1. family and parenting

Genomes: The good, the bad and the ugly
The limits of automatic biocuration
Michal Linial
Institute of Life Sciences, The Hebrew University of
Jerusalem, Israel
The Sudarsky Center for Computational Biology (SCCB)
The Israel Institute for Advanced Studies (IIAS)
Beijing, China
April 26th, 2015
Genomes: The good, the bad and the ugly
A classical movie - 1966
The story (dated to Civil War 1862) traces how 3 men gain information about
the location of a buried treasure of gold, and then uncover that treasure.
A community-based expedition
The “compass”:
Follow the footsteps of evolution
Biocuration - it is about changing the TOOLS
Compass - Sextant - GPS - WAZE
Where are we going?
 Create a MAP (for proteins)
 Develop NAVIGATION tools (for functions)
 Insights on HIDDEN FUNCTIONS
 The LESSON…
A treasure hunt for hidden functions
What for:
 Maximizing knowledge..
 Understand living systems…
 Expose design principles..
Instructions - some guideline
 Listen (carefully) to “big data”
 Listen (carefully) to “biological uniqueness”
It is a Tsunami.. You better accept it.
70 M
60 M
50 M
40 M
30 M
20 M
10 M
Bio
curation
April-2015
47 million sequences
(proteome redundancy)
1.5^10 amino acids
It is an unexplored territory ..
You better accept it
70 M
60 M
50 M
40 M
30 M
2015
1: Protein level 0.13%
2: Transcript level 2.05%
3: Inferred homology 20.8%
4: Predicted/Hypoth. 77.0%
20 M
Only 1 out of 770 proteins with evidence
10 M
1/770 is a small number..
China and Israel
1/ 180 in population
1/ 460 in area
The goal
Closing the gap..
Learn from the experts:
Observe the space and classify..
Houston we have a problem:
• High dimensional data
• What distance?
• Dark matter (hypotheticals)
• No “gold standard”
12 Sept 2013, 17:00
Abell 1689, Virgo Cluster
Distance: 2 billion light years
• The “known” is negligible
The challenge: Sequence based function
Known drawbacks in “search” for protein function
(1) Dominated by local alignments
Manual vs. Automatic vs. Hybrid:
Is it a realistic task ?
• Domination by Local alignments
• Statistical confidence score (meaningless ?)
• Minimal robustness, multi-parameters
• Faulty annotations propagation (1)
• Community based competition – i.e. CAFA (2)
•
•
(1) Kaplan N & Linial M (2005) On automatic detection of false annotations. BMC Bioinformatics
(2) Radivojac P et al. (2013) On computational protein function prediction. Nature Methods
Create a MAP - Classifying the space
The Goal: Charting the Protein Space
GUIDING PRINCILES
• Use only sequence information
• Treat all proteins equally (>5 Million “putative’)
• Use unbiased “automatic” methods
Homology derived from a common ancestor
ProtoNet as a Family Tree & Map
Guidelines: All sequences are included (UniProtKB / UniRef)
Preprocess : All against All BLAST (E-value)
Include very remote distance, E value=100 !
Bottom up clustering – ProtoNet Tree
Prune the tree – Report on STABLE clusters
N Rappoport, N Linial, M Linial (2013) ProtoNet: charting the expanding universe of protein sequences.
Nature Biotechnology
Bottom Up: Agglomerative Clustering
• A clustering algorithm based on
pre-calculated local ‘distances’
• Seek appropriate rules
according to which two clusters
merge
• Testing the robustness to data,
sensitivity to ‘merging rules’
• Prune the tree to keep only
stable clusters (data driven)
Creating a Map – A Historical View..
1995 2000 2005 2010 2014 2015 -
37K proteins (SWP) -ProtoMap
94K proteins (SWP)
114K (total 1M, UniProtKB)
3M proteins (UniRef90)
18M proteins (UniRef50) + expansion
+ Complete Genomes
M. Fromer
N. Linial
O. Sasson
E. Portugaly
N. Rappoport
N. Kaplan
The More the Merrier:
Leveraging huge-scale by clustering
Swiss-Prot similarities
S
S
S
S
S
S
S
S
S
S
S
S
Weak similarities between remote families
S
Adding missing sequences UniRef90
UniRef90
sample
(> 2.5 millions)
S
S
S
S
S
S
S
S
S
S
S
S
S
• Similarities amplified (similar on average), Detect consistency
• signal > noise
Boosting the sampling size
More is better
Signal>> noise
A
B
C
The “bad wolf” is still there (false transitivity)
A
1e-42
1e-42
B
8e-78
8e-78
C
 A and B are similar (homologous)
 B and C are similar (homologous)
 A and C are not similar and not homologous – false transitivity
 Mostly due to local similarities in a multi-class scenario
 Triangle invalidated - similarities inherently non-metric
The next merge.. Take the correct exit…
Avg. E=96
8e-78
Avg.
