This document provides an overview of next-generation sequencing technologies and their applications. It discusses genome enrichment techniques to isolate targeted regions for sequencing. It also describes template preparation methods like emulsion PCR and solid-phase amplification. Finally, it reviews various sequencing platforms like Illumina, SOLiD, 454 and details the sequencing and imaging processes. There are exercises proposed to work with sequencing data files in Galaxy.

1. [I0D51A] Bioinformatics: High-Throughput Analysis
Next-generation sequencing. Part 1: Technologies
Prof Jan Aerts
Faculty of Engineering - ESAT/SCD
jan.aerts@esat.kuleuven.be
TA: Alejandro Sifrim (alejandro.sifrim@esat.kuleuven.be)
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2. Announcements
May 27th (9am-noon): evaluation
open book
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3. Note to self...
Upload s_1_sequence.txt and s_2_sequence.txt to Galaxy first...
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4. Overview
• linux refresher (6/5)
• next-generation sequencing technologies and applications (6/5)
• sequence mapping (13/5)
• variant calling - SNPs (20/5)
• variant calling - structural variation (20/5)
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5. Linux Refresher...
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6. Next-generation sequencing technologies
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7. General principle
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8. Big data...
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9. First vs second generation sequencing
Sanger sequencing (1st gen) 2nd/next gen sequencing
Shendure & Ji, 2008
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10. Paired-end sequencing
Korbel et al, 2007
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11. General approaches
• 2nd generation: clonally amplified single molecules
• Roche 454 pyrosequencing
• Illumina Genome Analyzer -> HiSeq: reversible terminator technology
• ABI SOLiD: ligation-based extension
• Next-next-generation/3rd generation: true single molecule
• Helicos: Heliscore
• Pacific Biosciences: SMRT
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12. Mardis, 2011
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13. Steps
genome enrichment
template preparation
sequencing and imaging
data analysis
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14. A. Genome enrichment
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15. Sequencing costs
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16. What?
Only sequence relevant parts of the genome instead of whole genome, e.g.:
• specific Mb-scale regions known to be involved in particular disease (e.g.
based on GWAS)
• specific candidate genes belonging to disease pathway
• exome (= all exons)
=> how to isolate these from non-target sequence? “pulldown”
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17. Pulldown: on-array
Turner et al, 2009
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18. Pulldown: in-solution
Turner et al, 2009
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19. Performance metrics
• fold-enrichment: ratio of abundance of target sequences post-enrichment vs
pre-enrichment
• capture specificity: fraction of sequence reads that map to target
• uniformity: relative abundance of individual targets after enrichment
• completeness: fraction of target bases detectably captured
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20. B. Template preparation
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21. Problem: most imaging systems not designed to detect single fluorescent event
=> need amplified templates
Aim: to produce a representative, non-biased source of nucleic acid material
from the genome under investigation => population of identical templates
Steps:
1. shear DNA
2. amplify templates
Options: emulsion PCR (emPCR) or solid phase amplification
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22. Amplification by emulsion PCR
emulsion = mixture of two or more immiscible (unblendable) liquids; e.g.
mayonnaise, vinaigrette
emPCR: thousands of microreactors/micro-eppendorfs
one bead + one DNA molecule per microreactor => PCR to 1000s of copies
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23. Williams et al, 2006
Metzker et al, 2010
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24. Solid-phase amplification
http://bit.ly/6JYIUz
http://www.youtube.com/watch?v=77r5p8IBwJk&NR=1
Metzker et al, 2010
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25. C. Sequencing and imaging
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26. Sequencing and imaging
Technologies:
1. cyclic reversible termination
2. sequencing by ligation
3. pyrosequencing
4. real-time sequencing
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27. Cyclic reversible termination
DNA synthesis is terminated after adding single nucleotide
start/stop/start/stop/start/stop/...
Illumina: 4-colour
sequencing result
sequencing steps
Metzker et al, 2010
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28. Helicos: 1-colour
sequencing steps
sequencing result
Metzker et al, 2010
Metzker et al, 2010
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29. Sequencing by ligation
http://bit.ly/fPh22X
sequencing steps
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30. sequencing result
http://bit.ly/fPh22X
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31. Pyrosequencing
Metzker et al, 2010
Metzker et al, 2010 31

