Download Gene Products annotated

Bioinformatics and
Genome Annotation
Shane C Burgess
http://www.agbase.msstate.edu/
NIH WORKING DEFINITION OF
BIOINFORMATICS AND
COMPUTATIONAL BIOLOGY
July 17, 2000
Bioinformatics: Research, development, or application of
computational tools and approaches for expanding the use
of biological, medical, behavioral or health data, including
those to acquire, store, organize, archive, analyze, or
visualize such data.
Computational Biology: The development and application of
data-analytical and theoretical methods, mathematical
modeling and computational simulation techniques to the
study of biological, behavioral, and social systems.
Biocomputing:computational
biology & bioinformatics
Gene Ontology Consortium
members
Dr Fiona McCarthy
Dr Susan Bridges
Dr Teresia Buza
Dr Nan Wang
Cathy Grisham
Dr Divya Pedinti
Philippe Chouvarine Lakshmi Pillai
Sequencing is getting cheaper
Cost of human or similar sized genome
Source: Richard Gibbs,
Baylor College of Medicine
and biocomputing becomes more of an issue.
A.
1.
2.
3.
4.
5.
6.
7.
8.
9.
Complexity
Sequence itself and from all it’s compatriots and assorted microbes
SNPs
Transcripts (all of them…don’t forget alternative splicing, starts)
CNVs
Epigenetic changes to DNA
Proteome (expression, epigenetics, PTMs, location, flux, enzyme kinetics)
Metabolites
Phenotypes
Drugs
B. Statistical. 1. Multiple testing problem. 2. Search space
Both have potential computationally-intensive solutions (Monte Carlo/Resampling/
Permutation/Bootstrap and target/decoy).
C. Information: publications are no longer the sole source of “valid” or
“legitimate” information.
Trusted databases and not just publications used as research sources; not just data but
also community annotations etc
D. Biocomputing issues: LOCAL--storage, compute power (CPUs days), RAM;
DISTANT– linking, data movement, cyberinfrastucture (hard, soft and human).
E. How and who?
Titus Brown, Mich. SU
Storage costs
A. Simple Storage Service
(S3) e.g. Amazon. For the
first 50 TB = 15 US
cents/Gb ($7,500/50 TB)
plus pay for data transfer
and operations.
VS
Buy, store and scale as
needed e.g. Web Object
Scaler (WOS)
Immediate or “longer” term
solution
Putting Genomes in the Cloud. Making data sharing faster, easier and more scalable.
By M. May, May 18, 2010.
10 Gigabits (Gb)/second
Annotation: Nomenclature,
Structural & Functional
Nomenclature
Structural Annotation:
• Open reading frames (ORFs) predicted during genome assembly
• predicted ORFs require experimental confirmation
Functional Annotation:
• annotation of gene products = Gene Ontology (GO) annotation
• initially, predicted ORFs have no functional literature and GO
annotation relies on computational methods (rapid)
• functional literature exists for many genes/proteins prior to
genome sequencing
• Gene Ontology annotation does not rely on a completed
genome sequence
Livestock Gene Nomenclature:
Jim Reecy et al., International Society for Animal
Genetics from 26th – 30th July 2010, Edinburgh
Chicken Gene Nomenclature
• 1995: chicken gene nomenclature will follow HGNC
guidelines
• 2007: chicken biocurators begin assigning standardized
nomenclature
• 2008: first CGNC report; NCBI begins using standardized
nomenclature & CGNC links
• 2010: first dedicated chicken gene nomenclature
biocurator; NCBI/AgBase/Marcia Miller – structural
annotation & nomenclature for MHC regions (chr 16)
• Chicken gene nomenclature database – UK & US
databases sharing and co-coordinating data.
http://edit-genenames.roslin.ac.uk/
Available via BirdBase & AgBase
Experimental Structural
genome annotation
Proteogenomic mapping
Problems with Current
Structural Annotation Methods
• EST evidence is biased for the ends of the
genes
• Computational gene finding programs
– Misidentify some, and especially short, genes,
genes.
– Overlook exons
– Incorrectly demarcate gene boundaries,
especially splice junctions
Proteogenomic Mapping
• Combines genomic and proteomic data for structural
annotation of genomes
• First reported by Jaffe et al. at Harvard in 2004 in bacteria
• McCarthy et al. 2006 first applied in chicken (one of the
first uses in a eukaryote; the other two in human).
• Improves genome structural annotation based on expressed
protein evidence
– Confirms existence of predicted protein-coding gene
– Identifies exons missed by gene finder
– Corrects incorrect boundaries of previously identified
genes
– Identifies new genes that the gene finding programs
missed
CCV genome was
sequenced in 1992
But only 12 of predicted 76
ORFs confirmed to exist as
proteins.
