BioSci D145 Lecture #4
• Bruce Blumberg (blumberg@uci.edu)
– 4103 Nat Sci 2 - office hours Tu, Th 3:30-5:00 (or by appointment)
– phone 824-8573
• TA – Ron Leavitt (rleavitt@uci.edu)
– 4351 Nat Sci 2, 824-6873 – office hours M 2:30-3:30 4206 Nat Sci 2
• check e-mail daily for announcements, etc..
• Updated lectures will be posted on web pages after lecture
– http://blumberg.bio.uci.edu/biod145-w2016
– http://blumberg-lab.bio.uci.edu/biod145-w2016
–
Last year’s midterm is now posted.
–
Term paper outlines due Thursday (1/28) by midnight.
–
No office hours on Thursday 1/28
BioSci D145 lecture 4
page 1
©copyright
Bruce Blumberg 2004-2016. All rights reserved
Term paper outline
• Title of your proposal
• A paragraph introducing your topic and explaining why it is important; i.e.,
what impact will the knowledge gained have.
– Why should any funding agency give you money to pursue this research?
• NIH now requires a statement of human health relevance for all grant
applications
• NSF wants to know what is the intellectual merit of your proposed
research and what broader impacts of your proposed research
• Present your hypothesis
– A supposition or conjecture put forth to account for known facts; esp. in
the sciences, a provisional supposition from which to draw conclusions
that shall be in accordance with known facts, and which serves as a
starting-point for further investigation by which it may be proved or
disproved and the true theory arrived at.
• Enumerate 2-3 specific aims in the form of questions that test your
hypothesis
– At least one of these aims needs to have a strong “whole genome”
component
• Genomics, transcriptomic, proteomic, metabolomic, etc.
BioSci D145 lecture 4
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©copyright
Bruce Blumberg 2004-2016. All rights reserved
DNA Sequence analysis
• Complete DNA sequence (all nts both strands, no gaps)
– complete sequence is desirable but takes time
• how long depends on size and strategy employed
– which strategy to use depends on various factors
• how large is the clone?
– cDNA ?, genomic?
• How fast is sequence required?
• sequencing strategies
– Small-scale (not whole genome)
• primer walking
• cloning and sequencing of restriction fragments
• progressive deletions
– Bidirectional, unidirectional
– Genome sequencing – nearly always shotgun sequencing
• whole genome (traditional vs. nextgen)
• with mapping
– map first (C. elegans)
– map as you go (many)
BioSci D145 lecture 4
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©copyright
Bruce Blumberg 2004-2016. All rights reserved
DNA Sequence analysis (contd)
• Primer walking - walk from the ends with oligonucleotides
– sequence, back up ~50 nt from end, make a primer and continue
• Why back up?
– Need to see overlap to
be sure about sequence
you are reading
BioSci D145 lecture 4
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©copyright
Bruce Blumberg 2004-2016. All rights reserved
DNA Sequence analysis (contd)
• Primer walking (contd)
– advantages
• very simple
• no possibility to lose bits of DNA
– restriction mapping
– deletion methods
• no restriction map needed
• best choice for short DNA
– disadvantages
• slowest method
– about a week between sequencing runs
• oligos are not free (and not reusable)
• not feasible for large sequences
– applications
• cDNA sequencing when time is not critical
• targeted sequencing
– verification
– closing gaps in sequences
BioSci D145 lecture 4
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©copyright
Bruce Blumberg 2004-2016. All rights reserved
DNA Sequence analysis (contd)
• Cloning and sequencing of restriction fragments
– once the most popular method
• make a restriction map,
subclone fragments
• sequence
– advantages
• straightforward
• directed approach
• can go quickly
• cloned fragments often useful otherwise
– RNase protection, nuclease mapping, in situ hybridization
– disadvantages
• possible to lose small fragments
– must run high quality analytical gels
• depends on quality of restriction map
– mistaken mapping -> wrong sequence
• restriction site availability
– applications
• sequencing small cDNAs
• isolating regions to close gaps
BioSci D145 lecture 4
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©copyright
Bruce Blumberg 2004-2016. All rights reserved
