1. CO341: Introduction to Bioinformatics Prof. Yi-Ke Guo (yg@ic.ac.uk)
Filling in:
Ioannis Pandis, PhD

2. Sequencing and Genomics
DNA Sequencing Sequencing Analysis Gene Expression Gene Expression Analysis Functional Genomics

3. DNA Structure Double Helix (Crick & Watson) Nitrogenous Base Pairs
2 coiled matching strands
Backbone of sugar phosphate pairs
Nitrogenous Base Pairs
Roughly 20 atoms in a base
Adenine  Thymine [A,T]
Cytosine  Guanine [C,G]
Weak bonds (can be broken)
Form long chains called polymers
Read the sequence on 1 strand
GATTCATCATGGATCATACTAAC
Mentioned

4. Differences in DNA DNA differentiates: tiny 2% We share DNA with
Species/race/gender
Individuals
We share DNA with
Primates,mammals
Fish, plants, bacteria
Genotype
DNA of an individual
Genetic constitution
Phenotype
Characteristics of the resulting organism
Nature and nurture
tiny 2%
Share Material
Roughly 4%

5. Genes Chunks of DNA sequence Large percentage of human genome
Between 600 and 1200 bases long
22,000 human genes, 100,000 genes in tulips
Large percentage of human genome
termed “junk”: does not code for proteins
“Simpler” organisms such as bacteria
Are much more “evolved” (have hardly any junk)
Viruses have overlapping genes (zipped/compressed)
Often the active part of a gene is split into exons
Separated by introns

6. Transcription Take one strand of DNA
Write out the counterparts to each base
G becomes C (and vice versa)
A becomes T (and vice versa)
Change Thymine [T] to Uracil [U]
You have transcribed DNA into messenger RNA
Example:
Start: GGATGCCAATG
Intermediate: CCTACGGTTAC
Transcribed: CCUACGGUUAC
mentioned

7. The Synthesis of Proteins
Instructions for generating Amino Acid sequences
(i) DNA double helix is unzipped
(ii) One strand is transcribed to messenger RNA
(iii) RNA acts as a template
ribosomes translate the RNA into the sequence of amino acids
Amino acid sequences fold into a 3d molecule
Gene expression
Every cell has every gene in it (has all chromosomes)
Which ones produce proteins (are expressed) & when?
mentioned

8. Genetic Code How the translation occurs Think of this as a function:
Input: triples of three base letters (Codons)
Output: amino acid
Example: ACC becomes threonine (T)
Gene sequences end with:
TAA, TAG or TGA
Mentioned

9. Genetic Code 3 nt / aa = 43 = 64aa??? A=Ala=Alanine C=Cys=Cysteine
D=Asp=Aspartic acid
E=Glu=Glutamic acid
F=Phe=Phenylalanine
G=Gly=Glycine
H=His=Histidine
I=Ile=Isoleucine
K=Lys=Lysine
L=Leu=Leucine
M=Met=Methionine
N=Asn=Asparagine
P=Pro=Proline
Q=Gln=Glutamine
R=Arg=Arginine
S=Ser=Serine
T=Thr=Threonine
V=Val=Valine
W=Trp=Tryptophan
Y=Tyr=Tyrosine
Mentioned

10. Example Synthesis TCGGTGAATCTGTTTGAT AGCCACUUAGACAAACUA SHLDKL
Transcribed to:
AGCCACUUAGACAAACUA
Translated to:
SHLDKL
Mentioned

11. Proteins DNA codes for Amino acids strings Residue sequences
strings of amino acids
Amino acids strings
Fold up into complex 3d molecule
3d structures:conformations
Between 200 & 400 “residues”
Folds are proteins
Residue sequences
Always fold to same conformation
Proteins play a part
In almost every biological process
Mentioned

12. Evolution of Genes: Inheritance
Evolution of species
Caused by reproduction and survival of the fittest
But actually, it is the genotype which evolves
Organism has to live with it (or die before reproduction)
Three mechanisms: inheritance, mutation and crossover
Inheritance: properties from parents
Embryo has cells with 23 pairs of chromosomes
Each pair: 1 chromosome from father, 1 from mother
Most important factor in offspring’s genetic makeup

