1. Next-Gen Sequencing:
4 years in the trenches
C. Titus Brown
Asst Prof, CSE and Microbiology;
BEACON NSF STC
Michigan State University
ctb@msu.edu

2. These slides are available online.
“titus brown slideshare”
You can also e-mail me: ctb@msu.edu
Also note that these are my opinions and observations, culled
from personal experience, online material, and reading. I’m
happy to cite/explain further upon request, but:
Your Mileage May Vary

3. Things I won’t talk about
Don’t work on/with/have anything useful to say about:
Exome sequencing
Ancient DNA
ChIP-seq (protein-DNA interactions)
Work on but you’re probably not interested in:
Metagenomics (sequencing uncultured microbial communities)
Bioinformatics data structures and algorithms

4. Overview
 Shotgun sequencing basics
 Things everyone wants to know: how much $$...
 Various current problems & challenges
 Technology, now and future
 Some papers and projects worth looking at; & our own
experiences

7. Two specific concepts:
First, sequencing everything at random is very much easier
than sequencing a specific gene region. (For example, it will
soon be easier and cheaper to shotgun-sequence all of E. coli
then it is to get a single good plasmid sequence.)
Second, if you are sequencing on a 2-D substrate (wells, or
surfaces, or whatnot) then any increase in density (smaller
wells, or better imaging) leads to a squared increase in the
number of sequences.
These two concepts underlie the recent stunning increases
in sequencing capacity.

12. What are current costs for
Illumina?
Approximate costs from MSU sequencing center, a few
months ago, including labor:
RNAseq:
$200 prep / sample
Single-ended 1x50 -- $1100/lane – 100-150 mn reads
Paired-end 2x100 -- $2500/lane – 200-300 mn reads (/ 2)
Barcoding samples, etc, gets complicated.
Discuss biology, etc with a sequencing geek before going
forward!

13. What does this data really give
you??
 With RNAseq, you can do de novo (genome- and gene-annotation-
independent) gene & isoform discovery and quantification; 50-
100m reads/sample is probably “enough”
(see: http://blog.fejes.ca/?p=607 for a good discussion)
 With genome resequencing, you can do variant
analysis/discovery; I recommend 20x depth.
 De novo assembly of complex vertebrate genomes is not casual:
Cheap short-read sequencing does not yet deliver good long-range
contiguity; repeats, heterozygosity get in the way.
Assembly & scaffolding process itself is still evolving.

14. Why so much data?
Why do we need 10-20x coverage (resequencing) or 50-
100m reads (mRNAseq) with Illumina?
Two (linked) reasons:
Shotgun sequencing is random
Counting/sampling variation

15. 1. Useful minimum coverage
depends on high average coverage

16. 2. mRNAseq quantitation – must
overcome sampling variation

17. Coverage conclusions
More coverage rarely hurts (you can always discard data, but
it is harder/more $$ to get more data from an old sample)
Your desired coverage numbers should be driven by
sensitivity considerations.

18. Problems and challenges
Systematic bias in sequencing and software.
Genome assembly: scaffolding and sensitivity
Gene references
mRNAseq isoform construction

19. Resequencing: bias and error
Calling SNPs by mapping --
U. Colorado
http://genomics-course.jasondk.org/?p=395

20. Both sequencing and bioinformatics
yield many low-frequency artifacts!
“Obvious” things like misalignments to paralogous/repeat
sequences.
Indels are handled badly by current tools (up to 60% false
positive rate?!)
Oxidation of DNA during library prep step (acoustic
shearing) generated 8-oxoguanine “lesions” responsible for
artifacts involving C>A/G>T triplets.
=> With any data set, especially big ones, there will both
random and systematic error and bias.
http://pathogenomics.bham.ac.uk/blog/2013/01/sequencing-data-i-want-the-
truth-you-cant-handle-the-truth/

21. Suggestion: Cortex variant caller
Iqbal et al., Nat Genet. 2012, pmid 22231483

22. Genome assembly: scaffolding &
sensitivity
Everyone wants two things from a genome assembly --
Long/correct scaffolds
See http://www.slideshare.net/flxlex/a-different-kettle-of-fish-entirely-bioinformatic-challenges-and-solutions-
for-whole-de-novo-genome-assembly-of-atlantic-cod-and-atlantic-salmon
Complete genome content

23. Sequence data
Reads
original DNA
fragments
original DNA
fragments
Sequenced ends
http://www.cbcb.umd.edu/research/assembly_primer.shtml
slides from http://slideshare.net/flxlex/ ; Lex Nederbragt

24. Contigs
Building contigs
ACGCGATTCAGGTTACCACG
GCGATTCAGGTTACCACGCG
GATTCAGGTTACCACGCGTA
TTCAGGTTACCACGCGTAGC
CAGGTTACCACGCGTAGCGC
Aligned reads GGTTACCACGCGTAGCGCAT
TTACCACGCGTAGCGCATTA
ACCACGCGTAGCGCATTACA
CACGCGTAGCGCATTACACA
CGCGTAGCGCATTACACAGA
CGTAGCGCATTACACAGATT
TAGCGCATTACACAGATTAG
Consensus contig ACGCGATTCAGGTTACCACGCGTAGCGCATTACACAGATTAG
slides from http://slideshare.net/flxlex/ ; Lex Nederbragt

25. Scaffolds
Ordered, oriented contigs
mate pairs
contigs
gap size estimate
Scaffold
contig
gap
slides from http://slideshare.net/flxlex/ ; Lex Nederbragt
http://dx.doi.org/10.6084/m9.figshare.100940

26. slides from http://slideshare.net/flxlex/ ; Lex Nederbragt
Longer reads!
Repeat copy 1 Repeat copy 2
Long reads can span repeats and heterozygous regions
Polymorphic contig 22
Polymorphic contig
Contig 1 Contig 4
Polymorphic contig 33
Polymorphic contig

27. Cod: PacBio results
Mapping to the published genome
11.4 kbp subread
10.6 kbp subread
10.9 kbp subread
slides from http://slideshare.net/flxlex/ ; Lex Nederbragt

28. Sensitivity – does your genome
include everything?
Generally not!
For example, the chick genome is missing a substantial
number of genes from microchromosomes:
723 genes from HSA19q missing from chicken galGal4.
ESTs and RNAseq transcripts for many or most.

