7. Cell size and scale
http://learn.genetics.utah.edu/content/cells/scale/
8. 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
12. 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
13. 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
22. 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
23. Biologist talks to statistician
http://www.youtube.com/watch?v=Hz1fyhVOjr4
25. 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
65. 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
66. Challenges
• Need of new compression methods
• Data integration methods
• Lack of specialists
• Need for new:
– Analysis pipelines
– Methodology
– Algorithmic solutions
– Tools
67. 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