Confirmed 37/76.
Identified 17 novel ORFs
that were not predicted.
Structural Annotation of the Chicken
Genome
• Location of genes on the genome
• Computational gene finding programs such as
Gnomen (NCBI) based on Markov Models and also
use
– ESTs
– Known proteins
– Sequence conservation
ePST Generation Process
Peptide nucleotide sequence
chromosome
Map peptide nucleotide sequence to chromosome
Biological Sample
Trypsin Digestion
LC ESI-MS/MS Data
Search against protein
Database
Search against genome
translated in 6 reading frame
Peptide matches
Peptide matches
Generate ePST (expressed PeptideSequence Tags) from
peptides matching genome only
Confirm predicted proteincoding gene
Correction / validation of genome
annotation
Novel protein-coding gene
ePST Generation Process
Peptide nucleotide sequence
Stop codon
chromosome
Locate first downstream in-frame stop codon or canonical
splice junction
ePST Generation Process
Peptide nucleotide sequence
Stop codon
chromosome
Locate upstream canonical splice junction or in-frame
stop
ePST Generation Process
Peptide nucleotide sequence
Stop codon
chromosome
Start codon
Find 1st start codon between in-frame stop and peptide
ePST Generation Process
chromosome
Use splice junction or in-frame start as beginning of ePST
ePST Generation Process
chromosome
ePST coding nucleotide sequence
Translate
Expressed Peptide Sequence Tag (ePST) amino acid sequence
Functional annotation
No.
No. x 106
25000
18
16
20000
14
12
15000
10
8
10000
6
4
5000
2
0
0
‘00
‘01
‘02
‘03
‘04
‘05
‘06
‘07
‘08
‘09
YEAR
70
75
80
85
90
95
00
05
Ontologies
Canonical and
other Networks
GO Cellular Component
Pathway Studio 5.0
GO Biological Process
Ingenuity Pathway Analyses
GO Molecular Function
Cytoscape
BRENDA
Interactome Databases
Functional Understanding
Biological interpretation
Gene Ontology
Derived
Network Modeling
Implied
Physiology
(= Cellular Component + Biological Process + Molecular
Function)
What is the Gene Ontology?
“a controlled vocabulary that can be applied to all organisms even as
knowledge of gene and protein roles in cells is accumulating and
changing”
• the de facto standard for functional annotation
• assign functions to gene products at different levels, depending on
how much is known about a gene product
• is used for a diverse range of species
• structured to be queried at different levels, eg:
– find all the chicken gene products in the genome that are
involved in signal transduction
– zoom in on all the receptor tyrosine kinases
• human readable GO function has a digital tag to allow computational
analysis of large datasets
COMPUTATIONALLY AMENABLE ENCYCLOPEDIA OF
GENE FUNCTIONS AND THEIR RELATIONSHIPS
GO is the “encyclopedia” of gene functions
captured, coded and put into a directed acyclic
graph (DAG) structure.
In other words, by collecting all of
the known data about gene
product biological processes,
molecular functions and cell
locations, GO has become the
master “cheat-sheet” for our
total knowledge of the genetic
basis of phenotype.
Because every GO annotation
term has a unique digital code,
we can use computers to mine the
GO DAGs for granular functional
information.
Instead of having to plough through thousands of papers at the library and make notes
and then decide what the differential gene expression from your microarray experiment
means as a net affect, the aim is for GO to have all the biological information
captured and then retrieve it and compile it with your quantitative gene product
expression data and provide a net affect.
Use GO for…….
1. Determining which classes of gene products are
over-represented or under-represented.
2. Grouping gene products.
3. Relating a protein’s location to its function.
4. Focusing on particular biological pathways and
functions (hypothesis-testing).
Many people use “GO Slims” which capture only high-level terms which are more
often then not extremely poorly informative and not suitable for hypothesis-testing.
“GO Slim”
In contrast, we need to use the deep
granular information rich data suitable for
hypothesis-testing
Sourcing displaying GO annotations:
secondary and tertiary sources.
GO Consortium:
Reference Genome Project
• Limited resources to GO annotate gene products for every
genome
– rely on computational GO annotations
– most robust method is to transfer GO between orthologs
• Reference genome project: goal is to produce a “gold
standard” manually biocurated GO annotation dataset for
orthologous genes
– 12 reference genomes – chicken is only agricultural species
– Chicken RGP contributions provided via USDA CSREES
MISV-329140
http://www.geneontology.org/GO.refgenome.shtml
RGP & Taxonomy checks
• Transferring GO annotation between orthologs
requires:
– determining orthologs – computational prediction
followed by manual curation
– developing ‘sanity’ checks to ensure transferred
functions make sense phylogenetically (eg. no lactating
chickens!)
