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Spotted Microarray Workshop. Computational Approaches for Understanding Biological Significance of Microarray Data. Liangjiang (LJ) Wang [email protected] KSU Bioinformatics Center, Biology Division June, 2005. Sample acquisition. RNA: purification, labeling. Data acquisition.

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Computational Approaches for Understanding Biological Significance of Microarray Data

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Computational approaches for understanding biological significance of microarray data

Spotted Microarray Workshop

Computational Approaches for Understanding Biological Significance of Microarray Data

Liangjiang (LJ) Wang

[email protected]

KSU Bioinformatics Center, Biology Division

June, 2005


Computational approaches for understanding biological significance of microarray data

Sample

acquisition

RNA: purification, labeling

Data

acquisition

Microarray: hybridization,

washing, image analysis

Data

analysis

Data: preprocessing, statistical inference, clustering analysis, . . .

(Hypothesis generation)

Hypothesis

testing

Biological insight


Computational approaches for understanding biological significance of microarray data

Beyond Clustering Analysis

  • Using GO to understand significant functional associations of a gene cluster.

  • Mapping gene expression data onto biochemical pathways.

  • Discovering regulatory elements shared by the promoters of co-expressed genes.

  • Inferring gene regulatory networks.


Computational approaches for understanding biological significance of microarray data

Genes in a Cluster May Be Co-Regulated

Microarrays measure steady-state levels of mRNAs.

Multi-level regulation

Transcriptional

regulation

RNA synthesis

RNA processing

RNA turnover

Posttranscriptional

regulation

Translational

regulation

Protein synthesis

Protein modification

and degradation

Posttranslational

regulation


The gene ontology go

The Gene Ontology (GO)

  • Providing controlled vocabularies for describing gene products in the domain of molecular biology.

  • Enabling a common understanding of model organisms and between databases.

  • Consisted of three unlinked hierarchies:

    • Molecular function: elemental activity/task (What)(e.g., DNA-binding, polymerase, transcription factor)

    • Biological process: goal or objective (why)(e.g., mitosis, DNA replication, cell cycle control)

    • Cellular component: location or complex (where) (e.g., nucleus, ribosome, pre-replication complex)

  • Available at http://www.geneontology.org/.


Example of gene ontology hierarchy

Example of Gene Ontology Hierarchy

Biological process

(GO:0008150)

i

i

i

i

Development

(GO:0007275)

Cellular process

(GO:0009987)

Physiological

(GO:0007582)

Behavior

(GO:0007610)

i

i

i

i

i

… … …

… …

… … …

Communication

(GO:0007154)

Cell death

(GO:0008219)

Cell growth

(GO:0008151)

P

i

… … …

… … … …

Cell aging

(GO:0007569)

Programmed

(GO:0012501)

P

i

… … …

Induction

(GO:0012502)

Apoptosis

(GO:0006915)

is a

i

i

i

… … …

HS response

(GO:0009626)

Autophagic cell death

(GO:0048102)

part of

P


Gene annotation using go terms

Gene Annotation Using GO Terms

  • Association of GO terms with gene products based on evidence from literature reference or computational analysis.

  • The creation of GO and the association of GO terms with gene products (gene annotation) are two independent operations.

  • A gene can be associated with one or more GO terms (gene categories), and one category normally has many genes (many-to-many relationship between genes and GO terms).


Example of go annotation

Example of GO Annotation

(The Gene Ontology Consortium, 2000)


Genes from the same biological process tend to be co expressed

Genes from the Same Biological Process Tend to Be Co-Expressed

Gene Names

Bio Process

(The Gene Ontology Consortium, 2000)


Computational approaches for understanding biological significance of microarray data

How to Assess Overrepresentation of a GO Term?

Genes on an array:

Total number of genes (N): 2,285

Number of genes – cell cycle (R): 161

Genes in a cluster:

Number of genes in the cluster (n): 147

Number of genes – cell cycle (r): 25

Is the GO term (i.e., cell cycle) significantly overrepresented in the cluster?


