A description of the activities of the CIBERER BiER (Bioinformatics of Rare Diseases) and INB-ELIXIR-es

1. Joaquín Dopazo
Computational Genomics Department,
Centro de Investigación Príncipe Felipe (CIPF),
Functional Genomics Node, (INB-ELIXIR-es),
Bioinformatics in Rare Diseases (BiER-CIBERER),
Valencia, Spain.
Platforms CIBERER and
INB-ELIXIR-es
http://bioinfo.cipf.es
http://www.babelomics.org
@xdopazo
Symposium: International platforms for biomedical research:
A focus on rare diseases,
Fundacion Ramón Areces, Madrid 3-4 November, 2016

2. The CIBERER “1000 genomes”
Initiative to sequence rare disease patients
Diseases with
• Unknown genes
• No mutations in known genes
Search for:
• New genes
• Known genes with unknown modifier genes
• Susceptibility genes
http://www.gbpa.es/
Sample providers Sequencing platforms Data analysis
A total of 1044 patients
(including 300 controls) of
more than 30 diseases were
sequenced between 2012 and
2013.

3. The actors: MGP and CIBERER
MGP is a PPP between the Andalucia local government
and Roche. MGP roadmap is based on the availability of:
• More than14.000 clinically well characterized samples
• An automatically updated PATIENT HEALTH RECORD (PHR)
• SAMPLE INFORMATION (SI)
That will be used as the first steps towards the implementation
of genomic and personalized medicine in the Andalusian
HEALTHCARE SYSTEM. A system covering a population of 8.5
million. MGP spans from 2012 to 2014
The Spanish Network for Research in Rare Diseases
(CIBERER) is an initiative of the Spanish Health Ministry.
The CIBERER is composed of 60 research and clinic
groups distributed across the country and has been
running since 2005.

4. MAX, Pheochromocytome
NFU1, Mitochondrial disease
GlialCAM, MLC
The results: gene discovery at CIBERER
OTOG, Deafness
PLOD2, Osteogenesis
COQ4, CoQ10
BMP1, Osteogenesis
2011 2012
PHOX2B, Hirschprung
SERAC1, Aciduria
ERCC4, Fanconi anemia
PPM1K MSUD
TNPO3 Muscular dystrophy
CFHR1 DDD
SERPINF1, LEPRE1, CRTAP, PPIB. Osteogenesis
WNT1 Osteogenesis
2013
DNMT3B, Hirschprung
YWHAZ, DRP2, Retinitis pigmentosa
RD3 Retinitis pigmentosa
TUFM, IL27, Chromosomal rearrangements
LIPT1 Lipoiliation defects
BMP1 Osteogenesis
IFITM5 Osteogenesis
RNF125 Overgrowth
2014
ZNF408, Retinal dystrophy
ATP4A Carcinoid tumor
MDH2 Pheochromocytome
Junctophilin-1, CMT
EGR2 CMT
JMJD1C Rett syndrom
POT1 Cardiac angiosarcome
FAN1 Hereditary colorectal cancer
ALDH18A1 Hereditary paraplexy
MORC2 CMT
ZNF408 Retinitis pigmentosa AR
KITLG Waardenburg Syndrome Type 2
CAV1 Neonatal lipodystrophy syndrome
IL8, IL13 Renal cell carcinoma
2015
ATP4A Gastric tumor
CCNF ALS
2016
Sequencing initiative
ACCI projects

5. Data management, analysis and
storage = knowledge increase
http://www.gbpa.es/
GCGTATAG
CACGGGTA
TCTGTATTA
TGGTGGAT
ATCAGCGG
ATTGCGATT
GGCAGAGC
GGCAAAGT
GCGTATAG
CACGGGTA
TCTGTATTA
TGGTGGAT
ATCAGCGG
ATTGCGATT
GGCAGAGC
GGCAAAGT
GCGTATAG
CACGGGTA
TCTGTATTA
TGGTGGAT
ATCAGCGG
ATTGCGATT
GGCAGAGC
GGCAAAGT
GCGTATAG
CACGGGTA
TCTGTATTA
TGGTGGAT
ATCAGCGG
ATTGCGATT
GGCAGAGC
GGCAAAGT
Raw files
(FastQ)
DB
Analysis
Pipeline
Storage
K-DB
Diagnostic
portfolio
Gene 1 ksdhkahcka
Gene 2 jckacsksda
Gene 3 lkkxkccj<jdc
Gene 4 ksfdjvjvlsdkvjd
Gene 5 kckcksñdksd
Gene 6 ldkdkcksdcldl
Gene x kcdlkclkldsklk
Gene Y jcdksdkcdks
Prioritization
report
Dialog with experts in the
disease + validations
Samples
GCGTATAG
CACGGGTA
TCTGTATTA
TGGTGGAT
ATCAGCGG
GCGTATAG
CACGGGTA
TCTGTATTA
TGGTGGAT
ATCAGCGG
VCF BAM
Processed files

