Vendula Pospíchalová, PhD
(pospich@sci.muni.cz)
Department of Experimental Biology
Animal Physiology and Immunology
Bi5599 Applied Biochemistry and Cell Biology Methods
2019-10-30
Omics technologies:
genomics, transcriptomics, metabolomics,
databases and big data
Schematic representation of omics technologies, their corresponding analysis targets, and assessment
methods. Taken from Wu RD et al. JDR 2011; 90:561-572.
Contents
1. Introduction: what are –omics technologies + history
2. How does big data look and how to approach it
3. From –omics technologies to biomarkers and personalized medicine
4. Genomics: genomes vs exomes vs genotypes + DTC service
5. Cancer databases: COSMIC, TCGA, Oncomine and others
6. Transcriptomics: microarrays vs. RNA sequencing
7. Gene set analysis
8. Metabolomics
9. Cutting edge: single cell –omics and single cell multi-omics
10. Summary and take home messages
What are „-omics“ technologies
• Omics refers to a field of study in biology ending in -omics, such as
genomics, proteomics or metabolomics
• The related suffix -ome is used to address the objects of study of such
fields, such as the genome, proteome or metabolome
• -ome = many/collectivity in Latin, -omics = study of large sets of
biomolecules
• High-throughput experimental technologies characterized by
automation, miniaturized assays and large-scale data analysis
• Analytic part of the experiment is usually much longer than the experiment
itself – bioinformatics skills needed
• Raw data is the „gem“ but usually is in user unfriendly format
• Interpreting functional consequences of millions of discovered events is
one of the biggest challenges
Big –omics data challenges
https://www.laboratory-journal.com/science/information-
technology-it/big-data-genomics-challenges-and-solutions
EASY
RELATIVELY EASY
HARD HARDEST
Only skilled bioinformaticians can proces raw data
Integrated analyses of –omics studies are only possible in
large consortia (e.g. TCGA). Subsequently, the authorlist of
such articles can more than 2 pages long with substaintial part
of the authors being bioinformaticians. Among the reviewers,
bioinformaticians are also necessary …
Data sharing policy
• The concepts of data sharing and open data are becoming increasingly
important in science
• Funding bodies, journals and societies are now encouraging or mandating
data sharing (usually the raw data)
• Sharing data publicly is an important way of improving reproducibility and
showing that researchers are confident in their work
• Studies with raw data shared in a repository also receive more citations than
those without publicly available data
• But raw -omics data
are hard to analyse,
so many platforms
gather the publicly
available data,
thoroughly analyze it,
curate it and share it
in a user friendly
format
Leveraging Public Databases to Identify Actionable Targets
DIKW pyramide
Jennifer RowleyPublished 2007 in J. Information Science DOI:10.1177/0165551506070706
„Data is not information, information is not knowledge, knowledge is
not understanding, understanding is not wisdom.“ – Clifford Stoll
What is the aim of OMICS technologies
http://www.jpathinformatics.org/text.asp?2015/6/1/46/163985
The DIKW pyramid
metaphor:
“know-nothing”
(Data)
↓
“know-what”
(Information)
↓
“know-how”
(Knowledge)
↓
“Know-why”
(Wisdom)
Zeleny (2005)
In Genomics:
Sequence of 3 billions
letters in .txt file
↓
information where the
individual's genome varies
from reference sequence
↓
CYP2C9 or TPMT
genotype, which has
known pharmacogenomic
associations
↓
individualize the dose of a
new warfarin prescription
What is personalized health care?
https://pharma.bayer.com/en/research-and-development/research-
focus/oncology/personalized-medicine/index.php
Value of personalized medicine
And many more
examples, see
http://www.personalized
medicinecoalition.org
for more detailed
information on PM
https://invivo.pharmaintellig
ence.informa.com/
What is a biomarker?
