ADDING UTRs


1.   Zoom in to clearly resolve each exon as a dis(nct rectangle.

2.  Two exons from different tracks sharing the same start and/or end coordinates will display a red bar to indicate matching edges.

3.  Selec(ng the whole annota(on or one exon at a (me, use this ‘edge-‐matching’ func(on and scroll along the length of the annota(on, verifying exon boundaries against available data. Use square [ ] brackets to scroll from exon to exon.

4.  Check if cDNA / RNAseq reads lack one or more of the annotated exons or include addi(onal exons.

73 | 73

CHECK EXON INTEGRITY


To modify an exon boundary and match data in the evidence tracks: select both the offending exon and the feature with the expected boundary, then right click on the annota(on to select ‘Set 3’ end’ or ‘Set 5’ end’ as appropriate.

74 |

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.

74

EXON STRUCTURE INTEGRITY


Flags non-‐canonical splice sites.

Selec(on of features and sub-‐features

Edge-‐matching

Evidence Tracks Area

‘User-‐created Annota(ons’ Track

Apollo’s edi(ng logic (brain): §  selects longest ORF as CDS §  flags non-‐canonical splice sites

75

ORFs AND SPLICE SITES


76 |

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

76

SPLICE SITES


Zoom to review non-‐canonical splice site warnings. Although these may not always have to be corrected (e.g GC donor), they should be flagged with the appropriate comment.

Web Apollo calculates the longest possible open reading frame (ORF) that includes canonical ‘Start’ and ‘Stop’ signals within the predicted exons.

If ‘Start’ appears to be incorrect, modify it by selec(ng an in-‐frame ‘Start’ codon further up or downstream, depending on evidence (protein database, addi(onal evidence tracks).

It may be present outside the predicted gene model, within a region supported by another evidence track.

In very rare cases, the actual ‘Start’ codon may be non-‐canonical (non-‐ATG).

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‘START’ AND ‘STOP’ SITES


complex cases

Evidence may support joining two or more different gene models. Warning: protein alignments may have incorrect splice sites and lack non-‐conserved regions!

1.  In ‘User-‐created Annota=ons’ area shio-‐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.

79 | 79

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.

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COMPLEX CASES split a gene prediction


DNA Track

‘User-‐created Annota=ons’ Track

81

COMPLEX CASES correcting frameshifts and single-base errors


Always remember: when annota(ng gene models using Apollo, you are looking at a ‘frozen’ version of the genome assembly and you will not be able to modify the assembly itself.

82

COMPLEX CASES correcting selenocysteine containing proteins


83

COMPLEX CASES correcting selenocysteine containing proteins


1.  Apollo allows annotators to make single base modifica(ons or frameshios 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.

84 | 84 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.

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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


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 –  FrameshiYs

–  error in the genome assembly? –  Selenocysteines, single-‐base errors, etc

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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 89

A public Apollo Demo using the Honey Bee genome is available at hAp://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 submiAed

•  Data at i5K Workspace@NAL (annota(on hosted at USDA) -‐ 10,832 scaffolds: 23,288 transcripts: 12,906 proteins

Example 90

PubMed Search: what’s new?

Example 91

PubMed Search: what’s new?

Example 92

“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 93

•  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 94

>vgsc-‐Segment3-‐DomainII RVFKLAKSWPTLNLLISIMGKTVGALGNLTFVLCIIIFIFAVMGMQLFGKNYTEKVTKFKWSQDGQMPRWNFVDFFHSFMIVFRVLCGEWIESMWDCMYVGDFSCVPFFLATVVIGNLVVSFMHR

BLAT search

Example 95

>vgsc-‐Segment3-‐DomainII RVFKLAKSWPTLNLLISIMGKTVGALGNLTFVLCIIIFIFAVMGMQLFGKNYTEKVTKFKWSQDGQMPRWNFVDFFHSFMIVFRVLCGEWIESMWDCMYVGDFSCVPFFLATVVIGNLVVSFMHR

BLAT search

Example 96

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

Example 97

Creating a new gene model: drag and drop

Example 98

•  Apollo automatically calculates ORF. In this case, ORF includes the high-scoring segment pairs (hsp).

Available Tracks

Example 99

Get Sequence

Example 100

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

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

Example 101

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

BLAST hsps

Review alignments

Example 102

HaztTmpM006234

HaztTmpM006233

HaztTmpM006232

Hypothesis for vgsc gene model

Example 103

Editing: merge the three models

Example 104

Merge by dropping an exon or gene model onto another.

Merge by selec(ng two exons (holding down “Shio”) and using the right click menu.

or…

Editing: correct boundaries, delete exons

Example 105

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 106

Editing: modify boundaries

Example 107

Modify intron / exon boundary also at coord. 78,999.

Finished model

Example 108

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 buAon.

Example 109

Comments (if applicable)

Video demostración

APOLLOdemonstration

DEMO 111

Apollo demo video available at: hAps://youtu.be/VgPtAP_fvxY

CONTENIDO

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

112 OUTLINE

•  BIO-‐REFRESHER conceptos que neceistamos

•  ANOTACION predicciones automá(cas

•  ANOTACION MANUAL necesaria, en colaboración

•  APOLLO

avanzando la curación en colaboración •  EJEMPLO

demonstraciones

•  EJERCICIOS

Ejercicios

Exercises Live Demonstra(on using the Apis mellifera genome.

