**Real-time PCR Miner Version 4.0**

**1.**** ****Introduction**

** Real-time PCR Miner** provides a simple way for analyzing real-time
PCR data from raw fluorescence data without the need of standard curve. This
allows an automated calculation of amplification kinetics, as well as
performing the subsequent calculations for relative quantification and
calculating assay variability. Amplification efficiencies are also tested to
detect anomalous samples within groups and differences between
experimental groups (amplification equivalence). Moreover,

**Basic Use** **Explanation of the result** **Future improvement** **How to cite Miner** **How to support this website**

**2.**** ****Basic Use**

**1)****
Basic operation may be summarized
as follows**

i.
Import *raw fluorescence data*

ii.
Assign ** name** for each sample

iv.
Provide your ** email** and

v.
Set the ** direction** of the data / results

vi.
Submit your data and wait for the *result*

**2) Import
raw fluorescence data**

?

**i.
****You
can export your fluorescent raw data from the software of your Real-time PCR
machine to Microsoft Excel Sheet (can only handle data set less than 256
columns) by column or by row Please remember to include the cycle numbers of
each cycle in the first column (or row, when you have more than 255 samples).
Note, some platform like BioRad's MyIQ
and iCycler use fractional cycle number instead of
integer for each cycle. Also, you need give a name for each column (or row) at
the first row (or column). See example. For more
questions about how to exporting the pure raw data from different platforms,
please see Frequent asked
question.**

**ii.
****Please
make sure you have not imported blanks without any amplification.**

**iii.
****If
any cell miss data, Miner may response error /
warning messages. **

**iv.
****Conduct
modifications on raw data if necessary due to possible special requirements of
the platform before submit them, although it is usually unnecessary. **

**3)
****Assign
name for each sample**

**i.
****For
all requested analyses, user should give a name to each sample (at the first row of each sample column) in following format: ?/span>GeneName_ReplicateName_#No.#". Please do not use
any character except all letters, numbers, and "_". **

**ii.
****In
between of "GeneName", "ReplicateName", and "#No.#", there
must be a "_" as a
separating marker so that Miner
can use it to further calculate the mean and standard error for both replicate
group and gene group. "GeneName"
and "ReplicateName" are always required for running Miner.**

**iii.
****"GeneName" can be any name of gene you used in the
experiment.**

**iv.
****"ReplicateName" can be any label you want to use to the
sample. "ReplicateName" can be typically a label for treatment
type, time point, concentration point, or tissue type.**

**v.
****"#No.#" is a sequential label for triplicate
samples, like "1", "2", "3", etc. Although "#No.#" can be omitted, it is a good way to
distinguish the samples within replicate samples when looking for the detail
for individual sample. **

**vi.
****For
example, if there are triplicates for detecting Actin gene expression level after 24 hours
treatment, user can name the triplicate samples as "Actin_24 Hours_1", "Actin_24
Hours_2", and "Actin_24 Hours_3".**

**vii.
****Note,
please also remember to give a name for the cycle number column (or row).**

**viii.
****Copy
data from Microsoft Excel will automatically format the data. All
columns are separated by a "Tab" and each line in text area stands
for a row.**

The default setting of the ** Miner**
usually works well as long as the quality of the experiment is good. It's also
a good way to test the quality of the experiment by just simply run

**i.
****
MaxP-value:**

The *maximal *** P-value** allowed for non-linear regression by three-parameter simple
exponent model from which efficiency is calculated. The default

**ii.
****minEfficiency** and **maxEfficiency**:** **

The minimum and maximal efficiency can be custom
predefined. The default values for the minimum and maximal efficiency are 0.0 (0%)
and 2.0 (200%). In most case, those default values work fine. But if your
samples are suppose to have unusual efficiency out of the default range
(0%~200%), you can try to change these two values to cover it.
When calculating the data from lower quality or particular samples from which ** Miner**
produce error messages using default values, you can try lower minimum
efficiency or/and higher maximal efficiency.

**5)****
Provide your
email and platform you used for Real-time PCR**

**User must provide a valid Email address for
receiving result and choose or type the brand and model of the Real-time PCR
system they use for technique support. Once you submit your data, a Job_ID will generate automatically (e.g.
"976360283_381464") and you can use it to retrieve your results in
future.**

** **

**6)****
Set the
direction of the data / results **

**
Please indicate the direction of the sample in the submitted data and the
results. The default direction is by column. If you have more than 255 samples
and want use Microsoft Excel (can only handle data set with 256 columns
or less) to handle it, you need to set the direction as "by row". is The default setting is "by column".**

** **

**
i.****
Click "Submit raw data"
to submit your analysis request.**

** **

**
ii.****
Click "Reset raw data" to
reset the analysis request form to the example data and default options.**

** **

**
iii.****
Click "Clean raw data" to
clean the data in text area before you paste your own data.**

** In
most case, we recommend to use the average efficiency of each gene and the
average CTs of each replicates (replicates from the
same sample, or "PCR replicates") to do quantification. While when
you concerned the tissue or treatment specific inhibitory or activatory effect of PCR reaction for the same gene in your
experiment, you might need to use the average efficiency of replicates instead
of the average efficiency of each gene. But in this case, you had better use
more replicates (6 or more) for each group to get accurate average efficiency
from replicates because you have fewer samples to do the average (like only
three if you do triplicates). Here is an example in
Excel file for how to do the quantification using the average efficiency of
each gene and the average CT of replicates.**** **

