Biomedical Engineering Reference
Sample Processing and Standardization of Capture
and Sequencing Platforms
Sample processing for NGS is a much more complex process compared to Sanger
sequencing. It typically includes DNA isolation, library generation, target enrich-
ment, barcoding, and massive parallel sequencing (Mamanova et al. 2010 ). In addi-
tion, there are many new NGS platforms and chemistries that are being invented and
introduced by different companies. Each capture and sequencing platform has its
advantages and disadvantages. Unfortunately, clinical laboratories have not reached
an agreement as to which technology is best for clinical testing, in part, because the
technologies are new and keep rapidly improving. For example, the major suppliers
for pre-sequencing library generation and target enrichment include SeqCap EZ
Library series from NimbleGen-Roche, Nextera Rapid Capture Exome, TruSight
Portfolio from Illumina, and SequalPrep and SOLiD Fragment Library Construction
from Life Technologies.
Rapid adoption of the NGS technologies by clinical laboratories is often chal-
lenging because of the cost, validation time, specialized staff training, and lack of a
sequencing instrument standard. Different types of sequencing chemistries are com-
mercially available which include sequencing by synthesis, sequencing by ligation
with reversible terminators, capture hybridization, and ion sensing (Glenn 2011 ).
However, there is a lack of NGS instrument standard in clinical laboratories.
Adopting such NGS technologies in clinical laboratories takes time because each
sequencing chemistry requires its own instrument and the related purchasing cost,
needs its own standard operation protocols (SOPs), generates its unique format of
sequencing output, and the sequencing performance parameters must be established
by extensive validation studies. Even the most seasoned medical technologists often
have no NGS testing experience and it is the clinical laboratory's responsibility to
provide costly and time-consuming training opportunities to be competent in han-
dling patient samples for NGS clinical tests.
Given the large amount of sequence data produced by NGS platforms, it is critical
to have an effi cient, reliable, and fast data handling and processing pipeline. When
the sequencing is complete, sequence reads are aligned and analyzed against the
consensus sequences and existing public databases (dbSNP and HGMD) to detect
sequence variants. This requires extensive bioinformatics support and hardware
infrastructure. In addition, the clinical laboratory must have its SOP to guarantee
that the analytical pipeline can accurately track sample identity, particularly if bar-
coding is used. There are many challenges to assure the accuracy and reproducibil-
ity in data analyses. NGS data analysis can be divided into four basic steps: base
calling, read alignment, variant calling, and variant annotation and fi ltering.