DNA sequencing: methods
I. Brief history of sequencing
II. Sanger dideoxy method for sequencing
III. Sequencing large pieces of DNA
VI. The “$1,000 dollar genome”
On WebCT
-- “The $1000 genome”
-- review of new sequencing techniques by George Church
Why sequence DNA?
• All genes available for an organism to use -- a
very important tool for biologists
• Not just sequence of genes, but also positioning
of genes and sequences of regulatory regions
• New recombinant DNA constructs must be
sequenced to verify construction or positions of
mutations
• Etc.
History of DNA sequencing
History of DNA sequencing
MC chapter 12
Methods of sequencing
A.
Sanger dideoxy (primer extension/chain-termination)
method: most popular protocol for sequencing, very
adaptable, scalable to large sequencing projects
B.
Maxam-Gilbert chemical cleavage method: DNA is
labelled and then chemically cleaved in a sequencedependent manner. This method is not easily scaled and
is rather tedious
C.
Pyrosequencing: measuring chain extension by
pyrophosphate monitoring
for dideoxy sequencing you need:
1) Single stranded DNA template
2) A primer for DNA synthesis
3) DNA polymerase
4) Deoxynucleoside triphosphates and
dideoxynucleotide triphosphates
Primers for DNA sequencing
• Oligonucleotide primers can be synthesized by
phosphoramidite chemistry--usually designed
manually and then purchased
• Sequence of the oligo must be complimentary to
DNA flanking sequenced region
• Oligos are usually 15-30 nucleotides in length
DNA templates for sequencing:
• Single stranded DNA isolated from
recombinant M13 bacteriophage containing
DNA of interest
• Double-stranded DNA that has been
denatured
• Non-denatured double stranded DNA (cycle
sequencing)
One way for obtaining single-stranded DNA from a double
stranded source--magnets
Reagents for sequencing:
DNA polymerases
• Should be highly processive, and
incorporate ddNTPs efficiently
• Should lack exonuclease activity
• Thermostability required for “cycle
sequencing”
Sanger dideoxy sequencing--basic method
Single stranded DNA
3’
5’
3’
a) Anneal the primer
5’
Sanger dideoxy sequencing: basic method
5’
b) Extend the
primer with DNA
polymerase in the
presence of all four
dNTPs, with a
limited amount of a
dideoxy NTP
(ddNTP)
Direction of
DNA
polymerase
travel
3’
QuickTime™ and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
DNA polymerase incorporates ddNTP in a templatedependent manner, but it works best if the DNA pol
lacks 3’ to 5’ exonuclease (proofreading) activity
Sanger dideoxy sequencing: basic method
3’
5’
T
TT
T
3’
ddA
ddA
ddA
ddA
5’
ddATP in the
reaction: anywhere
there’s a T in the
template strand,
occasionally a ddA
will be added to the
growing strand
How to visualize DNA fragments?
• Radioactivity
– Radiolabeled primers (kinase with 32P)
– Radiolabelled dNTPs (gamma 35S or 32P)
• Fluorescence
– ddNTPs chemically synthesized to contain fluors
– Each ddNTP fluoresces at a different wavelength
allowing identification
Analysis of sequencing products:
Polyacrylamide gel electrophoresis--good
resolution of fragments differing by a single
dNTP
– Slab gels: as previously described
– Capillary gels: require only a tiny amount of
sample to be loaded, run much faster than
slab gels, best for high throughput
sequencing
DNA sequencing gels: old school
Different ddNTP used in
separate reactions
Analyze sequencing
products by gel
electrophoresis,
autoradiography
Radioactively labelled primer or
dNTP in sequencing reaction
cycle sequencing: denaturation
occurs during temperature cycles
94°C:DNA denatures
45°C: primer anneals
60-72°C: thermostable DNA
pol extends primer
Repeat 25-35 times
Advantages: don’t need a lot
of template DNA
Disadvantages: DNA pol
may incorporate ddNTPs
poorly
Animation of cycle sequencing: see
http://www.dnai.org/
Click on:
“manipulation”
“techniques”
“sorting and sequencing”
An automated sequencer
The output
Current trends in sequencing:
It is rare for labs to do their own sequencing:
--costly, perishable reagents
--time consuming
--success rate varies
Instead most labs send out for sequencing:
--You prepare the DNA (usually plasmid, M13, or PCR product),
supply the primer, company or university sequencing center
does the rest
--The sequence is recorded by an automated sequencer as an
“electropherogram”
BREAK UP THE GENOME,
PUT IT BACK TOGETHER
~160 kbp
Assemble sequences by
matching overlaps
BAC sequence
~1 kbp
BAC overlaps give genome sequence
Sequencing large pieces of DNA:
the “shotgun” method
• Break DNA into small pieces (typically sizes of around
1000 base pairs is preferable)
• Clone pieces of DNA into M13
• Sequence enough M13 clones to ensure complete
coverage (eg. sequencing a 3 million base pair genome
would require 5x to 10x 3 million base pairs to have a
reliable representation of the genome)
• Assemble genome through overlap analysis using
computer algorithms, also “polish” sequences using
mapping information from individual clones,
characterized genes, and genetic markers
• This process is assisted by robotics
Sequencing done by TIGR (Maryland) and The
Sanger Institute (Cambridge, UK)
“Here we report an analysis of the genome sequence of P.