13. Evolution of Genes: Mutation
Genes alter (slightly) during reproduction
Caused by errors, from radiation, from toxicity
3 possibilities: deletion, insertion, substitution
Substitution: ACGTTGACTC  ACGATGACTT
Deletion: ACGTTGACTC  ACGTGACTC
Insertion: ACGTTGACTC  AGCGTTGACTC
Frameshift: ACGTTGACTC  AGCGTTGACTC
Mutations are categorised into:
Neutral or
Deleterious
A single change has a massive effect on translation
Causes a different protein conformation

14. Evolution of Genes: Crossover (Recombination)
DNA sections are swapped
From male and female genetic input to offspring DNA

15. Sequencing for Medical Study
Genotype
Phenotype
Hypothesis
Test Hypothesis
By Genetic Manipulation
How do we answer this question?
Typically we start with a phenotype that we are interested in. And we find individuals that have that phenotype and individuals that do not have that phenotype. Then we find a genetic change that is predominantly in the individuals with the phenotype but not in those without the phenotype. This leads us to a hypothesis that genotype X is responsible for phenotype Y. To test this we must now take an organism, make genotype X, and see if we observe phenotype Y. If true, we can hypothesize that changes in related genes may also cause the phenotype of interest, so we repeat the cycle.

16. Typical Cycle of the Study
Mutation in APC
Gene
Genotype
Phenotype
Hypothesis:
Two groups:
1.Develop
Colorectal cancer
At Young Age
2. Do not
APC is a Tumor
Supressor Gene
Test Hypothesis
By Genetic Manipulation
A quick example:
Bert Vogelstein and colleagues were interested in a phenotype. They observed certain families developed colorectal cancer at at a very young age.
They found a mutation in a gene called APC that was overrepresented in affected individuals.
They hypothesized that APC suppresses tumors, and therefore by deleting APC, one promotes tumors.
They tested this by deleting the gene in mice. Their control is genetically identical except at the APC locus. Only the APC- mouse gets cancer.
Delete APC in Mouse
Control: Isogenic APC+

17. Technologies Required
In 2005
$9 million/genome
Not feasible
?Sequencing?
Genotype
Observation
Reading/Thinking
Phenotype
Hypothesis
Test Hypothesis
By Genetic Manipulation
As someone who is interested in technology development, I am interested in understanding how we can speed up this cycle. So this slide shows what geneticists actually do at each point in the cycle.
Gene Deletion/Replacement

18. The thing is changing rapidly: Bp/$$ increases exponentially with time
In 1980, the sequencing cost per finished bp ≈ $1.00
In 2003, the sequencing cost per finished bp ≈ $0.01
>>> a 100-fold reduction in years
Figure 1 | Exponential growth in computing and sequencing. The dark-blue plot indicates the Kurzweil/Moore’s Law: it describes the doubling of computer instructions per second per US dollar (IPS/US $) that has been occurring approximately every 18 months since The magenta plot indicates an exponential growth in the number of base pairs of accurate DNA sequence per unit cost (bp/US $) as a function of time. To some extent, the doubling time for DNA mimics the IPS/US $ curve because it is dependent on it. An even steeper segment occurs in the orange curve; this depicts the number of web sites (doubling time of four months) and shows how quickly a technology can explode when a protocol that can be shared spreads through an existing infrastructure. The turquoise plot is an ‘Open Source’ case study of ‘FLUORESCENT IN SITU SEQUENCING’ with polonies (see main text for details of this DNAsequencing technology) in bp/min on simple test templates (doubling time of one month).
Adapted from Shendure et al 2004

19. History of DNA Sequencing
Adapted from Eric Green, NIH; Adapted from Messing & Llaca, PNAS (1998)
Avery: Proposes DNA as ‘Genetic Material’
Watson & Crick: Double Helix Structure of DNA
Holley: Sequences Yeast tRNAAla
1870
1953
1940
1965
1970
1977
1980
1990
2002
Miescher: Discovers DNA
Wu: Sequences  Cohesive End DNA
Sanger: Dideoxy Chain Termination
Gilbert: Chemical Degradation
Messing: M13 Cloning
Hood et al.: Partial Automation
Cycle Sequencing
Improved Sequencing Enzymes
Improved Fluorescent Detection Schemes
1986
Next Generation Sequencing
Improved enzymes and chemistry
Improved image processing
1928???
Efficiency
(bp/person/year) 1 15
150
1,500
15,000
25,000
50,000
200,000
50,000,000
100,000,000,000
2008

