‘Omics’
- Analysis of high
dimensional Data
Achim Tresch
Computational Biology
Schedule
Monday
Lecture:
Introduction to Omics
Motivation: Transcriptomics
Sebastian
• Experimental techniques
Dümcke
• Data analysis (overview)
Data exploration of univariate data
• Measures of location and scale
• Bar plot, box plot, histogramm, density plot
Data exploration of bivariate data
• Odds ratio, correlation
crosstable, scatter plot, QQ-plot
Exercises:
Introduction to R and Bioconductor
Forensic bioinformatics
Arijit Das
Henrik
Failmezger
Omics
Omics (Wikipedia):
Omics informally refers to a field of study in biology such as
genomics, proteomics or metabolomics. Omics aims at the
collective characterization and quantification of pools of
biologically / biochemically similar molecules that translate into
the structure, function, and dynamics of an organism or organisms.
Ingredients for omics research:
- High throughput experimental techniques for the
simultaneous measurements of large numbers of molecules
- Statistical methods for the appropriate analysis of high
dimensional data.
Generally, Omics data analysis takes longer than data generation!
Genomics: Transcriptomics
Techniques for RNA quantification
- Northern Blot
- Reporter genes
low-medium
throughput
- Reverse Transkriptase PCR
- Microarrays
high throughput
- RNA-Sequencing
Northern Blot
RNA (or DNA) is separated by the size on
a gel, transfered to the membrane and
hybridized with gene-specific probe
RNA -> Nothern blot
DNA -> Southern blot
Low throughput and poor quantification
Molecular Biology of the Cell (© Garland Science 2008)
RT-PCR
RNA
DNA
Reverse transcription
PCR
The course of PCR (amount of doublestranded DNA) is monitored using a specific
fluorescent dye
N
Differences in concentration of particular
mRNA in different samples can be
calculated as 2N, with N being the difference
in the number of cycles to obtain the same
amount of product
Medium throughput, high precision
Molecular Biology of the Cell (© Garland Science 2008)
Microarrays
mRNA is converted to cDNA and labeled,
and subsequently hybridized to an array of
gene-specific probes (either spotted cDNA
samples or oligonucleotides, either one or
two sample(s) per microarray)
Differences in expression between samples
are determined as a ratio of fluorescence
signals at individual spots.
High throughput,
medium precision (low dynamic range)
Molecular Biology of the Cell (© Garland Science 2008)
Next generation sequencing (NGS)
Massively parallel sequencing
techniques enable sequencing of
genome-wide cellular RNA pools
Typical sequencing read lentgh is
30-100 nucleotides  RNA or
cDNA has to be fragmented
A single run comprises 106-108
reactions, depending on a
platform, so most RNAs are
covered by multiple “reads“
 read occurence for a particular
gene reflects expression level
Zyklusvorlesung
Molekularbiologie WS
2009/10
High throughput,
precision depends on sequencing
depth (#reads)
Next generation sequencing (NGS)
Illumina (Solexa) sequencing
DNA fragments are coupled to glass slide and subjected to Bridge amplification.
106-108 individual reads of 30-100 bp are produced at a time by using fluorescently
labeled removable terminator tags
Sample preparation
Sequencing
Transcriptomics with Microarrays
Workflow of a microarray experiment
Experimental
design
Frame a biological
question
Choose a microarray
platform
Decide on biological
and technical replicates
Design the series
of hybridization
days
Technical
performance
Obtain the samples
Isolate total RNA
Label cDNA or mRNA
Perform the
hybridizations
Scan the chips
weeks
Statistical
analysis
Data
mining
Extract fluorescence
intensities
Cluster analysis
and pattern recognition
Normalize data to
remove biases
Study lists of
genome ontologies
Estimate expression
changes
Identify differentially
expressd transcripts
Search for regulatory
motifs
Reconstruct
regulatory circuits
Design validation
and follow-up experiments
days-weeks
months
After: Gibson, G and SV Muse, 2004.
Transcriptomics with Microarrays
labeled
sample
Sample
amplification
and labeling
sample
injected into
microarray
RNA
sample
Fluorescence intensity
translated into mRNA
abundance
Probe array
scanning and
intensity
quantitation
Probe array
hybridization
Probe array
washing and
staining
RNA Sample preparation
RNA Sample preparation
Hybridization onto microarray
Quakenbush, 2006
Hybridization onto microarray
Hybridization onto microarray
mismatch probes
perfect match probes
probe pair
Each gene is represented by 11-20 probe pairs of 25nt length, consisting of a
perfect match probe and a mismatch probe.
Perfect match probes are complementary to specific sequences of the target
gene, preferentially located at the 3’ end of a gene.
The mismatch probe is identical to the perfect match probe, except for the
middle base. It is designed to detect unspecific binding.
Affymetrix Microarrays – Probe Synthesis
For the extension of all
oligonucleotides by one
base, four litographic steps
with complementary masks
are performed, one mask for
each base A, C, T, G.
Affymetrix raw Data
Greyscale and
false color image
of the fluorescence readout
Data Analysis
Detection of differentially expressed genes
genes
Identification of similar samples
and co-regulated genes in a
multi-sample comparison
samples
Data Analysis
A)
B)
C)
D)
E)
Cluster Analysis
Pearson correlation matrix
Venn diagram
Summary statistics up-/down regulation
[ Phenotypic analysis ]
Koschubs et al., EMBO J, 2009.
Data Analysis: Gene Ontology
35
% of genes in genome
% percentage in signifikant gene list
30
% of total changing genes
25
20
Response to chemical
stimulus
15
Vitamin metabolic
process
10
5
0
0
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38
GO te rm_biologica l process
Microarray Databases
ArrayExpress (European Bioinformatics Institute)
Gene Expression Omnibus (NCBI)
http://www.ebi.ac.uk/arrayexpress
Acknowledgement
Dietmar Martin
Gene Center,
LMU Munich