falciparum clone 3D7, including descriptions of chromosome
structure, gene content, functional classification of proteins,
metabolism and transport, and other features of parasite
biology.”
Sequencing strategy
A whole chromosome shotgun sequencing
strategy was used to determine the genome
sequence of P. falciparum clone 3D7. This approach
was taken because a whole genome shotgun
strategy was not feasible or cost-effective with the
technology that was available at the beginning of the
project. Also, high-quality large insert libraries of (A T)-rich P. falciparum DNA have never been
constructed in Escherichia coli, which ruled out a
clone-by-clone sequencing strategy. The
chromosomes were separated on pulsed field gels,
and chromosomal DNA was extracted…
The shotgun sequences were assembled into
contiguous DNA sequences (contigs), in some cases with
low coverage shotgun sequences of yeast artificial
chromosome (YAC) clones to assist in the ordering of
contigs for closure. Sequence tagged sites (STSs)10,
microsatellite markers11,12 and HAPPY mapping7 were
also used to place and orient contigs during the gap
closure process. The high (A /T) content of the genome
made gap closure extremely difficult7–9.
Chromosomes 1–5, 9 and 12 were closed,
whereas chromosomes 6–8, 10, 11, 13 and 14 contained
3–37 gaps (most less than 2.5 kb) per chromosome at the
beginning of genome annotation. Efforts to close the
remaining gaps are continuing.
Methods: Sequencing, gap closure and annotation
The techniques used at each of the three participating
centres for sequencing, closure and annotation are described in
the accompanying Letters7–9. To ensure that each centres’
annotation procedures produced roughly equivalent results, the
Wellcome Trust Sanger Institute (‘Sanger’) and the Institute for
Genomic Research (‘TIGR’) annotated the same100-kb
segment of chromosome 14. The number of genes predicted in
this sequence by the two centres was 22 and 23; the
discrepancy being due to the merging of two single genes by
one centre. Of the 74 exons predicted by the two centres, 50
(68%) were identical, 9 (2%) overlapped, 6 (8%) overlapped
and shared one boundary, and the remainder were predicted by
one centre but not the other. Thus 88% of the exons predicted
by the two centres in the 100-kb fragment were identical or
overlapped.
The $1000 dollar genome
Venter Foundation (2003): The first group to produce a
technology capable of a $1000 human genome will win
$500,000 …
X - Prize Foundation: no, $5 - 20 million …
National Institutes of Health (2004): $70 million grant program
to reach the $1000 genome
Previous sequencing techniques: one DNA molecule at a time
Needed: many DNA molecules at a time -- arrays
One of these: “pyrosequencing”
Cut a genome to DNA fragments 300 - 500 bases long
Immobilize single strands on a very small plastic bead (one
piece of DNA per bead)
Amplify the DNA on each bead to cover each bead to boost the
signal
Separate each bead on a plate with up to 1.6 million wells
Sequence by DNA polymerase -dependent chain extension,
one base at a time in the presence of a reporter (luciferase)
Luciferase is an enzyme that will emit a photon of light in
response to the pyrophosphate (PPi) released upon nucleotide
addition by DNA polymerase
Flashes of light and their intensity are recorded
Extension with individual dNTPs gives a readout
A
B
The readout is recorded by
a detector that measures
position of light flashes and
intensity of light flashes
A
B
25 million bases in
about 4 hours
APS = Adenosine phosphosulfate
From www.454.com
Height of peak indicates the number of
dNTPs added
This sequence: TTTGGGGTTGCAGTT
DNA sequencing: methods
I. Brief history of sequencing
II. Sanger dideoxy method for sequencing
III. Sequencing large pieces of DNA
VI. The “$1,000 dollar genome”
On WebCT
-- “The $1000 genome”
-- review of new sequencing techniques by George Church
Introduction to bioinformatics
1) Making biological sense of DNA
sequences
2) Online databases: a brief survey
3) Database in depth: NCBI
4) What is BLAST?
