The document discusses the impact of next-generation sequencing (NGS) technology on genomic medicine, highlighting its ability to produce large datasets that enhance disease research and diagnosis. It outlines the challenges posed by big data in terms of processing, storage, and analysis while emphasizing the need for advanced computational technologies and collaborative efforts in bioinformatics. Furthermore, it highlights the establishment of resources and facilities, including a cloud-based platform, to facilitate genomic sequencing and improve healthcare outcomes.

1. GUILLERMO ANTIÑOLO
DIRECTOR DE LA UNIDAD DE GESTIÓN CLÍNICA DE GENÉTICA, REPRODUCCIÓN Y MEDICINA
FETAL DEL HOSPITAL UNIVERSITARIO VIRGEN DEL ROCÍO
DIRECTOR CIENTÍFICO MGP/GBPA
PROFESOR TITULAR DE OBSTETRICIA Y GINECOLOGÍA
DE LA UNIVERSIDAD DE SEVILLA

5. Healthcare in the 21st Century
Genomic Medicine and Personalized Medicine

6. Most common applications of NGS
Resequencing
Resequencing
RNA-seq /Transcriptomics oo Mutation calling
Mutation calling
RNA-seq /Transcriptomics
oo Quantitative
Quantitative oo Profiling
Profiling
oo Descriptive
Descriptive ooGenome annotation
Genome annotation
Alternative splicing
Alternative splicing
oo miRNA profiling
miRNA profiling
De novo sequencing
De novo sequencing
Exome sequencing
Exome sequencing
Targeted
Targeted
ChIP-seq /Epigenomics
ChIP-seq /Epigenomics
oo Protein-DNA interactions
Protein-DNA interactions
sequencing
sequencing
oo Active transcription factor binding sites
Active transcription factor binding sites
ooHistone methilation
Histone methilation Copy number variation
Copy number variation
Metagenomics
Metagenomics
Metatranscriptomics
Metatranscriptomics

7. Introduction
Next-Generation Sequencing (NGS) technology is changing the way
Big data in Biology, a new scenario
how researchers perform experiments. Many new experiments are
being conducted by sequencing: exome re-sequencing, RNA-seq,
Meth-seq, ChIP-seq, ...
NGS is allowing researches to:
● Find exome and genomic variants responsible of diseases
● Study the whole transcriptome of a phenotype
● Establish the methylation state of a condition
● Locate DNA binding proteins
But experiments have increased data size by 1000x when compared
with microarrays, i.e. from MB to hundreds of GB in transcriptomics
Data processing and analysis are becoming a bottleneck and a
nightmare, from days or weeks with microarrays to months with
NGS, and it will be worse as more data become available

8. Nat Genet. 2010 Jan;42(1):13-4.
Exome sequencing makes medical genomics a reality.
Biesecker LG.

9. Relative throughput of the
different HT technologies
NGS emerges with a
potential of data production
that will, eventually wipe out
conventional HT technologies
in the years coming
Too many sequences to be handled and stored
in standard computers

10. The Pursuit of Better and more Efficient Healthcare
as well as Clinical Innovation through Genetic and
Genomic Research

11. Clinical Service, Hospital
& Health System
(AHS)
Text Text
Translationa Pharma
l Science MGP & Biotech
Institute Text Text
(GBPA)
Public-Private-Partnertship

12. MGP Research Goals
 To sequence the genomes of clinically well
characterized patients with potential mutations in
novel genes.
 To generate and validate a database of genomes
of phenotyped control individuals.
 To develop innovative bioinformatics tools for the
detection and characterisation of mutations using
genomic information.

13. 11 Megasequencing Platforms
Two technologies to scan for variations
Structural variation
•Amplifications
454 Roche
•Deletions
Longer reads
•CNV
Lower
•Inversions
coverage
•Translocations
Variants
SOLiD ABI •SNPs
Shorter reads •Mutations
Higher •indels
coverage

14. Big data challenges and solutions
 “Big data is a collection of data sets so large and complex that it
becomes difficult to process using on-hand database management
tools or traditional data processing applications”
 Big data is not a new scenario for other science areas: astronomy,
physics, internet search, finance, business, ...
 Which are the main Big data challenges?: curation, search, sharing,
storage, analysis and visualization
 We need to study and use new computational technologies :

High-Performance Computing (HPC): multi-core CPUs, SSE/AVX, GPUs

Distributed computing: Apache Hadoop MapReduce, MPI

Distributed and NoSQL databases: Apache Cassandra, HBase, …

Web apps: HTML5 (SVG, WebGL, ...), Javascript, RESTful WS, ...

