Next Generation Sequencing: Applications in Agriculture

Charles Johnson, PhD

Director, Genomics and Bioinformatics Charlie@ag.tamu.edu

www.txgen.tamu.edu

Norman BorlaugCredited with saving over 1 billonpeople from starvation

Nobel Prize winner

Plant Breeder – leader of theGreen Revolution

One of my graduate professors

THE TEXAS A&M UNIVERSITY SYSTEM

Texas A&M AgriLIFE11 Universities

7 State AgenciesHealth Sciences Center

Texas A&M Engineering

Dwight Look College of EngineeringTexas A&M University

Texas A&M Engineering Experiment Station

Texas A&M Engineering Extension Service

Texas A&M Transportation Institute

College of Agriculture and Life Sciences

Texas A&M University

Texas A&M AgriLIFE Research

Texas A&M AgriLIFE Extension

Texas A&M Forest Service

Texas A&M Veterinary MedicalDiagnostic Laboratory

Ranked #1 in Agriculture Research Expenditures in US

• Equipment• Inexperience• Lack of Training

Removing Roadblocks

Who we are…

Our Vision: Become the leading genomic and bioinformatics academic service provider through superior quality service, innovation, and technical excellence. Meeting the needs of scientists across the Texas A&M System, Texas and world.

Our Core Values:

Service Obsessed – Dedication to every client’s success. Striving to exceed expectation on a daily basis and provide the best serve possible. Trust and personal responsibility in all relationships.

Problem Solvers – “Let us sweat the details, we’ll make it happen and not give up!” Problems are solved and only viewed as temporary obstacles. Innovation that matters, for TAMUS, Texas & the World.

Excellence and Pride - We strive to be the best at what we do.

Department College

Animal Science College of Agriculture and Life Science

Biochem & Biophys College of Agriculture and Life Science

Biology College of Science

Ecosystem Science and MGMT College of Agriculture and Life Science

Electrical Engineering / Chem College of Engineering

Entomology College of Agriculture and Life Science

Forest Sciences College of Agriculture and Life Science

Health and Kinesiology College of Education and Human Development

Horticulture College of Agriculture and Life Science

Nutrition and Food Science College of Agriculture and Life Science

Plant Pathology College of Agriculture and Life Science

Poultry Science College of Agriculture and Life Science

Rural Public Health Health Science Center

Soil And Crop Sciences College of Agriculture and Life Science

Statistics College of Science

Texas Veterinary Medical Diagnostic Laboratory College of Agriculture and Life Science

Veterinary Integrative Biosciences College of Veterinary Medicine

Veterinary Large Animal Medicine College of Veterinary Medicine

Biomedical Sciences College of Veterinary Medicine

Physiology & Pharmacology College of Veterinary Medicine

Veterinary Pathobiology College of Veterinary Medicine

Wildlife Science College of Agriculture and Life Science

Total

Genomics Across The World

Over 650 Scientist; >20 Countries

Project Highlights

• Human & Animal Health• Plant pathogens

– Host/Plant Relationships• Plant & Animal Breeding

– > different 17 species• Over 3500 Bacterial genomes• 1st Quarter horse sequence• 1st Scarlet Macaw sequence• 1st Pacific Shrimp sequenced• Insects, Companion Animals, Wild life• Metagenomics

Center for Bioinformatics, Genomics and Systems

Engineering

Bioinformatics, Computational Biology,

Research in Agriculture & Life Science

Feb 2014 Texas A&M University System and IBM formed a partnership

Genomics, Geoscience, Engineering and Computer Science

25 IBM Scientist moving to TAMU

13PB storage

PowerPC and Intel (~18k nodes) Super Computers

Blue Gene system

“Fat Node” for Bioinformatics 2TB RAM/Node

TAMU and IBM

Texas A&M Seed Grant Program

Empowering Genomics

Current State of Art (Reality)

• 18,000 people per year per HiSeqx (10 pack) 30x coverage. (Only Human)

• True cost ~$72M/4 years – closer to $100M to sequence and store data for 72,000 people

• All new born 3M per year would take 1,666 HiSeqx $3B per year

Single Molecule Real Time Sequencing (SMRT): PacBio

Applications:• Genome (de novo and resequencing)• Transcriptome (expression profiling, Whole transcriptome, small RNA and miRNA)•Epigenome (ChIP-Seq, DNA Methylation).

