Transcript INTERPRO An integrated resource of protein families

Lecture Outline
• Introduction
• Data mining sources:
– GO, InterPro, KEGG, UniProt
• Tools to do the data mining:
– FatiGO
– FatiWISE
Data mining Microarray results
• Microarray experiments are done to answer a
biological question
• Results generate sets of numbers (intensities)
which are then clustered to find data points of
interest
• These themselves don’t necessarily answer the
research question, these need to be converted to
biological information first
Purpose of data mining
• Validation of results –understanding why
these genes are grouped together
• Using biological information to find
significant associations of biological terms
to sets of genes
• Understanding of the roles of the genes at
the molecular level
Data mining (1)
Add gene identifiers
-AB02387
-SB07593
-AA00498
-AC008742
-AB083121
Data mining (2)
Add gene descriptions
-RNA polymerase
-Glycosyl hydrolase
-Phosphofructokinase
-Transcripiton factor
-Glucose transporter
-AB02387
-SB07593
-AA00498
-AC008742
-AB083121
Data mining (3)
Add GO terms
-GO0003456
-GO0006783
-GO0142291
-GO0054198
-GO0000234
-RNA polymerase
-Glycosyl hydrolase
-Phosphofructokinase
-Transcripiton factor
-Glucose transporter
-AB02387
-SB07593
-AA00498
-AC008742
-AB083121
Data mining (4)
-GO0003456
-GO0006783
-GO0142291
-GO0054198
-GO0000234
-RNA polymerase
-Glycosyl hydrolase
-Phosphofructokinase
-Transcripiton factor
-Glucose transporter
Add functional annotation
-AB02387
-SB07593
-AA00498
-AC008742
-AB083121
Data mining (5)
-GO0003456
-GO0006783
-GO0142291
-GO0054198
-GO0000234
-RNA polymerase
-Glycosyl hydrolase
-Phosphofructokinase
-Transcripiton factor
-Glucose transporter
-AB02387
-SB07593
-AA00498
-AC008742
-AB083121
Store results
in database
Map onto pathways
Sources of biological information
• Free text: e.g. Medline
– Using text processing tools
• Curated repositories: e.g. GO, KEGG, UniProt,
InterPro etc.
– Using data mining
– Using tools e.g. FatiGO and FatiWISE
Free text mining
• Advantages:
– Vast amounts of data
– Many associated terms for each gene
• Disadvantages:
–
–
–
–
Synonyms and acronyms
Context information
Irrelevant terms
Need to divide into entities and relationships to
structure text
Example of problems
The Sch9 protein kinase regulates Hsp90dependent signal transduction activity in the
budding yeast Saccharomyces cerevisiae. This
interaction was suppressed by decreased signaling
through the protein kinase A (PKA) signal
transduction pathway.
Text is unstructured –needs to be divided into
entities and relationships
Example of problems
Protein
Pathway
Verb
The Sch9 protein kinase regulates Hsp90dependent signal transduction activity in the Organism
budding yeast Saccharomyces cerevisiae. This
interaction was suppressed by decreased signaling
through the protein kinase A (PKA) signal
transduction pathway.
Acronym –could be
Negative
term used
Some problems overcome using stats & better detection
of entities and relationships
used elsewhere for
different gene
Curated repositories
•
•
•
•
•
These have reliable annotation
Annotation is standardised
They are usually well structured
However, they usually have less annotation
Examples: GenBank, GO (FatiGO),
UniProt, InterPro, KEGG (FatiWISE)
Gene Ontology (GO)
• http://www.geneontology.org
• Many annotation systems are organism-specific
or different levels of granularity
• GO introduced standard vocabulary first used
for mouse, fly and yeast, but now generic
• An ontology is a formal specification of terms
and relationships between them
GO Ontologies
•Molecular function: tasks performed by gene product –e.g.
G-protein coupled receptor
•Biological process: broad biological goals accomplished by
one or more gene products –e.g. G-protein signaling
pathway
•Cellular component: part(s) of a cell of which a gene
product is a component; includes extracellular environment
of cells –e.g nucleus, membrane etc.