Clouds: Amazon AWS, Google Cloud, Microsoft Azure, …
 Biomed: Machine learning, data mining, clustering, probablistic
graphicals models, visualization, health infprmation management and genomic
data...

15. New Solutions for
Big Data Analysis,
Storage and
Visualization
HPC and Cloud-based solutions

16. Bioinformatics Unit at MGP/GBPA
24 High Performance Computing nodes – 72-192 Gb RAM
2 Control nodes - 24 Gb RAM
o 2 x Quad core CPU
o 16 threads
o 2 x 10Gb Network interface
Execution of 400 jobs in parallel
Storage 540 Tb total

17. NGS pipeline,
a HPC implementation for Bioinformatic analysis
NGS
Fastq file, up to hundreds of GB per run
sequencer
QC stats, filtering and
QC and preprocessing preprocessing options
HPG suite
High-Performance Genomics HPG Aligner, short read
Double mapping strategy:
Burrows-Wheeler Transform (GPU Nvidia CUDA)
aligner +
Smith-Waterman (CPU OpenMP+SSE/AVX)
More info at:
SAM/BAM file
http://bioinfo.cipf.es/docs/compbio/projects
/hpg/doku.php
QC stats, filtering and
QC and preprocessing preprocessing options
Variant calling analysis GATK and SAM mPileup HPC
Other analysis Implementation.
(HPC4Genomics consortium) Statistics genomic tests
RNA-seq (mRNA sequenced) VCF file
DNA assembly (not a real
analysis)
Meth-seq
Copy Number QC and preprocessing
Transcript isoform QC stats, filtering and
... Variant VCF viewer preprocessing options HPG Variant, Variant
HTML5+SVG Web based viewer analysis
Consequence type, GWAS, regulatory
variants and system biology information

19. “…Me parece increíble e injustificable el abismo
que existe entre los resultados de las
investigaciones y su aplicación cotidiana a los
enfermos…”
Eduard Punset

20. Genomic and Personalized Medicine
Patient Genomic core facility
1) Genomic sequencing
2) Database of markers/variants/mutations
3) Genetic/Genomic Diagnosis
4) Therapy/preventive intervention
Pre-symptomatic:
Clinician receives hints on • Genetic predisposition of acquired diseases (>6000.
some treatable)
Dx, and possible
Early and faster diagnosis of genetic
preventive therapeutic
diseases
and/or interventions
Symptomatic analysis
• Diagnostic of acquired diseases
• Early cancer detection
• Cancer treatment recommendation

22. Inherited Retinal Distrophies (IRDs)
 Prevalence 1 in 3000
 Clinically and genetically very heterogeneous
 190 GENES account for aprox. 50% of IRDs.
Families with
digenism
Families with Families with
unknown Families with
known
mutations one mutant
mutations
allele
Diagnosed
families

23. Genetic overlapping among IRDs
BBS
ARL6,, BBS2, BBS4,
BBS5, BBS7, BBS9,
LCA BBS10, BBS12,, INPP5E,
LZTFL1, MKKS, MKS1,
LCA5, SDCCAG8, TRIM32, TTC8 CORD/COD
RD3 CACNA1F,
CEP290
CACNA2D4
CVD
GNAT2
CRB1, IMPDH1, BBS1 CABP4, GRK1,
CORD/COD AIPL1, LRAT, MERTK, GRM6, NB
GUCY2D, RDH12, RPE65, PDE6B, NYX,
RPGRIP1 SPATA7, TULP1 RHO, TRPM1
ADAM9,
GUCA1A, CRX SAG
C2ORF71, C8ORF37,
HRG4/UNC119, LCA-Leber Congenital Amaurosis
CA4,CERKL, CNGA1, CNGB1,
KCNV2, PDE6H,
PITPNM3, RAX2,
RLBP1, DHDDS,EYS, FAM161A, IDH3B,KLHL7 CORD/COD- Cone and cone-rod dystro.
RDH5, RIM1
SEMA4A IMPG2, MAK, NRL, PAP1, PDE6A,
PDE6G, PRCD, PRF3, PRPF8, PRPF31
RP CVD- Colour Vision Defects
ABCA4, MD- Macular Degeneration
CNGA3, PROM1, RBP3, RGR, ROM1, RP1, RP2,
ERVR/EVR- Erosive and Exudative
CVD PDE6C PRPH2,
FSCN2,
SNRNP200, TOPORS, TTC8
ZNF513 Vitreoretinopathies
BCP, RPGR CLRN1,
GUCA1B USH2A USH- Usher Syndrome
GCP,
C1QTNF5,
BEST1
ABHD12, CDH23, CIB2, RP- Retinitis Pigmentosa
RCP EFEMP1, NR2E3 DFNB31, GPR98, NB- Night Blindness
ELOVL4, HARS, MYO7A,
PCDH15, USH1C,
BBS- Bardet-Biedl Syndrome
HMNC1, FZD4, KCNJ13,
RS1, LRP5, NDP, USH1G
TIMP3 TSPAN12, VCAN
MD USH
ERVR/EVR