DNA Sequence analysis (contd)
• nested deletion strategies - sequential deletions from one end of the clone
• Exonuclease III-mediated deletion
– cut with polylinker enzyme
• protect ends – 3’ overhang
– phosphorothioate
– cut with enzyme between first
cut and the insert
• can’t leave 3’ overhang
– timed digestions with Exo III
– stop reactions, blunt ends
– ligate and size select recombinants
– sequence
– advantages
• unidirectional
• processivity of enzyme
gives nested deletions
BioSci D145 lecture 4
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©copyright
Bruce Blumberg 2004-2016. All rights reserved
DNA Sequence analysis (contd)
• Exonuclease III-mediated
deletion (contd)
– disadvantages
• need two unique restriction
sites flanking insert on each side
• best used successively to get > 10kb total deletions
• may not get complete overlaps of sequences
– fill in with restriction fragments or oligos
– applications
• method of choice for moderate size sequencing projects
– cDNAs
– genomic clones
• good for closing larger gaps
• Small-scale sequence analysis – how is it practiced today?
– Primer walking
– ExoIII-mediated deletion with primer walking
BioSci D145 lecture 4
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©copyright
Bruce Blumberg 2004-2016. All rights reserved
Genome sequencing
• The problem
– Genome sizes for most eukaryotes are large (108-109 bp)
– High quality sequences only about 600-800 bp per run
• Nextgen sequencing is ~75-400 bp
• The solution
– Break genome into lots of bits and sequence them all
– Reassemble with computer
• The benefit
– Rapid increase in information about genome size, gene comparisons, etc
• The cost
– 3 x 109 bp(human haploid genome) ÷ 600 bp/reaction = 5 x 106 reactions
for 1x coverage!
– Need both strands (x2), need overlaps and need to be sure of sequences
– ~107-108 reactions/runs required for a human-sized genome
– About $1-2 per reaction these days, ~$8 commercially.
BioSci D145 lecture 4
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©copyright
Bruce Blumberg 2004-2016. All rights reserved
Genome sequencing (contd)
• Shotgun sequencing NOT invented by Craig Venter
– Messing 1981 first description of shotgun sequencing
– Sanger lab developed current methods in 1983
– approach
• blast genome into small chunks
• clone these chunks
– 3-5 kb, 8 kb plasmid
– 40 kb fosmid jump
repetitive sequences
• sequence + assemble by computer
– A priori difficulties
• how to get nice uniform distribution
• how to assemble fragments
• what to do about repeats?
• How to minimize sequence redundancy?
BioSci D145 lecture 4
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©copyright
Bruce Blumberg 2004-2016. All rights reserved
Genome sequencing(contd)
BioSci D145 lecture 4
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Bruce Blumberg 2004-2016. All rights reserved
Genome sequencing(contd)
“Mate pairs”
BioSci D145 lecture 4
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©copyright
Bruce Blumberg 2004-2016. All rights reserved
Genome sequencing (contd)
• Shotgun sequencing (contd)
– How to minimize sequence redundancy?
• Best way to minimize redundancy is map before you start
– C. elegans was done this way - when the sequence was finished,
it was FINISHED
» mapping took almost 10 years
– mapping much too tedious and nonprofitable for Celera
» who cares about redundancy, let’s sequence and make $$
» There is scientific value to draft genomes, too.
• why does redundancy matter?
– Finished sequence today costs
about $0.50/base
– Note that at 10x, 99.995%
coverage leaves at least
150 kb of the human genome
unsequenced
BioSci D145 lecture 4
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©copyright
Bruce Blumberg 2004-2016. All rights reserved
Genome sequencing (contd)
– Mapping by hybridization
– Mapping by fingerprinting
BioSci D145 lecture 4
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Bruce Blumberg 2004-2016. All rights reserved
Traditional (map first) vs STC (map as you go along) mapping
BioSci D145 lecture 4
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Bruce Blumberg 2004-2016. All rights reserved
DNA sequence analysis
• Landmarks in DNA sequencing
– Sanger, Nicklen and Coulson. Sequencing with chain terminating
inhibitors. Proc. Natl. Acad. Sci. 74, 5463-5467 (1977).