PacBio (2010)• 2900 bp (avergae) at 87-90% accuracy.• 35-75K reads per 30-120 min run.• $2/M basesPros: Longest reads, fast.Cons: Low yield at high accuracy,

Methodology:• Fluorescent dyes• Zero-mode waveguide

$695,000

Oxford Nanopore: MinIon• 1Gb in 6 hrs• 5kb reads• No decrease in quality over time

$1000

General NGS Applications

Genome (DNA-Seq)• De novo sequencing• Resequencing• Targeted Resequencing• Metagenomics• Genotyping/breeding (RAD-Seq).

Transcriptome (RNA-Seq)• Gene expression profiling• Small RNA• Whole transcriptome

Epigenome• ChIP-Seq• Methylation analysis.

Reading vs. Counting Applications

De novo SequencingSequencing and constructing a new genome or transcriptome.• Complicated by lack of reference.• Aided by long reads, paired ends and mate pair reads.• High coverage needed• Error rates not as important due to coverage.

Paired end Mate-Pair

ResequencingUsed to measure variation of a sample in relation to a known reference.• Single nucleotide polymorphisms (SNPs)• Copy number variations (CNVs).• Genomic rearrangements.• Insertion/deletions (indels).

Targeted Resequencing• Exomes (solution-based or microarray-based capture)• Amplicons• Genome-wide association studies (GWAS). Case studies of large populations lookiong at SNPs, etc.

Higher coverage with less reads.Read length not as critical.Error rates are critical as false positives/negatives may confound data.Multiplexing is critical to justify expense.

RNA-Seq

Total RNA, Poly A-enriched or rRNA-depleted

Chemical Fragmentation

cDNA Synthesis

cDNA Fragmentation

Adaptor ligation, amplification, purification

Size selection

Size selection

Size selection

Small RNA Prep is similar, but ligates adaptors first and relies on size selection• RNA isolation method must include small RNAs!

Prokaryotes, Eukaryotes,

Dealing with Ribosomes

Epigenomics

ChIP Seq: Identify DNA binding protein targets• Shear DNA to average of ~ 500 bp• Pulldown targets with antibody• Purify and make library

Whole Genome Bisulfite Seq (WGBS):Genome-wide DNA methylation status. • Uses bisulfite treatment to convert unmethylated cytosines to uracil (which is read as thymine).

MeDIP Seq: ChIP for Methylated DNA.

Reduced Representation Bisulfite Sequencing (RRBS): Uses restriction enzymes, size selection to reduce complexity and to enrich for CpGislands.

Metagenomics

• Usually primers to conserved regions of 16S/18s ribosomal RNA gene flanking highly variable regions are used to generate amplicons that are then sequenced and compared to a database.

• Shotgun metagenomics is gaining ground as it may reduce bias, but at the expense of increased cost (coverage).

Other Applications

Single Cell Seq: • Cancer cell evolution.

Ancient DNA: Sequencing highly fragmented DNA from recovered samples.

Non-coding RNA-Seq: Identify novel non-coding RNAs• Disease and other biomarkers

Cell Line-Seq: Clear, comprehensive genetic patterns of various cell lines• Mutation rates.

FFPE-Seq: Isolating nucleic acids from formalin-fixed, paraffin embedded tissues.

Custom: If you need it, it can probably be done.

It’s not just the

scale…

- it’s the complexity

Bioinformatics is Key

Crop Improvement

AG Genomics

AgriLife & Bayer Crop Science

Identify traits by phenotypic screening

Cross source material with elite cultivars

Select desirable progeny based on phenotype

Evaluate desirable lines for other agronomic traits

Repeat crossing and selection until quality reaches variety level

Traditional Breeding

High Plains Drought tolerance Water-use efficiency WSMV resistance Russian wheat aphid

Rolling Plains Drought / high temp Hessian fly

Blackland

Septoria diseases

Powdery mildew

Hessian fly

Central / South Texas

High temp tolerance

Low/intermediate vernalization

Hessian fly

Phenotyping

STATE WIDE Grain yield End-use quality Forage production Rust resistance Greenbug resistance Genotyping Marker development

Genotype Breeding lines

Identify traits by phenotypic screening

Develop molecular maps that link DNA markers to specific Phenotypes (traits).

Use the marker(s) for MAS to guide breeder to select lines with their trait of interest.

