The document discusses advancements in genomic sequencing, including the evolution of sequencing technologies from Sanger to next-generation sequencing methods. It emphasizes the significance of various sequencing technologies, their output capabilities, cost-effectiveness, and the increasing volume of data generated. Additionally, it highlights the role of bioinformatics in processing and analyzing sequencing data.

1. Surya Saha
Sol Genomics Network (SGN)
Boyce Thompson Institute, Ithaca, NY
ss2489@cornell.edu // @SahaSurya
BTI PGRP Intership Program 2015
http://www.acgt.me/blog/2015/3/7/next-generation-sequencing-must-die

2. Hello Experiment!
• Experimental design for survey
Sample size
Locations
Phenotypes
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Early Blight infected tomato plants
http://www.longislandhort.cornell.edu/vegpath/photos/early_blight.htm

3. Hello Experiment!
• Experimental design for survey
Sample size
Locations
Phenotypes
• Experimental design to identify
genetic differences
PCR-based
• Simple Sequence Repeats
• Other markers
Sequencing-based
• Genes of interest
• Single Nucleotide Polymorphisms
• Gene expression
• Genotyping by Sequencing
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Early Blight infected tomato plants
http://www.longislandhort.cornell.edu/vegpath/photos/early_blight.htm

4. Why Sequencing?
• Targeted interrogation
of genome
• Economical
• Technological
developments
• High-throughput assays
• But requires subsequent
validation
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5. Why Sequencing?
• Targeted interrogation
of genome
• Economical
• Technological
developments
• High-throughput assays
• But requires subsequent
validation
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6. 1953
DNA
Structure
discovery
1977
2012
Sanger DNA sequencing by
chain-terminating inhibitors
1984
Epstein-Barr
virus
(170 Kb)
1987
Abi370
Sequencer
1995
2001
Homo
sapiens
(3.0 Gb)
2005
454
Solexa
Solid
2007
2011
Ion
Torrent
PacBio
Haemophilus
influenzae
(1.83 Mb)
2013
Slide designcredit: AurelianoBombarely
Sequencing: Then and Now
Illumina
Illumina
Hiseq X
454
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Pinus
taeda
(24 Gb)
2014
Nanopore
MinION

7. First generation sequencing
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Sanger. Annu Rev Biochem. 1988;57:1-28.
Thanks to Nick Loman for the mention

8. Maxam-Gilbert method (1973)
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9. Maxam-Gilbert method (1973)
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http://en.wikipedia.org/wiki/File:Maxam-
Gilbert_sequencing_en.svg
https://www.nationaldiagnostics.com/electrophoresis
/article/maxam-gilbert-sequencing

10. Sanger method (1977)
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Frederick Sanger
13 Aug 1918 – 19 Nov 2013
Won the Nobel Prize for Chemistry in 1958 and
1980. Published the dideoxy chain termination
method or “Sanger method” in 1977
http://dailym.ai/1f1XeTB

11. Sanger method (1977)
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http://en.wikipedia.org/wiki/File:Sanger-sequencing.svg
http://en.wikipedia.org/wiki/File:
Radioactive_Fluorescent_Seq.jpg

12. First generation sequencing
• Very high quality sequences (99.999% or Q50)
• Very low throughput
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Run Time Read Length Reads / Run
Total
nucleotides
sequenced
Cost / MB
Capillary
Sequencing
(ABI3730xl)
20m-3h 400-900 bp 96 or 384 1.9-84 Kb $2400
http://www.hindawi.com/journals/bmri/2012/251364/tab1/

13. Next generation sequencing
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https://twitter.com/kbradnam/status/443153578429923328
• Second generation
• Third generation
• Fourth generation
• Next-next-generation
• Next-next-next
generation
http://www.acgt.me/blog/2015/3/10/next-generation-sequencing-must-diepart-2

15. Mention the specific technology
used to generate the data
– Illumina Hiseq/Miseq/NextSeq
– Pacific Biosciences RS1/RSII
– Ion Torrent Proton/PGM
– SOLiD
– Oxford Nanopore Minion
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http://www.acgt.me/blog/2015/3/10/next-generation-sequencing-must-
diepart-2

