1. Welcome to CS374 Algorithms in Biology

2. Overview Administrivia Molecular Biology and Computation
DNA, proteins, cells, evolution
Some examples of CS in biology
Computer Scientists vs Biologists

3. CS374: Algorithms in Biology cs374.stanford.edu
Attendance
At most 2 classes missed without affecting grade
Lectures
Most important requirement
Select an available topic and a day, send to Serafim
Read papers, meet with Serafim (1hr) 1-2 weeks before lecture
Schedule long (2 hr) meeting the day before lecture
Slides due at noon before lecture

4. CS374: Algorithms in Biology cs374.stanford.edu
Scribing
Please sign up on a first-come first-serve basis
Due 1 week after lecture, edited & distributed 2 weeks after lecture
Relly will help you edit
Summaries
Select 1 lecture among first 10, 1 lecture among rest
Find one relevant paper
Write a 1-page summary of the paper
Paper reference
Abstract
Discussion
Ask Relly for questions/feedback
Have fun!

5. Structure of DNA double helix
Phosphate
Group
Sugar
Nitrogenous
Base
A, C, G, T T C A G
DNA
Physicist
Ornithologist

6. DNA to RNA, and genes G A U C
RNA: carries the “message” for “translating”, or “expressing” one gene
DNA, ~3x109 long in humans
Contains ~ 22,000 genes
transcription
translation
folding

7. Structure of proteins Composed of a chain of amino acids. R |
H2N--C--COOH H
20 possible groups
Sequence of amino acids folds to form a complex 3-D structure.
The structure of a protein is intimately connected to its function.

8. All living organisms are composed of cells

9. Genetics in the 20th Century

10. 21st Century Technology drives an information revolution
AGTAGCACAGACTACGACGAGACGATCGTGCGAGCGACGGCGTAGTGTGCTGTACTGTCGTGTGTGTGTACTCTCCTCTCTCTAGTCTACGTGCTGTATGCGTTAGTGTCGTCGTCTAGTAGTCGCGATGCTCTGATGTTAGAGGATGCACGATGCTGCTGCTACTAGCGTGCTGCTGCGATGTAGCTGTCGTACGTGTAGTGTGCTGTAAGTCGAGTGTAGCTGGCGATGTATCGTGGT

11. DNA to RNA, and genes
RNA: carries the “message” for “translating”, or “expressing” one gene A
DNA, ~3x109 long in humans
Contains ~ 22,000 genes G C G
transcription
translation A
folding 1 C U G

12. Some examples of central role of CS 1. Sequencing
AGTAGCACAGACTACGACGAGACGATCGTGCGAGCGACGGCGTAGTGTGCTGTACTGTCGTGTGTGTGTACTCTCCT
3x109 nucleotides
~500 nucleotides

13. Some examples of central role of CS 1. Sequencing
AGTAGCACAGACTACGACGAGACGATCGTGCGAGCGACGGCGTAGTGTGCTGTACTGTCGTGTGTGTGTACTCTCCT
3x109 nucleotides
A big puzzle
~60 million pieces
Computational Fragment Assembly
Introduced ~1980
1995: assemble up to 1,000,000 long DNA pieces
2000: assemble whole human genome

14. Complete genomes today
More than 300 complete genomes have been sequenced

15. DNA to RNA, and genes
RNA: carries the “message” for “translating”, or “expressing” one gene A
DNA, ~3x109 long in humans
Contains ~ 22,000 genes G C G
transcription
translation 2 A
folding 1 C U G

16. 2. Gene Finding Where are the genes? In humans: ~22,000 genes
~1.5% of human DNA

17. 2. Gene Finding Exon 1 Exon 2 Exon 3 Intron 1 Intron 2 Splice sites
Start codon
ATG 5’ 3’
Exon 1
Exon 2
Exon 3
Intron 1
Intron 2
Stop codon
TAG/TGA/TAA
Splice sites
The problem of predicting genes means to give coordinates for the exon boundaries.
The first kind of information that prediction algorithms use, is the regular structure of a gene. Every gene starts with an ATG codon, and then exons alternate with introns; at the exon-intron boundaries, the splice sites, there are short words that are approximately preserved.