20. History of DNA Sequencing
Adapted from Eric Green, NIH; Adapted from Messing & Llaca, PNAS (1998)
Griffith's experiment, reported in 1928 by Frederick Griffith

21. History of DNA Sequencing
Adapted from Eric Green, NIH; Adapted from Messing & Llaca, PNAS (1998)
Avery: Proposes DNA as ‘Genetic Material’
Watson & Crick: Double Helix Structure of DNA
Holley: Sequences Yeast tRNAAla
1870
1953
1940
1965
1970
1977
1980
1990
2002
Miescher: Discovers DNA
Wu: Sequences  Cohesive End DNA
Sanger: Dideoxy Chain Termination
Gilbert: Chemical Degradation
Messing: M13 Cloning
Hood et al.: Partial Automation
Cycle Sequencing
Improved Sequencing Enzymes
Improved Fluorescent Detection Schemes
1986
Next Generation Sequencing
Improved enzymes and chemistry
Improved image processing
Efficiency
(bp/person/year) 1 15
150
1,500
15,000
25,000
50,000
200,000
50,000,000
100,000,000,000
2008

22. Sanger Sequencing (Chain-termination Methods)
DNA is fragmented
Cloned to a plasmid vector
Cyclic sequencing reaction
Separation by electrophoresis
Readout with fluorescent tags

23. Basics of the “old” technology
Clone the fragmented DNA.
Generate a ladder of labeled (colored) molecules that are different by 1 nucleotide.
Separate mixture on some matrix.
Detect fluorochrome by laser.
Interpret peaks as string of DNA.
Strings are 500 to 1,000 letters long
Assemble all strings into a genome
The Process Is Sequential

24. That is what old technology take
3 ∙ 109 bp
1x coverage
10x coverage
× 3 ∙ 109 bp
= 40 years
2 ∙ 106 bp/day
10x coverage × 3 ∙ 109 bp × $0.001/bp
= $30 million

25. New Generation Sequencing

26. Basics of the “new” technology
Get DNA and fragment it
Attach all fragments to glass slides.
Perform amplification by some form of PCR
Sequencing ALL these fragments in PARALLLE using chain termination or other methods such as pyro-sequencing
Extend and amplify signal with some color scheme.
Detect fluorochrome by microscopy.
Interpret series of spots as short strings of DNA.
Strings are letters long
Multiple images are interpreted as 0.4 to 1.2 GB/run (1,200,000,000 letters/day).
Map or align strings to one or many genome.
Making Millions Short Sequence Reads in Parallel

27. Technology Overview: Solexa/Illumina Sequencing
How does polony sequencing work?
Here are the steps in our protocol.
Step 1:
Make a Library of linear DNA molecules
This library can be made by random priming or ligation
Every molecule in this library has a Universal Primer on either end
In the middle is the Variable region that differs from molecule to molecule. This is the DNA that we want to sequence.
Made without cloning into E.coli so very high complexity.
Very Dilute amounts of this library and pour an acryalmide gel on a glass microscope slide. Include PCR reagents
Cycle in PCR machine adapted for slides.
Because solution so dilute each template mol is relatively far from other mole.
PCR proceeds - acrylamide restricts diffusion of amplification products so that tney remain localized to the template molecule. A polymerase colony forms (POLONY) 5,000,000

28. Immobilize DNA to Surface
The goal is to amplify millions of “polonies” on a single slide.
But then we want to perform enzymatic manipulations on this DNA
To facilitate this, one of the primers used in the PCR has a 5’ acrylic group that allows copolymerization into the gel.
Source:

29. Technology Overview: Solexa Sequencing
Now we’ve amplified polonies, How are we going to sequence them.
Here’s how we do this.

30. Sequence Colonies
The first thing I want to ask is can we amplify single molecules at high densities in acrylamide?
Alexander Chetverin had previously demonstrated growing RNA colonies in agarose using Q beta replicase system so we were optimistic that DNA colonies could be grown.
Add various amount of templatre (1 kind 236 base pairs), amplify, stain with DNA DYE SYBR GREEN - see fluorescent spheres?
Number of spheres linear with amount of template added
Pick sphere and run on gel – right size.
Conclude that these spheres were DNA colonies or polonies.
Now the size of the polonies shown here is much larger than we would like. We could fit 2100 of these polonies on a single slide and we want millions.

