Transcript GO annotations for ordered lists of genes

GOSt
a Gene Ontology mining tool
Jüri Reimand
Overview
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Introduction, bioinformatics
Gene Ontology (GO)
GOSt, a Gene Ontology mining tool
Statistics and thresholds
Ordered gene lists
Extending GO
Introduction
• Bioinformatics
– Analysis of experimental data
• Genes encode proteins
– Proteins : building blocks of living organisms
– Gene expression : protein production from
genetic code
• Microarray experiments measure gene
expression
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Thousands of genes simultaneously
Expression levels over time
Different biological conditions
Comparison of healthy and diseased cells
cluster similar
profiles
measures
over time
Introduction
• Biological experiments
give large amounts of data
• Groups of similar genes:
– top “most active” genes
– similar expression profiles over time
“steroid metabolism”
“biosynthesis”
“iron ion binding”
• Many genes have some available annotations
– Previous knowledge from databases
• How to describe the group as a whole?
– What are the common features?
– Which features are significantly overrepresented?
Gene Ontology (GO)
• GO - Directed Acyclic Graph (DAG)
– Vertices: terms
– Edges: relations between general and specific terms
• Hierarchically structured vocabulary
– 3 DAGs: processes, components, functions
• Annotations to vocabulary terms
– Association between
a gene g and a property t (GO term t)
– Based on biological discoveries
– Genes of many genomes are annotated to GO
• Annotation sets : for a fixed organism
– All genes associated with GO term t
GO example
• Graph fragment
with some terms
related to organ
development
• Vocabulary is
general to living
organisms
• Gene
annotations
organismspecific
• True Path Rule
hierarchical
annotations
ENSG00000163217
ENSG00000161202
GO example
• Graph fragment
with some terms
related to organ
development
• Vocabulary is
general to living
organisms
• Gene
annotations
organismspecific
• True Path Rule
hierarchical
annotations
ENSG00000163217
ENSG00000161202
GOSt – Gene Ontology Statistics
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GO annotations to groups of genes
Statistical significance of results
Thresholds for distinguishing significant results
Analysing ordered lists of genes
Visualisation methods, WWW interface
Command line toolset for large-scale analysis
GOSt example
45 mouse
genes
338 GO
Evidence
codes
Genes
P-value
GO
terms
Annotations to gene groups
Gq
Query
GO Term
e.g. Gt
heart
development
• Result: term t matches query Q
Statistical significance
• Is intersection QT significant?
• Fisher's one-tailed test
– Cumulative hypergeometric probability
– Get observed or more genes in intersection QT
– P ( pick k white balls out of K white and N-K black balls )
• Multiple testing
– Every query results in a number of p-values
– Matching GO terms are not independent
– Increased rate of false positive matches
• Which p-values are significant?
Experimental thresholds
• Simulation experiment
– Fix some gene query size k
– Repeat 1000 times:
• Generate synthetic query Q with k elements :
random subset of organism's genes
• Observe best p-value p for query Q
• Store p-value, p --> P
– Choose p', 50th smallest p-value from P
– Threshold p' – top 5% of p-values for random queries
of size k
• Calculate for query lengths k = [1,1000]
• Compare with standard multiple testing
corrections
– Bonferroni (1936), Benjamini-Hochberg (1995)
Analytical thresholds
• Analytical approach to simulated thresholds
– Fix gene query size k
– Observe all sizes and frequencies of GO annotation
sets T
– Presume events with different T independent
– Observe possible p-values p with query of k elements
– Always correct p by constant c=0.97 (set
dependencies!)
– Find such threshold p', that gives p ~= 0.95
• Repeat for query lengths k = [1,1000]
Significance thresholds
Significance thresholds
Significance thresholds
Significance thresholds
Ordered lists of genes
• Gene groups may be ordered
– Interesting gene and few most
similar genes
– Top “most active” genes
– Increasing distance from cluster
centre
• Top of the list, but how many?
– Compare list with GO term
– Which portion gives best p-value?