Computational approaches for understanding biological significance of microarray data

Using the Z-Statistic

  • Assume the hypergeometric distribution.

  • The z-score:

  • For the example:


Computational approaches for understanding biological significance of microarray data

Using the Fisher Exact Test

  • Contingency table:

  • Probability of finding a genes of the GO class in the cluster:

  • The p value:

Cluster

in out

a = r

b = R - r

c = n - r

d = N - R - n + r

in

out

GO

class


Computational approaches for understanding biological significance of microarray data

MAPPFinder

  • A tool for mapping gene expression data to the GO hierarchies.

  • Part of the free software package GenMAPP.

  • Available at http://www.genmapp.org/.

(Doniger et al., 2003)


Computational approaches for understanding biological significance of microarray data

MAPPFinder Sample Output

(Doniger et al., 2003)


Computational approaches for understanding biological significance of microarray data

GoMiner

  • A client-server application using Java (data on the server side).

  • Available at http://discover.nci.nih.gov/gominer/.

(Zeeberg et al., 2003)


Computational approaches for understanding biological significance of microarray data

p GO # genes

(Genes linked to poor breast cancer outcome)

Onto-Express

  • A web application for GO-based microarray data analysis (http://vortex.cs.wayne.edu/Projects.html).

  • The input to Onto-Express is a list of Affymetrix probe IDs, GenBank sequence accessions or UniGene cluster IDs.

  • Part of the integrated Onto-Tools, including:

    • Onto-Compare: compare commercial arrays.

    • Onto-Design: help array design (probe selection).

    • Onto-Translate: provide mapping of different IDs.


Pathway visualization of microarray data

Pathway Visualization of Microarray Data

Yeast metabolic pathways


Pathway tools software

Pathway Tools Software

  • A software package for the creation, curation, querying and visualization of metabolic pathways.

  • The Pathway Tools Omics Viewer is used to paint data from high-throughput experiments onto the metabolic pathway diagram for an organism.

  • For microarray data, each reaction line is color-coded according to the gene expression level of the enzyme that catalyzes the reaction step.

  • The Omics Viewer can also be used to display an animated time series.

  • The Pathway Tools software is freely available to academics (http://biocyc.org/download.shtml).


Web access to the omics viewer

Web Access to the Omics Viewer

  • Yeast biochemical pathways at http://pathway.yeastgenome.org/biocyc/.

  • Arabidopsis thaliana biochemical pathways at http://www.arabidopsis.org/tools/aracyc/.

  • For other species (including E. coli and human), http://biocyc.org/ECOLI/expression.html.


Other tools for pathway visualization

Other Tools for Pathway Visualization

  • KEGG (Kyoto Encyclopedia of Genes and Genomes at http://www.genome.jp/kegg/kegg2.html):

    • A database of curated metabolic and signaling pathways (mostly reference maps).

    • KEGG Expression is for mapping microarray data to pathways and genomes (static view).

  • GenMAPP (http://www.genmapp.org/):

    • A software package for viewing and analyzing microarray data in the context of pathways.

    • Allowing users to modify pathways or design new pathways for their own use.

    • MAPP files can be used for data exchange.


Computational approaches for understanding biological significance of microarray data

Transcriptional Regulation

  • Cells respond to various stimuli by regulating the expression of particular genes.

  • Transcription factors regulate gene expression by binding to specific

  • DNA sequence motifs.

MyoD HLH Dimer

  • Transcription factor binding sites are often short (5 - 25 bases) and degenerate DNA motifs.

  • Co-expressed genes may have common regulatory motifs in their promoters.

H2

H2

L

L

DNA

H1

H1

CAACTGAC


Computational approaches for understanding biological significance of microarray data

AGTCC

AGTCC

AGTAC

AGTAC

AGTGG

AGTGG

AACTT

AAGTT

How to Represent a Promoter Motif?