** **

**Keywords:
SampleNames Logistic_a Logistic_b
Logistic_X0 Logistic_Y0 Logistic_Pvalue Noise(SPE) EndofExpPhase(SDM) CP(SPE) CP(SDM)
DynamicThreshold LowerCycleNumber UpperCycleNumber PointsForRegression Number_OfRegressionWindows
WeightedAverage_OfPvalue
Stderr_OfWeightedPvalue
WeightedAverage_OfEfficiency
StdErr_OfWeightedEfficiency
CT TotalSampleNumber
ReplicateSampleNames
AverageEfficiency_OfReplicatesamples
StdErr_OfReplicateSamplesEfficiency
CoefficienctVariation(CV%)_OfReplicateSamplesEfficiencies
AverageCT_OfReplicatesamples
StdErr OfReplicateSamplesCT
CV%_OfReplicateSamplesCTs
GeneNames AverageEfficiency_OfGenes
Stdev_OfGenesEfficiencies
MeanCV%_OfGenesEfficiencies
MeanCV%_OfGenesCTs Warnings Errors
**

** **

**Sample name given by user**

**Parameter "a" in four parameters Logistic model**

**Parameter "b" in four parameters Logistic model**

**4)****
Logistic_X0:**

**Parameter "X _{0}" in four parameters
Logistic model**

** **

**5)****
Logistic_Y0:**

**Parameter "Y _{0}" in four parameters
Logistic model**

**P-value of F-statistic computed for the regression using
four parameters Logistic model**

** **

**Standard deviation of noise cycles, also considered as
the start point of the exponential phase**

** ?**

**Fluorescence reading at secondary derivative maximal, also
considered as the end of the exponential phase**

** **

**Crossing point of the start point of
the exponential phase**

**Crossing point of the second positive second
derivative maximum**

**A threshold chosen for each sample dynamically based on its
own kinetics**

** **

**The lower boundary row number (or column number when data
submitted by row) of the exponential phase**

** **

**The higher boundary row number (or column number when data
submitted by row) of the exponential phase**

**The number of point eventually used for exponential phase
regression**

**15)**** Number_OfRegressionWindows:**

**The number of windows eventually used in exponential phase
for regression**

**The weighted average of the P-value of F-statistic for the
regression windows**

** **

**The standard error of the weighted average of the P-value
of F-statistic for the regression windows**

**18)**** WeightedAverage_OfEfficiency:**

**The weighted averaged efficiency of each sample**

**19)**** StdErr_OfWeightedEfficiency:**

**The standard error of the weighted averaged efficiency for
each sample**

**20)**** CT:**

**The fractional cycle number of dynamic threshold value of
the sample**

**The total number of the sample in the submitted data set**

**Partial name of the sample ("GeneName_TreatmentName")
of the full name ("GeneName_TreatmentName_No."),
representing the replicate samples**

**23)**** AverageEfficiency_OfReplicateSamples:**

**The mean Efficiency of the replicate samples**

**24)**** StdErr_OfReplicateSamplesEfficiency:**

**The standard error of the Efficiency for replicate
samples**

**25)**** CoefficientVariation (%)_OfReplicateSamplesEfficiencies:**

**Coefficient Variation (%) of Efficiencies within replicate
samples:**

** ?**

**26)**** AverageCT_OfReplicateSamples:**

**The mean CT of replicate samples**

**27)**** StdErr_OfReplicateSamplesCT:**

**The standard error of the CT for replicate samples**

**28)**** CV%_OfReplicateSamplesCTs:**

**Coefficient Variation (%) of CT within replicate samples:**

**Partial name of the sample ("GeneName")
of the full name ("GeneName_TreatmentName_No."),
representing the detecting gene for a set of samples**

**30)**** AverageEfficiency_OfGenes:**

**The mean Efficiency of each gene**

**The standard deviation of the Efficiency for each gene**

**32)**** MeanCV%_OfGenesEfficiencies:**

**The mean coefficient variation (%) of Efficiency for each
gene**

**The mean coefficient variation (%) of CT for each gene**

**34)**** Warnings:**

**Warning messages found by Miner**

**35)**** Errors:**

**Warning messages found by Miner**

** **

**1)**** Currently, Miner works
perfectly with MyIQ and iCycler
system from Bio-Rad. Some raw data from ABI PRISM^{TM} 7700, 7900,
Stratagen MX 3000, 4000, Roche LightCycler,
MJ Research DNA Engine Opticon2, etc. also passed tests successfully. Data
samples from various platforms are welcome to be sent to us for additional
tests so that we can make Miner more powerful and cross more platforms. **

** **

**2)**** We are continually updating Miner and this
website. Your inputs and discussions will always welcome.**

** **

**3)**** Additional options for data analysis may be
included in the future.**

** **

**5.**** ****How to cite Miner**

** **

**Sheng Zhao, Russell D. Fernald. Comprehensive algorithm for quantitative real-time polymerase chain
reaction. J. Comput.
Biol. 2005 Oct;12(8):1045-62. PubMed and PDF **