The document presents an overview of sequencing technologies and genome assembly processes, highlighting the evolution from Sanger sequencing to next-generation sequencing methods. It emphasizes the cost reductions and advancements in throughput over the years, detailing different sequencer types, their capabilities, and the implications for genomic research. Additionally, it discusses the integration of bioinformatics tools and data management systems in applications such as cassava breeding and genomic selection.

1. Surya Saha, Ph.D.
Cornell University & Boyce Thompson Institute
suryasaha@cornell.edu @SahaSurya
Centre for Agricultural Bioinformatics
Pusa, New Delhi
June 13,2014
Slides: http://bit.ly/CABin_Pusa_2014
http://www.acgt.me/blog/2014/3/7/next-generation-sequencing-must-die
Genome Assembly
Jason Chin http://www.bit.ly/SZPKIG

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Sequencing

4. 1953
DNA Structure
discovery
1977
2012
Sanger DNA sequencing by
chain-terminating inhibitors
1984
Epstein-Barr
virus
(170 Kb)
1987Abi370
Sequencer
1995
2001
Homo
sapiens
(3.0 Gb)
2005
454
Solexa
Solid
2007
2011
Ion
Torrent
PacBio
Haemophilus
influenzae
(1.83 Mb)
2013
Slide credit: Aureliano Bombarely
Sequencing over the Ages
Illumina
Illumina
Hiseq X
454
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Pinus
taeda
(24 Gb)
2014
MinION
The Next Generation

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Its all about the $£€¥
http://www.genome.gov/sequencingcosts/

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First generation sequencing

7. Sanger method
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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

8. Sanger method
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http://bit.ly/1g6Cudq
http://bit.ly/1lcQO4J

9. First generation sequencing
• Very high quality sequences (99.999%)
• 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 386 1.9-84 Kb $2400
http://bit.ly/1clLps3
http://1.usa.gov/1cLqIRd

10. Use the specific technology used
to generate the data
– Illumina Hiseq/Miseq/NextSeq
– Pacific Biosciences RS I/RS II
– Ion Torrent Proton/PGM
– SOLiD
– 454
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http://www.acgt.me/blog/2014/3/10/next-generation-
sequencing-must-diepart-2

11. 454 Pyrosequencing
One purified DNA
fragment, to one bead, to
one read.
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http://bit.ly/1ehwxWN
GS FLX
Titanium
http://bit.ly/1ehAcEh

12. Illumina
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Output 15 Gb 120 GB 1000 GB 1800 GB
Number
of Reads
25 Million 400 Million 4 Billion 6 Billion
Read
Length
2x300 bp 2x150 bp 2x125 bp
(2x250 update mid-2014)
2x150 bp
Cost $99K $250K $740K $10M
Source: Illumina
$1000 human
genome??

13. Illumina
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http://1.usa.gov/1fP9ybl

14. Illumina:Moleculo
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http://bit.ly/1aEPOBn

15. Pacific Biosciences SMRT sequencing
Single Molecule Real
Time sequencing
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http://bit.ly/1naxgTe

16. Pacific Biosciences SMRT sequencing
Error correction methods
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Hierarchical genome-assembly
process (HGAP)
PBJelly
Enlish et al., PLOS One. 2012
PBJelly

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Pacific Biosciences SMRT sequencing
Read Lengths

18. Oxford Nanopore
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https://www.nanoporetech.com/
• No data yet??
• Error model
http://erlichya.tumblr.com/post/66376172948/hands-on-
experience-with-oxford-nanopore-minion

19. Others
• Ion Torrent Proton/PGM
• Nabsys
• SOLiD
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20. Comparison
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21. Next generation sequencing
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Run Time Read Length Quality
Total
nucleotides
sequenced
Cost /MB
454
Pyrosequencing
24h 700 bp Q20-Q30 0.7 GB $10
Illumina Miseq 27h 2x250bp > Q30 15 GB $0.15
Illumina Hiseq
2500
11days 2x125bp >Q30 1000 GB $0.05
Ion torrent 2h 400bp >Q20 50MB-1GB $1
Pacific
Biosciences
2h 10-20kb
>Q30 consensus
>Q10 single
400-800MB
/SMRT cell
$0.33-$1
http://bit.ly/1clLps3
http://1.usa.gov/1cLqIRd

22. http://omicsmaps.com/
Next Generation Genomics:
World Map of High-throughput Sequencers
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http://bit.ly/18pfUId

24. Real cost of Sequencing!!
Sboner, Genome Biology, 2011
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25. 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: Aureliano Bombarely

26. Implications of Choice of Library
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Slide credit: Aureliano Bombarely
Consensus sequence
(Contig)
Reads
Scaffold
(or Supercontig)
Pair Read information
NNNNN
Pseudomolecule
(or ultracontig)
F
Genetic information (markers)
NNNNN NN