– Sanger, F. et al. The nucleotide sequence of bacteriophage ΦX174. J Mol
Biol 125, 225-46. (1978).
– Sutcliffe, J. G. Complete nucleotide sequence of the Escherichia coli
plasmid pBR322. Cold Spring Harb Symp Quant Biol 43, 77-90. (1979).
– Sanger et al., Nucleotide sequence of bacteriophage lambda DNA. J Mol
Biol 162, 729-73. (1982).
– Messing, J., Crea, R. & Seeburg, P. H. A system for shotgun DNA
sequencing. Nucl.Acids Res 9, 309-21 (1981).
– Anderson, S. et al. Sequence and organization of the human
mitochondrial genome. Nature 290, 457-65 (1981).
– Deininger, P. L. Random subcloning of sonicated DNA: application to
shotgun DNA sequence analysis. Anal Biochem 129, 216-23. (1983).
– Baer et al. DNA sequence and expression of the B95-8 Epstein-Barr virus
genome. Nature 310, 207-11. (1984). (189 kb)
– Innis et al. DNA sequencing with Taq DNA polymerase and direct
sequencing of PCR-amplified DNA Proc. Natl. Acad. Sci. 85, 9436-9440
(1988)
BioSci D145 lecture 4
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©copyright
Bruce Blumberg 2004-2016. All rights reserved
DNA sequence analysis (contd)
• Landmarks in DNA sequencing (contd).
– 1995 - Haemophilus influenzae (1.83 Mb)
• first bacterium sequenced, human pathogen
– 1995 - Mycoplasma genitalium (0.58 Mb)
– 1996
– 1996
– 1997
– 1997
– 1997
– 1997
• smallest free living organism
- Saccharomyces cerevisiae genome (13 Mb)
- Methanococcus jannaschii (1.66 Mb)
• first Archaebacterium
- Escherichia coli (4.6 Mb)
- Bacillus subtilis (4.2 Mb)
- Borrelia burgdorferi (1.44 Mb)
• Lyme disease
- Archaeoglobus fulgidus (2.18 Mb)
• first sulfur metabolizing bacterium
– 1997 - Helicobacter pylori (1.66 Mb)
• first bacterium proven to cause cancer
BioSci D145 lecture 4
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©copyright
Bruce Blumberg 2004-2016. All rights reserved
DNA sequence analysis (contd)
• Landmarks in DNA sequencing (contd)
– 1998 - Treponema pallidum (1.14 Mb)
– 1998 - Caenorhabditis elegans genome (97 Mb)
– 1999 - Deinococcus radiodurans (3.28 Mb)
• resistant to radiation, starvation, ox stress
– 2000 - Drosophila melanogaster (120 Mb)
– 2000 - Arabidopsis thaliana (115 Mb)
– 2001 - Escherichia coli O157:H7 (4.1 Mb)
• Pathogenic variant of E. coli
– 2001 – draft Human “genome”
– 2002 – mouse genome
– 2002 – Ciona intestinalis
–
–
–
–
• Primitive chordate
2003 – “complete “human genome
2004 – rat genome
2006 – Human “genome” complete sequence of all chromosomes
Many more genomes underway, check JGI, Sanger and other web sites
BioSci D145 lecture 4
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©copyright
Bruce Blumberg 2004-2016. All rights reserved
The human genome
• In Feb 12 2001, Celera and Human Genome project published “draft” human
genome sequencs
– Celera -> 39114
– Ensembl -> 29691
– Consensus from all sources ~30K
• Number of genes
– C. elegans – 19,000
– Arabidopsis - 25,000
• Predictions had been from 50-140k human genes
– What’s up with that?
