Surya Saha Cornell University & Boyce Thompson Institute

suryasaha@cornell.edu @SahaSurya

Directorate of Soybean Research, Indore June 7,2014

Slides: http://bit.ly/Soybean_Indore_2014

http://www.acgt.me/blog/2014/3/7/next-generation-sequencing-must-die

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Sequencing

19

53

DNA Structure discovery

19

77

20

12

Sanger DNA sequencing by chain-terminating inhibitors

19

84

Epstein-Barr virus

(170 Kb)

19

87

Abi370

Sequencer

19

95

20

01

Homo sapiens (3.0 Gb)

20

05

454

Solexa

Solid

20

07

20

11

Ion Torrent

PacBio

Haemophilus influenzae (1.83 Mb)

20

13

Slide credit: Aureliano Bombarely

Sequencing over the Ages

Illumina

Illumina Hiseq X

454

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Pinus taeda

(24 Gb)

20

14

MinION

The Next Generation

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Its all about the $£€¥

http://www.genome.gov/sequencingcosts/

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First generation sequencing

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

Sanger method

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http://bit.ly/1g6Cudq

http://bit.ly/1lcQO4J

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

Next generation sequencing

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http://bit.ly/1keDtZQ

• Second generation • Third generation • Fourth generation • Next-next-generation • Next-next-next

generation http://www.acgt.me/blog/2014/3/10/next-generation-sequencing-must-diepart-2

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

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

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

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??

Illu

min

a

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Illu

min

a: M

ole

culo

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http://bit.ly/1aEPOBn

Pacific Biosciences SMRT sequencing

Single Molecule Real Time sequencing

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http://bit.ly/1naxgTe

Pacific Biosciences SMRT sequencing Error correction methods

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Hierarchical genome-assembly process (HGAP)

PB

Jelly

Enlish et al., PLOS One. 2012

PBJelly

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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

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

Others

• Ion Torrent Proton/PGM

• Nabsys

• SOLiD

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Comparison

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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 5.5-8.5kb

>Q30 consensus

>Q10 single

400-800MB

/SMRT cell $0.33-$1

http://bit.ly/1clLps3 http://1.usa.gov/1cLqIRd

http://omicsmaps.com/

Next Generation Genomics: World Map of High-throughput Sequencers

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http://bit.ly/18pfUId

Real cost of Sequencing!!

Sboner, Genome Biology, 2011

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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

F R Illumina

Illumina

Slide credit: Aureliano Bombarely

Implications of Choice of Library

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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

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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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

What's new on SGN?

• Tomato genome release 2.5 • Incorporates results from FISH • Nicotiana benthamiana genome sequence • Genome sequence and annotation • VIGS Tool • Select specific probes for VIGS • New BLAST interface • New Breeder functions • Later this year: Tomato genome release 3.0

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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

Locus Locus Editor Data

Community Data Curation

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

Locus Locus Editor Data

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Tools

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Genome Browser

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Genomes in SGN

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CassavaBase

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Cassava

● Tropical and subtropical regions

● Mainly grown for starchy roots

● Native to South America

● Major crop in Africa

● Food for 500 million people around the world

● Clonally propagated

● Accumulates toxic cyanogenic glucosides

● Requires processing before consumption

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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

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CassavaBase

http://cassavabase.org/

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

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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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

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● Tassel 4 pipeline from Ed Bucker's group

● Discovery vs production

● Filtering

● Imputation

● Storing in Cassavabase

Slide credit: Jeremy Edwards

Genotyping by sequencing (GBS)

Genotyping by sequencing (GBS)

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SolGS: A tool for genomic selection

Phenotyped

& Genotyped Lines

Prediction Model

Predicted Breeding Values

Genotyped Lines

Slide credit: Jeremy Edwards

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

Thank you!! Questions??

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