31. Sequence Colonies
But, by increasing the length of the DNA template and increasing the acrylamide concentration, we can get polonies as small as 5 microns.
This would enable 15,000,000 distinguishable polonies assuming Poisson statistics.

32. Call Sequence We established that we could amplify polonies
But can we sequence them?
Rather than sequencing them directly, I decided to try to work out the protocol using oligonucleotide templates attached to acrylamide to work out issues with attachment chemistry, as well as the nucleotide and polymerase chemistries.
First question, can we get a specific single base extension with our

33. From Debbie Nickerson, Department of Genome Sciences, University of Washington,

34. Sequence Alighment
Meyerson et al, 2011

35. So, how fast is cost going down?
2006: $10 million
2008: $100,000
2009: $10,000
2010: $5,000
2012: $1,000
??? $100

36. Informatics Informatics challenge : ample applications
All the genomics research can be uniformly done through sequencing (with the help of proper assay design)
Bioinformatics turns the sequencer into universal genomics interpreter
Not a challenge, rather a big opportunity!!!
For Edison, phonograph was not primarily designed for playing music but …….

37. One Stone, Many Birds: NGS May Enable a Uniform Bioinformatics
Mapped Position : Structure/functionality
(Mapping)
Read Numbers:
Quantified Abundance
(Counting)
BP Variant:
SNP & Mutation Pattern
(Detecting)

38. Match These Sequences How do we match this sequence: gattcagacctagct
With this sequence:
gtcagatcct

39. Possible Answers 1. gattcagacctagct (no indels) gtcagatcct
2. gattcaga-cctagct (with indels)
g-t-cagatcct
3. gattcagacctagc-t (no overhang)
4. gattcagacctagct (with overhang)

40. Sequence Matching Algorithms #1
Without indels
Hamming distance
Scoring schemes
Certain changes in sequence more likely
Due to chemical properties of the residues
BLAST algorithm
Idea: match local regions and expand
Seven part process
In information theory, the Hamming distance between two strings of equal length is the number of positions at which the corresponding symbols are different. In another way, it measures the minimum number of substitutions required to change one string into the other, or the number of errors that transformed one string into the other.

41. Sequence Matching Algorithms #2
With indels
Drawing of Dotplots
Dynamic Programming
(getting from A to B)
Quickest route to Z
+ Quickest route
from Z
VPFLLMMVLG V P F M L G A C D G Z E B F

42. Searching Databases We have ways to score how well 2 seqs match
Now want to use this in databases
Given a known gene sequence
Which genes in the database are closely related
Have to worry about:
Repeated subsequences biasing matches
Accuracy and significance of matches
Sensitivity and specificity (false + and false -)

43. Functional Genomics—Transcriptomics
Transcriptome – the complete set of coding and non-coding RNA molecules in a cell at a particular time: Varies between cell types
Transcriptomics – the study of the transcripts in a cell, cell type, organism, etc.

44. Methods for Transcriptomics
Microarray-based:
High-throughput gene expression profiling
Hybridization of labeled cDNAs to an array of complementary DNA probes
Measurement of expression levels based on hybridization intensity
Sequence-based:
Full-length cDNA (FLcDNA) sequencing: complete sequencing of cDNA clone
Expressed sequence tag (EST) sequencing: Single-pass sequencing of cDNA clone
Serial Analysis of Gene Expression (SAGE):
Short sequence tags at 3’ end of transcript
Tags concatenated and sequenced
NGS enables whole transcriptome sequencing : Sequence Census Method

45. Machine Learning Machine learning (inductive reasoning)
Automatic proposing of hypotheses based on data
Has many applications in bioinformatics, such as microarray analysis
Example: predictive toxicology
Given: set of toxic drugs and a set of non-toxic drugs
Given: background information (chemistry, etc.)
Produces: hypothesis why drugs are toxic/toxis mechanism
Overview of machine learning
Aims, techniques, methodologies, representations
Artificial neural networks
Support vector machine et.al

46. Machine Learning
Larrañaga et al. 2005

47. The End!
Questions?