– Are we only slightly more complicated than a weed?
– How can we possibly get a human with less than 2x the number of genes
as C. elegans
– Implications?
• UNRAVELING THE DNA MYTH: The spurious foundation of genetic
engineering, Barry Commoner, Harpers Magazine Feb, 2002
BioSci D145 lecture 4
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©copyright
Bruce Blumberg 2004-2016. All rights reserved
The human genome
• The answer – Gene sets don’t overlap completely (duh)
– Floor is 42K
– 130056build #236 UniGene Clusters (from EST and mRNA sequencing)
– http://www.ncbi.nlm.nih.gov/unigene
– Up from 123,459 in 2013 (85,793, 105,680, 128,826, 123,891 previous
years) (“final” count
• Important questions to be
answered about what
constitutes a “gene”
– Crick genes?
DNA-RNA-protein
– How about RNAs?
– miRNAs?
– Antisense transcripts?
– lncRNAs?
BioSci D145 lecture 4
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©copyright
= 42113
Bruce Blumberg 2004-2016. All rights reserved
Genome sequencing(contd)
– Whole genome shotgun sequencing (Celera)
• premise is that rapid generation of draft sequence is valuable
• why bother trying to clone and sequence difficult regions?
– Basically just forget regions of repetitive DNA - not cost effective
• using this approach, genomes rarely are completely finished
– rule of thumb is that it takes at least as long to finish the last 5%
as it took to get the first 95%
• problems
– sequence may never be complete as is C. elegans
– much redundant sequence with many sparse regions and lots of
gaps.
– Fragment assembly for regions of highly repetitive DNA is dubious
at best
– “Finished” fly and human genomes lack more than a few already
characterized genes
BioSci D145 lecture 4
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©copyright
Bruce Blumberg 2004-2016. All rights reserved
Genome sequencing (contd)
• Knowing what we know now – how to approach a large new genome?
– Xenopus tropicalis 1.7 Gb (about ½ human)
– BAC end sequencing
– Whole genome shotgun
– HAPPY mapping and radiation hybrid mapping to order scaffolds
– Gaps closed with BACS
– 8.5 x coverage (but > 9000 scaffolds for 18 chromosomes)
– Finishing now in process
• But how “finished” will it be?
• 2016 update – now version 9.0
– FINALLY integrated BAC end sequences
– Integrated genetic map
– 50% of contigs > 72 kb
– Xenopus laevis – v9.1 –
• >90% of genome in chromosomal scaffolds
• 2 “subgenomes” fully characterized.
• annotation remains a big challenge.
BioSci D145 lecture 4
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©copyright
Bruce Blumberg 2004-2016. All rights reserved
Functional Genomics - Analysis of gene function on a whole genome basis
• Genome projects
– DNA sequencing
– Human genome, mouse, rat, Drosophila, C. elegans “finished”
– model organisms progressing rapidly
– Lots of new genes, but many lack known function
• Functional genomics
– Identification of gene functions
• associate functions with new genes coming from genome projects
• function of genes identified from characterizing diseases or mutants
– Identification of genes by their function
• discovery of new genes
BioSci D145 lecture 4
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©copyright
Bruce Blumberg 2004-2016. All rights reserved
*Methods of profiling gene expression – large scale to whole genome
• What are the possibilities
– Array – micro or macro
– Sequence sampling (EST generation)
– SAGE – serial analysis of gene expression
– Massively parallel signature sequencing (RNA-seq, Illumina, 454)
• DNA microarray analysis was, until now totally dominant method
– Two basic flavors
• Spotted (spot DNA onto support)
– cDNA microarrays
– Oligonucleotide arrays
– Moderately expensive
• Synthesized (use photolithography to synthesize oligos onto silicon or
other suitable support
– Affymetrix Gene Chips dominate
– VERY expensive
– Both are in wide use and suitable for whole genome analysis
BioSci D145 lecture 4
