Introduction to bioinformatics
Elena Sügis
elena.sugis@ut.ee
Bioinformatics Seminar MTAT.03.242, 2016
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VSVS
Questions we ask
Questions we ask
Questions we ask
Image credit:https://siteman.wustl.edu/glossary/cdr0000046470/
1014 cells in
human body
23 000 genes
23 pairs of
chromosomes
3 billion pairs
Cell size and scale
http://learn.genetics.utah.edu/content/cells/scale/
Central dogma
What is DNA?
What is the difference
between DNA and RNA?
Image credit: Genome Research Limited
100 000 different proteins
23 000 genes
Technology
Image credit: Genome Research Limited
Mass spectrometry
Sequencing technologies
v
v
v
Measuring levels of gene expression
CELL
DNA
ON ONOFF
RNA
PROTEIN
OFF
G1 G2 G3 G4
Gene products
carry out cellular
function
v
Gene expression profile
Gene Expression
profile
Expression
level
G1 OFF 0
G2 ON 30
G3 ON 20
G4 OFF 0
Each cell has its own gene
expression profile
Gene Expression
profile
Expression
level
G1 OFF 0
G2 ON 30
G3 ON 20
G4 OFF 0
Gene Expression
profile
Expression
level
G1 OFF 0
G2 ON 30
G3 ON 20
G4 ON 20
HEALTHY CELL CANCER CELL
Single Nucleotide polymorphism
•  variation in a single nucleotide that occurs at a specific position in the
genome, where each variation is present to some appreciable degree
within a population (e.g. >1%)
•  underlie differences in our susceptibility to disease
Image credit: Wikipedia
Experiments
Good old times
Science
knowledgeexperimenthypothesis analysis
v v v
How we do it now
lots of
experiments
v
analysis
Science
knowledge
hypothesis
v
v
Bioinformatics is an interdisciplinary field
that develops methods and software tools
for understanding biological data
Bioinformatics is
•  an interdisciplinary field
•  that develops methods and software tools
•  for understanding biological data
Moore’s law
Cost per genome
Why do we need
bioinformaticians?
Because Excel can’t help anymore
Why do we need bioinformaticians?
•  Amount of generated biological data
requires sophisticated data analysis and
management
•  Programmers & statisticians lack biological
knowledge
•  Biologists don’t program
•  These two don’t understand each other
Slide is inspired by from “How to be a bioinformatician” by Christian Frech
Biologist talks to statistician
http://www.youtube.com/watch?v=Hz1fyhVOjr4
What do bioinformaticians do?
What do bioinformaticians do?
Word cloud from manuscript titles published in
Bioinformatics from Jan 2013 to April 2014
Slide credit: “How to be a bioinformatician” by Christian Frech
Machine learning
Algorithms
Proteomics
Black magic
Sequences
Modeling
Bioinformatics is a broad field
What Does the Perfect
Bioinformatician Look Like?
Computer
Science
Computer
Science
Biology
Computer
Science
Biology
Statistics
Perfect bioinformatician
Computer
Science
Biology
Statistics
Data comes in different forms
Slide credit: D.Fishman, Introduction to ML in bioinformatics
Data ≠ Knowledge
Slide credit: D.Fishman, Introduction to ML in bioinformatics
R. Matthiesen (ed.), Bioinformatics Methods in Clinical Research, Methods in Molecular Biology 593,
DOI 10.1007/978-1-60327-194-3 2, © Humana Press, a part of Springer Science+Business Media, LLC 2010
Adopted from P.Vincent http://videolectures.net/deeplearning2015_vincent_machine_learning/
What is the main ingredient?
What is the main ingredient?
Adopted from P.Vincent http://videolectures.net/deeplearning2015_vincent_machine_learning/
Databases
Data management systems
Data source
Simple data analysis pipeline
Data Black Magic Result
high quality data machine learning
method
awesome result
Simple data analysis pipeline
Data Black Magic Result
poor quality data machine learning
method
not so
awesome result
Data Preprocessing
Clean
Massage your data
80 %
Predictive modeling
Tools & Services
Understand the disease better
Make more precise diagnosisML in Bioinformatics (part II)
Medical Imaging
Cancer
Images credit: D.Fishman, Introduction to ML in bioinformatics
image by Krista Fisher et al.
Genetic risk assessment
Genetic risk assessment
Genetic risk assessment
Pharmacogenetics
Personalized medicine
http://www.pfizer.ie/personalized_med.cfm
Population genomics
large-scale comparison of DNA sequences of populations
What do these books contain?
The Wellcome Human Genome Library in London (Left CC: Russ London, Right Source: Wellcome Collection)
This is a single
human genome
~4GB
The Wellcome Human Genome Library in London (Left CC: Russ London, Right Source: Wellcome Collection)
Moore’s law
Cost per genome
Big Data: Astronomical or Genomical?, Zachary et al, PlosOne Biology 2015
The Age Of Big Data
Labs that produce data
Research groups that can
Labs that produce data
Experts who can analyze data
Analyze that
Data analysis
Need to develop new:
•  Preprocessing methods
•  Analysis pipelines
•  Methodology
•  Algorithmic solutions
•  Tools
IF YOU TORTURE
THE DATA
LONG ENOUGH
IT WILL CONFESS
TO ANYTHING
Ronald Coase, Economist, Nobel Prize winner
http://baranzinilab.ucsf.edu/integration-large-scale-omics-data-sets
Data
Integration
Data storage
The amount of sequencing data created is growing at
a faster rate than economically available storage
http://geschwindlab.neurology.ucla.edu/protocols/next-generation-sequencing
N Goldman et al. Nature 000, 1-4 (2013) doi:10.1038/nature11875
Using DNA as a digital archive media
store sequenced DNA information in … DNA
N Goldman et al. Nature 000, 1-4 (2013) doi:10.1038/nature11875
Scaling properties of DNA-based
storage
If the cost of DNA synthesis falls by two orders of magnitude,
it will become a sensible way to store data for the medium term
(below 50 years)
Challenges
•  Need of new compression methods
•  Data integration methods
•  Lack of specialists
•  Need for new:
–  Analysis pipelines
–  Methodology
–  Algorithmic solutions
–  Tools
Challenges of being
bioinformatician
•  Biology is complex, not black and white
–  As many exceptions as rules (e.g.: define “gene”)
–  No single optimal solution to a problem
–  Results interpretable in many ways (story telling, cherry picking)
•  Understanding the biological question
•  Field is moving incredibly fast
–  Lack of standards, immature/abandoned software
–  Much time spent on collecting/cleaning-up data, troubleshooting
errors
•  Hundreds of software tools and databases out there
–  Easy to get lost
–  Important to understand their strengths and weaknesses
Genes
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EUROPEAN,BIOINFORMAT,ICS,INSTITUTE
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Introduction to Bioinformatics.