16. 454 Pyrosequencing
One purified DNA
fragment, to one bead, to
one read.
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http://www.genengnews.com/
GS FLX
Titanium
https://mariamuir.com/wp-
content/uploads/2013/04/rip.gif

17. Illumina
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Output 0.3-15 Gb 20-120 GB 10-1500 GB 900-1800GB
Number
of Reads/
Flow cell
25 Million 130-400 Million 300 million – 2.5 Billion 3 Billion
Read
Length
2x300 bp 2x150 bp 2x250 - 2x125 bp 2x150 bp
Cost $99K $250K $740K $10M(10 units)
Source:Illumina
2500
3000
4000
500

18. Illumina
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Output 0.3-15 Gb 20-120 GB 10-1500 GB 900-1800GB
Number
of Reads/
Flow cell
25 Million 130-400 Million 300 million – 2.5 Billion 3 Billion
Read
Length
2x300 bp 2x150 bp 2x250 - 2x125 bp 2x150 bp
Cost $99K $250K $740K $10M(10 units)
Source:Illumina
2500
3000
4000
$1000 human
genome??
500

19. Illumina
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Mardis 2008. Annu. Rev. Genomics Hum. Genet. 2008. 9:387–402

20. Illumina
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Mardis 2008. Annu. Rev. Genomics Hum. Genet. 2008. 9:387–402

21. Illumina:TruSeqLongRead
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Voskoboynik eLife2013;2:e00569

22. Pacific Biosciences SMRT sequencing
Single Molecule Real
Time sequencing
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http://smrt.med.cornell.edu/images/pacbio_library_prep-1.gif

23. Pacific Biosciences SMRT sequencing
Error correction methods
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Hierarchical genome-assembly
process (HGAP)
Englishetal., PLOSOne.2012
PBJelly

24. Pacific Biosciences SMRT sequencing
Error correction methods
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PBcRPipeline

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Pacific Biosciences SMRT sequencing
Read Lengths
http://www.igs.umaryland.edu/labs/grc/
Mean Read Length: 8391 bp
Maximum Subread Length: 24585 bp

26. 6/11/2015 BTI PGRP SummerInternshipProgram2015 26
Pacific Biosciences SMRT sequencing
Read Lengths

27. Genome Assembly with Long Reads
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28. Oxford Nanopore
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https://www.nanoporetech.com/
http://erlichya.tumblr.com/post/66376172948/hands-on-
experience-with-oxford-nanopore-minion
http://halegrafx.com/vector-art/free-vector-despicable-me-minions/

29. Oxford Nanopore
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30. Oxford Nanopore
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https://theconversation.com/how-a-small-backpack-for-fast-genomic-sequencing-is-helping-
combat-ebola-41863

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32. Sequencing Trends
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https://www.google.com/trends/

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0
5000
10000
15000
20000
25000
30000
2008 2009 2010 2011 2012 2013 2014
Number of Publications
Illumina Pacific Biosciences Roche 454 Ion Torrent
-2000
-1000
0
1000
2000
3000
4000
5000
6000
2009 2010 2011 2012 2013 2014
Increasein Number of Publications
Illumina Pacific Biosciences Roche 454 Ion Torrent
0.00%
20.00%
40.00%
60.00%
80.00%
100.00%
120.00%
2009 2010 2011 2012 2013 2014
% Increasein Number of Publications
Pacific Biosciences Roche 454 Ion Torrent

34. Hi-C Crosslinking
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35. Others
• Ion Torrent Proton/PGM
• SOLiD
• Helicos
• Supporting technologies
– BioNano
– Nabsys
– OpGen
– 10X Genomics
– Fluidigm
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36. Comparison
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37. Next generation sequencing
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Run Time Read Length Quality
Total
nucleotides
sequenced
Cost/MB
454
Pyrosequencing
24h 700 bp Q20-Q30 1 GB $10
Illumina Miseq 27h 2x300bp > Q30 15 GB $0.15
Illumina Hiseq
2500
1 - 10days 2x250bp >Q30 3000 GB $0.05
Ion torrent 2h 400bp >Q20 50MB-1GB $1
Pacific
Biosciences
30m - 4h 10kb - >40kb
>Q50 consensus
>Q10 single
500 - 1000MB
/SMRT cell
$0.13 - $0.60
http://www.hindawi.com/journals/bmri/2012/251364/
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3431227