32. Real-time sequencing
“ZMW” zero-mode waveguide
DNA polymerase
“strobe sequencing”
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33. Run time Gb/run
Roche 454 8.5 hr 45
Illumina 9 days 35
SOLiD 14 days 50
Helicos 8 days 37
PacBio ? ?
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34. Accuracy - base calling error
• base quality drops along read
Sanger > SOLiD > Illumina > 454 > Helicos
(“dephasing” within clusters)
• base calling errors
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35. Accuracy - homopolymer runs
Issue for Roche 454:
39% of errors are homopolymers
A5 motifs: 3.3% error rate
A8 motifs: 50% error rate
Reason: use signal intensity as a measure for homopolymer length
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36. 36

37. Ronaghi, Genome Res 11:3-11 (2001)
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38. http://mammoth.psu.edu/labPhotos/imageOfFlowgram.jpg
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39. Is it 4? Is it 5? Is it 4?
http://mammoth.psu.edu/labPhotos/imageOfFlowgram.jpg
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40. Consensus accuracy
Increase accuracy for SNP calling by increasing coverage:
Illumina: 20X
SOLiD: 12X
454: 7.4X
Sanger: 3X
Factors: raw accuracy + read length
How deep do you have to sequence? => Poisson distribution: “If you sequence at
average of 10X, how much of the genome will be covered at least 5X”?
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41. Bentley et al, Nature 456:53-56 (2008)
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42. FASTQ file format
example fasta entries (n=2)
“@” + identifier example fastq entries (n=2)
sequence
“+” + identifier (optional)
phred-based quality scores
phred quality score encoding
Wikipedia
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43. Sequence quality control
Is this good sequence? (essential!)
E.g.: using FastQC tool (Babraham Institute, UK; http://
www.bioinformatics.bbsrc.ac.uk/projects/fastqc/)
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44. Sequence quality control
per base sequence quality
good bad
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45. Sequence quality control
per sequence quality scores
good bad
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46. Sequence quality control
per base sequence content
good bad
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47. Sequence quality control
per base GC content
good bad
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48. Sequence quality control
per sequence GC content
good bad
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49. Sequence quality control
k-mer content
good bad
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50. Intermezzo: Galaxy
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51. Online genome analysis
http://galaxy.psu.edu/
“Galaxy allows you to do analyses you cannot do anywhere else without the
need to install or download anything. You can analyze multiple alignments,
compare genomic annotations, profile metagenomic samples and much much
more...”
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53. 53

54. Applications of next-generation sequencing
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55. Kahvejian et al, 2008
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56. DNA-seq
ChIP-seq
RNA-seq
Kahvejian et al, 2008
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57. identify
sequence
variations
DNA-seq
ChIP-seq
RNA-seq
identify
pathogens
Kahvejian et al, 2008
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58. Exercises
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59. Try to login to the server mentioned on Toledo with username and password
provided there.
There are 2 FASTQ files in /mnt/homes/jaerts/: s_1_sequence.txt and
s_2_sequence.txt (= paired ends)
• How many sequences are in s_1_sequence.txt?
• What encoding was used for the quality score? Illumina? Sanger?
• What are the numerical quality scores for the first sequence in
s_1_sequence.txt (i.e. 7172283/1)?
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60. • Create an account on the Galaxy server
• Download s_1_sequence.txt and s_2_sequence.txt from Toledo and upload
them into Galaxy. These files are also available on the linux server
• Have a look at the contents of s_1_sequence.txt.
• Convert quality scores to numeric values for s_1_sequence.txt (“FASTQ
Groomer”)
• Draw the quality score boxplot for s_1_sequence.txt
• Draw the nucleotide distribution chart for s_1_sequence.txt
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61. References
Bentley DR et al. Accurate whole human genome sequencing using reversible
terminator chemistry. Nature 456: 53-59 (2008)
Kahvejian A, Quackenbush J & Thompson JF. What would you do if you could
sequence everything? Nature Biotechnology 26: 1125-1133 (2008)
Korbel JO et al. Paired-end mapping reveals extensive structural variation in the
human genome. Science 318: 420-426 (2007)
Mardis ER. A decade’s perspective on DNA sequencing technology. Nature
470: 198-203 (2011)
Metzker ML. Sequencing technologies - the next generation. Nature Reviews
Genetics 11:31-46 (2010)
Shendure J & Ji H. Next-generation DNA sequencing. Nature Biotechnology
26:1135-1145 (2008)
Turner EH et al. Methods for genomic partitioning. Annual Review of Genomics
and Human Genetics 10 (2009)
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