Hands-on Application Workshops @GQ2023 Please direct your enquiry to our scientific organisation team, headed by Michael W. Pfaffl GeneQuan.physio@ls.tum.de
Basic hands-on qPCR (2-days) hosted by
TATAA Biocenter
© 2022 - 2023
Seminar room 85 @HEF Target Audience Researchers interested in analyzing nucleic acids Experience Qualifications Basic knowledge about DNA & RNA and molecular analyses Description This
is a basic real-time qPCR course for people
starting with qPCR or is working with the
technique and want to understand it deeper.
The course starts with describing how the
technology works, different types of detection
technologies and instruments. Then you will
learn how to design primers and probes and
optimize new assays. You will get an
understanding of the qPCR reaction so you are
able to troubleshoot and improve your own
experiments. During the first day a standard
curve hands-on experiment will be performed
where we will practically show how to evaluate
amplification curves and handle qPCR
instrument software.
The second
day will cover how to do relative
quantification with qPCR, how to find stable
reference genes and do proper normalization.
Several quantification examples will be
demonstrated with calculations of ΔCq, ΔΔCq
and efficiency corrected ratios. During
hands-on lab you will receive relative
quantification data that you will perform
calculations with yourself with a few
different methods. You will get an
understanding of how Cq-values, thresholds and
efficiency affect your quantification results.
During the day you will also learn how to do
absolute quantification, work with standard
curves and how to properly validate your qPCR
assay performance.
MicroRNA Analysis (2-days) hosted by TATAA Biocenter & MultiD Seminar room 86-87 @ HEF Target Audience Researchers interested in analyzing microRNA Experience Qualifications Basic knowledge about microRNA and molecular analyses Description This course
introduces miRNA and its variant forms.
Methods to extract and purify microRNA and
prepare it for analysis, including quality
control. Methods for microRNA expression
analysis are presented and compared, to learn
how to choose the best method for each study.
Analysis of microRNA data is also discussed,
including challenging normalization.
Web-based
qPCR data analysis (1-day)
hosted by Dr A Untergasser, Dr MJB van den
Hoff and Dr JM Ruijter
Unbiased platform-independent analysis of qPCR data - from fluorescent data to publishable graph. Seminar
room 88 @ HEF
Target Audience Advanced qPCR users interested in measuring nucleic acid levels using DNA binding dyes Experience Qualifications Experience in analyzing qPCR data beyond the Cq Description The Web-based qPCR data
analysis workshop includes the analysis
of amplification and melting curves,
calculation of efficiency-corrected
target quantities, reference gene
validation, between-run correction and
basic statistical comparison of groups
of biolgoical replicates. This workshop
uses the web-based qPCR data analysis
pipeline implemented in the RDML-tools.
You can find the free RDML-tools
=> RDML-tools
Though discussed for two decades, the Covid pandemic has univocally illustrated the poor interlaboratory reproducibility of RT-qPCR results reported as just Cq (or Cp or Ct) values. Although integrating the PCR efficiency in the analysis has been shown to overcome most of this problem, its application was never considered essential. Moreover, determining the PCR efficiency seemed to require extra reactions and analysis steps. To overcome this limitation, a free, fast, assumption-free, platform-independent, and publicly available integrated pipeline for the analysis of qPCR experiments was developed. This set of tools, based on earlier papers and programs of our group, is based on the RDML platform and hosted on the EMBL server. Upon input of the raw fluorescence data, the amplification curve and melting curve of each individual qPCR reaction are analyzed automatically and interactively, and for each individual reaction the efficiency-corrected starting concentration of the target is reported. A comprehensive error assignment flags reactions and assays that do not conform the criteria for positive and negative controls. Optionally, statistical outlier detection can be applied to identify deviant replicates. Moreover, the platform holds tools to identify valid references genes and to remove between-plate variation in multi-plate qPCR experiments. Target quantities per reaction can then be converted into normalized and relative expression levels according to the experimental design and bar graphs, including data points per reaction, can be drawn. The 1-day workshop will provide you with the skills to analyze qPCR runs in an unbiased way and present the efficiency-corrected results in graphs or in spreadsheets for further analysis. The workshop will consist of short background lectures and extensive hands-on exercises which you will run on your own laptop. Program Thursday March 23, 2023 from 9.00 to 17.00 1: Basics on amplification curve analysis (9.00-10.00) Theory: Basics of amplification curve analysis, with PCR Efficiency determination from the exponential phase and calculation of the efficiency-corrected target quantity (N0) per reaction (with Nq, Cq and mean E) Hands-on: Using RDML-LinRegPCR with a provided RDML-data file to get acquainted with the RDML-tools 2: Basics on melting curve analysis (10.15-11.15) Theory: Basics on melting curves analysis and the option to correct N0 with the fraction fluorescence in the correct melting peak Hands-on: Using RDML-Melting curve analysis with a provided RDML-data file to evaluate the effect of the applied procedure 3: Between plate correction (11.30-12.30) Theory: How to determine the multiplicative factor per plate and remove the systematic between-plate variation Hands-on: Using RDML-LinRegPCR to analyze two runs within one experiment and using RDML-Merge to merge the data into one experiment from which the systematic between-plate variation is removed 4: Import of data into RDML (13.30-14.30) Theory: RDML structure; from RDML output of the thermocycler to compiling an RDML-dataset from these raw fluorescence data Hands-on: Using RDML-Edit and RDML-TableShaper to illustrate the file structure, to adapt the format and to create an RDML file using example data from different qPCR machines 5: Reference gene identification (14.45-15.45) Theory: The need for reference genes and how to identify reference genes using RDML-geNorm Hands-on: Using RDML-LinRegPCR and RDML-geNorm to identify a set of valid reference genes 6: From qPCR run to results graph and statistics (16.00-17.00) Theory: How to apply reference genes Hands-on: Using RDML-LinRegPCR, RDML-Bargraph and RDML-Statistics with a provided data set you will perform the full analysis from raw fluorescent data till the presentation of the results in a publishable graph Please direct your enquiry to our scientific organisation team, headed by Michael W. Pfaffl GeneQuan.physio@ls.tum.de
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