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©copyright
Bruce Blumberg 2004-2016. All rights reserved
Spotted arrays
• Source material is prepared
– cDNAs are PCR amplified OR
– Oligonucleotides synthesized
• Spotted onto treated glass slides
• RNA prepared from 2 sources
– Test and control
• Labeled probes prepared from RNAs
– Incorporate label directly
– Or incorporate modified NTP
and label later
– Or chemically label mRNA directly
• Hybridize, wash, scan slide
• Express as ratio of one channel to
other after processing
BioSci D145 lecture 4
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©copyright
Bruce Blumberg 2004-2016. All rights reserved
DNA microarray types
• Stanford type
microarrayer
– http://cmgm.stanford
.edu/pbrown/mguide/
index.html
• Printing method
– Reminiscent of
fountain pen
BioSci D145 lecture 4
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©copyright
Bruce Blumberg 2004-2016. All rights reserved
Strategy to identify RAR target genes
Agonist - TTNPB
Antgonist - AGN193109
Harvest st 18
Poly A+ RNA
Poly A+ RNA
Amino-allyl labeled
1st strand cDNA
Amino-allyl labeled
1st strand cDNA
Alexa Fluor
555 (cy3)
Alexa Fluor
647 (cy5)
Alexa Fluor
555 (cy3)
Alexa Fluor
647 (cy5)
Probe microarrays
upregulated
BioSci D145 lecture 4
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©copyright
downregulated
Bruce Blumberg 2004-2016. All rights reserved
DNA microarray
• Statistical analysis of output – VERY IMPORTANT!
• Replicates are very important
• Preprocessing of data is needed
– To remove spurious signals
BioSci D145 lecture 4
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©copyright
Bruce Blumberg 2004-2016. All rights reserved
DNA microarray
• Advantages
– Custom arrays possible and
affordable
– Ratio of fluorescence is robust
and reproducible
• Disadvantages
– Availability of chips
– Expense of production on your own
– Technical details in preparation
BioSci D145 lecture 4
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©copyright
Bruce Blumberg 2004-2016. All rights reserved
Affymetrix GeneChips
• High density arrays are synthesized directly on support
– 4 masks required per cycle -> 100 masks per chip (25-mers)
– Pentium IV requires about 30 masks
– G.P. Li in Engineering directs a UCI facility that
can make just about anything using photolithography
BioSci D145 lecture 4
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©copyright
Bruce Blumberg 2004-2016. All rights reserved
Affymetrix GeneChips
Streptavidin/phycoerythrin
BioSci D145 lecture 4
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©copyright
Bruce Blumberg 2004-2016. All rights reserved
Affymetrix GeneChips
– Each gene is represented by a series of oligonucleotide pairs
• One perfect match
• One with a single mismatch
– Only hybridization to perfect match
but not mismatch is considered to be real
– Gene is considered “detected” if
> ½ of oligo pairs are positive
– Number of pairs depends on
organism and how well
characterized array behavior is
• Human uses 8 pairs
• Xenopus uses 16 pairs
BioSci D145 lecture 4
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©copyright
Bruce Blumberg 2004-2016. All rights reserved
Affymetrix GeneChips
• Result is in single color
– Always need two chips – control and experimental for each condition
– Also need replicates for each condition
– For diverse biological samples (e.g., humans) 10 replicates required!
– For less diverse samples (cell lines) probably 5 replicates needed
• Advantages
– Commercially available
– Standardized
• Disadvantages
– About $700 to buy, probe and
process each chip (at UCI)!
• About $500 elsewhere
– May not be available for your
organism of interest
– No ability to compare probes
directly on the same chip
• Must rely on technology
BioSci D145 lecture 4
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©copyright
Bruce Blumberg 2004-2016. All rights reserved
DNA microarrays
• What are they good for?