29. Approach - Digital normalization
(a computational version of library normalization)
Digital normalization “smooths
out” coverage from different
loci, and can “recover” low
coverage regions for assembly.

30. Applying diginorm to increase
sensitivity
Reassembled chick genome from 70x Illumina ->
normalized reads in ~24 hours.
Contig assembly contained partial or complete matches to
70% of previously unmappable transcripts assembled from
chick mRNAseq
Together with Wes Warren (WUSTL), Hans Cheng (USDA
ADOL), Jerry Dodgson (MSU) proposing to apply PacBio
and normalization to improve chick genome; should be
generalizable approach.

31. Mapping => mRNAseq quantitation
Reference transcriptome required.

32. Existing chick gene models lack exons,
isoforms
Our data
Models
*This gene contains at least 4 isoforms.
Likit Preeyanon

33. (Exon detection is pretty good.)
Likit Preeyanon

34. Gene Modeler Pipeline (“gimme”?)
Merge transcripts together based on transcript mapping to
genome; can include existing gene predictions, iterate.
Construct gene models
Remove redundant sequences
Predict strands and ORFs
Likit Preeyanon

35. Some thoughts on bioinfo
Software is evolving very fast. Don’t worry about using the
latest, but keep an eye on possible artifacts/problems with
what you do use.
In NGS, online information (seqanswers, biostar, Twitter) is
generally far less behind than publications.

36. Technology – where next?
Most slides taken from Lex Nederbragt:
http://www.slideshare.net/flxlex/updated-new-high-throughput-sequencing-
technologies-at-the-norwegian-sequencing-centre-and-beyond

37. High-throughput sequencing
Phase 1: more is better
2005 GS20 200 000 reads 100 bp
0.02 Gb/run
2011 GS FLX+ 1.2 million reads 750 bp
0.7 Gb/run
2006 GA 28 million reads 25 bp
0.7 Gb/run
2011 HiSeq 2000 3 billion reads 2x100 bp
600 Gb/run
slides from http://slideshare.net/flxlex/ ; Lex Nederbragt

38. High-throughput sequencing
Phase 2: smaller is better
GS Junior from Roche/454
0.04 GB/run
400 bp reads
0.7 GB/run
700 bp reads
MiSeq from Illumina
4.5 GB/run
2x150 bp reads
600 GB/run
2x100 bp reads
PGM from Ion Torrent/
Life Technologies
0.01, 0.1 or 1 GB/run
100 or 200 bp reads
slides from http://slideshare.net/flxlex/ ; Lex Nederbragt

39. slides from http://slideshare.net/flxlex/ ; Lex Nederbragt
High-throughput sequencing
Why benchtop sequencing instruments?
Diagnostics
Affordable price
per instrument Small projects
Fast turn around time
http://pennystockalerts.com/ http://www.highqualitylinkbuildingservice.com/
http://www.vetlearn.com/ http://vanillajava.blogspot.com

40. Which instrument to choose?
slides from http://slideshare.net/flxlex/ ; Lex Nederbragt

41. High-throughput sequencing
Phase 3: single-molecule
C2 (current) chemistry:
Average read length 2500 bp
36 000 reads
90 MB per ‘run’
slides from http://slideshare.net/flxlex/ ; Lex Nederbragt

42. S
High-throughput sequencing
Real-time sequencing Technology
Phospholinked hexaphosphate nucleotides
G A T C
b
Lim of detection zone
it
Fluorescence pulse
Intensity
e detection Time
slides from http://slideshare.net/flxlex/ Nature Reviews |Genetics
; Lex Nederbragt
Figure 4 |Real-time sequencing. Pacific Biosciences’ four-colour real-tim sequencing m
e ethod is shown.

43. Need to combine Illumina + PacBio still.
P_errorCorrection pipeline from
 93% of reads recovered
2.7x
Alignments of at least 1kb to cod published assembly
+
Error-corrected reads
23x
s
+ w
rea
d
Ra
24 cpus
4.5 days
100 Gb RAM
slides from http://slideshare.net/flxlex/ ; Lex

44. My perspective on tech:
Illumina HiSeq + benchtop sequencers (MiSeq) currently
most reliable for data generation: data in hand, decent
quality.
PacBio data is an excellent add-on for situations where long
reads are needed (to bridge repeats or het regions).

45. Two final pieces of advice
Should you work with genome centers? Maybe.
Genome centers are good at large, well funded projects.
Their default pipelines are reliable but not always cutting edge.
“Weird” problems (high heterozygosity, or complex repeats)
may require more attention than they can give.
They also have their own schedules and incentives.
Where should you go for contract sequencing?
I get asked this a lot!
My best recommendation is UC Davis.
“Cheaper” is not always “better”; data quality can vary
immensely.

46. Advertisement: next-gen sequence
course http://bioinformatics.msu.edu/ngs-summer-course-2013
June 10-June 20, Kellogg Biological Station; < $500
Hands on exposure to data, analysis tools.

47. Acknowledgements
I showed work from Likit Preeyanon and Alexis Black
Pyrkosz, in my lab
Hans Cheng is primary collaborator on chick work
USDA funded our technology development.
Lex Nederbragt for his slides :)