Four Factors for Improving Genomic Data for Personalized Medicine
Four Factors for Improving Genomic
Data for Personalized Medicine
Genome Sequencing |Personalized Medicine | Transforming Health Care
We’ve come a long way in improving the way
that a patient’s genome sequence data is
analyzed and interpreted to realize the full
potential of personalized medicine.
Here are four factors helping to overcome
barriers and achieve new milestones for using
genomic data to provide faster, more accurate,
and more in-depth information to guide
clinicians in delivering personalized care for
Factor #1: Fast database query of
Problem: Relational database architectures make it possible to store
large quantities of sequencing data, but querying whole genome data
can be time-consuming and take days to weeks.
Solution: The GOR (Genomic Ordered Relations) database is able to
query whole-genome sequences in real time. The reason is that GOR
understands the genome in terms of chromosomes, its natural
structure, rather than as a continuous string of sequence.
When searching for a variant, tools in the GOR architecture don’t have
to scan each individual’s entire sequence; they retrieve the variant
straight from its location.
The GOR database was pioneered a decade ago by deCODE genetics,
one of the first organizations to manage truly large genetic datasets,
and is now being used by NextCODE for clinical applications of
Factor #2: Fast, reliable
identification of gene variants
Problem: Many sequencing analysis pipelines are only powered to
process data in a compressed format called Variant Call Format (VCF)
files. These comprise only a tiny fraction of the genome, and being
working only with VCF files makes it difficult to correct common
alignment and allele-calling errors. That can result in both false
positive and negative results, or missing the key variants altogether.
Solution: The foundation for improved sensitivity and specificity is the
ability to use VCF data on top of the raw sequence data from which it
was derived. NextCODE’s pipeline and clinical interfaces, powered by
GOR, give users the ability to visualize raw sequence data at a click.
This approach enables genomic analysis and interpretation by seeking
out disease-causing genetic variants, either in specific patients, or for
research studies in a clinical setting.
Factor #3: Clinician access to
patient genomic information
Problem: Many of today’s genomic interpretation tools are too
complex and difficult to use by clinicians who may have minimal
experience with genetic informatics tools.
Solution: All of the complex informatics required by a clinical analysis
tool should disappear at the fingertips of a clinician. It starts by having
a robust foundation to the informatics platform, and using the GOR
database architecture enables rapid cycling between personal
sequence data and broad clinical knowledge.
The result is the Clinical Sequence Analyzer (CSA) in which clinicians
can simply type in a patient’s symptoms, and CSA will search the
patient’s whole genome for variants that may be relevant.
Factor #4: Applying the full
power of WGS to tumor analysis
Problem: Many of today’s approaches to the cancer genome analysis only
look at the immediate next step for a course of treatment – an important
capability, but only part of a holistic view of the genetic profile of a patient’s
cancer and what can be done to fight it.
Solution: The Tumor Mutation Analyzer makes a more holistic approach
possible, analyzing a whole exome or whole genome sequence from a
patient’s own genome and from tumor cells. The distinguishing feature of
TMA is the depth of the data it stores and the unprecedented level of detail it
provides to more accurately identify variations.
This level of detail is especially important in cancer genetics, where the
chances of finding previously unknown variants are very high. Even if a
mutation is successfully targeted with a course of treatment, another
potential driver is often waiting in the wings.
The pace of progress has been astounding with
advances in the use of genomic information for
patient care. How will the path continue in the
future? Stay tuned.