1. Bruce Blumberg (blumberg@uci.edu)
BioSci D145 Lecture #4
Bruce Blumberg
4103 Nat Sci 2 - office hours Tu, Th 3:30-5:00 (or by appointment)
phone
TA – Riann Egusquiza
4351 Nat Sci 2– office hours M 1-3
Phone
check and noteboard daily for announcements, etc..
Please use the course noteboard for discussions of the material
Updated lectures will be posted on web pages after lecture
Last year’s midterm is now posted.
Term paper outlines due Friday (2/3) by midnight.
BioSci D145 lecture 1 page 1 ©copyright Bruce Blumberg All rights reserved

2. Why should any funding agency give you money to pursue this research?
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
BioSci D145 lecture 4 page 2 ©copyright Bruce Blumberg All rights reserved

3. Modern DNA sequence analysis
Cycle sequencing
Virtually all commercial DNA sequencing today is done by cycle sequencing with fluorescent ddNTPs
ABI Big Dye chemistry
Template preparation still tedious for small scale
TempliPHi used in genome centers (obviated need for most automation)
Capillary sequencers predominant form of technology in use
But, next generation sequencing is already coming online and will rapidly displace old technology in genome centers.
454 sequencing (Roche)
Solexa (Illumina)
SoLID (Applied Biosystems)
3rd generation sequencing (individual DNA molecule) now available
e.g., Pacific Biosciences (sequence reads of 1,000-10K bases)
BioSci D145 lecture 4 page 3 ©copyright Bruce Blumberg All rights reserved

4. Other sequencing technologies
Sequencing by hybridization
Construct a high-density microchip with all possible combinations of a short oligonucleotide
Up to 25-mers
By photolithography
Synthesized on chip directly
Label and hybridize fragment to be sequenced
Wash stringently
Read fluorescent spots
Reconstruct sequence by computer
BioSci D145 lecture 5 page 4 ©copyright Bruce Blumberg All rights reserved

5. Other sequencing technologies (contd)
Sequencing by hybridization rarely used for de novo sequencing
Extremely fast and useful to sequence something you already know the sequence of but want to identify mutation - resequencing
Disease causing changes
e.g in mitochondrial DNA
SNP discovery
Works best for examining sequence of <10 kb
BioSci D145 lecture 5 page 5 ©copyright Bruce Blumberg All rights reserved

6. Other sequencing technologies (contd)
SNP discovery
Photo shows mitochondrial chip
Right panel shows pairs of normal (top) vs disease (bottom) (Leber’s Hereditary Optic Neuropathy)
Top 3 disease mutations
Bottom control with no change
BioSci D145 lecture 5 page 6 ©copyright Bruce Blumberg All rights reserved

7. Other sequencing technologies – Next Generation sequencing
2nd generation = high throughput, short sequences
3rd generation = single molecule sequencing
Small number of sequence templates (thousands) but very long reads (~105 bp)
What is the immediate implication of this technology for genome assembly?
Key review is Metzger, M.L. (2010) Sequencing technologies — the next generation, Nature Reviews Genetics 11,
We should now be able to completely sequence large insert clones directly and avoid fragmentation by repetitive elements!
BioSci D145 lecture 5 page 7 ©copyright Bruce Blumberg All rights reserved

8. 3rd generation

9. Other sequencing technologies (contd)
Illumina (Solexa) sequencing
Based on synthesis of complementary strand to a template (like Sanger)
Detection of elongation with labeled terminators
Steps
Library generation - fragment genome to appropriate size (depends on application) and add adapters to each end
Cluster generation – capture fragments on lawn of oligos and amplify
Sequencing – reversible terminator
Data analysis –
align reads to reference genome
Analysis of reads
BioSci D145 lecture 5 page 9 ©copyright Bruce Blumberg All rights reserved

10. Other sequencing technologies (contd)
Illumina sequencing (contd)
Library preparation – fragment target and add adapters.
Can multiplex to gain additional capacity
That is, Hiseq-X can generate 1.8 Tb of data per run, but don’t need this much for most applications so use different adapters and “bar-code” samples.
BioSci D145 lecture 5 page 10 ©copyright Bruce Blumberg All rights reserved

11. Bar coding sequence analysis
BioSci D145 lecture 5 page 11 ©copyright Bruce Blumberg All rights reserved

12. Other sequencing technologies (contd)
Deep sequencing
What is the point?
Can generate huge number of reads in parallel
Miniseq – 7.5 Gb (25 million reads/run 2 x 150 bp)
MiSeq – 15 gb (15 million reads/run 2 x 300 bp)
NextSeq – 120 Gb (400 million reads/run 2 x 150 bp)
HiSeq – 1.5 Tb (5 billion/run 2 x 150 bp)
HiseqX – 1.8 Tb (6 billion/run 2 x 150 bp)
What is massively parallel sequencing good for?
Rapid sequencing of genomes, or resequencing of known sequences
Ancient DNA (even dinosaurs? – Svante Pääbo says ~200K years is limit)
ChIP-sequencing (week 6)
Sequencing ESTs or other tags
Determining microbial diversity in field samples
Transcriptome sequencing
Identifying variations in
Viral populations
Gene sequences in mixed populations
BioSci D145 lecture 5 page 12 ©copyright Bruce Blumberg All rights reserved