1e-42
E=1e-07
ProtoNet Clusters & Expert families
Correspondence Score (CS)
Jaccard Score (J)
1.0 = perfect,
Size of the intersection divided by
the size of the union
0 = no match
TN
Expert
Specificity = TP /(TP+FP)
Sensitivity = TP /(TP+FN)
Jaccard (CS) = TP/(TP + FP +
FN)
(CATH,
InterPro..)
FP
ProtoNet
TP
Cluster
FN
Quality assessment for the tree
Clusters tested w.r.t. external families:
InterPro
Pfam
E.C. enzyme
SCOP, CATH
SF, Gene3D
GO …
Evolutionary relatedness is captured
Continuous granularities of superfamilies
Average UPGMA* vs.‘naïve’
clustering
10,000 Pfam keywords
PNet-UPGMA Jw=0.86
Single-linkage Jw=0.59
*UMPGA -Unweighted Pair Group Method with Arithmetic Mean
Merging rules make a difference
Geometric
Quality measure w.r.t. Pfam (>10 proteins)
Arithmetic
The numbers keep growing…
*Efficient / exact clustering
algorithm for (most) sparse date
Input:
Similarity data
cluster
similarities
clusterer:
MC-UPGMA
Output:
Complete tree
to next
round
Problem: Too Big
(partial)
tree
merger:
edge re-calculation
Clusterer – partial clustering
Merger – creating next round’s input
Loewenstein et al. (2008) Efficient Algorithms for Exact Hierarchical Clustering of
Huge Datasets Bioinformatics
A critical assessment on 1.8 M sequences
InterPro Families
InterPro Domains
J
spec.
sens.
J
spec.
sens.
MC-UPGMA
.90
.97
.93
.74
.90
.80
ProtoNet4
Single-linkage
CluSTr Slim
.80
.81
.28
.94
.95
.93
.83
.84
.29
.67
.57
.24
.90
.88
.89
.72
.62
.25
A MAP – What for?
New discoveries:
Family definition, Superfamilies, Hidden connections…
Evolution of genomes…
Improved data quality:
Detecting faulty annotations
New insights:
The ‘unexplored’ territory for 3D – Target selection
The family MAP / Isolated families
Family models
No internal distances
Family clusters
Internal distances
Follow the footstep of evolution
Identity
of > fold
35% inproteins
sequence ensures homology
Globins
Identity of 20-35% in sequence - very hard (Twilight)
Identity of <20% deep in the noise (Midnight)
Short (~120-160 aa) Oxygen transport
Low sequence similarity <15%
Partition to subfamilies (no FP)
~1000 proteins, 50< 3D ‘globin-like fold’
78 % of all potential BLAST pairs are with E-value
100 or worse
a
ee
b, d
Functional Road Map of Enzymatic Activity - UREASE
Hypothetical: Urease like
Urease alpha subunit
3.5.1.5
Urease
3.5.1.5
17 subfamilies
beta
subunit
Dihydropyrimidinase
3.5.2.2
D-hydantoinase
3.5.2.2
allantoinase 3.5.2.5
Imidazolonepropionase 3.5.2.7
N-acyl-D-aspartate deacylase 3.5.1.83
adenine deaminase
3.5.4.2
Hypotetical
Guanine deaminase
3.5.4.36
N-acetylglucosamine-6-phosphate
deacetylase 3.5.1.25
Dihydroorotase
3.5.2.3
Hypothetica
l
AMP
deaminase
3.5.4.6
Adenosine
deaminase
3.5.4.4
Shachar and Linial (2004)
On remote homologues
In Proteins
ProtoNet navigation tool
Overlooked connections:
Connect the Dots
Functional connection?