24. Molecular Genetics of RP
RPLX UN
ADRP 7% 3% RPE
 Variety of inheritance patterns. 15%
40%
 Autosomic Recessive RP (arRP)  most common.
 Allelic and locus heterogeneity. 35%
 62 genes have been associated with RP  responsible
of 2/3 of cases ARRP
 EYS  one of the most prevalent responsible of 15 % of arRP cases.
EYS
RP22, RP29, RP32 ABCA4,
BEST1, C2ORF71, CERKL,
CNGA1, CNGB1, CRB1,
FAM161A, IDH3B, IMPG2,
LRAT, MERTK, NR2E3, NRL,
PDE6A, PDE6B, PDE6G,
PRCD, PROM1, RBP3, RGR,
RHO, RLBP1, RP1, RPE65,
SAG, SEMA4A, SPATA7, TTC8, Unknown
TULP1, USH2A, ZNF513…

25. Clinical Diagnosis: ARRP
APEX RESEQUENCING
(Commercially available) (Custom design)
CERKL
CNGA1, EYS
CNGB1, PROM1
MERTK PRCD
PDE6A NR2E3
PDE6B LRAT
PNR IDH3B
RDH12 CERKL
RGR, TULP1
RLBP1 RPE65
SAG RLBP1
TULP1 RHO
CRB RGR
RPE65 PDE6B
USH2A CRB1
USH3A CNGA1
LRAT, MERTK
PROML1
PBP3

26. Summary after WES
INITIAL
INCORRECT
CLINICAL
DIAGNOSIS
INITIAL
INCOMPLETE
CLINICAL
DIAGNOSIS

27. Mutación si
conocida?
Diagnóstico
no si
Mutación si Se
en gen Validación confirma?
conocido?
no no
Mutación en si
gen
relacionado?
no

28. Next steps
cloud-based and open solutions
 cloud-based environment integration ready, codename: GASC

Storage: efficient storage and data retrieval of ~TB, transparent
connection to others clouds such as Amazon AWS or Microsoft Azure

Analysis: many tools ready to use (aligners, GATK, …), users can
upload their tools to extend functionality, SGE queue, …
 Search and access: data is indexed and can be queried efficiently,
RESTful WS allows users to access to data and analysis programatically
 Sharing: users can share their data and analysis, public and private data
 Visualization: HTML5-SVG based web applications to visualize data
 Open development initiative

HPG project, CellBase, Genome Maps, GASC, … released as open
source development initiative
 Source code controlled with Git, hosted freely in GitHub
 Scientist are encouraged to collaborate and extend functionality, a
HPC4G consortium from universities already created

29. High-throughputtechnologies such as NGS is
pushing BioMedicine into Big Data
We must learn how to deal with this huge amount
of data to translate it into clinically relevant
information
This new scenario demands new solutions as well
as new computational technologies
Open development model allows researchers to
join forces and build up better solutions

30. Joaquín Dopazo Javier Santoyo

31. UGC Genética, Reproducción y
Medicina Fetal
Hospital Universitario
Virgen del Rocío
Sevilla
Salud Borrego Alicia Vela Nacho Medina
Cristina méndez Antonio Rueda
María Gónzalez F.J. López
Lorena Fernández
Nereida Bravo