GO relationships
•“is-a” e.g.
mitochondrial
membrane is a
membrane
•“part of” e.g.
nuclear membrane is
part of nucleus
DAG
structure
Current Mappings to GO
• Consortium mappings -MGD, SGD, RGD,
FlyBase, TAIR
• GOA (Gene Ontology Anotation):
• Swiss-Prot keywords
FatiGO
• EC numbers
• InterPro entries
• Manual mappings
• Unigene
• Medline ID mappings, etc.
Evidence codes NB
GO Slim
• “Slimmed down” version of GO ontologies
• Selection of high level terms covering all or most
biological functions processes and cell locations
• Many different GO Slim’s available with different
depths and detail
• Used to make comparisons between annotated
gene/protein sets easier (each gene may be
mapped to different granularity)
Applications of GO slim
GO consortium page
UniProt annotation
• Protein sequence database from EMBL
translations and direct sequencing
• Structured into specific fields e.g. description,
comments, feature table, keywords
• Each field may have controlled vocabulary or
specific syntax
• Swiss-Prot is well annotated, TrEMBL is not, and
may have less structured text
Example
SwissProt entry
Annotation
KEGG
• Kyoto Encyclopedia of Genes and Genomes
– Molecular interaction networks in biological processes
-PATHWAY database
– Genes and proteins -GENES/SSDB/KO databases
– Chemical compounds and reactions COMPOUND/GLYCAN/REACTION databases
• Includes most organisms and info on orthologues
Example
KEGG
entry
InterPro
• Integrates protein signature databases e.g. Pfam,
PROSITE, Prints etc.
• Classifies proteins into families and domains and
lists all UniProt proteins belonging to each
• Provides annotation on the family/domain and
links to 3D structure, GO, Enzyme Classification
• Used to functionally characterise a protein
Example
InterPro
entry
FatiGO
• Connecting microarray results with these
biological data sources –answers questions e.g do
my differentially expressed genes have different
functions?
• FatiGO is used to extract relevant GO terms for a
group of genes with respect to a set of reference
genes (the rest)
• Can be used to list proportions of GO terms in a set
of genes
http://fatigo.bioinfo.cnio.es
FatiGO data sources
• Uses tables of correspondences between genes and their
GO terms (human, mouse, Drosophila, yeast, worm and
UniProt proteins –curated if possible)
• Uses genes from GenBank, UniProt (SwissProt/TrEMBL), Ensembl etc.
• Problem in lack of standardisation of names –use EBI
xrefs to link them, and for other databases they use their
own gene IDs
• For GO associations they include GO evidence codes,
e.g. IEA
Using the GO hierarchy
• Different levels in the GO hierarchy can be chosen,
depending on specificity required
• FatiGO suggest using level 3 –questionable?
• Deeper you go (more specific) –fewer genes annotated to
the terms
• Once level is set, for each gene FatiGO moves up
hierarchy until set level is reached –increases no. of
terms mapped to this level –easier to find relevance in
different distributions of GO terms
• Repeated genes are counted once
How FatiGO works
• Given two sets of genes, and selected GO level
• Retrieves GO terms for each gene on correct level
• Applies Fisher’s exact test for 2x2 contingency tables for
comparing 2 sets of genes (to get p-values)
• Extracts GO terms with significantly different
distributions
• After correcting for multiple testing, provides adjusted pvalues for 3 tests:
– Step-down minP method (Westfall and Young)
– FDR independent (Benjamini & Hochberg)
– FDR arbitrary dependent (Benjamini & Yekutieli )
Testing sets of GO terms
Gene set 2
Gene set 1
Set 1
6
7
8
Significantly higher
distribution in 1 than 2
2
1
0
Transport 20%
Transport 60%
Same distribution
Regulation 20%
Set 2
Regulation 20%
Observed
difference and
possible stronger
differences
Multiple testing
• P-value: is the probability, under the null hypothesis of
obtaining the observed result or a more extreme result than
one observed
• Testing multiple null hypotheses (one per GO term) that
there is no difference in the frequency of terms in each set
• For 1 test, type I error rate (probability of rejecting a true null
