bioinformatics and genome annotation

Bioinformatics and Genome Annotation

Shane C Burgess

http://www.agbase.msstate.edu/

NIH WORKING DEFINITION OF BIOINFORMATICS AND

COMPUTATIONAL BIOLOGYJuly 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 Nan Wang

Dr Susan Bridges

Dr Divya Pedinti

Dr Fiona McCarthy

Dr Teresia Buza

Philippe Chouvarine

Cathy Grisham

Lakshmi Pillai

Source: Richard Gibbs, Baylor College of Medicine

and biocomputing becomes more of an issue.

Sequencing is getting cheaperCost of human or similar sized genome

A. Complexity1. Sequence itself and from all it’s compatriots and assorted microbes2. SNPs3. Transcripts (all of them…don’t forget alternative splicing, starts)4. CNVs5. Epigenetic changes to DNA6. Proteome (expression, epigenetics, PTMs, location, flux, enzyme kinetics)7. Metabolites8. Phenotypes9. 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

Putting Genomes in the Cloud. Making data sharing faster, easier and more scalable.By M. May, May 18, 2010.

Storage costsA. 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

10 Gigabits (Gb)/second

Annotation: Nomenclature, Structural & Functional

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

Nomenclature

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.

Livestock Gene Nomenclature:Jim Reecy et al., International Society for Animal Genetics from 26th – 30th July 2010, Edinburgh

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

chromosome

Map peptide nucleotide sequence to chromosome

Peptide nucleotide sequence

Biological Sample

Trypsin Digestion

LC ESI-MS/MS Data

Search against genome translated in 6 reading frame

Search against protein Database

Peptide matches Peptide matches

Correction / validation of genome annotation

Novel protein-coding gene

Generate ePST (expressed PeptideSequence Tags) from peptides matching genome only

Confirm predicted protein-coding gene


chromosome


Locate first downstream in-frame stop codon or canonical splice junction

Stop codon


chromosome

Locate upstream canonical splice junction or in-frame stop


Stop codon


chromosome

Find 1st start codon between in-frame stop and peptide


Stop codon

Start codon


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

Functional annotation

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OntologiesCanonical and other Networks

GO Cellular Component

GO Biological Process

GO Molecular Function

BRENDA

Pathway Studio 5.0

Ingenuity Pathway Analyses

Cytoscape

Interactome Databases

Functional Understanding

Physiology (= Cellular Component + Biological Process + Molecular

Function)

Gene Ontology Network Modeling

Biological interpretation

ImpliedDerived

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).

“GO Slim”

In contrast, we need to use the deep granular information rich data suitable for 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.

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 Biocurators

AgBasebiocuration

interface

AgBase database

‘sanity’ check

‘sanity’ check& GOC QC

EBI GOA Project

GO Consortiumdatabase

‘sanity’ check& GOC

QC ‘sanity’ check

GO analysis tools Microarray developers

UniProt dbQuickGO browserGO analysis toolsMicroarray developers

Public databases AmiGO browserGO analysis toolsMicroarray developers

AgBase Quality Checks & Releases

‘sanity’ check: checks to ensure all appropriate information is captured, no obsolete GO:IDs are used, etc.

Comparing AgBase & EBI-GOA Annotations

computational

manual - sequence

manual - literature

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Complementary to EBI-GOA: Genbank proteins not represented in UniProt & EST sequences on arrays

AgBase EBI GOA

EBI-IntAct

Roslin

HGNC

UCL-Heart project

MGI

Reactome

Contribution to GO Literature Biocuration

Chicken

Cow

< 0.50%

< 1.50%

97.82%

88.78%

INPUT: functional genomics data (e.g. Microarray data)

GOanna

Biocuration from literature

Manual interpretation of GOanna output

gene products with NO orthologs OR with orthologs but NO GO annotations

GOModeler

Generic: qualitative data presentation. Analysis can only be changed if user has programming skills

Specific: user-defined, hypothesis-driven, quantitative data presentation

must wait on experimental evidence or new electronic inference

NO literature or specialist knowledge that can be used to make GO annotations

gene products with orthologs and GO annotations

gene products with NO GO annotations

gene products with GO annotations

BLAST output

biocurated annotations from literature or specialist knowledge

GOSlimViewer

GORetriever

data visualization

ArrayIDer

GOanna2ga

comprehensive GO annotation

(existing GO analysis programs)

GA2GEO

GAQ Score

To request a workshop contactFiona McCarthy

[email protected]

[email protected]

2010 GO Training Opportunities

- on site training by request/interest - webinar: notification via ANGENMAP & GO discussion groups

GO trainingWorkshop Surveys

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Topics covered were relevant

Topics were well explained

I am confident in using GO for modeling

I am confident I can get GO questions answered

I would recommend this workshop

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2009 Workshop hosts:ISU – Dr Susan LamontNCSU – Dr Hsiao-Ching Liu

ARK-Genomics

AffymetrixAgilent 44K array

UD 7.4K Metabolic/Somatic

UD_Liver_3.2K

Arizona 20.7K

Neuroendocrine

Chicken Array Usage

Number of participants: 25Number of arrays: 22Number of votes: 41

Bovine array usageNumber of participants: 26Number of arrays: 26Number of votes: 42

UIUC 13.2K

Affymetrix

UIUC 7,872-element

Bovine Total Leukocyte cDNA

Agilent 44k

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, 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

Mean GAQ score

DoD: Bobwhite Quail Toxicogenomics• Reads in annotated gene regions + 20 kb radius

• 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 own 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).

GO Cellular Component DAG

Differential Detergent Fractionation

2 3 41

DDF Fraction

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.

CytC

B-cells Stroma

Apaf1

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Cancer Immunology and Immunotherapy, 2008. 57:1253-62

Shack et al., Cancer Immunology and Immunotherapy, 2008. 57:1253-62

IL-18 distribution: it matters where proteins are

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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.

Translation to clinical research

(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.

Bindu Nanduri

hemorrhage

Proportional distribution of protein functions differentially-expressed by Electro and Harmonic Scalpel

Total differentially-expressed proteins: 509

Electroscalpel

Total differentially-expressed proteins: 433

Harmonic Scalpel

immunity (primarily innate)

inflammation

Wound Healing

Lipid metabolism

response to Thermal Injury

angiogenesis

HYPOTHESIS TERMS

8 6 4 2 0 2 4 6

immunity (primarily innate)

classical inflammation(heat, redness, swelling, pain, loss of function)

Wound healing

Lipid metabolism

response to thermal injury

angiogenesis

sensory response to pain

hemorrhage

Relative bias

Net functional distribution of differentially-expressed proteins

Electroscalpel Harmonic Scalpel

bioinformatics and genome annotation

Documents