Shane C Burgess

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.

Gene Ontology Consortium members

Dr Fiona McCarthy
Dr Susan Bridges
Dr Nan Wang Cathy Grisham
Dr Teresia Buza
Philippe Chouvarine Lakshmi Pillai
Dr Divya Pedinti

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. Complexity
1.
Sequence itself and from all it’s compatriots and assorted microbes
2. SNPs
3.
Transcripts (all of them…don’t forget alternative splicing, starts)
4. CNVs
5. Epigenetic changes to DNA
6. Proteome (expression, epigenetics, PTMs, location, flux, enzyme kinetics)
7. Metabolites
8. Phenotypes
9. 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

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

International Society for Animal
Genetics from 26th – 30th July 2010, Edinburgh
• 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

• 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

• 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.

• 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

chromosome
Peptide nucleotide sequence
Map peptide nucleotide sequence to chromosome

Search against protein
Database
Peptide matches
Biological Sample
Trypsin Digestion
LC ESI-MS/MS Data
Search against genome translated in 6 reading frame
Peptide matches
Confirm predicted proteincoding gene
Generate ePST (expressed PeptideSequence Tags) from peptides matching genome only
Correction / validation of genome annotation
Novel protein-coding gene

chromosome
Peptide nucleotide sequence Stop codon
Locate first downstream in-frame stop codon or canonical splice junction

chromosome
Peptide nucleotide sequence Stop codon
Locate upstream canonical splice junction or in-frame stop

chromosome
Peptide nucleotide sequence Stop codon
Start codon
Find 1 st start codon between in-frame stop and peptide

chromosome
Use splice junction or in-frame start as beginning of ePST

chromosome
ePST coding nucleotide sequence
Translate
Expressed Peptide Sequence Tag (ePST) amino acid sequence

No.
25000
20000
No. x 10 6
18
16
14
15000
10000
12
10
8
6
5000
4
2
0
‘00 ‘01 ‘02 ‘03 ‘04 ‘05 ‘06 ‘07 ‘08 ‘09
YEAR
0
70 75 80 85 90 95 00 05

Ontologies
GO Cellular Component
GO Biological Process
GO Molecular Function
BRENDA
Canonical and other Networks
Pathway Studio 5.0
Ingenuity Pathway Analyses
Cytoscape
Interactome Databases
Functional Understanding

Biological interpretation
Gene Ontology Network Modeling
Derived
Implied
Physiology
(= Cellular Component + Biological Process + Molecular
Function)

“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.

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

• 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

• 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
Biocurators
‘sanity’ check
AgBase biocuration interface
‘sanity’ check
& GOC
QC
AgBase database
‘sanity’ check
GO analysis tools
Microarray developers
‘sanity’ check: checks to ensure all appropriate information is captured, no obsolete GO:IDs are used, etc.
EBI GOA
Project
‘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
12,000
10,000
8,000
6,000
4,000
2,000
0
AgBase EBI-GOA AgBase EBI-GOA
Chick Chick Cow Cow
Project computational manual - sequence manual - literature
Complementary to
EBI-GOA : Genbank proteins not represented in UniProt
& EST sequences on arrays

Contribution to GO Literature Biocuration
Chicken
97.82%
AgBase EBI GOA
Cow
< 0.50%
EBI-IntAct
Roslin
HGNC
UCL-Heart project
MGI
Reactome
88.78%
< 1.50%

INPUT: functional genomics data (e.g. Microarray data)
ArrayIDer
GORetriever gene products with NO GO annotations gene products with GO annotations
GOanna
BLAST output
Manual interpretation of GOanna output gene products with orthologs and GO annotations
GOanna2ga
GAQ Score comprehensive GO annotation
GA2GEO gene products with NO orthologs OR with orthologs but NO GO annotations data visualization
(existing GO analysis programs) biocurated annotations from literature or specialist knowledge
Biocuration from literature
NO literature or specialist knowledge that can be used to make GO annotations must wait on experimental evidence or new electronic inference
GOModeler GOSlimViewer
Specific: user-defined, hypothesis-driven, quantitative data presentation
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 fmccarthy@cvm.msstate.edu
OR agbase@cse.msstate.edu

200
150
100
50
GO training
Annual
Cumulative
0
2007 2008
Year workshops offered
2009
2009 Workshop hosts:
ISU – Dr Susan Lamont
NCSU – Dr Hsiao-Ching Liu
Workshop Surveys strongly agree agree uncertain disagree strongly disagree
I would recommend this workshop
I am confident I can get GO questions answered
I am confident in using GO for modeling
Topics were well explained
Topics covered were relevant
10 20 30 40 50 60
% of respondents

Chicken Array Usage
Number of participants: 25
Number of arrays: 22
Number of votes: 41
Neuroendocrine
Arizona 20.7K
UD_Liver_3.2K
UD 7.4K
Metabolic/Somatic
Agilent 44K array
Bovine array usage
Number of participants: 26
Number of arrays: 26
Number of votes: 42
ARK-Genomics
UIUC 13.2K
Affymetrix
Agilent 44k
Bovine Total
Leukocyte cDNA
UIUC 7,872element
Affymetrix

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 CD3 e
,
IL-1 β, 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
# previous annotations
Specificity
# chicken annotations
# re-annotations # human/mouse annotations
Quality
Gene Ontology Annotation Quality (GAQ) score

Number of gene products with GO
• Reads in “RNAFAR” regions i.e. clustered
30 000
4 000
3 500 reads forming novel transcripts (these reads do
20 000 2 500
10 000
2 000
1 500
1 000
500
0 0 gene models, if they are within a specified
Mean GAQ score
450 000 predicted transcript model.
400 000
350 000 • Repeats with > 10 alignments
250 000 • Reads overlapping annotated repeat regions
150 000
Reannotated discarded as poor quality).

GO Cellular Component DAG

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 Stroma
C
CytC
M Apaf1
AMID
N
EndoG
AIF
Smac

100000
10000
1000
100
10
1
0
-1
-2
-3
4
3
2
1 mRNA
Protein
Cancer Immunology and Immunotherapy, 2008. 57:1253-62

IL-18 distribution: it matters where proteins are
Hyperplastic Lymphocytes
Extracellular
20
15
10
5
0
35
30
25
1
1 2 3 4
2 3
DDF Fraction
4 Nuclear
Neoplastic Lymphocytes
(T-reg)
Shack et al., Cancer
Immunology and
Immunotherapy, 2008.
57:1253-62
80
70
60
50
40
30
20
10
0
1 2 3
DDF Fraction
4

Bindu Nanduri
Translation to clinical research
Pig
Total mRNA and protein expression was measured from quadruplicate samples of control, electroscalple and harmonic scalple-treated tissue.
Differentiallyexpressed mRNA’s and proteins identified using
Monte-Carlo resampling 1 .
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

8
Net functional distribution of differentially-expressed proteins
Electroscalpel
Harmonic Scalpel hemorrhage sensory response to pain
6 angiogenesis response to thermal injury
4
Lipid metabolism
Wound healing classical inflammation
(heat, redness, swelling, pain, loss of function) immunity (primarily innate)
2 4 6 2 0
Relative bias