i ii iii iv a b c d
1 2 3 4
A and C coincide
CB (CA ‘s sibling)
A and C coincide on a multi-domain protein = false-transitivity
Cluster A
Cluster B
Beware of false inference…
Cluster A
Cluster B
Connect the dots
A-B
JA >0.6
JB >0.6
710 A-B pairs
4 80 (non-coinciding) pairs
Connect the Dots
Passing the structural test
SCOP Superfamily
Aerolisin / ETX pore-forming
1preB
Pfam: Aerolysin toxin (PF01117)
SCOP (SF & Fold):
Aerolisin/ETX pore-forming domain
1uyjA
Pfam: ETX_MTX2 (PF03318)
SCOP (SF & Fold):
Aerolisin/ETX pore-forming domain
A - B: Estimating Safe Predictions
480 safe A-B pairs
Correct A-B
Wrong A-B
Pfam Clans
Unknown A-?
Unknown ?-?
Let the data speak
Distribution of Pfam AB-pairs
1
No clan
One clan
Correct
wrong
J(B)
0.9
0.8
0.7
0.6
0.6
0.7
0.8
J(A)
0.9
1
Functional insight on DUFs
32% are DUFs (Domains of Unknown Function)
???
Spore surface
determinant
47
DUF1429
YabP family
J=1.0 (24/0)
J=1.0 (21/0)
20
2
22
3
Unknown to Known
50% in Bacillales
40% in Clostridia
40% in Bacillales
50% in Clostridia
Small packages:
50% A-B pairs (most top scoring)
A parallel space: Virus world
On viral evolution
Evolution forces that are unique to the virus‘biology’
• Fast recombination (co-infected host cell)
• High mutation rate (specialized polymerase)
• Fast selection (antigenic shift)
• Host-co evolution
Viruses proteins are often isolated in the protein family tree
Rappoport, Linial (2012) Plos Comp Biology
Herpes Virus
No 3D / No external support
PF04541 Herpesvirus virion protein U34
A
B
PF05900 Gammaherpesvirus BFRF1 protein
Connect Herpes Gamma-Herpes (Epstein-Barr virus (EBV) and Kaposi’s sarcoma herpesvirus
(KSHV)*
Assessment of the hidden connections
Clan True
Clan True
Clan-Wrong
Clan-Wrong
Pfam-Obsolete
Pfam-Obsolete
Name-True
Name-True
A-B TrueA-B True
A-B-Possible
A-B-Possible
A-B Worng
A-B Worng
Protein sequences come in bulk…
The complete genomes flood
>30 Insect’s
genomes !!
Gaining insight on the life style of an
insect
~1,100,000 species of insects
The most diverged known class
HUGE variation in: morphology, life span,
life cycle, development, sex determination,
medical impact, genome size, behaviour...
18 complete
genomes
insects &
crustacean
Removed redundancy
(10 Drosophilae )
Not yet included:
Butterfly and Moth
….
Arthropods and Insects genomes:
300 MY of evolution
Hypotheticals
Others
Ants complete genomes
•
•
•
•
•
•
•
Leaf cutter ant Atta cephalotes
Fungus-growing ants Acromyrmex exchinatior
Florida Carpenter ant Camponotus floridanus
Jerdon's Jumping ant Harpegnathos saltator,
Argentine ant Linepithema humile,
Red harvester ant Pogonomyrmex barbatus
Fire ant Solenopsis invicta.
14,000 species
Dominating Biomass of Animals
Lesson 1: 300 K proteins, 300 M years
Secured functional inference to 1000s of sequences
1398 Root SF
(Superfamilies)
To Root
(PL)
ProtoLevel
Depth
?
.
.
..
…
Life Time
77,988 unannotated
20,134 families
PL70
.
..
…
….
PL50
ProtoBug:
Insights from COMPLETE proteomes - ‘secure’ inference
Pure families
w.r.t. Pfam
1
CS Median - 0.932
CS Average - 0.888
100% Pure
no FP
Specificity
0.8
0.6
0.4
0.2
1
401
801
1201
1601
2001
2401
2801
3201
Pfam keywords assigned to ProtBug families (PL70, ≥ 10 proteins)
ProtoBug: Automatic view on expanded &
contracted families
.
.
..
…
Life Time
?
Depth
ProtoLevel (PL)
To Root
18 representatives
300,000 proteins
20,000 families
~5000 clusters / species
22% - Annotation inference
.