38. http://omicsmaps.com/
Next Generation Genomics:
World Map of High-throughput Sequencers
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https://flxlexblog.wordpress.com/2014/06/11/developments-in-next-generation-sequencing-june-2014-edition/

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https://flxlexblog.wordpress.com/2014/06/11/developments-in-next-generation-sequencing-june-2014-edition/

41. Real cost of Sequencing!!
Sboner,Genome Biology,2011
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42. Sequencing Data and Concepts
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43. Library Types
Single end
Pair end (PE, 150-800 bp, Fwd:/1,Rev:/2)
Mate pair (MP, 2Kb to 20 Kb)
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F
F R
F R 454/Roche
FR Illumina
Illumina
Slide credit:AurelianoBombarely
BTI PGRP SummerInternshipProgram2015

44. Implications of Choice of Library
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Slide credit:AurelianoBombarely
Consensus sequence
(Contig)
Reads
Scaffold
(or Supercontig)
Pair Read information
NNNNN
Pseudomolecule
(or ultracontig)
F
Genetic information (markers) or Optical maps
NNNNN NN
BTI PGRP SummerInternshipProgram2015

45. Multiplexing Libraries
Use of different tags (4-6 nucleotides) to identify
different samples in the same lane/sector.
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Slide credit:AurelianoBombarely
AGTCGT
TGAGCA
AGTCGT
AGTCGT
AGTCGT
AGTCGT
TGAGCA
TGAGCA
TGAGCA
TGAGCA
AGTCGT
AGTCGT
AGTCGT
AGTCGT
TGAGCA
TGAGCA
TGAGCA
TGAGCA
Sequencing
BTI PGRP SummerInternshipProgram2015

46. Fasta files:
It is a text-based format for representing either nucleotide sequences or peptide
sequences, in which nucleotidesor amino acids are represented using single-lettercodes.
-Wikipedia
File Formats
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Slide credit:AurelianoBombarely
BTI PGRP SummerInternshipProgram2015

47. Fastq files:
FASTQ format is a text-based format for storing both a biologicalsequence (usually
nucleotidesequence) and its corresponding qualityscores.
-Wikipedia
• Single line ID with at symbol (“@”) in the first column.
• Sequences can be in multiple lines after the ID line
• Single line with plus symbol (“+”) in the first column to represent the quality line.
• Quality ID line may contain ID
• Quality values are in multiple lines after the + line but length is identical to sequence
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Slide credit:AurelianoBombarely
File Formats
BTI PGRP SummerInternshipProgram2015

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Quality control: Encoding
Fastq files:
!"#$%&'()*+,-./0123456789 Offset by 33 (Phred+33)
KLMNOPQRSTUVWXYZ[]^_`abcdefgh Offset by 64 (Phred+64)
BTI PGRP SummerInternshipProgram2015

49. Quality control: Encoding
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!"#$%&'()*+,-./0123456789 Offset by 33 (Phred+33)
KLMNOPQRSTUVWXYZ[]^_`abcdefgh Offset by 64 (Phred+64)
BTI PGRP SummerInternshipProgram2015

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Quality control: Encoding
http://en.wikipedia.org/wiki/Phred_quality_score
Phred score of a base is:
Qphred = -10 log10 (e)
where e is the estimated probabilityof a base
being wrong
BTI PGRP SummerInternshipProgram2015

51. Pre-processing: Tools
Trimming
• FastQC
• FASTX toolkit
• Trimmomatic
• Scythe
Joining paired-end reads
• fastq-join
• FLASH
• PANDAseq
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52. Sequencing done!
Now What??
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53. Sequencing done! Now What??
• 1 Hiseq run can produce up to 1500GB or 1.5TB
of data
• How much is 250GB of data?
– 250,000,000,000 characters
– 3000 characters per sheet
– 100 sheets / cm
– Stack of ~8000m
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Mount Everest - 8848m

54. Increase in Sequencing Data
L. Stein,Genome Biology,2010
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Slide credit:LukasMueller
BTI PGRP SummerInternshipProgram2015

55. Big Data
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Slide credit:LukasMueller
BTI PGRP SummerInternshipProgram2015

