qPCR dPCR & NGS Application Workshops

The workshops are aimed at giving participants a deep and objective understanding of real-time quantitative PCR, digital PCR, Next Generation Sequencing (NGS), biostatistics, expression profiling, and its applications in various fields of molecular diagnostics. The courses are intended for academic or industrial persons considering working with qPCR, dPCR or NGS or scientists currently working with thes technologies seeking a deeper understanding. All workshops offer extensive hands-on training by expertsin the field on Thu 6 to Fri 7th April 2017.

The workshop laboratories and seminar rooms are very close to the central lecture hall.
The workshops are hosted by:

In the qPCR data analysis workshop  the data conversion, normalisation procedure, biostatistical calculations and the expression profiling will be done with the newest GenEx software by MultiD.
You can download a free trial version from our webpage => Genex.gene-quantification.info

logo MultiD
The courses cover all aspects in qPCR and is held during 2-days. Each course is app. 50% hands-on and is limited to app. 20 participants, resulting in very interactive teaching and everybody is given the opportunity to try the instrumentation. 
After the course participants will be able to plan and perform qPCR experiments themselves, as well as interpret and analyze data. Detailed course material and full catering (lunch, coffee, soft drinks and snacks) are included in the course fee.

Course dates                 6 and 7 April 2017
Course starts at            9 am
Course ends around    5 pm

Please register for the workshops using the ConfTool registration platform => Registration.qPCR-dPCR-NGS-2017.net

Workshop short agendas:

Basic real-time qPCR Application Workshop   (2-days)  
hosted by TATAA Biocenter
Seminar room -- S2

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.

Day 1

Introduction to PCR and qPCR
-    How does qPCR work?
-    Different detection chemistries, dyes or probes?
-    Different applications

qPCR data evaluation
-    How does qPCR software process the data
-    How to evaluate curves and set threshold

Primer and probe design
-    How to do proper primer design
-    How to avoid primer dimer formation
-    Other important considerations for primer design
-    How to design hydrolysis probes
-    Practical exercises in primer design

Optimization of qPCR protocols
-    Which factors affect the PCR?
-    Which factors can be optimized?
Day 2

-    Different ways to normalize
-    How to find stable reference genes

Basic quantification theory
-    Quantification methods and equations
-    How to do interplate calibration

Absolute quantification strategies
-    How to do absolute quantification
-    What is a suitable standard?

Validation of assays
-    How to determine LOD and LOQ
-    Precision estimation
-    Which controls to use

Quantification calculation examples
-    Practical examples with relative quantification calculations
-    Calculations with own generated qPCR data

Analysis of qPCR data - how to get reliable results compliant with guidelines   (2-days)   hosted by TATAA Biocenter
Computer seminar room -- PU26

Description:  This training course is aimed at those working with real-time qPCR and would like to learn how appropriate statistics shall be selected and applied correctly to get the most out of the qPCR data. It is a comprehensive course teaching the basics of statistics including the most common methods to analyze qPCR data generated both in large and small studies. Participants are expected to have knowledge and experience of the qPCR method.
The course includes extensive computer based exercises using the software GenEx (MultiD Analyses).
You can download a free trial version from our webpage => Genex.gene-quantification.info

The first day of the course focuses on the principles of statistics going through the terminology used in statistics and how to approach statistics when analysing qPCR data. Day one will describe the most common terms used in descriptive statistics and the most frequently used statistical tests to extrapolate from sample to population. It will also discuss how to setup qPCR experiments to be able to make statistical analysis of the data.

In addition day one focuses on the ability to detect difference and factors that influence the power analysis that can be made to optimize an experiment.  The last part will discuss the pre-treatment that is required before analysing qPCR data, how to use relative quantification to compare samples and the use of reference genes to normalise between samples. Day one will also discuss the importance of controlling for genomic DNA contamination in gene expression analysis and how we can correct for gDNA presence using NoRT controls or ValidPrime.

Day two of this course focuses mainly on two things. The first is absolute quantification using standard curves that is used to quantify unknown samples. To do this in a proper way one need to know the limits between which we can make a good detection and quantification. How to identify limit of detection (LOD), limit of quantification (LOQ) and the dynamic range of a qPCR test will be handled.

Second main topic of the day is gene expression profiling where more than one gene is analysed in many samples. Analysing many genes in many samples often mean that the analysis cannot be performed with a single plate, to account for this a proper design is vital for your multi-plate experiment and analysis. We will finally discuss the most common multivariate methods used in expression profiling where samples (or genes) are classified based on the similar response of different genes (or samples)

Day 1

Principles of statistics
-    Introduction to the basic principles of statistics
-    What does the basic terms mean?

Design of experiments
-    How should the experiment be designed?
-    How to do statistical hypothesis testing

Statistical tests
-    Overview of the most common statistical tests
-    How do we apply the different tests and when are they valid?

