It is not the strongest of the species
that survives, not the most intelligent, but the one most responsive to change."
High Throughput Life Science Research
high throughput research
process optimization software
enables gene sequencing, gene expression, genotyping, proteomics,
biomolecular screening, and other high throughput research laboratories to
optimize their processes, increase their yield, improve their throughput, and
lower their overall operating cost.
analyzes the performance of
all individual factors (libraries, machines, personnel, reagents, etc.) and
factor sets (Library=Human7 and Machine=Thermocycler41 and Operator=JTS)
associated with the process on an hourly or daily basis and pinpoints the
factors and factor sets associated with all yield losses and gains. Once
is configured, it runs
automatically and notifies appropriate personnel of performance anomalies based
on user specified criteria. Focus
enables high throughput
research laboratories to not only survive but to thrive by accelerating their
response to change.
Competitive pressures have
forced both the not-for-profit and the for-profit life science research
communities to embrace high throughput research. While high throughput processes
have dramatically increased the pace of life science research, they have also
dramatically increased the complexity of research processes. As these processes
have grown more complex, they have also grown more sensitive to even small
process changes, making process optimization and process yield management
essential to remain competitive.
Yield A simple concept with a complex reality
Research process yield
is a simple concept. The diagram
below represents the process flow through a typical high throughput gene
sequencing laboratory. Samples are
transformed through ten process steps into DNA sequence chromatograms.
Each chromatogram is then processed by a base calling program such
as Phred and assigned a quality value.
Sequencing attempts that meet an established quality threshold are a
success. Those that do not are a
failure. The yield for this example
process is simply the ratio of the number of successful sequencing attempts to
the total number of sequencing attempts for a given period.
yield is simple in concept, it has a complex reality.
The diagram below provides an example of the complexity inherent in any
high through research process.
Samples are likely to come from many different sources.
Process step throughput rate incompatibilities require that each step be
performed by a number of different work cells.
All of the work cells in any given process step may not use the same make
and model of equipment or batch or manufacturer of reagents.
Add to this, shift-to-shift and operator-to-operator differences and the
many other factors that affect overall process performance and the simple
concept of yield displays a very complex reality.
Eliminating Yield Loss
Overall yield loss is made up of a number of specific
individual yield losses, each with a unique cause.
The cause of simple, single factor induced losses, such as when the
temperature of a single machine is outside of its specified operating range, can
easily be identified by SPC (Statistical Process Control).
The comparative yield performance of members of a single resource type,
such as the electrophoresis instruments in a gene sequencing process, can be
determined by POV (Partition Of Variance) or other ANOVA (Analysis Of Variance)
However, these techniques are of little use in the extremely difficult
task of identifying the specific factors involved in a multi-factor-induced loss
where two or more process factors interact or conspire to cause a specific
For example, a temperature on a thermocycler at one process step
interacts with a reagent used in the next process step, and an electrical field
produced by an instrument several steps later in the process to produce an
unexpected yield change.
In complex high throughput research processes, the interactions of even
within specified operating range process factors are far too complicated
to predict the emergence of such yield changes.
In the past, process managers have relied on DOE (Design Of Experiments) to
identify the factor set associated with a multi-factor induced yield loss.
However, using DOE requires that an expert in the process under study
hypothesize theories of cause and effect for each yield loss, then design,
precisely execute, and rigorously analyze the results of experimental process
scenarios to test the accuracy of each hypothesis.
DOE often requires a number of experimental cycles before the process
factors associated with a specific yield loss can be identified.
Due to the time and cost associated with the experimental process, DOE is
usually reserved for only the largest individual yield losses.
Smaller individual yield losses, aggregated together, often represent a
significant portion of overall yield loss, but are usually not viewed as
practical DOE candidates.
DOE is hypothesis
driven. Focus, on the other hand, is
discovery driven. Focus does not require
an hypothesis. Focus treats each sample
run through a research process as an experiment and uses proprietary algorithms
to test all possible hypotheses by those experiments.