– Peak significance of ordered query
GOSt algorithms
• Unordered query
– Intersections with all annotation sets T
• Exhaustive algorithm for ordered queries:
– intersections with all Qi and annotation sets T
• Approximate algorithm for ordered queries:
– for every annotation set T, view only list portions
that give local p-value extremes
• local best p : list ends with matching gene
• local worst p : list ends just before matching
gene
Example: Ordered list analysis
Peak significance
at ordered list of
28 genes
p-value
query length
List of genes, and matches for “Biosynthesis of steroids”
Evidence
codes
Genes
P-value
Ordered
list query
GO
categories
Algorithm speed comparison
24 sec
2.8 sec
GOSt features
• Command line interface (C/C++ and Perl)
• Graphical user interface in web
http://bioinf.ebc.ee/GOST
– SWOG (Graphics language, Jaanus Hansen 2005)
• Data for multiple organisms
– yeast, chicken, cow, mouse, rat, human...
• Wrappers for parallel applications (GRID, MPI)
• Pipelines for gene expression data analysis
Extending GO ( i )
• Pathway – a network of interacting genes
and proteins
– metabolism pathways, disease pathways, ..
• Include pathway data to GO vocabulary
– KEGG Pathway database
– pathways as vocabulary terms
– related genes as annotations to terms
• KEGG terms independent of GO vocabulary
GO
GO:0003674
molecular_function
GO:0005575
cellular_component
GO:0008150
biological_process
KEGG:00000 KEGG pathways
KEGG:05010 - Alzheimer's disease
Extending GO ( ii )
• Gene expression started by transcription
factors (TF)
• TFs bind to certain patterns in DNA
– Transcription Factor Binding Sites (TFBS)
– Often found in regions close to gene (1k bp)
• Include TFBS data from TRANSFAC
– Patterns (putative TFBS) as vocabulary terms
– annotations to genes near patterns
Transcription factor
ATATAATAAAGATGAGGCGAATATAAATATACCGGCCCTTAGCGCGAAGCAATTCATCATATAAGCGAGAGAGGCCAATATGCAATCTTCGACAGCAT
TF binding site
gene
TRANSFAC motifs
• Motifs added in a hierarchy
– according to PWM score
– 5 levels:
• near_threshold
• ...
• near_MAX_score
depth in
hierarchy
• Work in progress
TF:M00431_4
TF:M00431_3
TF:M00431_2
TF:M00431_1
TF:M00431_0
TF:M00328_4
TF:M00328_3
TF:M00328_2
TF:M00000
TTTSGCGS:4
TTTSGCGS:3
TTTSGCGS:2
TTTSGCGS:1
TTTSGCGS:0
NCNNTNNTGCRTGANNNN:4
NCNNTNNTGCRTGANNNN:3
NCNNTNNTGCRTGANNNN:2
TRANSFAC motifs
– Hedi Peterson
GO
GO:0003674
molecular_function
GO:0005575
cellular_component
GO:0008150
biological_process
KEGG:00000 KEGG pathways
Summary
• We investigated means for finding GO annotations to
groups of genes, and statistical methods for determining
significance of results.
• We combined GO vocabulary with various types of
biological data, such as KEGG pathways and TRANSFAC
regulatory elements.
• We proposed analytical thresholds for distinguishing
significant results from structured and partly dependent
GO annotations, and verified thresholds with simulation
experiments.
• We proposed a novel concept of analyzing GO
annotations for ordered lists of genes, and implemented
fast algorithms for the purpose.
• The practical result of our work is GOSt, a GO mining tool.
Command line interface is suitable for large-scale automatic
analysis, while graphical web interface enables highly
visualized and interactive analysis.
Sneak preview
• GO analysis of
hierarchical clustering
tree
– Cluster genes according
to expression similarity
and ..
– .. “Wrap up” nodes that
show no significant
annotations in GO
• Work in progress
– Meelis Kull
– Darja Krushevskaja
Acknowledgments
Jaak Vilo
BIIT group
Hedi Peterson
Meelis Kull
Jaanus Hansen
Priit Adler
Ilja Livenson
Raivo Kolde
Konstantin Tretjakov
Pavlos Pavlidis
Asko Tiidumaa
Darja Krushevskaja
FunGenES Consortium