5) Using BLAST for sequence analysis
6) “Biology workbench”, etc.
www.ncbi.nlm.nih.gov
www.tigr.org
http://workbench.sdsc.edu
There’s plenty of DNA to make sense of
http://www.genomesonline.org/
(2006)
Making sense of genome sequences:
1)
Genes
a)
b)
2)
Protein-coding
•
Where are the open reading frames?
•
What are the ORFs most similar to? (What is
the function/structure/evolution history?)
RNA
Non-genes
a)
b)
Regulation: promoters and factor-binding sites
Transactions: replication, repair, and
segregation, DNA packaging (nucleosomes)
Sequence output
Raw data
Computer calls
GNNTNNTGTGNCGGATACAATTCCCCTCTAGAAATAATTTTGTTTAACTTTAAGAAGGAGATATACATATGCACCACCAC
CACCACCACCCCATGGGTATGAATAAGCAAAAGGTTTGTCCTGCTTGTGAATCTGCGGAACTTATTTATGATCCAGAAAG
GGGGGAAATAGTCTGTGCCAAGTGCGGTTATGTAATAGAAGAGAACATAATTGATATGGGTCCTAAGTGGCGTGCTTTTG
ATGCTTCTCAAAGGGAACGCAGGTCTAGAACTGGTGCACCAGAAAGTATTCTTCTTCATGACAAGGGGCTTTCAACTGCA
ATTGGAATTGACAGATCGCTTTCCGGATTAATGAGAGAGAAGATGTACCGTTTGAGGAAGTGGCANTCCANATTANGAGT
TAGTGATGCAGCANANAGGAACCTAGCTTTTGCCCTAAGTGAGTTGGATAGAATTNCTGCTCAGTTAAAACTTCCNNGAC
ATGTAGAGGAAGAAGCTGCAANGCTGNACANAGANGCAGNGNGANAGGGACTTATTNGANGCAGATCTATTGAGAGCGTT
ATGGCGGCANGTGTTTACCCTGCTTGTAGGTTATTAAAAGNTCCCGGGACTCTGGATGAGATTGCTGATATTGCTAGAGC
atgttgtatttgtctgaagaaaataaatccgtat
ccactccttgccctcctgataagattatctttga
tgcagagaggggggagtacatttgctctgaaact
ggagaagttttagaagataaaattatagatcaag
ggccagagtggagggccttcacgccagaggagaa
agaaaagagaagcagagttggagggcctttaaac
aatactattcacgataggggtttatccactctta
tagactggaaagataaggatgctatgggaagaac
tttagaccctaagagaagacttgaggcattgaga
tggagaaagtggcaaattaga
What does this sequence do?
Could it encode a protein?
Looking for ORFs (Open Reading Frames)
using “DNA Strider”
ORF map
1) Where are the potential starts (ATG)
and stops (TAA, TAG, TGA)?
2) Which reading frame is correct?
= ATG
= stop
codon
Reading frame #1 appears to encode a protein
Cautions in ORF identification
• Not all genes initiate with ATG, particularly in certain
microbes (archaea)
• What is the shortest possible length of a real ORF? 50
amino acids? 25 amino acids? Cut-off is somewhat
arbitrary.
• In eukaryotes, ORFs can be difficult to identify because
of introns
• Are there other sequences surrounding the ORF that
indicate it might be functional?
– promoter sequences for RNA polymerase binding
– Shine-Dalgarno sequences for ribosome binding?
What is the function of
the sequenced gene?