-OMICS
technologies
and their
integration
is crucial for
biomarker
discovery
and
validation
History of „-omics“ technologies
• Genome – central part of all
–omics technologies
• NGS = next generation
sequencing
© slideshare.net
Sanger vs next generation sequencing
• Sanger sequencing
• https://www.youtube.com/watch?v=e2G5zx-OJIw
• Next generation sequencing (Illumina is shown as an example)
• https://www.youtube.com/watch?v=9YxExTSwgPM
https://slideplayer.com/slide/5799907/
(1.5%)
Used to confirm NGS results
Seeing is believing
• www.illumina.com/t
echnology/next-
generation-
sequencing.html
Illumina NGS overview
Cost of sequencing over time
© slideshare.net
DTC (direct-to-customer) genetic testing
http://ancestry.com/
Genotyping vs Sequencing
• Genotyping - determining which genetic variants an individual possesses
through a variety of different methods, especially genotyping chips
(based mostly on SNPs – single nucleotide polymorphisms)
- cheap, but require prior identification of the variants of interest
https://www.23andme.com/
How SNP genotyping works
• https://www.youtube.com/watch?v=Naona1y_I2U
• For more information see YouTube Channel Useful Genetics:
https://www.youtube.com/channel/UCtXCrx28msMBQ-vFUIOIReA
https://www.jax.org/news-and-insights/jax-blog/2016/september/genomes-versus-exomes-versus-genotypes
How SNP genotyping works
There are two types of microarray commonly used in multiplexing SNP analysis: allele-specific oligonucleotide (ASO)
hybridization and allele-specific primer (ASP) extension. (A) ASO hybridization: The allele-specific oligonucleotide for every
SNP is synthesized and separately immobilized onto the glass plate. Fluorescence labeled targets containing SNP sites are
produced from a PCR reaction and plotted separately into each well to conduct the hybridization reaction. The mismatched
base pair between target and oligonucleotide can decrease the binding strength with the fluorescence-labeled target removed
after a stringent washing. A fluorescence signal is detected on a perfectly matched base pair; (B) Allele-specific primer (ASP)
extension: The specific primer for SNP location is designed and separately immobilized onto a microarray. A different
fluorescence labeled dNTP is individually used in an extension reaction. The extended fragment showing fluorescence signal
can only be found when the 3′ end of primer pair is perfectly matched (AA type in this case) in contrast to the mismatched
primer pair (GG type in this case); (C) The SNP genotype can be determined according to fluorescent intensity from the
products/target DNA. https://doi.org/10.3390/microarrays4040570
DTC genome sequencing as popular demand
https://www.dantelabs.com
Coverage (or depth) in sequencing
Sequencing – WGS and WES
~3,000,000,000,000
https://www.mygenefood.com/finding-best-dna-test-genotype-sequence/
• Determining the exact DNA sequence
Genomes vs exomes vs genotypes
https://2wordspm.wordpress.com/2017/10/30/ngs-%EA%B2%80%EC%82%AC-whole-genome-exome-targeted-sequencing-%EB%B9%84%EA%B5%90/
• Most sensitive – able to
detect rare tumor cells in
biopsy
• Results are sometimes
challenging to interpret
• Good alternative to WGS
in terms of clinical use
What to expect
• Genetic testing provided by most of the
companies is moreless for fun
(ancestry, health and wellness,
nutrigenetics, skincare, sports,…)
• More expensive, and complete, sequencing like the one provided by Illumina can be
used for medical investigation
• Do not expect your genome sequencing to tell you how long is your life expectation,
whether you are likely to get cancer and so on
• So far our knowledge on the “implication” of the genome are quite limited
• What we can already do in health care is to look at the genome once you have been
diagnosed a specific ailment and look for specific genes that would make one cure more
effective than another (this has become normal practice in some form of cancer cure)
Based on http://sites.ieee.org/futuredirections/2017/12/26/did-you-get-your-genome-sequenced-for-christmas/
Example of genetic testing in clinical practise
• BRCA genes testing for PARP inhibitor treatment
• https://www.youtube.com/watch?v=ilwMGRH276M
PARP inhibitors
• In December 2014, the drug olaparib (Lynparza)
became the first of a new class of treatments
known as PARP (poly(ADP-ribosa)polymerase)
inhibitors to be licensed for clinical use,
heralding in a new era for personalised, targeted
treatment — and turning the promise of ‘synthetic
lethality’ into reality.
More info on PARPi:
https://www.youtube.com/watch?v=mgW30YyaJz4 https://doi.org/10.1016/j.ygyno.2015.02.017
Synthetic lethality concept
BER = base excision repair
BRCAness ovarian tumors and PARPi
• Only women with mutation in
BRCA genes are now eligible
for PARPi treatment
• BRCA mutations underlie only
a small portion of tumors
defective in HR
• But 50% of ovarian tumors are
HR deficient = ‚BRCAness‘
phenotype
• PARPi are effective also in
BRCAness tumors
• Can we identify the BRCAness
tumors/patients and provide
them also with this novel and
highly promissing treatment
option?
DOI: 10.1158/2159-8290.CD-15-0714
Homologous Recombination Deficiency:
Exploiting the Fundamental Vulnerability of Ovarian Cancer
The Present and Future of Genome Sequencing
• Genomics England - 100,000 patients
with rare diseases, their
families, and cancer patients
• Precision Medicine Initiative (PMI)
1-million-volunteer health
study, data including genetics
and lifestyle factors
• GenomeAsia 100K - genomic data
for Asian populations
• … a many more initiatives
https://labiotech.eu/features/genome-sequencing-review-projects/
• How to handle such huge amount of data and the ethical implications?
