& NGS Application Workshops
workshops are aimed at giving participants a deep and objective
understanding of real-time quantitative PCR, digital PCR, Next
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
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.
workshop laboratories and seminar rooms are very close to the
central lecture hall.
The workshops are
|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
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.
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.
6 and 7 April 2017
around 5 pm
register for the workshops using the ConfTool
registration platform => Registration.qPCR-dPCR-NGS-2017.net
Workshop short agendas:
qPCR Application Workshop
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
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.
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?
- 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 with own generated qPCR data
Analysis of qPCR
data - how to get reliable results compliant with guidelines
by TATAA Biocenter
seminar room -- PU26
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
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)
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
- Overview of the most common statistical tests
- How do we apply the different tests and when are
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
- How are the calculations for relative
- How should qPCR data be pre-treated before
- How do we get Cq-values, what is delta-Cq and
- How should we plan the experiment to analyze
multi-plate measurements in the best way?
- How do we use interplate calibrators
- 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
- How to find and validate optimal reference genes
- How can gene expression profiling be performed?
- How to classify genes or samples with scatter plots
- How to handle missing data in multivariate analysis
- Practical computer based training in principal
component analysis, self-organizing maps and clustering analysis
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.
- Welcome and introductions
- Introduction to Digital PCR
- Droplet generation and PCR start for CNV
- ddPCR applications: CNV
- Start DR for CNV experiment
- Droplet generation and PCR start for RED and
- ddPCR Applications: RED and ABS
- CNV results analysis
- Start DR for RED/ABS experiment
- Review of the day
- ddPCR: basic statistics
- RED/ABS results analysis
- Other ddPCR Applications: gene expression and
- When qPCR and when ddPCR? Moving from qPCR to
- Open Q&A session
- Review of the workshop
NGS – Library construction and quality
(2-days) hosted by TATAA Biocenter
room -- S3
This course gives an introduction to massively
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.
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:
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?
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?
analysis workshop (2 days) hosted by Genomatix
seminar room – PU26A -- GIS room
Description: 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.
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
GGA: integrating RNA-Seq
and ChIP-Seq data
correlation of different data sets
combining predicted targets and differentially expressed genes
GeneGrid: small variant
small variant annotation
using annotation to filter for relevant variants
comparative analysis scenarios: trios, cancer and others
variant interpretation: predicting pathogenic and benign variants
direct your enquiry to our
scientific organisation team, headed by Michael W.
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