Classical methods:
-- mutate gene, characterize phenotype for clues to function
(genetics)
-- purify protein product, characterize in vitro (biochemistry)
Comparison to previously characterized genes:
-- genes sequences that have high sequence similarity
usually have similar functions
-- if your gene has been previously characterized
(using classical methods) by someone else, you want
to know right away! (avoid duplication of labor)
NCBI
NCBI home page --Go to www.ncbi.nlm.nih.gov for the following
pages
Pubmed: search tool for literature--search by author, subject, title
words, etc.
All databases: “a retrieval system for searching several linked
databases”
BLAST: Basic Local Alignment Sequence Tool
OMIM: Online Mendelian Inheritance in Man
Books: many online textbooks available
Tax Browser: A taxonomic organization of organisms and their
genomes
Structure: Clearinghouse for solved molecular structures
What does BLAST do?
1) Searches chosen sequence database
and identifies sequences with similarity
to test sequence
2) Ranks similar sequences by degree of
homology (E value)
3) Illustrates alignment between test
sequence and similar sequences
Alignment of sequences:
The principle: two homologous sequences derived from the
same ancestral sequence will have at least some identical
(similar) amino acid residues
Fraction of identical amino acids is called “percent identity”
Similar amino acids: some amino acids have similar
physical/chemical properties, and more likely to substitute for
each other--these give specific similarity scores in
alignments
Gaps in similar/homologous sequences are rare, and are
given penalty scores
Homology of proteins
Homology: similarity of biological structure, physiology,
development, and evolution, based on genetic inheritance
Homologous proteins: statistically similar sequence, therefore
similar functions (often, but not always…)
Pho TFB1
1 - - - - - - - - - - - - - - - - - M T K Q K1V C-P-V-C-G-S-T-----E-F-I-Y-D-P-E-R-GMETIKVQCKAVRCCPGVYC
Pab TFB
1 - - - - - - - - - - - - - - - - - M T K Q R1V C-P-V-C-G-S-T-----E-F-I-Y-D-P-E-R-GMETIKVQCRAVRCCPGVYC
Pfu TFB1
1 - - - - - - - - - - - - - - - - - M N K Q K1V C-P-A-C-E-S-A-----E-L-I-Y-D-P-E-R-GMENIKVQCKAVKCCPGAYC
Tko TFB1
1 - - - - - - - - - - - - - - - - - M S G K R1V C-P-V-C-G-S-T-----E-F-I-Y-D-P-S-R-GMESIGVKCRKVVCCPGVYC
Tko TFB2
1 - - - - - - - - - - - - M R G - - I S P K R1V C-P-I-C-G-S-T-----E-F-I-YMDRPGR-R-GIESIPVKCRAVKCCPGIYC
Pfu TFB2
1 - - - - - - - M S S T E P G G G W L I Y P V1K C-P-Y-C-K-S-R--M-SDSLTVEYPDGRGQGHWGLEIVYFPCVKKKCCPGYSC
o mPBhLoATSFTB_2 _ d e d1u c-e-d-N-T-D-i-s-f-r-o-m-BYLGAGS-T-_- - S K I1R C-P-V-C-G-S-S-----K-I-I-YYDGPGE-H-G-E-YSYKCIARECCPGVHC
Sso TFB1
1 - - - - - - - - - - - - M L Y L S E E N K S1V S-T-P-C-P-P-D-----K-I-I-FMDLAYELRSGEEEYNIKCSSVESTTGPEC
Sso TFB2
1 - - - - - - - - - - - - - - - - - - - - - M1K C-P-Y-C-K-T-D-N---A-I-T-Y-D-V-E-K-G-M-Y-V-CMTKNCCPAYSC
Sce TFIIB
1 M M T R E S I D K R A G R R G P N L N I V L1T CMPMETCRKEVSYIPDPKKRIAVGERRRFGSPENGLDNVIVVCLATLCCPGELC
con sensu s 1
m
k1v c p v C g s t
e l i y d p e r Gme i v CkavrccpgvyC
G
G
E
G
G
K
G
P
K
K
g
Pho TFB1
3 2 V I E E N I I D M G P E W R A F D A S Q R3-2- EVKIRESERNTIGIADPMEGSPIELWLRHADFKDGALSSQTRD-I-GEIKDRRS
Pab TFB