..
…
….
1010101010101010111111010110110101
1010101011101110111100001011011011
1010101000101010111111010110110101
1010101011101110101100001011011011
1010101010101010111111010110110101
1010101011101110111100001011011011
1010101010101010110111010110110101
1010101011101110111100001011011011
1010101011101110101100001011011011
1010101010101010111111010110110101
1010101011101110111100001011011011
1010101010101010110111010110110101
The basis for GAIN and LOST of a family
Vectors of families
Gained and lost families are traceable
Hymenoptera
H. saltator
C. floridanus
L. humil
P. barbatus
S. invicta
A. echinatior
A. cephalotes
A. melifera
N. vitripennis
D. virilis
D. melanogaster
A. darligini
A. gamiae
C. quiquefasciatus
A. aegypti
T. castaneum
P. humans
D. pulex
Diptera
{b,c}
Parent
Uncle
{a,b,c}
{b,c,e}
n
Sibling
{a,b,d}
{a,c}
Lesson 2: 1000s of gained and lost families
at a (surprising) variable rate
Gain
Diptera
Hymenoptera
H. saltator
C. floridanus
L. humil
P. barbatus
S. invicta
A. echinatior
A. cephalotes
A. melifera
N. vitripennis
D. virilis
D. melanogaster
A. darligini
A. gamiae
C. quiquefasciatus
A. aegypti
T. castaneum
P. humans
D. pulex
628
973
1215
1327
579
1441
2214
494
777
926
512
1656
427
896
685
2329
674
4969
Loss
351
334
245
657
1568
1623
556
629
322
74
89
838
492
296
380
330
353
0
Lesson 2: 1000s of gained and lost families
at a (surprising) variable rate
Diptera
800
Normalized TOR
Hymenoptera
H. saltator
C. floridanus
L. humil
P. barbatus
S. invicta
A. echinatior
A. cephalotes
A. melifera
N. vitripennis
D. virilis
D. melanogaster
A. darligini
A. gamiae
C. quiquefasciatus
A. aegypti
T. castaneum
P. humans
D. pulex
787 750
600
412 410
400
247 245 241
200
194 171 169 163 159
151 132
24 12
0
TuroOver Rate ( Dynamics of functions)
Benefit from the ProtBug MAP
Seek a phylogenetic signal
114 Root SF at least
200 protein each
82.3K proteins
1398 Root SF
(Superfamilies)
To Root
ProtoLevel
(PL)
Depth
?
.
.
..
…
Life Time
77,988 unannotated
20,134 families
PL70
.
..
…
….
PL50
Lesson 3: Transposition
/ DNA dynamic functions are
enriched in Hymenoptera..
A secret for
adaptation?
ProtoBug: Navigating tools
1. Cluster ID
2. Cluster name
3. Tree view
5. Species view
4. Cluster summary
6. LT
8. Kw statistics
7. PANDORA
9. Proteins /
features
Insights from Genome-Centric Curation
Evolution insight: 100s of families/ functions were gained / lost
NA dynamics is enriched in some taxa
Resource: ProtoBug – a Genome view on 18 insect representatives
Rappoport and Linial (2015) ProtoBug… DATABASE (Oxford)
Bio-curators: A tsunami is coming
insects & crustacean
….
5000 genomes
The i5k initiative is a transformative project that aims to sequence and analyze the genomes of
5,000 arthropod species.
Hexapoda 702
Chelicerata 64
Crustacea 20
Myriapoda 6
Not all genomes are born equal..
The case of Daphnia pulex
~200
Mb
31 K genes
Hypotheses:
A phenotypic adaptability for changeable environments ?
A fitness in Daphnia’s aquatic toxic environment ?
Colbourne et al. (2011) Science
Proteins arrive in Bulk (a new genome..)
31,000 genes
10,000 are unknown
X2 w.r.t insects…
Expansion of Daphnia’s paralogs
• Extreme number of paralogs
• Many clusters have >10 paralogs
D.