hypothesis) is 0.05, but for multiple tests this increases Family wise error rate (probability that one or more of
rejected nulls are true )
• Multiple testing allows controlling of Family Wise Error
Rate (FWER) and False discovery rate (FDR)
Step down min-P method
• Controls FWER
• Procedure with a test statistic equivalent to Fisher's
exact test for 2x2 contingency tables
• No. of random permutations set at 10000
• Examines how many of the permuted p-values are
smaller than the one under consideration
• Adjusted p-value for hypothesis H is level of entire
test set procedure at which H would be rejected,
given values of all test statistics involved
Controlling False Discovery Rate
• Tends to be more liberal than controlling FWER
• Controlling expected no. of false rejections (Type 1
errors) among rejected hypotheses
• Consider the proportions of erroneous rejections to the
total number of rejections. Average value of proportion =
FDR
• FDR can be dependent on or independent of test
statistics, FatiGO gives:
• adjusted p-value using the FDR method of Benjamini &
Hochberg –control of FDR under independence
• adjusted p-value using the FDR method of Benjamini &
Yekutieli –control of FDR under arbitrary dependent structures
Using FatiGO -Input
• Search for Unigene cluster ID, or specific gene IDs
• Input results from SotaTree or Pomelo
• Or input Excel or text file with list of gene or protein
IDs, each on a new line
• Input reference set of genes
• Select GO ontology and level (inclusive)
• Select whether multiple test should include adjusted pvalues for minP test
FatiGO interface (1)
FatiGO interface (2)
FatiGO output
• FatiGO returns four columns: the unadjusted p-value (pvalue from Fisher’s exact test without adjusting for
multiple comparisons) and adjusted p-values based on the
three methods
• Results are ordered by increasing value of the adjusted pvalue, facilitating the selection of GO terms with the
most significant differences.
• P-value of 0.01-0.05 –some evidence, 0.01-0.001 –strong
evidence and < 0.001 –very strong evidence against null
Query set
Reference set
FatiGO
example
output
Unadjusted p-value
FRD (indep) adjusted
FDR (depend) adjusted
Link to AmiGO
Other features of FatiGO
• You can input a list of genes and extract the GO
terms sorted by percentages
• You can use GO results as a way to find
differentially expressed genes –see if after
correcting for multiple testing, some GO terms
are overrepresented (provides more resolution
where p-value has no meaning)
Percentages of GO terms within a set of genes
FatiWISE
• Data mining to retrieve additional biological info
on InterPro motifs, KEGG pathways and SwissProt keywords
• Uses Fishers exact test for 2x2 contingency tables
for comparing two sets of genes and finding
significantly different distributions
• Corrects for multiple testing to get adjusted p-value
• Can get stats for one set of genes or compare 2 sets
FatiWISE input and output
• Data sources: KEGG, InterPro, UniProt
• Input:
– one or two sets of genes
– Selection of organism (for pathway)
• Output:
– Unadjusted p-value
– Step-down min P adjusted p-value
– FDR (arbitrary dependent) adjusted p-value
FatiWISE
interface
FatiWISE
InterPro
output
FatiWISE
KEGG
output
FatiWISE
keyword
output
Summary
• Data mining is used to bring the biology into
results
• Curated data sources are the best for this, due to
structure and controlled vocabulary
• FatiGO and FatiWISE are simple web tools
enabling data mining on 1 or 2 sets of genes
• Exercises: http://cbio.uct.ac.za/courses/MicroDM/
Websites for Annotation
• Webgestalt:
http://genereg.ornl.gov/webgestalt/login.php
• Fatigo: http://babelomics.bioinfo.cipf.es/
Websites for Sequence Analysis and
Motif Finding
• Martview: http://www.ensembl.org/Multi/martview
• TOUCAN:
http://homes.esat.kuleuven.be/~saerts/software/tutorial1/TOUCAN
_Tutorial_Overview.html
• SeqVista: http://zlab.bu.edu/SeqVISTA/tutorials/motif.htm
• Mitra: http://fluff.cs.columbia.edu:8080/domain/mitra.html
• Spex: http://ep.ebi.ac.uk/EP/SPEXS/
• Gene Expression Analysis:
http://geneontology.org/GO.tools.microarray.shtml
•