3 2 V I E E N I V D M G P E W R A F D A S Q R3-2- EVKIRESERNTIGVADPMEGSPIELWLRHADFKDGALSSQTRD-I-GEIKDRRS
Pfu TFB1
3 2 V I E E N I I D M G P E W R A F D A S Q R3-2- EVRIRESERNTIGIADPMEGSPIELWLRHADFKDGALSSQTRE-I-GEIRDRRS
Tko TFB1
3 2 V I E E N V V D E G P E W R A F D P G Q R3-2- EVKIREAERNVVGVADPEEGSPIELWLRHADFKDGPLGSQTRD-I-GEIKDRRA
Tko TFB2
3 5 V I E E N V V D E G P E W R A F E P G Q R3-5- EVKIREAERNTVGVADPEMGTPLEMWIRHADFKEGPLGSQTRD-I-DEWKRRDA
Pfu TFB2
4 2 I L A T N L V D S E L - - - - - - - - - -4-2- -I-LSARTKNTLKVTDNSDEILP-R-Y---T-K-R-I-G------------S-R
o mPBhLoATSFTB_2 _ d e3d3u cVeIdKNST-D-iFsDfTrRoVm-B-L-A-S-T-_- - - -3-3- -V-IRKTSF-S-SFPD-T-R-VP-K-F-R-S-K-G-T-S------------R-T
R
R
R
R
R
K
F
Alignment of TFB and TFIIB sequences
High sequence similarity correlates with functional similarity
enzymes
Non-enzymes
40-20% identity: fold can be predicted by similarity but precise
function cannot be predicted (the 40% rule)
Programs available for BLAST searches
Protein sequence (this is the best option)
blastp--compares an amino acid query sequence against a protein
sequence database
tblastn--compares a protein query sequence against a nucleotide
sequence database translated in all reading frames
DNA sequence
blastn--compares a nucleotide query sequence against a nucleotide
sequence database
blastx--compares a nucleotide query sequence translated in all reading
frames against a protein sequence database
tblastx--compares the six-frame translations of a nucleotide query
sequence against the six-frame translations of a nucleotide sequence
database.
BLAST considers all possible combinations of
matches
mismatches
gaps
in any given alignment
Gives the “best” (highest scoring) alignment of sequences
Three scores
1) percent identity
2) similarity score
3) E-value--probability that two sequences will have
the similarity they have by chance (lower number, higher
probability of evolutionary homology, higher probability of
similar function)
What is the E-value?
The E value represents the chance that the similarity is
random and therefore insignificant. Essentially, the E value
describes the random background noise that exists for
matches between sequences. For example, an E value of 1
assigned to a hit can be interpreted as meaning that in a
database of the current size one might expect to see 1
match with a similar score simply by chance.
You can change the Expect value threshold on most main
BLAST search pages. When the Expect value is increased
from the default value of 10, a larger list with more lowscoring hits can be reported.
E values (continued)
From the BLAST tutorial:
Although hits with E values much higher than 0.1 are
unlikely to reflect true sequence relatives, it is useful
to examine hits with lower significance (E values
between 0.1 and 10) for short regions of similarity. In
the absence of longer similarities, these short
regions may allow the tentative assignment of
biochemical activities to the ORF in question. The
significance of any such regions must be assessed
on a case by case basis.
Relationship between E-value and function
Single domain proteins
Multi-domain proteins
E value greater than 10-10, similar structure but possibly
different functions
What does this sequence do? Cue up BLAST…..