Further taxon checking comments may be added here, or
contact the AgBase database.
AgBase Quality Checks & Releases
AgBase
Biocurators
‘sanity’ check
AgBase
biocuration
interface
‘sanity’
check
& GOC
QC
AgBase
database
‘sanity’ check
EBI GOA
Project
‘sanity’ check: checks
to ensure all appropriate
information is captured,
no obsolete GO:IDs are
used, etc.
GO analysis tools
Microarray developers
‘sanity’ check
& GOC QC
GO Consortium
database
UniProt db
QuickGO browser
GO analysis tools
Microarray developers
Public databases
AmiGO browser
GO analysis tools
Microarray developers
Comparing AgBase & EBI-GOA Annotations
14,000
computational
manual - sequence
Gene Products
annotated
12,000
manual - literature
10,000
8,000
Complementary to
EBI-GOA: Genbank
proteins not
represented in UniProt
& EST sequences on
arrays
6,000
4,000
2,000
0
AgBase
Chick
EBI-GOA AgBase
Chick
Cow
Project
EBI-GOA
Cow
Contribution to GO Literature Biocuration
AgBase
EBI GOA
Chicken
97.82%
EBI-IntAct
Roslin
HGNC
< 0.50%
UCL-Heart project
MGI
Reactome
Cow
88.78%
< 1.50%
INPUT:
functional genomics data (e.g. Microarray data)
ArrayIDer
GORetriever
gene products with GO annotations
gene products with NO GO
annotations
GOanna
BLAST output
Manual interpretation of GOanna
output
gene products with orthologs and GO
annotations
GAQ Score
GOanna2ga
comprehensive GO
annotation
gene products with NO orthologs OR with orthologs but NO GO
annotations
GA2GEO
(existing GO analysis
programs)
data visualization
Biocuration from
literature
biocurated annotations from literature or
specialist knowledge
GOModeler
GOSlimViewer
NO literature or specialist knowledge that can be used to
make GO annotations
Specific: user-defined, hypothesis-driven,
quantitative data presentation
must wait on experimental evidence or new electronic
inference
Generic: qualitative data presentation. Analysis can
only be changed if user has programming skills
2010 GO Training Opportunities
- on site training by request/interest
- webinar: notification via ANGENMAP & GO discussion groups
To request a workshop contact
Fiona McCarthy
[email protected]
OR
[email protected]
200
Workshop Surveys
GO training
Annual
strongly agree
uncertain
agree
disagree
strongly disagree
Cumulative
No. of people
150
I would recommend this workshop
100
I am confident I can get GO
questions answered
50
I am confident in using GO for
modeling
Topics were well explained
0
2007
2008
2009
Topics covered were relevant
Year workshops offered
2009 Workshop hosts:
ISU – Dr Susan Lamont
NCSU – Dr Hsiao-Ching Liu
10
20
30
40
% of respondents
50
60
Chicken Array Usage
Number of participants: 25
Number of arrays: 22
Number of votes: 41
Bovine array usage
Number of participants: 26
Number of arrays: 26
Number of votes: 42
Neuroendocrine
Arizona 20.7K
UD_Liver_3.2K
ARK-Genomics
UD 7.4K
Metabolic/Somatic
Agilent 44K array
UIUC 13.2K
Agilent 44k
Affymetrix
Bovine Total
Leukocyte cDNA
Affymetrix
UIUC 7,872element
Quality improvement Microarray annotations
• Most microarray analysis tools do not readily accept EST clone
names (abundantly on arrays).
• Manual re-annotation of microarrays is impracticable
• Retrieves the most recent accession mapping files from public
databases based on EST clone names or accessions and rapidly
generates database accessions.
•Fred Hutchinson Cancer Research Centre 13K chicken cDNA array
• structurally re-annotated 55% of the array; decreased non-chicken
functional annotations by 2 fold; identified 290 pseudogenes, 66 of
which were previously incorrectly annotated.
Zhou H, Lamont SJ:
Global gene expression profile after Salmonella enterica Serovar
enteritidis challenge in two F8 advanced intercross chicken lines.
Cytogenet Genome Res 2007;117:131-138 (DOI: 10.1159/000103173)
1. Increased the pathway coverage of several major immune
response pathways and provided more comprehensive
modelling of signalling pathways e.g. FAS :originally not
annotated but now pathways involving FAS identified.
2. Confirm and consolidate previous suggestions that CD3e,
IL-1β, and CCL5 differential expression involved in the
immune response to SE. Chicken-specific functional
annotation of these genes allowed identification of these
gene’s related pathways with statistical confidence.