High Throughput Research Process Optimization
Internationals Focus high throughput
research process optimization software
rapidly identifies the specific
process factors associated with every individual cause of yield loss, reducing
from days to minutes the time required to identify the factors associated with
any specific yield loss. Focus
also identifies the factors associated with every yield gain enabling unexpected
process improvements to be identified and capitalized on.
to the generic high throughput research process example used above, a sample may
take the path through that process shown in blue in the diagram below. At each process step a sample tracking or laboratory information
management system would collect data about the factors associated with that step
at the time the sample was being processed. These factors could include instrument manufacturer, instrument model,
instrument ID, sample hold time from the previous process step, date, time,
shift, operator(s), reagent manufacturer(s), reagent lot numbers, protocols,
pressures, temperatures, flow rates, voltages, currents, hours since the
equipment was last serviced, number of samples processed since the equipment was
last serviced, technician(s) last servicing the equipment, and any other
compares the performance with every process factor, individually and in
combination with every other process factor associated with every process step
providing a comparison between the performance of individual factors and groups
of factors or factor sets in the process overall.
In addition, Focus
calculates the impact on the number of samples successfully processed based on
the performance of each factor set. The
data contained on the first line of the segment of a Focus
Results Table shown below indicates that for the 13,248 samples that where
processed through (associated with) both Amplifier Cycler 1229 and Cycle
Sequence Cycler 192 the Yield loss was 2.92 times the target yield loss which
resulted in the loss of an additional 1275 samples.
The number in parentheses around each of the individual factors in a
multi-factor Factor Set indicates the Relative Yield Loss for that factor alone.
As is evident from the data on this line, the interaction between two
poorly performing factors with a relative yield loss of 1.75 (75% worse than the
target yield loss) and 1.53 (53% worse than the target yield loss) respectively,
created a much worse performing Factor Set with a Relative Yield Loss of 2.92
(192% worse than the target yield loss). However,
even though the first lines Factor Set was the worst performing of all of
those show, each of the other three Factor Sets had a greater impact on the
number of samples lost do to their association with a greater number of samples
overall. This is because Impact is
based on Relative Yield Loss AND Units In.
of the Focus Results Table shown below is from the same analysis as the one
above and shows some of the best performing Factor Sets analyzed.
While most Aliquot Primer Lots had a Relative Yield Loss of between .90
and 1.10 in this analysis, Aliquot Primer Lot performed significantly better
with a Relative Yield Loss of .70, 30% better than the target yield loss.
Such unexpected yield gain may warrant further investigation as they may
represent process enhancement opportunities.
facilitate further study, Focus can produce
trend charts, loss category charts, histograms, summary charts, and source data
statistics reports based on any individual factor set or group of factor sets.
most high throughput life science research is done using 96, 384, or 1536 well
microtiter trays or some other multi-unit carrier to facilitate automation, Focus
can analyze both tray/plate based data and well/sample based data.
When analyzing well/sample data, Focus
can produce well charts graphically illustrating the performance of each
individual well location. Well
charts allow for the rapid identification of well related problems with
automation systems and plate region (edge, corner, center, etc.) problems.
is a Microsoft Windows client server application. Well/sample or tray/plate process history records are transferred hourly
or daily from the sample tracking system or laboratory information system server
to the Focus server. The Focus
server automatically runs user defined analysis, charts analysis results, and
notifies the specified users by e-mail and/or pager when specific user defined
conditions are present in the analysis results.
client software is used to define the analysis, charting, and notification
parameters to be run automatically and to perform ad hoc analysis and charting.
of life science research today is based on high throughput research processes.
The key to successful high throughput research is to optimize research
BergenShaw Internationals Focus
software product is the worlds most advanced high throughput research process
enables high throughput laboratories to optimize their research processes, thus
their yield, improving their throughput, and
lowering their overall operating cost. Once configured, Focus
enables your laboratory to be the one most responsive to change.