6. 3-Methylglutaconic aciduria (3-MGA-uria) is a
heterogeneous group of syndromes
characterized by an increased excretion of 3-
methylglutaconic and 3-methylglutaric acids.
WES with a consecutive filter approach is
enough to detect the new mutation in this
case.
The prioritization process is actually a
Heuristic Filtering strategy that reduces
the inmense list of candidate variants
An example with 3-Methylglutaconic aciduria syndrome

7. Prioritization programs: making the
prioritization report interactive
Numerous interactive filters to
discard unlikely candidate variants
- Mutational impact
- Population frequency
- Family segregation
- Inheritance mode
- Consequence type
- Functional considerations (GO,
HPO, etc.)
- Etc.
Different views, including the genomics perspective
with GenomeMaps

8. On the importance of the local
variability in the prioritization process
And… on how to
use local
variability without
compromising the
confidential
nature of
genomic data

9. The CIBERER Spanish Variant Server (CSVS): the first
repository of variability of the Spanish population
Only another similar initiative
exists: the GoNL
http://www.nlgenome.nl/
http://ciberer.es/bier/exome-server/
And more recently
the Finnish
and the Icelandic
populations

10. The CSVS is a crowdsourcing project
Scenario: Sequencing projects of healthy
population are expensive and funding
bodies are reluctant to fund them
CSVS Aim: To offer increasingly accurate
information on variant frequencies
characteristic of Spanish population.
CSVS Main use: Frequency-based
filtering of candidate variants
Main data source: Sequencing projects
of individual researchers (CIBERER and
others)
Problem: Most of the contributions
correspond to patient exomes
Idea: Patients of disease A can be
considered healthy pseudo-controls for
disease B (providing no common genetic
background exist between A and B)
Beacon: CSVS will soon appear in the
Beacon server
http://ciberer.es/bier/exome-server/

11. The CSVS Interface
CSVS is organized in
disease categories
CSVS can be queried
about chromosomal
regions or genes

12. Why binning data into ICD-10
categories?
ICD-10 first level of diseases offer two
advantages:
• No (or very low) common genetic
background among ICD categories
• Classes big enough to preserve data
confidentiality. Attempts to identify
individuals within them will produce very
vague phenotype clues
Binning into ICD-10 high level categories
endorsed by CIBERER experts in bioethics.
D1 D2 D3 D4 D5 D6 D7 …… D22
(pseudo) control s for D3

13. Statistics
As of 11/09/2016
CSVS contains 790
unrelated Spanish
individual exomes.
About 1000
expected by the
end of the year

14. Information provided
Genotype frequencies
in the different
reference populations
Genomic coordinates, variation, gene.
SNPid
if any

15. Information provided
Pathogenicity indexes
Phenotype,
if available

16. Variants can also be seen
within their genomic context
GenomeMaps viewer (Medina et al., 2013, NAR) embedded in the application.
GenomeMaps is the official genome viewer of the ICGC (http://dcc.icgc.org/)

17. CSVS provides insights on the portion of
the variability already contained in it

18. Table of Spanish
Frequencies
(TSF)
DB of Spanish
variants (DBSV)
Chr Position Ref Alt 0/0 0/1 1/1
1 1365313 A T 75 0 0
1 1484884 G A 70 4 1
2 326252 T C 25 35 15
CES
use
Other countries
CSVS
input
External
Unrelated?
(DBSV)
VCFs Spanish?
(TSF)
YES YES
NO NO
Counts
Internal
Regional
AIM (Ancestry-informative
markers) are used to
discard kinship and
different ethnicity

19. ?
SIP
Diagnosis+ biomarker discovery: an ongoing
integrated CIBERER initiative
Ongoing CIBERER pilot project with the collaboration of seven hospitals: La
Paz, FJD, Ramón y Cajal, CBM (Madrid), Virgen del Rocio (Sevilla), Hospital del
Mar (Barcelona), HU La Fe (Valencia)
http://team.babelomics.org
http://BiERapp.babelomics.org

