Annotating
PLANT

genomes

Richard Smith-Unna 

What we'll cover

  • what annotation is
  • what the important steps are
  • how those steps work
  • what software to use
  • what to do with the data

and we'll get our hands dirty
with some real (rice) data

RESOURCES

  • these slides
  • software list
  • exercises
  • recommended pipeline

all online at:

STRUCTURAL ANNOTATION


where are the genes?


what features do they have?


what products do they encode?


FUNCTIONAL ANNOTATION

  • what is the biological role of those gene products?
  • what should we call them?
  • how is the gene regulated?

don't expect too much

Annotation is an imperfect science.

Even the best model genomes (e.g. human)
have very incomplete annotations:
  • over 90% of protein loci correctly identified
  • ~50% have structure incorrectly annotated
    • EGASP 2006
  • ~30% lacking any functional annotation

community standards

don't reinvent the wheel

the Generic Model Organism Database project

GMOD TOOLS

THE EVIDENCE TRAIL

annotation is about building evidence to support
our conclusions about structure and function

  •  GFF3 file format for accumulating and sharing evidence
  •  CHADO database for storage and querying

GFF3

ctg123 example gene            1050 9000 . + . ID=EDEN;Name=EDEN;Note=protein kinase

ctg123 example mRNA            1050 9000 . + . ID=EDEN.1;Parent=EDEN;Name=EDEN.1;Index=1
ctg123 example five_prime_UTR  1050 1200 . + . Parent=EDEN.1
ctg123 example CDS             1201 1500 . + 0 Parent=EDEN.1
ctg123 example CDS             3000 3902 . + 0 Parent=EDEN.1
ctg123 example CDS             5000 5500 . + 0 Parent=EDEN.1
ctg123 example CDS             7000 7608 . + 0 Parent=EDEN.1
ctg123 example three_prime_UTR 7609 9000 . + . Parent=EDEN.1

ctg123 example mRNA            1050 9000 . + . ID=EDEN.2;Parent=EDEN;Name=EDEN.2;Index=1
ctg123 example five_prime_UTR  1050 1200 . + . Parent=EDEN.2
ctg123 example CDS             1201 1500 . + 0 Parent=EDEN.2
ctg123 example CDS             5000 5500 . + 0 Parent=EDEN.2
ctg123 example CDS             7000 7608 . + 0 Parent=EDEN.2
ctg123 example three_prime_UTR 7609 9000 . + . Parent=EDEN.2

ctg123 example mRNA            1300 9000 . + . ID=EDEN.3;Parent=EDEN;Name=EDEN.3;Index=1
ctg123 example five_prime_UTR  1300 1500 . + . Parent=EDEN.3
ctg123 example five_prime_UTR  3000 3300 . + . Parent=EDEN.3
ctg123 example CDS             3301 3902 . + 0 Parent=EDEN.3
ctg123 example CDS             5000 5500 . + 1 Parent=EDEN.3
ctg123 example CDS             7000 7600 . + 1 Parent=EDEN.3
ctg123 example three_prime_UTR 7601 9000 . + . Parent=EDEN.3
http://en.wikipedia.org/wiki/General_feature_format


TYPICAL workflow

REPEATS


refers to multiple different biological phenomena

  • low complexity sequences
    • AAAAATTTTTTAAAAAAAAACCAAAA
  • short tandem repeats
    • ACACACACACACACAACACACACAC
  • transposable elements 
    • LINEs, SINEs and viruses

REPEATS ARE IMPORTANT


GOOD

  • useful in population genetics, phylogenetics and forensics
  • fascinating to study in their own right
  • cause genome expansions
  • affect evolution of genes and regulatory elements

REPEATS ARE IMPORTANT


bad

  • sometimes look like real genes
  • cause spurious alignments
  • want to prevent them affecting analysis

REPEATS ARE IMPORTANT


69% of the Gossypium arboreum genome
 


IDENTIFYING REPEATS


homology

do they look like known repetitive sequences?

DE-NOVO

compare a genome to itself and cluster
OR
use regular expressions

MASKING REPEATS


SOFT: ACAGTAAaaaattttaaaaACTGCATGCAAAT

HARD: ACAGTAANNNNNNNNACTGCATGCAAAT

exercise 1

GENES

shared structure allows de-novo prediction 

  • start and stop codons
  • splice-sites
  • promoter elements


low accuracy (~30%)

GENES


GENES


using data from biological experiments
gives evidence-based predictions

  • align proteins from related species
  • ESTs, cDNAs from same or very close spp.
  • assembled transcripts
  • RNAseq reads

much more accurate (~90%)

WHAT DOES THE EVIDENCE SAY?

STRUCtURAL aNNOTATION PIPELINES

  1. take in multiple sources of evidence
  2. balance them based on confidence
  3. create a merged annotation

examples:

  • MAKER (runs all the tools for you!)
  • AUGUSTUS
  • PASA-EVM

MAKER-P

THE EVIDENCE TRAIL

however you annotate,
you need to record the evidence

pipelines do this for you

present the evidence in a genome browser

exercise 2

FUNCTIONAL ANNOTATION

homology (sequence alignment)

  • use predicted protein sequences
  • perform pairwise alignment against related spp.
  • transfer functional info between species

THE CUTOFF PROBLEM

FUNCTIONAL ANNOTATION

homology (sequence profiles)

  • search the sequence into a database of models
  • can be whole-sequence or domain specific

PROTEIN PROFILES

Piwi domain

FunCTIONAL ONTOLOGIES


  • GO
  • KEGG
  • MapMan

FUNCTIONAL ANNOTATION PIPELINES

  1. use multiple methods
  2. search multiple databases and references
  3. collect the evidence

example tools:

  • InterProScan
  • Mercator
  • Trinotate

exercise 3

STANDARD PIPELINE

MANUaL CURATION

important to keep the evidence trail

gene family experts required for high confidence

exercise  4

MAKING THE ANNOTATION USEFUL


create a  public genome database to
present all your work and allow expert curation

GMOD tools make this (fairly) easy:

  • WebApollo (community annotation editor)
  • Tripal (genome data browser, BLAST server)
  • G/JBrowse (genome track browser)
  • biomart (data mining system)
  • PathwayTools (metabolic pathway server)

THANKS!


Don't forget the resources:

Annotating plant genomes

By Richard Smith-Unna

Made with Slides.com

Annotating plant genomes

Genome annotation course originally written for TGAC 2014 SeqAhead course.

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