Community Standards and Comparative Genomics

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Community Standards and Comparative Genomics

• Doreen Ware USDA-ARS
• Cold Spring Harbor Laboratories
• Plant Ontology Workshop May 30, 2006

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Outline

• Introduction to Gramene
• Data integration why is it important
• Example using Genome Sequencing and Mapping
• Why standards are important for data management
  and integration?
• Repositories, protocols, nomenclature,
  vocabulary, formats, user interfaces
• Comparative Resources
• PlexDB www.plexdb.org, Pathways tools in
  Gramene, Reactome www.reactome.org

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Gramenewww.gramene.org
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Gramene

• As an information resource, Gramene's purpose is
  to provide added value to data sets available
  within the public sector, which will facilitate
  researchers' ability to leverage the rice genomic
  sequence to identify and understand corresponding
  genes, pathways and phenotypes in other crop
  grasses.
• This is achieved by building automated and
  curated relationships between rice and other
  cereals for both sequence and biology. The
  automated and curated relationships are queried
  and displayed using controlled vocabularies and
  web-based displays.
• The controlled vocabularies (Ontologies),
  currently being utilized include Gene ontology,
  Plant ontology, Trait ontology, Environment
  ontology and Gramene Taxonomy ontology.
• The web-based displays for phenotypes include the
  Genes and Quantitative Trait Loci (QTL) modules.
  Sequence based relationships are displayed in the
  Genomes module using the genome browser adapted
  from Ensembl, in the Maps module using the
  comparative map viewer (CMap) from GMOD, and in
  the Proteins module displays. BLAST is used to
  search for similar sequences. Literature
  supporting all the above data is organized in the
  Literature database.

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Why data integration?

• It is often the case that two data sets, when
  integrated, are far more useful than the two data
  sets taken individually.

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Genome Sequence and ESTs
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Genome Sequence and SNP data
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SNPs in context to the a protein sequence
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Automated Annotation
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Synteny view of Rice Chromosome 1 to the maize
genome
http//www.gramene.org/Oryza_sativa/syntenyview?ot
herspeciesZea_mayschr1x15y13
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Bioinformatic Food Chain
Plant Biology Databases A needs Assessment
November 2005
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What are the appropriate repositories for the
data sets?

• Standards for release

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Static repositories for long term storage

• GenBank (Benson et al 2004), sequence submissions
  
• GEO (Barrett et al 2005), a repository of
  microarray expression data
• PDB (Westbrook et al 2003), a repository of x-ray
  crystallographic structures

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If no static repository exist what to keep and
why?

• Examplegenotype data from recombinant inbred
  (RI) lines
• Multiple labs use the same germplasm for trait
  evaluation
• Analysis tools change overtime
• Integrate data from multiple experiments

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What is the method for producing the alignments?
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Provide information on the data set, the method
used and the when the analysis was completed
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Standard Operating ProtocolsSOPs

• Data integration often requires additional
  analysis, and these require decisions.
• Document these to allows end-users and the
  individuals working on the project to understand
  the process and what decisions were made along
  the way.
• Establish Quality Assurance and Control at each
  step

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Separate the technical infrastructure from the
human infrastructure

• Many automated computational tasks that do not
  require specialized species- or biology-specific
  knowledge.
• In order to avoid redundant and inconsistent
  efforts, encourage partnerships between groups
  that can provide technical infrastructure for
  automated annotation tasks and groups that are
  knowledgeable about the underlying biology
  associated with the data set.

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Standardize Nomenclature
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Standardize Nomenclature

• Example Genomic Clones used as templates for the
  physical map and genomic sequence
• 59P7
• c59P7
• c0059P07
• ZM0059P07
• ZMC0059P07
• ZMCUNK_0059P07

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Standardize Vocabulary
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Ontologies to describe attributes of the data set

• Ontologies are sets of vocabulary terms whose
  meanings and relations with other terms are
  explicitly stated in such a way as to be
  comprehensible to humans and computer programs.
• Ontology-building has emerged as a major activity
  of curated repositories because by annotating
  data sets using a shared set of ontologies,
  repositories can establish connections both
  within the data sets they curate and across data
  sets contained within different repositories.

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GOGene Ontologyhttp//www.geneontology.org

• molecular function
• describes activities, such as catalytic or
  binding activities, at the molecular level
• biological process
• is series of events accomplished by one or more
  ordered assemblies of molecular functions
• cellular component
• a component of a cell but with the proviso that
  it is part of some larger object, which may be an
  anatomical structure (e.g. rough endoplasmic
  reticulum or nucleus) or a gene product group
  (e.g. ribosome, proteasome or a protein dimer)

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POPlant Ontologywww.plantontology.org

• Plant Structure
• A controlled vocabulary of botanical terms
  describing morphological and anatomical
  structures representing organ, tissue and cell
  types and their relationships. Examples are
  stamen, gynoecium, petal, parenchyma, guard cell,
  etc.
• Growth and Developmental Stages
• A controlled vocabulary of terms describing
  growth and developmental stages in model plant
  species and their relationships. Examples are
  embryo development stage, seedling stage,
  flowering stage, etc

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OBOOpen biomedical Ontologieshttp//obo.sourcefo
rge.net
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Example ontologies currently available from OBO
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Evidence and Attribution tracking

• Evidence tracking links an assertion contained
  within a repository to the underlying evidence
  that supports that assertion.
• Attribution tracking links a data set and
  annotations on the data set to the individual or
  group that produced it

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Standardize data formats and user interfaces

• The lack of standard file formats provides
  friction that increases the cost and decreases
  the pace of active curation and data integration.
  
• The lack of standardization of user interfaces
  leads to frustration on the part of researchers
  who cannot easily move from one repository to
  another.

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Description of the fields in the database and
intended use

• Example the database field was location
• Mexico
• Jalisco
• third field off of the first left on the main
  road out of town

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End-Users Who are they and what are their needs?

• Naïve end-users require easy-to-use and intuitive
  interfaces that nevertheless provide them with
  access to the full data set. These users are
  often satisfied with one-object-at-a-time
  interfaces, such as those provided by almost all
  biological databases.
• More sophisticated users require query interfaces
  that allow them to integrate multiple data sets
  within the current repository.
• The most sophisticated users wish to integrate
  multiple data sets across multiple repositories.

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Generic Model Organism Database

• The Generic Model Organism Database (GMOD)
  Project is a largely open source project to
  develop a complete set of software for creating
  and administering a model organism database.
  Components of this project include genome
  visualization and editing tools, literature
  curation tools, a robust database schema,
  biological ontology tools, and a set of standard
  operating procedures.

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GMODwww.gmod.org
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Comparative Resources
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Comparative Resources

• Microarray Resource
• PlexDB
• MIAME Standard compliant
• Pathway Databases
• MetaCyc pathway tools
• Biochemcial pathways
• Reactome
• Biological processes

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MIAME Standards
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Pathway Database

• Standardized formats are maturing for data
  exchange
• User meeting are necessary to facilitate the
  development
• Existing resources provide useful cross species
  comparisons, to leverage experimental validation
  between organisms, identify gaps in pathways,
  and overlap between species

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Inference of a rice reaction set from Arabidopsis
with Pathway Tools in Gramene Focus is on
biochemical pathways
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SkyPainter view from Reactome
Inference of a rice reaction set from human with
Reactome / OrthoMCL Focus on biological process
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http//dev.gramene.org/about/personnel.html
PlexDB Roger Wise Julie Dickerson Pathway
Tools SRI Peter Karp Reactome Peter
dEstachio Guanming Wu Lincoln Stein Funding
NSF and USDA-ARS