Lecture Outline

1
Lecture Outline

• Introduction
• Data mining sources
• GO, InterPro, KEGG, UniProt
• Tools to do the data mining
• FatiGO
• FatiWISE

2
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 dont necessarily answer the
  research question, these need to be converted to
  biological information first

3
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

4
Data mining (1)
Add gene identifiers
-AB02387 -SB07593 -AA00498 -AC008742
-AB083121
5
Data mining (2)
Add gene descriptions
-AB02387 -SB07593 -AA00498 -AC008742
-AB083121
-RNA polymerase -Glycosyl
hydrolase -Phosphofructokinase
-Transcripiton factor -Glucose transporter
6
Data mining (3)
Add GO terms
-AB02387 -SB07593 -AA00498 -AC008742
-AB083121
-RNA polymerase -Glycosyl
hydrolase -Phosphofructokinase
-Transcripiton factor -Glucose transporter
-GO0003456 -GO0006783
-GO0142291 -GO0054198 -GO0000234
7
Data mining (4)
-AB02387 -SB07593 -AA00498 -AC008742
-AB083121
-RNA polymerase -Glycosyl
hydrolase -Phosphofructokinase
-Transcripiton factor -Glucose transporter
-GO0003456 -GO0006783
-GO0142291 -GO0054198 -GO0000234
Add functional annotation
8
Data mining (5)
-AB02387 -SB07593 -AA00498 -AC008742
-AB083121
-RNA polymerase -Glycosyl
hydrolase -Phosphofructokinase
-Transcripiton factor -Glucose transporter
-GO0003456 -GO0006783
-GO0142291 -GO0054198 -GO0000234
Store results in database
Map onto pathways
9
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

10
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

11
Example of problems

• The Sch9 protein kinase regulates
  Hsp90-dependent 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
12
Example of problems
Protein
Verb
Pathway

• The Sch9 protein kinase regulates
  Hsp90-dependent 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.

Organism
Acronym could be used elsewhere for different
gene
Negative term used
Some problems overcome using stats better
detection of entities and relationships
13
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)

14
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

15
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.

16
GO relationships

• is-a e.g. mitochondrial membrane is a membrane
• part of e.g. nuclear membrane is part of nucleus

DAG structure
17
Current Mappings to GO

• Consortium mappings -MGD, SGD, RGD,
  FlyBase, TAIR
• GOA (Gene Ontology Anotation)
• Swiss-Prot keywords
• EC numbers
• InterPro entries
• Manual mappings
• Unigene
• Medline ID mappings, etc.

FatiGO
Evidence codes NB
18
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 Slims available with different
  depths and detail
• Used to make comparisons between annotated
  gene/protein sets easier (each gene may be mapped
  to different granularity)

19
Applications of GO slim
20
GO consortium page
21
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

22
Example Swiss-Prot entry
Annotation
23
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

24
Example KEGG entry
25
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

26
Example InterPro entry
27
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
28
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
  (Swiss-Prot/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

29
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

30
How FatiGO works

• Given two sets of genes, and selected GO level
• Retrieves GO terms for each gene on correct level
• Applies Fishers 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 p-values for 3 tests
• Step-down minP method (Westfall and Young)
• FDR independent (Benjamini Hochberg)
• FDR arbitrary dependent (Benjamini Yekutieli )

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Testing sets of GO terms
Gene set 2
Gene set 1
Set 1
Set 2
Significantly higher distribution in 1 than 2
Transport 20
Transport 60
Observed difference and possible stronger
differences
Same distribution
Regulation 20
Regulation 20
32
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)

33
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

34
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

35
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 p-values for minP test

36
FatiGO interface (1)
37
FatiGO interface (2)
38
FatiGO output

• FatiGO returns four columns the unadjusted
  p-value (p-value from Fishers 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 p-value, 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 lt 0.001 very strong
  evidence against null

39
FatiGO example output
Query set
Reference set
Unadjusted p-value
FRD (indep) adjusted
FDR (depend) adjusted
40
(No Transcript)
41
Link to AmiGO
42
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)

43
Percentages of GO terms within a set of genes
44
FatiWISE

• Data mining to retrieve additional biological
  info on InterPro motifs, KEGG pathways and
  Swiss-Prot 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

45
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

46
FatiWISE interface
47
FatiWISE InterPro output
48
FatiWISE KEGG output
49
FatiWISE keyword output
50
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/

51
Websites for Annotation

• Webgestalt http//genereg.ornl.gov/webgestalt/log
  in.php
• Fatigo http//babelomics.bioinfo.cipf.es/

52
Websites for Sequence Analysis and Motif Finding

• Martview http//www.ensembl.org/Multi/martview
• TOUCAN http//homes.esat.kuleuven.be/saerts/soft
  ware/tutorial1/TOUCAN_Tutorial_Overview.html
• SeqVista http//zlab.bu.edu/SeqVISTA/tutorials/mo
  tif.htm
• Mitra http//fluff.cs.columbia.edu8080/domain/mi
  tra.html
• Spex http//ep.ebi.ac.uk/EP/SPEXS/
• Gene Expression Analysis http//geneontology.org/
  GO.tools.microarray.shtml