20. Diagnostic using NGS and
virtual panels
Diagnostic SNV
Variants of unknown
significance (VUS) and
unexpected findings
management
Medical reports
Generation and management
of virtual panels http://team.babelomics.org
100% traceability of
data management
and decisions

21. The CIBERER CNV server
Stores CNVs found in
patients of different
hospitals, along with
some interesting
information on
ethnicity, location,
phenotype (HPO), etc.,
that can be studied in
the genomic context
(using GenomeMaps)
If everything goes as
planned it will contain
data on more than
15.000 patients from 5
CIBERER hospitals by
the end of the year

22. What is inside? OpenCGA
Overview and goals
Open-source Computational Genomics Analysis (OpenCGA) aims to provide a high performance
and scalable solution for genomic big data processing and analysis
OpenCGA is built on OpenCB: CellBase, Genome Maps, Cell Maps, HPG Aligner,
HPG BigData, Variant annotation. Project at GitHub: https://github.com/opencb/opencga

23. 6 node Hadoop cluster:
• Transform: 97 min
• Load: 80 sec
• Merge: 84 sec
• Millisecond response
times for regional
queries
• Whole genome filtering
queries for all individuals
within seconds
OpenCGA: storage
Extensive capabilities to query across genotype and phenotype relationships
https://github.com/opencb/opencga

24. Tools developed to improve the pipeline:
CellBase, the knowledge DB
Now at: https://github.com/opencb/cellbase
Project: http://bioinfo.cipf.es/compbio/cellbase
CellBase (Bleda, 2012, NAR), a
comprehensive integrative database and
RESTful Web Services API, more than
250GB of data:
● Core features: genes, transcripts, exons,
cytobands, proteins (UniProt),...
● Variation: dbSNP and Ensembl SNPs, HapMap,
1000Genomes, EVS, EXAC, etc.
● Pathogenicity indexes and conservation: SIFT,
Polyphen, CADD, PhastCons, philoP, GERP, etc.
● Disease: ClinVar, OMIM, HGMV, Cosmic, etc.
● Functional: 40 OBO ontologies (Gene Ontology,
HPO, etc.), Interpro, etc.
● Regulatory: TFBS, miRNA targets, conserved
regions, etc.
● System biology: Interactome (IntAct), Reactome
database, co-expressed genes.
● Compared in testing against VEP: more than
99.999% similarity in Consequence types
● Annotation tool of GEL
● More than 10000 genomes annotated so far

25. Tools developed to improve the pipeline
Genome Maps, the genome viewer
o Genome scale data visualization plays an important role in the data analysis process. It is a big data
management problem.
o Features of Genome Maps (Medina, 2013, NAR; ICGC data analysis portal)
● First 100% HTML5 web based: HTML5+SVG (inspired in Google Maps)
● Always updated, no browser plugins or installation
● Data taken from CellBase, remote NGS data, local files and DAS servers: genes, transcripts, exons, SNPs, TFBS, miRNA
targets, etc.
● Other features: Multi species, API oriented, easy integration, plugin framework, etc.
BAM
viewer
VCF viewer
ICGC genomic viewer
www.genomemaps.org
Currently GM is being
implemented in RDConnect

26. Although already implementing genomic biomarkers we are still in the empirical
medicine era. Without the knowledge of the functional relationship between genotype
and disease we only have (increasingly better) probabilistic associations.
What is next?
The transition to precision medicine
Intuitive
Based on trial
and error
Identification of
probabilistic
patterns
Decisions and
actions based
on knowledge
Intuitive Medicine Empirical Medicine Precision Medicine
Today Tomorrow
Degree of personalization
Genomic biomarkers
Molecular biomarkers

27. We think “gene-centric”
http://www.fda.gov/drugs/scienceresearch/researchareas/pharmacogenetics/ucm083378.htm
• Thinking in terms of the unique
causative gene is still reasonable for a
number of rare diseases, but not for all
of them.
• Current GWAS, NGS and gene
expression analyses are eminently
gene-centric
As a consequence of this, most existing
diagnostic and personalized treatments are
based on single-gene biomarkers
Data analysis biomedical platforms need to go beyond
supporting gene-centric pipelines / algorithms / procedures
and evolve towards a systems biology based perspective

28. Genetic diseases have a modular nature
and, consequently, must be addressed
from a systems biology perspective
• With the development of systems biology, studies have shown that phenotypically
similar diseases are often caused by functionally related genes, being referred
to as the modular nature of human genetic diseases (Oti and Brunner, 2007; Oti
et al, 2008).
• This modularity suggests that causative genes for the same or phenotypically
similar diseases may generally reside in the same functional module, either a
protein complex, a sub-network of protein interactions, or a pathway
• Perturbed modules account for disease better than individual perturbed genes
Disease genes are close in the interactome
Goh 2007 PNAS
Same disease
in different
populations is
caused by
different genes
affecting the
same functions
Fernandez, 2013, Orphanet J Rare Dis.