Pulex Low divergence - similar functions…
Divergence among Daphnia’s paralogs
High divergence
High similarity
Number of clusters
Daphnia ≥ 10 paralogs
High divergence
Tree Score (TS)
High similarity
Number of clusters
Drosophila ≥ 10 paralogs
Expansion in signaling cascade
Some General Conclusions
•
The MORE, the Merrier. Improving S/N
•
LET the DATA lead you
•
SCALABILITY is critical
•
CONNECTED MAP is a key for DISCOVERY
•
NAVIGATION TOOLS are important
Fascinating Biology is always in front of you
Hidden functions
New Genomes, New functions
‘Maybe’
Boarder line similarity
Only part of protein
Conflicting exp/ lit
Having
Function
Experiments
Literature
Expert view
‘Wrong’
Fault annotation
Wrong inference
Inferring
Function
Models, Maps
Predictions
No Function
No similarity
No evidence
Hidden niches: Unique “life style”
Hypothesis: Unexplored world of functions
Short peptides
Signaling molecules
Viruses & pathogens
Metagenomics
Innovation, novelty, evolutionary games
Short proteins: Hidden niche
Hypothesis: Short peptides – hidden functions
uncharacterized
Average
24.2%
Short <100 aa NOT fragments
SwissProt: 9.7 %
TrEMBL: 8.0 %
Protein Length
Short Proteins: When sequence similarity fails
• Genomics:
– Similarity-based searches: short proteins, non-significant results (weak signal)
– NGS: few reads
• Proteomics:
– Best for long seq: “global” MS experiments, low coverage, low DB scores ..
– Missed spectra (modification?)
– DB search:
If the proteins do not exist in the database they cannot be found.
Many animal toxins are short
Eukaryotes short proteins (<100 aa, no fragments, SWP, 15,224)
Property
Amount
P-value
Toxin
2421/3342
0
Neurotoxin
1468/1505
0
Ion channel inhibitor
270/304
1E-247
Why searching toxin-like sequences
•
•
•
•
Secrets of evolution
Unexplored niche
Unexpected surprises
Peptide therapy
Source
Name
Action
Application
Cone snail
ω-conotoxin
Ca2+ channel inhibitor
Chronic pain
Cone snail
μ-conotoxin
Na2+ channel inhibitor
Epilepsy, pain,
arrhythmias
Scorpion,
Sea-anemone
margatoxin
ShK
K+ channel inhibitor
Cone snail
α-conotoxins
Cone snail
Conantokins
Immunosuppressant
Multiple Sclerosis
nAChR inhibitor
Pain
NMDA inhibitor
Pain
ICI in sporadic metazoa
Eukaryota
Bacteria
Archea
Metazoa
(animals)
Cnidaria
Mammals
Bilateria
Arthropods
Sea anemone
Spiders
Molluscs
Scorpions
Vertebrata
Insects
Cone snails
Reptiles
Breaking the rules for the 3D perspective
MANY folds – ONE target
K+
MANY targets – ONE fold
Ca2+, Na+, Cl-, K+,
No simple relation between the folds and the targets of the ICIs
Ion channel inhibitor (ICI) toxins
Weak (no) signal in sequence
•No sequence consensus
•No phylogenetic tree
•No 3D folds specificity
Conotoxin, 112 seed proteins
What’ s common among ICI?
•
•
•
•
•
All EXTRACELLULAR expression
SHORT & COMPACT
Act by blocking a wide range of RECEPTORS & CHANNELS
STABLE protein (stored in venomous gland, often modified)
SPECIFIC, HIGH AFFINIRY
Alternative “curation” tools: A search for features
A collection of 600 relevant features
• Amino acid frequency (20)
• Amino acid pairs frequency (202=400)
• Length (1)
• Hydrophobic binary pattern (25=32)
• Charged entropy
• Amino acid entropy
• Cysteine binary pattern (25=32)
• Amino acid “center of mass” (40)
• More…
The Key:
Design features that capture your biological intuition
Noam Kaplan
From sequence to a prediction machine
Short proteins
Supervised learning
A classisfier
machine
Predicting label for
each protein seq
CONFIDENCE SCORE
for being ICI
ClanTox: the “Classifier Machine”
Applying of SWP short proteins
3-fold cross validation results:
AUC: mean 0.9934, sd 0.0026
Kaplan, Morpurgo, Linial J. Mol. Biol (2007)
Naamati, Askenazi, Linial (2010) Bionformatics
TOLIP= toxin like
proteins
TOLIPs discovery platform
Input - Discovery
TOLIPs Predictor
Genome
Positive
Set
Proteome
Negative
Negative
Set
Negative
Set
Negative
Set
Set
TOLIPs Annotation
Functional
TOLIPs
Candidate
TOLIPs
Nega ve
Short
proteins
ClanTox
P1
P2 P3
ClanTox: a “Classifier Machine”
Guy Naamati
www.clantox.cs.huji.ac.il
Where should we search: Unexplored Proteomes
INSECT: Honey Bee (10K proteins)
NEW Toxin-like (TOLIP): OCLP1
Not in the venom gland, similar to w-conotoxin
AMPHEBIA (121K, 2.4K short proteins)
Huge expansion in Amphebia
When needed… D. pulex TOLIPs
•31K ORFs, 11% < 100 aa
•ClanTox identified 6 top score predictions
Signal pep de
Metallothionein Daphnia pulex and Arthropods
TOLIP paralogs - A cassette for protection
(local duplication)
Metallothionein (MT) - Cys-rich short proteins.