56. High Performance
Computing
Powerful servers with large
amounts of memory,
compute cores, and disk
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57. What is bioinformatics?
 Bioinformatics /baɪ.oʊˌɪnfərˈmætɪks/is the
applicationof computer science and
information technology to the field of biology
and medicine.
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Slide credit:LukasMueller
BTI PGRP SummerInternshipProgram2015

58. Bioinformatics deals with
 Algorithms, databases and information systems, web
technologies, artificial intelligence and soft computing,
information and computation theory, software
engineering, data mining, image processing, modeling
and simulation, signal processing, discrete mathematics,
control and system theory, circuit theory, and statistics.
 Generation of new knowledge in biology and medicine,
and improving & discovering new models of computation
(e.g. DNA computing, neural computing, evolutionary
computing, immuno-computing, swarm-computing,
cellular-computing).
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Slide credit:LukasMueller
BTI PGRP SummerInternshipProgram2015

59. Bioinformatics can...
 Identify similar sequences
 Provide a putative function for a sequence
 Assemble sequences (genomes, transcriptomes)
 Annotate genomes
 Identify differentially expressed genes
 Build networks of genes or metabolites
 Determine phylogenetic relationships
 Mine literature for biological information
 Uncover differences between two genomes
 Calculate how a protein folds
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Slide credit:LukasMueller
BTI PGRP SummerInternshipProgram2015

60. What can bioinformatics do for me?
 Majority of projects involve large datasets
 Speed up your research
 Enable you to ask new questions
 Basic knowledge of bioinformatics needed
 Extract information
 Transform information
 Run analyses
 Build hypotheses, etc.
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Slide credit:LukasMueller
BTI PGRP SummerInternshipProgram2015

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62. Linux
 UNIX-based, free and open source
operating system
 Very stable, easy to use
 Created by Linus Torvalds in 1990s
as a student
 Adopted for most bioinformatics
work
 Also: installed on cell phones,
laptops, desktops,clusters,
supercomputers
 Can run on your computer!
 Virtualized or native
http://www.linux-netbook.com/linux/distributions/
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Slide credit:LukasMueller
BTI PGRP SummerInternshipProgram2015

63. Linux
 UNIX-based,free and open source operating
system
 Very stable, easy to use
 Created by Linus Torvalds in 1990s as a student
 Adopted for most bioinformaticswork
 Also: installed on cell phones, laptops, desktops,
clusters, supercomputers
 Can run on your computer!
 Virtualized or native
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64. Further Reading
Plant Bioinformatics Course
• Virtual machine setup instructions
• Slides for Linux, Sequencing, RNAseq, NGS Read
Mapping and R graphics
• http://btiplantbioinfocourse.wordpress.com
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Slide credit:LukasMueller
BTI PGRP SummerInternshipProgram2015

65. Scripting
 Scripts: Small programs written by the end-
user that control the execution of other
programs or perform a simple algorithm
 Extremely flexible
 Written in Shell, Perl, Python
 You can write them yourself!!!
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Slide credit:LukasMueller
BTI PGRP SummerInternshipProgram2015

66. Perl
 Developed since 1980s by Larry Wall
 Useful for bioinformatics and web development
 Support for objects
 Excellent integration of regular expressions (text
handling language)
 Vast open source code library (http:/cpan.org/)
 BioPerl (http://bioperl.org/)
 Easy to learn
 http://www.perl.org/
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Slide credit:LukasMueller
BTI PGRP SummerInternshipProgram2015

67. Python
 Created by Guido van
Rossum in 1989
 Very elegant language
 BioPython libraries
 The “new” popular
language
 Many frameworks(Django
for web etc.)
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Slide credit:LukasMueller
BTI PGRP SummerInternshipProgram2015

68.  Language designed for statistics
 Support for matrix calculations, graphics
 Expression analysis, Next-Gen sequence analysis,
Graphics, genome annotation statistics, phylogeny
 Interactive
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Slide credit:LukasMueller
BTI PGRP SummerInternshipProgram2015

69. Databases
 Need to store and query
data
 Biological data is highly
structured
 Relational database
systems
 Non-relationalsystems
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Slide credit:LukasMueller
BTI PGRP SummerInternshipProgram2015

70. Thank you!!
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