13. Idea is to sequence many copies of the same thing Gene sequence
Amplicon sequencing
Idea is to sequence many copies of the same thing
Gene sequence
mRNA transcript
BioSci D145 lecture 5 page 13 ©copyright Bruce Blumberg All rights reserved

14. Amplicon sequencing (contd)
What is amplicon sequencing good for?
Discovery of rare somatic mutations in complex samples (e.g., cancerous tumors - mixed with germline DNA) based on ultra-deep sequencing of amplicons
Sequencing collections of exons from populations of individuals to identify diversity
Sequencing collections of human exons from populations of individuals for the identification of rare alleles associated with disease
Analysis of viral quasispecies present within infected populations in the context of epidemiological studies
Evolutionary biology in populations
BioSci D145 lecture 5 page 14 ©copyright Bruce Blumberg All rights reserved

15. Consensus from all sources ~30K Number of genes C. elegans – 19,000
The human genome
In Feb , 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 k 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 page 15 ©copyright Bruce Blumberg All rights reserved

16. The answer – Gene sets don’t overlap completely (duh) Floor is 42K
The human genome
The answer – Gene sets don’t overlap completely (duh)
Floor is 42K
130029build #236 UniGene Clusters (from EST and mRNA sequencing)
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?
= 42113
BioSci D145 lecture 4 page 16 ©copyright Bruce Blumberg All rights reserved

17. 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 page 17 ©copyright Bruce Blumberg All rights reserved

18. 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 page 18 ©copyright Bruce Blumberg All rights reserved

19. Human genome, mouse, rat, Drosophila, C. elegans “finished”
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 page 19 ©copyright Bruce Blumberg All rights reserved

20. *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 page 20 ©copyright Bruce Blumberg All rights reserved

21. Source material is prepared cDNAs are PCR amplified OR
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 page 21 ©copyright Bruce Blumberg All rights reserved

22. Stanford type microarrayer
DNA microarray types
Stanford type microarrayer
Printing method
Reminiscent of fountain pen
BioSci D145 lecture 4 page 22 ©copyright Bruce Blumberg All rights reserved

23. Amino-allyl labeled 1st strand cDNA
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
downregulated
BioSci D145 lecture 4 page 23 ©copyright Bruce Blumberg All rights reserved

24. Statistical analysis of output – VERY IMPORTANT!
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 page 24 ©copyright Bruce Blumberg All rights reserved

25. Custom arrays possible and affordable
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 page 25 ©copyright Bruce Blumberg All rights reserved

26. High density arrays are synthesized directly on support
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 page 26 ©copyright Bruce Blumberg All rights reserved

27. Affymetrix GeneChips Streptavidin/phycoerythrin
BioSci D145 lecture 4 page 27 ©copyright Bruce Blumberg All rights reserved

28. Each gene is represented by a series of oligonucleotide pairs
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 page 28 ©copyright Bruce Blumberg All rights reserved

29. Result is in single color
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 page 29 ©copyright Bruce Blumberg All rights reserved

30. Identifying genes expressed in one condition vs. another
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
BioSci D145 lecture 4 page 30 ©copyright Bruce Blumberg All rights reserved

31. What are they good for? (contd)
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 page 31 ©copyright Bruce Blumberg All rights reserved

32. Signal intensity (or signal/noise) Improved dyes, label uniformly
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
BioSci D145 lecture 4 page 32 ©copyright Bruce Blumberg All rights reserved

33. 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 is modern implementation
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 page 33 ©copyright Bruce Blumberg All rights reserved

34. 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 page 34 ©copyright Bruce Blumberg All rights reserved

35. 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 page 35 ©copyright Bruce Blumberg All rights reserved

36. Assumes you know all the transcripts
Microarray vs. RNAseq
Microarray
Assumes you know all the transcripts
Any sequence you did not know was expressed will not be there.
except whole genome tiling arrays – Kapranov paper
Detection limit issues
Signal-noise ratio
Well validated , expression analysis can be quantitative
RNAseq
No assumption re transcripts but need genome sequence
Can discover novel sequences or new splice forms not yet characterized (if you have genome)
Detection limits are not a problem – can detect small #
Getting better, expression analysis can be quantitative
BioSci D145 lecture 4 page 36 ©copyright Bruce Blumberg All rights reserved