Localized to the membrane of the Golgi apparatus.
MTs bind to metal ions (Zn, Cu, Se, Ag, Cd, As, Hg )
TOLIPs and New functions: Brain & Immune system
The playground of creativity and innovation
Toxin-like sequences without a venom ??
Some (wild) thoughts as a Biocurator…
1. Short proteins – EASY to create (evolution)
2. The 3D constrains are simple – analogy for A CORK
Mouse TOLIPs – RNA-Seq data
•Defensin
•Testis expressed
•Cancer related
•Immunological signaling
•Developmental cues
reproductive
HIGHLY RESPONSIVE
Immunity
Beetle, Spider and Human
The use of a successful scaffold
• Rodent-Primate Signaling molecules
• Regulator of GPCR
• Regulators of Energy Consumption
ASIP (Agouti Signaling)
Mammals / Primate
AgRP (Agouti Related)
A statement from EVOLUTION:
"If it looks like a duck, swims like a duck, and
quacks like a duck, then it probably is a duck”
ANLPs: Shared 3D, but minimal similarity
Yitzhak Tirosh
Treasure hunt: An overlooked loci
1.1M, Chr 9q4A , 19 mouse genes – testis related
19 TOLIPs in 1.1M bp
Unknown function
Testis expression
ANLP
Lynx1-2 SLURP12
UPAR/ LY6 254
M=60; H=40
Activin receptor 203
M=29; H=22
BAMBI 16
M=2; H=3
PLA2 inh 45
M=2; H=0
Toxin 375
M=0; H=0
TOLIPs “the power of many”
WAP proteins (e.g., Elafin)
Chr 2 0.55 Mb
• Serine-proteinase inhibitors
• Antimicrobial and anti-inflammatory activity.
• Amplified in cancers
• Carcinogenesis ? Tumor progression ?
WAP proteins
Yet another TOLIP cluster
Ly6 - gene expansion
mouse Chr 15 & human Chr
Cell8identity code?
B cells
T cells
NK cells
Monocytes
Neutrophils
Dendritic cells
All defense lines of the cellular immune system
J Leukoc Biol. 2013
ANLP-LY6-Toxin functions in the clinics
Skin disease (psoriasis)
Pain control ‘drug’
Immune system (sorting)
Alzheimer (soluble marker)
TOLIPs in Metazoa
~100 TOLIPs
Real toxins
~100 TOLIPs
no toxins
Target receptors
Target receptors
Very short (<5 kDa)
Short (~10 kD)
Simple modifications
Rich modifications
Chr. amplification
Chr. amplification
Irreversible
action
Reversible action
Feature based curation
Neuropeptides - Powerful modulators
Dan Ofer
neuropid.cs.huji.ac.il
Ofer D. and Linial, M. (2013) Bioinformatics
Ofer, D. et al (2014) Nucl. Acids. Res
Closing remarks
 Maps - the way to follow evolution footsteps
 Innovators and replicators - work hand in hand
 Plasticity / adaptation calls for ‘innovation’
 Unification in function – a common phenomenon
 BIOCURATION - essential (sequence & features)
My “take home message”
for knowledge sharing
•
Develop methodologies, tools and resources
•
Share ideas and Share data
•
Keep crossing from Data to Biology & Back
Give a man a fish and you feed him for a day;
teach a man to fish and you feed him for a lifetime
10-14 July 2015
Thank You