• In the US, the Genetic Information Nondiscrimination Act (2008) but mostly
no act in other countries and somewhat grey legal position in Europe
COSMIC: Cataloque of Somatic Mutations in Cancer
https://cancer.sanger.ac.uk/cosmic/about
• How to use COSMIC database:
https://www.youtube.com/watch?v=2FD5RabgK6o, https://www.youtube.com/watch?v=k477uAiKx74
TCGA: The Cancer Genome Atlas
https://cancergenome.nih.gov/
https://www.
youtube.com/
watch?time_c
ontinue=249
&v=epsZjJ_A1
y4
TCGA: Overview
• Initiated in 2005
• A joint effort of the National Cancer Institute (NCI)
and the National Human Genome Research
Institute (NHGRI).
• 27 participating Institutes in US and Canada.
• The overarching goal of TCGA is to improve our
ability to diagnose, treat and prevent cancer,
through the application of genome analysis
technologies, including large-scale genome
sequencing.
• The Cancer Genome Atlas Network have published
more than 20 papers since the project began
(https://tcga-data.nci.nih.gov/docs/publications/)
TCGA Data Portal
TCGA: A Valuable Resource for Research Community
TCGA Data Types
• Clinical data
• DNA sequencing
• miRNA sequencing
• Protein expression
• mRNA sequencing
• Total RNA sequencing
• Array-based expression
• DNA methylation
• Copy number variations
+ Computational tools
How to use TCGA: https://www.youtube.com/playlist?list=PL-hYJ1isbXhURdasc-RmwDRLhrHzdzKtN
Oncomine database
www.Oncomine.org
How to use Oncomine:
https://www.youtube.com/wat
ch?v=b8ckDiVNrFE
Transcriptomics
• Study of transcriptome, the
sum of all RNA transcripts
• Two most widely studies
types of RNA
• mRNA - transcriptome or
the expressed genes.
Usually contains genes
with poly A tail.
• miRNA - Small non-coding
RNA (containing about 21-
25 nucleotides), important
in gene regulation.
• Array-based Expression Profiling:
• https://www.youtube.com/watch?v=6ZzFihESjp0
Microarrays vs RNA-seq
• While methods for analyzing microarray data are fully mature and straightforward, there is no
consensus on which pipelines—or series of computational steps—to use to analyze RNA-seq data.
https://www.the-scientist.com/lab-tools/an-array-of-options-35381
Overview of RNA-seq
By Malachi Griffith, Jason R. Walker, Nicholas C. Spies, Benjamin J. Ainscough, Obi L. Griffith http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004393,
CC BY 2.5,
https://commons.wikimedia.org/w/index.php?curid=53055894
RNA sequencing downstream analysis
• https://www.youtube.com/watch?v=tlf6wYJrwKY (from 13:10)
• More info about microarray vs. RNA-seq at:
https://www.youtube.com/watch?v=2c3t3tDEmsU
• More info RNA seq at:
• https://www.youtube.com/watch?v=MFRkwXq6v_I
• Useful detailed info about anything connected toRNA-seq
• https://www.rna-seqblog.com
Examples of transcriptomics data outputs
• Volcano Plot
• Heat map
Cellular/functional/pathway analysis
Usually hundreds to
thousands of genes
• Useful info in Czech language:
https://portal.matematickabiologie.cz
• Cellular/functional/pathway
analysis is a valuable tool to
summarize high-dimensional
gene expression data in terms
of biologically relevant sets.
• Genes are aggregated into
gene sets on the basis of
shared biological or
functional properties as
defined by a reference
knowledge base.
• Knowledge bases are
database collections of
molecular knowledge which
may include molecular
interactions, regulation,
molecular product(s) and even
phenotype associations.
Database resources for understanding high-level
functions and utilities of the biological system
• Database tools:
• KEGG (Kyoto
Encyclopedia of
Genes and
Genomes)
• (https://www.geno
me.jp/kegg/)
• Disadvantage –
does not provide
statistical
significance of
particular pathways
• And many others
available online
KEGG analysis
• Gene Ontology (GO) analysis (http://geneontology.org/)
Gene-set analysis (GSA)/Pathway analysis
• GO enrichment analysis
• One of the main uses of the GO is to
perform enrichment analysis on gene
sets. For example, given a set of
genes that are up-regulated under
certain conditions, an enrichment
analysis will find which GO terms are
over-represented (or underrepresented)
using annotations for
that gene set.