115

1. Evidence in support of protein coding gene models. 1.1 Consensus Gene Sets: Official Gene Set v3.2 Official Gene Set v1.0 1.2 Consensus Gene Sets comparison: OGSv3.2 genes that merge OGSv1.0 and RefSeq genes OGSv3.2 genes that split OGSv1.0 and RefSeq genes 1.3 Protein Coding Gene Predic=ons Supported by Biological Evidence: NCBI Gnomon Fgenesh++ with RNASeq training data Fgenesh++ without RNASeq training data NCBI RefSeq Protein Coding Genes and Low Quality Protein Coding Genes

1.4 Ab ini,o protein coding gene predic=ons: Augustus Set 12, Augustus Set 9, Fgenesh, GeneID, N-‐SCAN, SGP2 1.5 Transcript Sequence Alignment: NCBI ESTs, Apis cerana RNA-‐Seq, Forager Bee Brain Illumina Con(gs, Nurse Bee Brain Illumina Con(gs, Forager RNA-‐Seq reads, Nurse RNA-‐Seq reads, Abdomen 454 Con(gs, Brain and Ovary 454 Con(gs, Embryo 454 Con(gs, Larvae 454 Con(gs, Mixed Antennae 454 Con(gs, Ovary 454 Con(gs Testes 454 Con(gs, Forager RNA-‐Seq HeatMap, Forager RNA-‐Seq XY Plot, Nurse RNA-‐Seq HeatMap, Nurse RNA-‐Seq XY Plot


Exercises Live Demonstra(on using the Apis mellifera genome.

116

1. Evidence in support of protein coding gene models (Con=nued). 1.6 Protein homolog alignment: Acep_OGSv1.2 Aech_OGSv3.8 Cflo_OGSv3.3 Dmel_r5.42 Hsal_OGSv3.3 Lhum_OGSv1.2 Nvit_OGSv1.2 Nvit_OGSv2.0 Pbar_OGSv1.2 Sinv_OGSv2.2.3 Znev_OGSv2.1 Metazoa_Swissprot

2. Evidence in support of non protein coding gene models 2.1 Non-‐protein coding gene predic=ons: NCBI RefSeq Noncoding RNA NCBI RefSeq miRNA 2.2 Pseudogene predic=ons: NCBI RefSeq Pseudogene


Instrucciones 117 | 117

APOLLO EN LA WEBinstrucciones

Servidor URL 1 hAp://54.94.132.228:8080/apollo/annotator/index 2 hAp://54.94.132.228:8080/apollo/annotator/index 3 hAp://54.94.132.228:8080/apollo/annotator/index 4 hAp://54.94.132.228:8080/apollo/annotator/index 5 hAp://54.94.132.228:8080/apollo/annotator/index

Email: [email protected]

Contraseña:

nombreapellido

Email Contraseña Servidor Empezar en [email protected] userone 1 1 [email protected] usertwo 2 1 [email protected] userthree 3 1 [email protected] userfour 4 1 [email protected] userfive 5 1 [email protected] usersix 1 7 [email protected] userseven 2 7 [email protected] usereight 3 7 [email protected] usernine 4 7 [email protected] userten 5 7 [email protected] usereleven 1 1 [email protected] usertwelve 2 1 [email protected] userthirteen 3 1 [email protected] userfourteen 4 1 [email protected] userfioeen 5 1 [email protected] usersixteen 1 7 [email protected] userseventeen 2 7 [email protected] usereighteen 3 7 [email protected] usernineteen 4 7 [email protected] usertwenty 5 7 [email protected] usertwentyone 1 1 [email protected] usertwentytwo 2 1 [email protected] usertwentythree 3 1 [email protected] usertwentyfour 4 1 [email protected] usertwentyfive 5 1 [email protected] usertwentysix 1 7 [email protected] usertwentyseven 2 7 [email protected] usertwentyeight 3 7 [email protected] usertwentynine 4 7

Thank you. 118

•  Berkeley Bioinforma=cs Open-‐source Projects (BBOP), Berkeley Lab: Apollo and Gene Ontology teams. Suzanna E. Lewis (PI).

•  § Chris1ne G. Elsik (PI). University of Missouri.

•  * Ian Holmes (PI). University of California Berkeley.

•  Arthropod genomics community: i5K Steering CommiAee (esp. Sue Brown (Kansas State)), Alexie Papanicolaou (UWS), and the Honey Bee Genome Sequencing Consor(um.

•  Stephen Ficklin GenSAS Washington State University

•  Apollo is supported by NIH grants 5R01GM080203 from NIGMS, and 5R01HG004483 from NHGRI. Both projects are also supported by the Director, Office of Science, Office of Basic Energy Sciences, of the U.S. Department of Energy under Contract No. DE-‐AC02-‐05CH11231

• 

•  For your a"en=on, thank you!

Apollo

Nathan Dunn

Colin Diesh §

Deepak Unni §

Gene Ontology

Chris Mungall

Seth Carbon

Heiko Dietze

BBOP

Apollo: hAp://GenomeArchitect.org

GO: hAp://GeneOntology.org

i5K: hAp://arthropodgenomes.org/wiki/i5K

¡Gracias!

NAL at USDA

Monica Poelchau

Christopher Childers

Gary Moore

HGSC at BCM

fringy Richards

Kim Worley

JBrowse Eric Yao *

Apolo Taller en BIOS

Science

Transcript of Apolo Taller en BIOS