Raw data
Computer calls
GNNTNNTGTGNCGGATACAATTCCCCTCTAGAAATAATTTTGTTTAACTTTAAGAAGGAGATATACATATGCACCACCAC
CACCACCACCCCATGGGTATGAATAAGCAAAAGGTTTGTCCTGCTTGTGAATCTGCGGAACTTATTTATGATCCAGAAAG
GGGGGAAATAGTCTGTGCCAAGTGCGGTTATGTAATAGAAGAGAACATAATTGATATGGGTCCTAAGTGGCGTGCTTTTG
ATGCTTCTCAAAGGGAACGCAGGTCTAGAACTGGTGCACCAGAAAGTATTCTTCTTCATGACAAGGGGCTTTCAACTGCA
ATTGGAATTGACAGATCGCTTTCCGGATTAATGAGAGAGAAGATGTACCGTTTGAGGAAGTGGCANTCCANATTANGAGT
TAGTGATGCAGCANANAGGAACCTAGCTTTTGCCCTAAGTGAGTTGGATAGAATTNCTGCTCAGTTAAAACTTCCNNGAC
ATGTAGAGGAAGAAGCTGCAANGCTGNACANAGANGCAGNGNGANAGGGACTTATTNGANGCAGATCTATTGAGAGCGTT
ATGGCGGCANGTGTTTACCCTGCTTGTAGGTTATTAAAAGNTCCCGGGACTCTGGATGAGATTGCTGATATTGCTAGAGC
Find the open reading frame(s)
Translate it:
MKCPYCKSRDLVYDRQHGEVFCKKCGSILATNLVDSELSRKT
KTNDIPRYTKRIGEFTREKIYRLRKWQKKISSERNLVLAMSE
LRRLSGMLKLPKYVEEEAAYLYREAAKRGLTRRIPIETTVAA
CIYATCRLFKVPRTLNEIASYSKTEKKEIMKAFRVIVRNLNL
TPKMLLARPTDYVDKFADELELSERVRRRTVDILRRANEEGI
TSGKNPLSLVAAALYIASLLEGERRSQKEIARVTGVSEMTVR
NRYKELA
BLAST against (go to genomes page):
-- Microbial genomes
-- environmental sequences (genomes)
Results:
1) Distribution of hits: query sequence and positions in
sequence that gave alignments
2) Sequences producing significant alignments
1) Accession number (this takes you to the sequence that
yielded the hit: gene or contig)
2) Name of sequence (sometimes identifies the gene)
3) Similarity score
4) E-value
3) Alignments arranged by E value, with links to gene reports
Two problems with BLAST
1) Homology? the function is
only inferred (NOT known)
2) Large percentages of
coding proteins cannot be
assigned function based
on homology
For a current list of databases and bioinformatics
tools see: Nucleic Acids Research annual
bioinformatics issue (comes out every January).
List of all the databases described, by category:
http://www.oxfordjournals.org/nar/database/cap/
Guide to NCBI: see Webct
Bioinformatics:
making sense of biological sequence
• New DNA sequences are analyzed for ORFs
(Open Reading Frames: protein)
• Any DNA or protein sequence can then be
compared to all other sequences in databases,
and similar sequences identified
• There is much more -- a great diversity of
programs and databases are available
Massively parallel measurements of gene
expression: microarrays
•
•
•
•
•
Defining the “transcriptome”
The northern blot revisited
Detecting expression of many genes: arrays
A typical array experiment
What to do with all this data?
Brown and Botstein (1999) “Exploring the new world
of the genome with DNA microarrays” Nature
Genetics 21, p. 33-37.
(we have this)
genome
(we want these)
DNA
“transcriptome”
RNA
“proteome”
protein
The value of DNA microarrays for
studying gene expression
1)
Study all transcripts at same time
2)
Transcript abundance usually correlates with level of gene
expression--much gene control is at level of transcription
3)
Changes in transcription patterns often occur as a response to
changing environment--this can be detected with a microarray
Detection of mRNA transcripts
• Northern Blot -- immobilize mRNA on membrane,
detect specific sequence by hybridization with one
labeled probe--requires a separate blotting for
each probe
• DNA microarray -- immobilize many probes
(thousands) in an ordered array, hybridize (base
pair) with labelled mRNA or cDNA
Generating an array of probes
•
Identify open reading frames (orfs)
1) PCR each orf (several for each orf), attach
(spot) each PCR product to a solid support in a
specific order (pioneered by Pat Brown’s lab,
Stanford)
2) Chemically synthesize orf-specific
oligonucleotide probes directly on microchip
(Affymetrix)
The chip defines
the genes you are
measuring
http://derisilab.ucsf.edu/microarray/
(Derisi Lab at UCSF)
The RNA comes
from the cells and
conditions you are
interested in
The hybridization
represents the
measurement
A print head for generating arrays
of probes
Print head
Print head travels from DNA probe
source (microtiter plate) to solid
support (treated glass slide)
Small amount of DNA probe is put
on a specific spot at a specific
location
Each spot (DNA probe sequence)
has a specific “address”
Printing needles
QuickTime™ and a
TIFF (Uncomp resse d) de com press or
are nee ded to s ee this picture.