• 3 main GO aspects (molecular
function, biological process, cellular
component)
• http://geneontology.org/docs/go-
enrichment-analysis/
GO analysis
Example data of GO enrichment analysis
Reactome Knowledgebase
• More info at:
• https://www.
youtube.com
/user/Reacto
me/videos
Metabolomics
https://polypdx.com/for-healthcare-
providers/metabolomics
• Metabolomics – large-scale systematic
study of the metabolome
• Metabolome - total complement of
metabolites present in a biological sample
under given genetic, nutritional or
environmental conditions
- the unique biochemical fingerprint of
all cellular processes
• Metabolite - low molecular (usually 50 –
1,500 Da) weight organic compound,
typically involved in a biological process
as a substrate or product.
• Metabolomics yield many insights into basic
biological research in areas such as systems
biology, metabolic modelling, pharmaceutical
research, nutrition and toxicology
Metabolites are important
Metabolomics can therefore be seen
as bridging the gap between
genotype and phenotype
Metabolomics technologies
Metabolomics – ‚a snapshot‘ in time
• Metabolomics: employs complementary analytical methodologies, for example, LC-MS/MS, GC-MS, and/or
NMR, in order to determine and quantify as many metabolites as possible, either identified or unknown
compounds.
• Metabolic fingerprinting: a metabolic “signature” or mass profile of the sample of interest is generated and
then compared in a large sample population to screen for differences between the samples. When signals that
can significantly discriminate between samples are detected, the metabolites are identified and the biological
relevance of that compound can be elucidated, greatly reducing the analysis time.
Conceptual approaches in metabolomics:
• Target analysis: has been applied for many decades
and includes the determination and quantification of a
small set of known metabolites (targets) using one
particular analytical technique of best performance for
the compounds of interest.
• Metabolite profiling: aims at the analysis of a larger
set of compounds, both identified and unknown with
respect to their chemical nature. This approach has
been applied for many different biological systems
using GC-MS, including plants, microbes, urine, and
plasma samples.
Metabolomics data analysis
Where to look for metabolomics data
https://www.slideshare.net/TNAUgenomics/metabolomics-13725538?next_slideshow=1
Cutting edge: Single cell -omics
• Application of whole genome, whole transcriptome sequencing and other
–omics methods to single cells, sc RNA-seq is now the top method
https://community.10xgenomics.com/t5/10x-Blog/Single-Cell-RNA-Seq-An-Introductory-Overview-and-Tools-for/ba-p/547
sc RNA-seq data visualization
Molecular Architecture of the Mouse Nervous System
DOI:https://doi.org/10.1016/j.cell.2018.06.021
Cca 500,000 analyzed cells
Common applications of sc RNA-seq
https://f1000research.com/articles/5-182/v1
For more info go at: https://omicstools.com
Single-cell multi-omics
https://www.frontiersin.org/articles/10.3389/fcell.2018.00028/full
Challenges:
• There are no commercial
kits available yet for any
single-cell multi-omics
techniques, and many
are technically
challenging.
• Researchers must modify
existing single-cell
protocols so that they’re
compatible with multiple
types of molecules and
take great care to
minimize the loss or
contamination of samples
https://www.the-scientist.com/lab-
tools/integrating-multiple--omics-in-
individual-cells-64829
Summary
• Omics technologies are revolutionizing
science and medicine
• From data to actionable knowledge Integrated
Omics data
• Precision medicine is the ultimate goal
of many –omics efforts
• Despite the progress made we have
still a long way to go …
8
• Omics technologies - „the data deluge“
• Genomics and Transcriptomics rely on two main approaches:
microarrays (hybridization) and NGS (sequencing by synthesis)
• Proteomics and Metabolomics rely heavily on mass spectrometry
Take home messages
• We have been generating Big data, but we hardly understand
it 
• Big data is publicly available, go through the databases before
you even start even planing your experiment – it can save you
enourmous time and money
• Databases contain huge datasets of patients you would never
be able to gather by yourself, test your hypothesis in silico
before the „wet-lab“ work
• If you cannot find the „yes/no“ or „a few genes“ answer, use
the Cellular/functional/pathway analyses to help you out 
• Learning bioinformatics skills (e.g. programing in R) is a good
investment plan for your future career
• Jay Flatley, Executive Chairman of Illumina:
• „Everyone is going to get sequenced, it is
gonna be part of their health record and it
will be used to manage their health care
throughout their lifetime“.
Thank you for your attention
Any Questions?