18. atg caggtg ggtgag cagatg ggtgag cagttg ggtgag caggcc ggtgag tga
Topic in CS374:
Finding genes by comparing
genomes of different species
atg
caggtg
ggtgag
cagatg
ggtgag
cagttg
ggtgag
caggcc
ggtgag
tga

19. 2 3 1 DNA to RNA, and genes easy
RNA: carries the “message” for “translating”, or “expressing” one gene A
DNA, ~3x109 long in humans
Contains ~ 22,000 genes G C G
transcription
translation 2
easy A 3
folding 1 C U G

20. 3. Protein Folding
The amino-acid sequence of a protein determines the 3D fold
The 3D fold of a protein determines its function
Can we predict 3D fold of a protein given its amino-acid sequence?
Holy grail of compbio—35 years old problem
Molecular dynamics, robotics, machine learning, computational geometry
Topics on Proteins in CS374
Protein Structure
Finding the -helix motif
Protein Domains
Molecular Dynamics & Drug Targets
2. Protein Classification
Machine Learning
Graph Flow techniques
Protein Comparison
Latest multiple alignment tools

21. More than 200 complete genomes have been sequenced

22. Evolution

23. Evolution at the DNA level
next generation OK OK OK X X
Still OK?

24. 4. Sequence Comparison Sequence conservation implies function
Sequence comparison is key to
Finding genes
Determining function
Uncovering the evolutionary processes

25. Sequence Comparison—Alignment
AGGCTATCACCTGACCTCCAGGCCGATGCCC
TAGCTATCACGACCGCGGTCGATTTGCCCGAC
-AGGCTATCACCTGACCTCCAGGCCGA--TGCCC---
| | | | | | | | | | | | | x | | | | | | | | | | |
TAG-CTATCAC--GACCGC--GGTCGATTTGCCCGAC
query DB
BLAST
Sequence Alignment
Introduced ~1970
BLAST: 1990, most cited paper in history
Still very active area of research

26. Comparison of Human, Mouse, and Rat

27. More DNA is coming… Topics on Genomics in CS374
Indexing Large Databases
Newest encoding techniques
2. Genomic Rearrangements
Finding the order of shuffles
between two genomes
Repeat Detection
Identifying selfish sequences
that replicate across DNA
Gene Finding
Finding genes by comparing
DNA of different mammals
Finding conserved elements
How do we quantify how much
evolution “likes” a given region?

28. 5. Clustering of Microarrays Clinical prediction of Leukemia type
2 types
Acute lymphoid (ALL)
Acute myeloid (AML)
Different treatment & outcomes
Predict type before treatment?
Bone marrow samples: ALL vs AML
Measure amount of each gene

29. 6. Protein networks Fresh research area
Topics on Protein Networks in CS374
Integration
Build networks from
multiple sources
2. Alignment
Compare networks
across species
Mathematical properties
Modular, scale free
Systems Biology
The cell as a dynamic system
5. Graph Algorithms
Fresh research area
Construct networks from multiple data sources
Navigate networks
Compare networks across organisms
Statistics
Machine learning
Graph algorithms
Databases

30. Some goals of biology for the next 50 years
List all molecular parts that build an organism
Genes, proteins, other functional parts
Understand the function of each part
Understand how parts interact
Study how function has evolved across all species
Find genetic defects that cause diseases
Design drugs rationally
Sequence the genome of every human, use it for personalized medicine

31. Computer Scientists vs Biologists

32. Computer scientists vs Biologists
(almost) Nothing is ever true or false in Biology
Everything is true or false in computer science

33. Computer scientists vs Biologists
Biologists strive to understand the complicated, messy natural world
Computer scientists seek to build their own clean and organized virtual worlds

34. Computer scientists vs Biologists
Biologists are obsessed with being the first to discover something
Computer scientists are obsessed with being the first to invent or prove something

35. Computer scientists vs Biologists
Biologists are comfortable with the idea that all data have errors
Computer scientists are not

36. Computer scientists vs Biologists
Computer scientists get high-paid jobs after graduation
Biologists typically have to complete one or more 5-year post-docs...

37. Computer Science is to Biology what Mathematics is to Physics
“Antedisciplinary” Science
What is computational biology?