3. Identified additional genes involved in major immune
pathways important in bacterial gut disease but not
identified in the original work e.g. tyrosine phosphatase
type IVA member 1 (PTP4A1); CD28; T-cell co-stimulator
(ICOS, CD287) and NK-lysin and associated pathway
genes.
Bacterial functional
genomic
responses to
structural
differences in
explosive
compounds.
KTR9 and
V. fischeri
proteomics
Quantifying re-annotation
Metrics
Granularity
Specificity
# previous annotations
# chicken annotations
# re-annotations
# human/mouse annotations
Quality
Gene Ontology Annotation Quality (GAQ) score
• DoD:
ReadsBobwhite
in annotated
regions + 20 kb
Quailgene
Toxicogenomics
radius
Number of GO annotations
Number of gene products with GO
• Reads in “RNAFAR” regions i.e. clustered
reads forming novel transcripts (these reads do
not belong to any gene model the reference set
and can either be assigned to neighboring
gene models, if they are within a specified
threshold
radius,
or assigned their
Total GAQ
score
Meanown
GAQ score
predicted transcript model.
• Repeats with > 10 alignments
• Reads overlapping annotated repeat regions
• Unmapped reads
• Other (regulatory, etc. do not include reads
discarded as poor quality).
35,000
4,500
30,000
4,000
3,500
25,000
3,000
20,000
2,500
15,000
2,000
1,500
10,000
1,000
5,000
500
0
0
Original
Reannotated
450,000
400,000
350,000
300,000
250,000
200,000
150,000
100,000
50,000
0
Original
Reannotated
Original
Reannotated
GO Cellular Component DAG
Differential Detergent Fractionation
DDF Fraction
1 2
3
4
2007. Non-electrophoretic differential detergent fractionation proteomics using frozen whole organs. Rapid Commun Mass
Spectrom 21:3905-9.
2007. Sequential detergent extraction prior to mass spectrometry analysis. Methods in Molecular Medicine: Proteomic analysis of
membrane proteins. Humana Press. 117 (1-4):278-87.
2005. Differential detergent fractionation for non-electrophoretic eukaryote cell proteomics. Journal of Proteome Research. 4 (2),
316-324.
Sub-cellular localization of pro-PCD proteins.
One mechanism controlling PCD is the release of “pro-death” proteins
mitochondria into the cytoplasm or nucleus.
B-cells
C
Stroma
CytC
Apaf1
M
AMID
N
EndoG
AIF
Smac
100000
mRNA
Neoplastic compared to Hyperplastic
lymphoma cells (%)
10000
1000
100
10
1
4
3
Protein
2
1
0
-1
-2
-3
Cancer Immunology and Immunotherapy, 2008. 57:1253-62
IL-18 distribution: it matters where proteins are
35
1 2
3
4
30
Hyperplastic Lymphocytes
25
20
15
10
5
Extracellular
0
1
2
3
4
DDF Fraction
1
2
3
4
DDF Fraction
80
70
Neoplastic Lymphocytes
(T-reg)
60
50
40
30
Shack et al., Cancer
Immunology and
Immunotherapy, 2008.
57:1253-62
20
10
0
Nuclear
Translation to clinical research
Bindu Nanduri
Pig
Total mRNA and protein expression was measured from
quadruplicate samples of control, electroscalple and harmonic
scalple-treated tissue.
Differentially-expressed mRNA’s and proteins identified using
Monte-Carlo resampling1.
Using network and pathway analysis as well as Gene
Ontology-based hypothesis testing, differences in specific
phyisological processes between electroscalple and harmonic
scalple-treated tissue were quantified and reported as net
effects.
(1) Nanduri, B., P. Shah, M. Ramkumar, E. A. Allen, E. Swaitlo, S. C. Burgess*, and M. L. Lawrence*. 2008.
Quantitative analysis of Streptococcus Pneumoniae TIGR4 response to in vitro iron restriction by 2-D LC
ESI MS/MS. Proteomics 8, 2104-14.
Proportional distribution of protein functions
differentially-expressed by Electro and Harmonic Scalpel
Electroscalpel
HYPOTHESIS TERMS
Harmonic Scalpel
immunity (primarily innate)
inflammation
Wound Healing
Lipid metabolism
response to Thermal Injury
angiogenesis
hemorrhage
Total differentially-expressed
proteins: 509
Total differentially-expressed
proteins: 433
Net functional distribution of differentially-expressed proteins
Harmonic Scalpel
Electroscalpel
hemorrhage
sensory response to pain
angiogenesis
response to thermal injury
Lipid metabolism
Wound healing
classical inflammation
(heat, redness, swelling, pain, loss of function)
immunity (primarily innate)
8
6
4
2
0
Relative bias
2
4
6