01. Introduction to Bioinformatics.pptx

Introduction to bioinformatics
CUVAS, Bahawalpur

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

Measuring levels of gene expression
CELL
DNA
ON ON
OFF
RNA
PROTEIN
OFF
G1 G2 G3 G4
Gene products
carry out cellular
function

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
Gene Expression
profile
Expression
level
G1 OFF 0 G1 OFF 0
G2 ON 30 G2 ON 30
G3 ON 20 G3 ON 20
G4 OFF 0 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

Good old times
Science
knowledge
experiment
hypothesis analysis

How we do it now
lots of
experiments
analysis
Science
knowledge
hypothesis

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

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 have knowledge aprogram
• 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?
Word cloud from manuscript titles published in
Bioinformatics from Jan 2013 to April 2014
Slide credit: “How to be a bioinformatician” by Christian Frech

Algorithms
Proteomics
Black magic
Sequences
Machine learning
Modeling
Bioinformatics is a broad field

What Does the Perfect
Bioinformatician Look Like?

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

Medical Imaging
Cancer
Images credit: D.Fishman, Introduction to ML in bioinformatics

image by Krista Fisher et al.
Genetic risk assessment

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 asingle
human genome
~4GB
The Wellcome Human Genome Library in London (Left CC: Russ London, Right Source: Wellcome Collection)

Big Data: Astronomical or Genomical?, Zachary et al, PlosOne Biology 2015
The Age Of Big Data

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

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
are
us…
and them.
SOURCE: JAVIER HERRERO, EUROPEAN MOLECULAR BIOLOGY LABORATORY—
EUROPEAN BIOINFORMAT ICS INSTITUTE

Genes
are
us…
and them.
SOURCE: JAVIER HERRERO, EUROPEAN MOLECULAR BIOLOGY LABORATORY—
EUROPEAN BIOINFORMAT ICS INSTITUTE
50%

01. Introduction to Bioinformatics.pptx

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