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Genome 
Data 
Interpreta-on: 
How 
to 
Ease 
the 
Bo5leneck 
Hannes 
Smarason 
Genome 
Sequencing 
|Personalized 
Medicine 
| 
Transforming 
Health 
Care
Breaking 
the 
Data 
“Bo5leneck” 
While 
various 
systems 
have 
been 
established 
in 
recent 
years 
to 
store 
the 
large 
amounts 
of 
genomic 
data 
from 
pa>ents’ 
DNA, 
a 
remaining 
obstacle 
is 
to 
“break 
the 
boFleneck” 
so 
that 
researchers 
can 
process 
the 
vast 
data 
in 
mul>ple 
human 
genomes 
in 
order 
to 
iden>fy 
and 
isolate 
a 
small, 
useful 
piece 
of 
informa>on 
about 
disease.
Breaking 
the 
Data 
“Bo5leneck” 
• Conven>onal 
databases 
and 
algorithms 
have 
not 
been 
able 
to 
efficiently 
and 
reliably 
iden>fy 
subset 
informa>on 
among 
the 
millions 
of 
gene>c 
markers 
in 
order 
to 
inform 
clinical 
decisions. 
This 
has 
become 
a 
major 
data 
management 
roadblock. 
• The 
key 
is 
to 
find 
new 
approaches 
for 
databases 
and 
algorithms 
that 
accommodate 
the 
unique 
ways 
that 
genomic 
informa>on 
is 
analyzed 
and 
interpreted.
Improving 
the 
Efficiency 
of 
Storage 
and 
Analysis 
As 
discussed 
in 
Bloomberg 
BNA, 
Diagnos>c 
Tes>ng 
& 
Emerging 
Technologies, 
NextCODE 
is 
already 
easing 
this 
boFleneck 
by 
providing 
a 
qualita>vely 
different 
way 
to 
store 
and 
analyze 
genomic 
informa>on 
and 
apply 
it 
to 
meet 
the 
growing 
opportuni>es 
for 
personalized 
medicine.
Efficient 
Database 
Organiza-on 
• NextCODE’s 
Genomically 
Ordered 
Rela>onal 
(or 
GOR) 
database 
infrastructure 
is 
a 
truly 
different 
way 
of 
storing 
this 
huge 
amount 
of 
data. 
The 
principle 
is 
very 
simple: 
– Rather 
than 
store 
sequence 
and 
reference 
data 
in 
vast 
unwieldy 
files, 
it 
>es 
data 
directly 
to 
its 
specific 
genomic 
posi>on. 
– As 
a 
result, 
the 
algorithms 
are 
vastly 
more 
efficient 
compared 
to 
a 
tradi>onal 
rela>onal 
database 
because 
they 
can 
isolate 
by 
loca>on 
in 
the 
genome. 
• That 
makes 
analysis 
faster, 
more 
powerful 
and 
radically 
more 
efficient, 
both 
in 
terms 
of 
clinicians’ 
and 
researchers’ 
>me, 
as 
well 
as 
computer 
infrastructure, 
I/O 
and 
CPU 
usage.
Holis-c 
Approach, 
Broad 
Applicability 
This 
holis>c 
approach 
applies 
broadly 
to 
the 
priori>es 
of 
genome 
scien>sts 
around 
the 
world, 
helping 
them 
eliminate 
the 
data 
management 
boFleneck 
to 
iden>fy 
more 
culprits 
to 
many 
inherited 
diseases, 
more 
quickly 
and 
cost 
effec>vely. 
Read 
more 
about 
NextCODE’s 
work 
here.

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Genome Data Interpretation: How to Ease the Bottleneck

  • 1. Genome Data Interpreta-on: How to Ease the Bo5leneck Hannes Smarason Genome Sequencing |Personalized Medicine | Transforming Health Care
  • 2. Breaking the Data “Bo5leneck” While various systems have been established in recent years to store the large amounts of genomic data from pa>ents’ DNA, a remaining obstacle is to “break the boFleneck” so that researchers can process the vast data in mul>ple human genomes in order to iden>fy and isolate a small, useful piece of informa>on about disease.
  • 3. Breaking the Data “Bo5leneck” • Conven>onal databases and algorithms have not been able to efficiently and reliably iden>fy subset informa>on among the millions of gene>c markers in order to inform clinical decisions. This has become a major data management roadblock. • The key is to find new approaches for databases and algorithms that accommodate the unique ways that genomic informa>on is analyzed and interpreted.
  • 4. Improving the Efficiency of Storage and Analysis As discussed in Bloomberg BNA, Diagnos>c Tes>ng & Emerging Technologies, NextCODE is already easing this boFleneck by providing a qualita>vely different way to store and analyze genomic informa>on and apply it to meet the growing opportuni>es for personalized medicine.
  • 5. Efficient Database Organiza-on • NextCODE’s Genomically Ordered Rela>onal (or GOR) database infrastructure is a truly different way of storing this huge amount of data. The principle is very simple: – Rather than store sequence and reference data in vast unwieldy files, it >es data directly to its specific genomic posi>on. – As a result, the algorithms are vastly more efficient compared to a tradi>onal rela>onal database because they can isolate by loca>on in the genome. • That makes analysis faster, more powerful and radically more efficient, both in terms of clinicians’ and researchers’ >me, as well as computer infrastructure, I/O and CPU usage.
  • 6. Holis-c Approach, Broad Applicability This holis>c approach applies broadly to the priori>es of genome scien>sts around the world, helping them eliminate the data management boFleneck to iden>fy more culprits to many inherited diseases, more quickly and cost effec>vely. Read more about NextCODE’s work here.