  • Multiple sequence alignment.

  • Consensus: e.g., TATAAAA (the TATA box).

  • Position Weight Matrices (PWM): relative frequencies of nucleotides at different positions.

  • Sequence logo: information content of each site (a measure of tolerance for substitution).


Computational approaches for understanding biological significance of microarray data

Discovery of Shared Promoter Motifs

Co-expressed

gene cluster

Promoter retrieval from

SCPD, GBrowser, etc.

Promoters

Motif discovery using

MEME, AlignACE, etc.

Shared

motifs

Factor identification in

TransFac, TESS, etc.

Transcription

factors

Hypothesis testing


Computational approaches for understanding biological significance of microarray data

Retrieval of Promoter Sequences

  • Yeast promoters may be retrieved from SCPD (http://cgsigma.cshl.org/jian/HTML/retrieveseq.html) using gene or ORF names (batch retrieval).

  • For model organisms, promoter sequences of RefSeq genesmay be downloaded from the UCSC Genome Browser site at: http://hgdownload.cse.ucsc.edu/downloads.html.

  • Gene prediction tools predict transcription start sites (TSS). The sequence upstream of TSS may be used as the promoter.


Computational approaches for understanding biological significance of microarray data

Statistical Overrepresentation of Motifs

Enumerate all the possible motif patterns with ambiguous characters.

e.g., CWTNC, CRTGTW, YCGGAYRRAWG, …… over {A, C, G, T, R, Y, S, W, M, K, V, H, D, B, N}

Count the occurrences of all the motif patterns in the promoter sequences.

e.g., z-score:

Compute statistical significance based on the background distribution.

(The method has been used to discover novel transcription factor binding sites in yeast)


Computational approaches for understanding biological significance of microarray data

MEME: Multiple EM for Motif Elicitation

  • A widely used motif discovery tool available at http://meme.sdsc.edu/meme/website/meme.html.

  • Based on the Expectation Maximization (EM) algorithm with several extensions.

  • A sequence motif is represented as a Position Weight Matrix (PWM).


Computational approaches for understanding biological significance of microarray data

Gibbs Sampling Methods

  • For motif discovery, Gibbs sampling can be viewed as a stochastic analog of EM.

  • Gibbs sampling may be less susceptible to local minima than EM.

  • Gibbs Motif Sampler at http://bayesweb.wadsworth.org/gibbs/gibbs.html.

  • AlignACE at http://atlas.med.harvard.edu/.


Computational approaches for understanding biological significance of microarray data

Search for Transcription Factors

  • TransFac(http://www.gene-regulation.com/):

    • A database on eukaryotic transcription factors and their DNA binding sites (PWMs).

    • Provide classification of transcription factors.

    • MatchTM uses the PWMs to search for potential transcription factor binding sites.

  • TESS (Transcription Element Search System athttp://www.cbil.upenn.edu/cgi-bin/tess/tess?RQ=WELCOME):

    • A web tool for predicting TF binding sites.

    • Using PWMs from TransFac and others.


Computational approaches for understanding biological significance of microarray data

(Segal et al., 2003. Bioinformatics, 19:i273-i282)

Inferring Gene Regulatory Networks

  • Integrate gene expression and promoter data into a unified model.

  • Use a probabilistic graphical model trained using the EM algorithm.

  • Validate the model using GO and protein complex information.


Computational approaches for understanding biological significance of microarray data

Yeast Respiration and Carbon Regulation

(Segal et al., 2003.

Nature Genetics,

34:166-176)


Computational approaches for understanding biological significance of microarray data

Summary

  • “Statistical significance is fine, but biological significance is better” (Baxevanis and Ouellette, 2005).

  • Gene Ontology (GO) can be used to assess significant functional associations of a gene cluster or a list of significant genes.

  • Pathway visualization allows one to interpret microarray results in a pathway context.

  • Motif discovery tools can be used to identify common regulatory elements shared by the promoters of co-expressed genes.


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