QuickTi me™ and a
TIFF ( Uncompressed) decompressor
are needed to see thi s pi ctur e.
A yeast array experiment
vegetative
sporulating
Isolate mRNA
Prepare fluorescently
labeled cDNA with two
different-colored fluors
hybridize
read-out
Example microarray data
Green: mRNA
more abundant
in vegetative
cells
Yellow: equivalent
mRNA abundance
in vegetative and
sporulating cells
Red: mRNA more
abundant in
sporulating cells
What to do with all that data?
Overarching patterns may become apparent
1) Organize data by hierarchical clustering,
profiling to find patterns
2) Display data graphically to allow
assimilation/comprehension
(Cell synchronization method)
All yeast cell cycleregulated genes
(phase in which
gene is
expressed)
High mRNA
levels
low mRNA
levels
MIAME:
The Minimum Information About a Microarray Experiment
(#6 helps correct for variations in the quantity of
starting RNA, and for variable labelling and
detection efficiencies)
(we have this)
genome
(we want these)
DNA
“transcriptome”
RNA
“proteome”
protein
Analysis of the proteome: “proteomics”
• Which proteins are present and when?
• What are the proteins doing?
– What interacts with what?
• Protein-DNA interactions (chromatin
immunoprecipitation)
• Protein-protein interactions
– Functions of proteins?
Phizicky et al. (2003) “Protein analysis on a proteomic
scale” Nature 422, p. 208-215
Which proteins are expressed?
Classical method
– Detect presence of a specific protein
• Using antibodies or specific assay
• Measure changes in protein levels with
changing environment, in different tissues
– Very labor intensive, expensive to scale up to
proteome
Massively parallel detection and
identification of proteins
• 2D gel electrophoresis
– Separate proteins in a given organism or tissue type by migration in gel
electrophoresis
– Identify protein (cut out of gel, sequence or mass-spec)
– Pattern of spots like a barcode for hi-throughput studies
• Mass spectrometry
– Separate individual proteins from cell by charge and mass, individual
proteins can be identified (but need genome sequence information for
this)
• Microarrays: isolate things that bind proteins
2D gel electrophoresis
1) Separate proteins on the basis of isoelectric point
4
This technique is usually
done on a long, narrow gel
10
2D gel
electrophoresis
Lay gel containing
isoelectrically focused
protein on SDS page
gel, separate on the
basis of size
E.coli protein profile
From swissprot database,
www.expasy.ch
Mass spectrometry for identifying proteins in a
mixture
Liquid chromatography
and tandem mass
spectrometry
Software for processing
data
From J.R. Yates 1998 “Mass spectrometry and the age
of the proteome” J Mass Spec. 33, p 1-19
Defining protein function
• Classical methods:
– Define activity of protein, develop an assay for
activity
• Biochemistry: use assay to purify protein from
cell, characterize structure/function of protein in
vitro
• Genetics: obtain mutants with change in activity,
characterize phenotype of mutant, obtain
suppressors to identify genes that interact with
protein of interest
– Time intensive, expensive
Protein activity at the proteome level
• Protein-DNA interactions: identifying binding
sites for DNA-binding proteins: regulation of
gene expression
• Massively parallel screens for activity--protein
arrays
“chromatin immunoprecipitation” (ChIP)
1) Grow cells, add
formaldehyde to cross-link
everything to everything
(including DNA to protein)
2) Lyse cells, break up DNA
by shearing
3) Retrieve protein of interest
(and the DNA it is bound to)
using specific antibody to that
protein (immunoprecipitation)
4) Determine presence of
DNA by quantitative PCR
V. Orlando (2000) TIBS 25, p. 99
Massively
parallel ChIP
PCR, label with
fluorescent dyes
Protein arrays for function
Proteins immobilized,
usually by virtue of a tag
sequence (6 x his tag,
biotin, etc.)
Probe all proteins
at once for a
specific activity
Example of a protein microarray
Proteins fused to GST with
6 x histidine tags,
immobilized on Ni++ matrix
Anti-GST tells how much
protein is immobilized on
surface
Specific assays identify
proteins with specific
activities--calmodulin
binding, phosphoinositide
binding
(we have this)
genome
(we want these)
DNA
“transcriptome”
RNA
“proteome”
protein