Marker Assisted Selection

Genotyping By Sequence

1. Genome Size and complexity

2. Lack of reference

Key factors1. Cost effective2. Reproducible3. Sensitivity, specificity4. Flexible5. Uniform Sampling6. *No Reference needed

Whole genome?Cost - # individuals

Large and Complex Genomes

Polyploidy, repeats, high GC

Lack of Annotated Genome References

Genotyping with NGS

Reduce Representation or complexity reduction

Targeted Sequencing

Random shallow multiplex sequencing

Genotype-by-Sequencing

RADSeq

GBS (2dGBS)

Digital Genotyping (nested RE) 8,6,4

ddRADSeq

RESTSeq

bbRADSeq (double cutters)

nextRAD (no RE)

EzRad

Flavors of Genotyping by NGS

Markers for recombination binsfor phenotype association

SNP(s) discovery is done via reproducible reduced representation sequencing

Shotgun sequencingFull genome coverage

RE sequencingReduced representation

Restriction site Restriction site

Restriction enzyme aided DNA Sequencing

Uses restriction enzymes to reduce complexity and increase coverage of a small subset of the genome.

• Genotyping by Sequencing• Useful for large scale breeding and mapping studies• SNP detection.

Flexibility:• Choice of site(s) • Length of site (4-, 6-, 8-base cutters)

• DNA status: Methyl-sensitive Cutters

Shotgun sequence

RAD-Seq

Restriction site Restriction site

Restriction Enzymes

Primers

Barcodes

Fragmentation

Size Section

Sage Pippin!

Lab Variables

Genotyping by Sequencing

RAD-Seq

ddRAD-Seqwith size selection

FseI site FseI site

FseIsite

FseIsite

MspIsite

MspIsite

MspIsite

MspIsite

MspIsite

Peterson BK, Weber JN, Kay EH, Fisher HS, et al. (2012) Double Digest RADseq: An Inexpensive Method for De Novo SNP Discovery and Genotyping in Model and Non-Model Species. PLoS ONE 7(5): e37135. doi:10.1371/journal.pone.0037135http://www.plosone.org/article/info:doi/10.1371/journal.pone.0037135

Double Digest Benefits

1.Anchored paired end readsBoth ends usable as markersBuilt-in redundancy check

2.Size selection controls fragment length biasUniform coverageReduced sequence requirementsPloidy/dosage estimation

J. Catchen, A. Amores, P. Hohenlohe, W. Cresko, and J. Postlethwait. ``Stacks: building and genotyping loci de novo from short-read sequences’’. G3:

Genes, Genomes, Genetics, 1:171-182, 2011

Challenges

Users issues

Core issues

User * Core issues (Timing)

Working with your NGS CORE

Challenges-Users

Not main problem

Cost

Equipment

Samples

Main Problem

Training

Lack of Bioinformatics Training

Lack of Computer skills

Lack of Statistical training

Lack of laboratory experience

Students often don’t like learning new things (?)

Challenges- Core

Collaborators

Best and hardest part of the job

Juggling 20-50 Projects at a time

Everyone wants their project done first

P of failure = other lab methods, cost >>

Lack of planning- hint Google is your friend

“Who long will it take”

Authorship

Challenges- Timing

Tricks to getting your samples done quickly Provide lots of high quality DNA/RNA

The list is roughly in order of speed MiSeq run

Full flow cell of HiSeq rapid (2 lanes)

Full flow cell HiSeq High Output (HO) (8 lanes)

One lane HiSeq rapid (flexible type (PE or SE and any length)

One lane HiSeq Rapid (specific length and end type) *

More then one lane HiSeq HO (flexible)

More then one lane HiSeq HO (specific length and end-type)

One lane HiSeq HO (specific length and end-type)

Part of a lane (spike-in) (flexible for length and end-type)

Part of a lane (spike-in) specific length and end-type)

* Different run types can very considerably in terms of popularity so a run configuration that is less popular will take a long time to fill up.

Challenges- Spike-ins

Spike-in study

Why is it taking so long!!

Things that must happen

A run has to fit your length and end-type requirement

Someone else must want only part of a lane as well with same size and end-type

there must be enough samples to fill a flow cell (2 or 8 lanes)

Administration:

- Dr. Gail Martin

Laboratory:

- Dr. Richard Metz- R&D Lead

- Dr. Joshua Hill

- Shannon Parma

- Hiring - Postdoc

Bioinformatics:

- Dr. Noushin Ghaffari- scientist

- Dr. Marcel Brun- visiting scientist

- John Thiltges- IT Lead/computer engineer

- Hiring – (2) Bioinformatics Scientist, (2) Postdocs

Genomics & Bioinformatics Service

http://www.txgen.tamu.edu/