29. In fact, predictions made with proper models of
functional modules overtake the predictions of
their components
The activity of the pathway is
best correlated to survival
than individual gene activities
Fey et al., Sci. Signal. (2015).
ODE used to solve the dynamics of a model
from the expression values of their
components
Problem:
ODE can
efficiently solve
only small systems

30. Two problems: defining
functional modules and
modeling their behavior
Gene ontology:
descriptive; unstructured
functional labels
Networks of Interaction,
regulation, etc.:
relationships among
components but unknown
function
Pathways: relationships
among components and
their functional roles
Models
Enrichment methods. GO, etc. (simple
statistical tests)
Connectivity models. Protein-protein, protein-
DNA and protein-small molecule interactions
(tests on network properties)
Low resolution models. Models of signalling
pathways, metabolic pathways, regulatory
pathways, etc. (executable models)
Detailed models. Kinetic models including
stoichiometry, balancing reactions, etc.
(mathematical models)

31. The behavior of a functional module can be
estimated from the behavior of their
components
Transforming gene expression levels into a different metric
that accounts for a function. Easiest example of modeling
function: signaling pathways. Function: transmission of a
signal from a receptor to an effector
Receptors Effectors
Important assumption:
collective changes in gene
expression within the
context of a signaling
circuit are proxies of
changes in protein
activation
Important fact: when the
signal reaches the end of a
circuit triggers a function

32. Signaling activity trigger cell functions
directly related to cancer progression
Estimations of signal intensity received by the effectors
that trigger a cancer-related function can be related to
clinical parameters, such as survival

33. Actually, signal activity triggers
all the cancer hallmarks
Hanahan, Weinberg, 2011
Hallmarks of cancer: the next
generation. Cell 144, 646
Negative regulation of release of cytochrome c
from mitochondria (inhibition of apoptosis)

34. Mechanistic biomarkers
show high specificity and
sensitivity
Models used for obtaining
mechanistic biomarkers
can integrate different
omics data (e.g. mutations)
Mechanistic biomarkers
can be used in the context
of prediction
Specificity Sensitivity
Some interesting features of mechanistic
biomarkers derived from models of pathway
activity

35. Future prospects:
Actionable models
The real advantage of models is that, the same way they can be used
to convert omics data into measurements of cell functionality that
provide information on disease mechanisms and drug MoA, they can
be used to test hypothesis such as “what if I suppress (or over-
express) this gen?” This lead to the concept of actionable models.
By simulating changes of gene expression/activity it is easy to:
• Direct study of the consequences of induced gene over-expressions
or KOs
• Reverse study of genes that need to be perturbed to change cell
functionalities, such as:
• Reverting the “normal” functional status of a cell
• Selectively kill diseased cells without affecting normal cells
• Enhancing or reducing cell functionalities (e.g., apoptosis or
proliferation, respectively, to fight cancer)
• Etc.

36. Actionable pathway models
KO in RAF1 geneDrugs that
target RAF1
Selected
drugs
extra
targets
Other
pathways
affected
by the KO
Specific
circuits
affected
Action
button
http://pathact.babelomics.org/

37. The use of new algorithms that enable the transformation of genomic
measurements into cell functionality measurements that account for
disease mechanisms and for drug mechanisms of action will ultimately
allow the real transition from today’s empirical medicine to precision
medicine and provide an increasingly personalized medicine
Biomedical Platforms need to evolve to
provide a real support to the transition to
precision medicine
Intuitive
Based on trial
and error
Identification of
probabilistic
patterns
Decisions and
actions based
on knowledge
Intuitive Medicine Empirical Medicine Precision Medicine
Today Tomorrow
Degree of personalization

38. The Computational Genomics Department at the Centro de
Investigación Príncipe Felipe (CIPF), Valencia, Spain, and…
...the INB-ELIXIR, National Institute of Bioinformatics
and the BiER (CIBERER Network of Centers for Research in Rare Diseases)
@xdopazo @bioinfocipfFollow us on twitter