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Quality control: Encoding
http://bit.ly/N28yUd
Phred score of a base is:
Qphred = -10 log10 (e)
where e is the estimated probability of a base
being incorrect

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Genome Assembly

29. Whole Genome Shotgun Sequencing
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Slide credit: cbcb.umd.edu

30. Genome Sequencing Strategies
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Slide credit: Aureliano Bombarely

31. Genome Sequencing Strategies
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International Human Genome Sequencing Consortium 2001
Overlap Layout Consensus
http://contig.wordpress.com/
cbcb.umd.edu

32. DeBruijnGraph
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33. Ingredient for a Good Assembly
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Slide credit: Mike Schatz

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Bird Snake

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• You have the expertise to install and run
• You have the suitable infrastructure (CPU & RAM) to run the assembler
• You have sufficient time to run the assembler
• Is designed to work with the specific mix of NGS data that you have
generated
• Best addresses what you want to get out of a genome assembly (bigger
overall assembly, more genes, most accuracy, longer scaffolds, most
resolution of haplotypes, most tolerant of repeats, etc.)
The BEST?? Genome Assembler for YOU
http://haldanessieve.org/2013/01/28/our-paper-making-pizzas-and-genome-assemblies/

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38. Which technology to use??
• Microbial genomes
• Eukaryotic genomes
• Resequencing genomes
• RNAseq and other XXXseq methods
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http://bit.ly/1ko9Kgh

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SOL Genomics Network

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41. The SGN Team!!
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Surya Saha, Tom Fisher-York, Hartmut Foerster, Suzy Strickler, Jeremy Edwards,
Noe Fernandez, Naama Menda, Aure Bombarely, Aimin Yan, Isaak Tecle

42. SGN Website
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http://solgenomics.net

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Main web page (front page):
WEB ICONS
TOOL BAR

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Main web page (front page):
TOOL BAR
(MENUS)

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But the DATA also can be
edited
LocusLocus Editor Data
Community Data Curation

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You need
• SGN account.
• Activate submitter / Locus Editor privileges by SGN curator
LocusLocus Editor Data

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Tools

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Genome Browser: GBrowse

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Genome Browser: JBrowse

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CassavaBase
http://cassavabase.org/
Slide credit: Jeremy Edwards

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NextGen Cassava Project
● Project: Adapt SGN database for Cassava Breeding
● Goal: Apply Genomic Selection to cassava breeding
● Predict breeding values from genotype information
● Shorten the breeding cycle
● Massive amounts of genotypic data (GBS)
● Phenotypic data
● Data management challenge
● Improve flowering
● http://nextgencassava.org
Slide credit: Jeremy Edwards

53. SGN/Cassavabase behind the scenes
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● Perl/Catalyst MVC Framework
● PostgreSQL Database
● Generic Model Organism Database (GMOD)
– Chado relational database schema
– GBrowse
– JBrowse
● R
– Experimental design
– QTL mapping
– Genomic selection
Slide credit: Jeremy Edwards

54. Objectives
Provide cassava breeders and researchers access
to data and tools in a centralized, user-friendly
and reliable database.
– Improve partner breeding program information
tracking
– Streamline management of genotypic and
phenotypic data
– Pipeline genotypic and phenotypic data through
Genomic Selection prediction analyses
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Slide credit: Jeremy Edwards

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Genomic Selection
The 'training population' is genotyped and phenotyped to 'train'
the genomic selection (GS) prediction model. Genotypic
information from the breeding material is then fed into the
model to calculate genomic estimated breeding values (GEBV)
for these lines. From Heffner et al. 2009 Crop Sci. 49:1–12
Information from a majority of lines in the breeding population (the training set) is used to create the
prediction model. The model is then used to predict the phenotypes of the remaining lines (the validation
set), using genotypic information only. The results from the model are compared to the actual data to give
the prediction accuracy. Image courtesy of Martha Hamblin, Cornell University
Flow diagram of a genomic selection breeding program.
Breeding cycle time is shortened by removing phenotypic
evaluation of lines before selection as parents for the next
cycle. From Heffner et al. 2009 Crop Sci. 49:1–12
Slide credit: Jeremy Edwards

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Data collection in the field
● Android tablets
● Field book app
– Jesse Poland's group at
USDA-ARS / Kansas
State University
Slide credit: Jeremy Edwards

57. Cassava Trait Ontology
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Kulakow et al. 2011
Kulakow et al. 2011
● Standard terminology
● Facilitate the sharing of information
● Allow users to query keywords related to traits
Slide credit: Jeremy Edwards

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Position available at Solgenomics
Cassavabase project
Plant Breeding + Bioinformatician
● Familiar with breeding
● Programming in Perl, R, SQL, Hadoop
● Linux
● Africa
● Genius
http://www.cassavabase.org/forum/posts
.pl?topic_id=9

59. Thank you!!
Questions??
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