Ability to detect a difference (Power Testing)
-    How to define the power of a test
-    How to test the null hypothesis
-    Type I and typ II errors

Relative quantification
-    How are the calculations for relative quantification performed?
-    How should qPCR data be pre-treated before comparison?
Day 2

Multiplate measurements
-    How do we get Cq-values, what is delta-Cq and delta-delta-Cq?
-    How should we plan the experiment to analyze multi-plate measurements in the best way?
-    How do we use interplate calibrators

Absolute quantification
-    How is absolute quantification performed?
-    How to interpret standard curves

Limit of detection
-    How to calculate LOD and LOQ
-    How to identify the source of variance in experiments

Reference genes
-    How to find and validate optimal reference genes

Expression profiling
-    How can gene expression profiling be performed?
-    How to classify genes or samples with scatter plots and dendograms
-    How to handle missing data in multivariate analysis
-    Practical computer based training in principal component analysis, self-organizing maps and clustering analysis

digital PCR  (2-days)   hosted by Bio-Rad
Seminar room -- S1

Description:  Learn how to plan, perform, and analyze digital PCR experiments and how digital PCR can help your research to overcome the limitations of real-time qPCR.

Day 1
  • Welcome and introductions
  • Introduction to Digital PCR
  • Droplet generation and PCR start for CNV experiment
  • ddPCR applications: CNV
  • Start DR for CNV experiment
  • Droplet generation and PCR start for RED and ABS experiments
  • ddPCR Applications: RED and ABS
  • CNV results analysis
  • Start DR for RED/ABS experiment
  • Review of the day
Day 2
  • ddPCR: basic statistics
  • RED/ABS results analysis
  • Other ddPCR Applications: gene expression and NGS
  • When qPCR and when ddPCR? Moving from qPCR to ddPCR
  • Open Q&A session
  • Review of the workshop

NGS – Library construction and quality control (2-days)   hosted by TATAA Biocenter      fully booked
Seminar room -- S3   

Description:  This course gives an introduction to massively parallel sequencing (also called Next Generation Sequencing, NGS), and its many applications. The course consists of a theoretical part, which will focus on considerations for the NGS experiment design, the different sequencing platforms, quality control of samples, library preparation techniques, and quantification of libraries for sequencing. The course also includes practical parts where the participants will prepare libraries and perform quality control and compare libraries.

Day 1

Introduction to NGS:

  The history of DNA sequencing
  What are the advantages with NGS? Disadvantages?
  What is the difference between microarray and NGS?

  What are the possibilities for analysis of the genome? Transcriptome? Epigenome? Metagenome?
  RNA sequencing; investigating differential expression, splice variants, novel transcripts and much more.
  DNA sequencing; finding Single-Nucleotide Polymorphisms (SNPs), Copy Number Variants (CNVs), structural variations and more.
  NGS for epigenetic studies; analyzing the methylation state of DNA or discovering protein-binding sites in DNA.
  Sample preparation and quality control:

Day 2
How to prepare samples for NGS:
  Which starting material can be used for NGS?
  Which quality controls are included in the experimental workflow?
  How to choose the right library prep protocol.
  Practical experiment to learn the basics of library preparation and quality control.

  Which platforms are available?
  How do they work?
  Which platform is suitable for my experiment?
  Experimental design:

What to think about when designing your NGS experiment:
  How much to sequence?
  How to calculate coverage and depth?
  How many samples can I multiplex?

NGS data analysis workshop   (2 days)   hosted by Genomatix
Computer seminar room – PU26A -- GIS room

Instructor:  Christian Zinser

The large amounts of data derived from next generation sequencing projects makes efficient data mining strategies necessary. In the course you will learn strategies for the analysis of different kinds of next generation sequencing data. The workshop is based on real world examples and will use the Genomatix software, which provides a graphical user interface; no programming, scripting, or command line tool knowledge is necessary to attend.

Day 1

General introduction to the Genomatix Genome Analyzer (GGA)
GGA: RNA-Seq analysis
                mapping of RNA-Seq data
                differential expression analysis
                classification and pathway analysis of differentially expressed genes

GGA: ChIP-Seq analysis
                peak detection and classification
                transcription factor binding site analysis in ChIP peaks
                de novo definition of common sequence motifs in ChIP data
                next-neighbor analysis and regulatory target prediction for ChIP regions

Day 2

GGA: integrating RNA-Seq and ChIP-Seq data
                correlation of different data sets
                combining predicted targets and differentially expressed genes

GeneGrid: small variant analysis
                 small variant annotation
                 using annotation to filter for relevant variants
                 comparative analysis scenarios: trios, cancer and others
                 variant interpretation: predicting pathogenic and benign variants

New workshop -- title to be announced !!!
Computer seminar room -- HU34A       


Day 1

Day 2

Please direct your enquiry to our scientific organisation team, headed by Michael W. Pfaffl  qPCR-dPCR-NGS-2017@wzw.tum.de
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