Post on 11-Jan-2015
description
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
6/15/2014 Centre for Agricultural Bioinformatics, Pusa 2
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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
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
$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
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 10-20kb
>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
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Genome Assembly
Whole Genome Shotgun Sequencing
6/15/2014 Centre for Agricultural Bioinformatics, Pusa 29 Slide credit: cbcb.umd.edu
Genome Sequencing Strategies
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Genome Sequencing Strategies
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International Human Genome Sequencing Consortium 2001
Overlap Layout Consensus
http://contig.wordpress.com/
cbcb.umd.edu
De
Bru
ijn G
rap
h
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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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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
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: 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
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
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
6/15/2014 Centre for Agricultural Bioinformatics, Pusa 54 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
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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