– Identifying genes expressed in one condition vs. another
• One tissue vs. another (heart vs liver)
• Tissue vs. tumor
(liver vs. hepatocarcinoma)
• In response to a treatment
(e.g., RA)
• In response to disease
(e.g., after viral infection)
– Building expression profiles
• Tissues
• Cancers
• Developmental stages
• Expressed genes
– Identifying organisms in food
• Array can identify which
animals are present in a mix
• http://www.dnavision.com/files/FOODIDBrosh%20En.pdf
BioSci D145 lecture 4
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©copyright
Bruce Blumberg 2004-2016. All rights reserved
DNA microarrays
• What are they good for? (contd)
– Response of animal to drugs or chemicals
• Toxicogenomics
• Pharmacogenomics
– Diagnostics
• SNP analysis to identify disease loci
• Specific testing for known diseases
BioSci D145 lecture 4
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©copyright
Bruce Blumberg 2004-2016. All rights reserved
DNA microarrays
• What are the limitations of microarray technology? What sorts of factors
might confound the experiment?
– Signal intensity (or signal/noise)
• Improved dyes, label uniformly
– Biological variation (samples are inherently different)
• Sufficient # of replicates is key
• keep individuals separate
– Not all mRNAs will be present at sufficient levels to detect
• Amplification, but beware of bias
– Good statistical analysis is required
• Bayesian statistics are best (Pierre Baldi is local expert)
– calculating the probability of a new event on the basis of earlier
probability estimates which have been derived from empiric data
– i.e., don’t assume random distribution in datasets, calculate
probability based on real data
– Bayesian approach great for small number of replicates,
converges on t-test at high number of replicates
• http://cybert.microarray.ics.uci.edu/
BioSci D145 lecture 4
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©copyright
Bruce Blumberg 2004-2016. All rights reserved
Other methods of transcriptome analysis - parallel
• Microarray was once the dominant
method
– Direct RNA sequencing methods
are rapidly displacing microarrays
– SAGE (serial analysis of gene
expression)
• Nanostring N-Counter is
modern implementation
• Very short sequences
– RNAseq
• Directly sequence large
numbers of RNAs
• Longer sequences
• SAGE
– Relies on generating many very
short sequences and matching
these to the genome
– 10 bp = short SAGE
– 17 bp = “long” SAGE
BioSci D145 lecture 4
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©copyright
Bruce Blumberg 2004-2016. All rights reserved
Other methods of transcriptome analysis - parallel
• SAGE (continued)
– What is the obvious shortcoming
of this method?
– Sequences may not be unique and
could have difficulty mapping to
the genome
BioSci D145 lecture 4
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©copyright
Bruce Blumberg 2004-2016. All rights reserved
Other methods of transcriptome analysis - parallel
• RNA seq – Ali Mortazavi is
local expert
– Use of massively parallel
sequencing allows precise
quantitation of transcript
– Also allows discovery of
rare splice forms
– Discovery of unexpected
transcripts
– Main problem is in
mapping sequence calls to
genome
• Sequencing has 1-2%
errors which can make
mapping to genome
fail
• or induce “in silico
cross-hybridization”
– Mapping to
incorrect genomic
location
BioSci D145 lecture 4
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©copyright
Bruce Blumberg 2004-2016. All rights reserved
Microarray vs. RNAseq
• Microarray
– Assumes you know all the
transcripts that are expressed in
the organism/tissue of interest
• RNAseq
– No assumption re transcripts but
best with genome sequence.
– Works less well without
– Any sequence you did not know
was expressed will not be there.
• except whole genome tiling
arrays
– Can discover novel sequences or
new splice forms not yet
characterized (if you have
genome)
– Detection limit issues
• Signal-noise ratio
– Detection limits are not a
problem – can detect small #
– Well validated , expression
analysis can be quantitative
• Not usually performed
quantitatively
– Getting better, expression
analysis is quantitative with read
depth ≥ 20 x106 mapped reads
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©copyright
Bruce Blumberg 2004-2016. All rights reserved