Assembly and Annotation of a 22Gb
Conifer Genome, Loblolly Pine
Jill Wegrzyn
Pieter de Jong, Chuck Langley, Dorrie Main, Keithanne
Mockaitis, Steven Salzberg, Kristian Stevens, Nick
Wheeler, Jim Yorke, Aleksey Zimin, David Neale
Univ. of Calfornia, Davis; Children’s Hospital of Oakland
Research Institute; Indiana Univ.; Washington State Univ.;
Univ. of Maryland; and Johns Hopkins Univ.
PineRefSeq
Goal
To provide the benefits of conifer reference genome
sequences to the research, management and policy
communities.
Specific Objectives
– Provide a high-quality reference genome sequence of loblolly
pine looking toward sugar pine and then Douglas-fir.
– Provide a complete transcriptome resource for gene
discovery, reference building, and aids to genome assembly
– Provide annotation, data integration, and data distribution
through Dendrome and TreeGenes databases.
The Large, Complex Conifer Genomes Present a
Challenge
• Challenges
– The estimated 22 Gigabase loblolly pine genome is 8 times larger than
the human genome
– Conifer genomes generally possess large gene families (duplicated and
divergent copies of a gene), and abundant pseudo-genes.
– The vast majority of the genome appears to be repetitive DNA
• Approaches to Resolving Challenges
– Complementary sequencing strategies that seek to reduce complexity
through use of actual or functional haploid genomes and reduced size
of individual assemblies.
Plant Genome Size Comparisons
40000
35000
3000
2000
1C DNA content (Mb)
1000
30000
0
Pinus
Picea taeda
Picea glauca
Pseudotsuga abies
menziesii
25000
20000
15000
Arabidopsis
Oryza
Populus
Sorghum
Glycine
Zea
Pinus
lambertiana
Pinus
pinaster
P. menziesii
Taxodium
distichum
10000
5000
0
Image Credit: Modified from Daniel Peterson, Mississippi State University
Existing and Planned Angiosperm Tree Genomes
Genome Size1
Species
In Progress With Draft Assemblies
Populus trichocarpa
Black cottonwood
423 Mbp
Populus nigra
Black poplar
480 Mbp
Eucalyptus grandis
Rose gum
691 Mbp
Eucalyptus globulus
Blue gum
530 Mbp
Eucalyptus camaldulensis
River red gum
624 Mbp
Corymbia citriodora
Lemon-scented gum
370 Mbp
Betula nana
Dwarf birch
450 Mbp
Fraxinus excelsior
European ash
900 Mbp
Malus domestica
Apple
881 Mbp
Prunus persica
Peach
227 Mbp
Citrus sinensis
Sweet orange
319 Mbp
Azadirachta indica
Neem
363 Mbp
Castanea mollissima
Chinese Chestnut
800 Mbp
Quercus robur
Pedunculate Oak
740 Mbp
Populus spp and ecotypes
Various
various
In Progress Or Planned
Existing and Planned Gymnosperm Tree Genomes
Species
Genome Size1
Status
Conifers
Picea abies
Norway Spruce
20,000 Mbp
Draft Complete
Picea glauca
White Spruce
22,000 Mbp
Draft Complete
Pinus taeda
Loblolly Pine
22,000 Mbp
Draft Complete
Pinus lambertiana
Sugar Pine
34,000 Mbp
Pending
Pseudotsuga menziesii
Douglas-fir
18,700 Mbp
Pending
Larix sibirica
Siberian Larch
12,030 Mbp
Pending
Pinus sibirica
Siberian Pine
30,000 Mbp
Pending
Pinus pinaster
Maritime Pine
23,810 Mbp
Pending
Pinus sylvestris
Scots Pine
~23,000 Mbp
Pending
1) Genome size: Approximate total size, not completely assembled.
Elements of the Conifer Genome Sequencing Project
Acquiring the DNA
Haploid
Haploid megagametophyte tissue
1N
Shotgun sequenced
Diploid
Diploid needle tissue
2N
40 Kb cloned fosmids, pooled
and sequenced
Figure Credit: Nicholas Wheeler, University of California,
Sequencing Strategy
65X
12X
Technology for De Novo Sequencing
of the Conifer Genomes
Parallel and Complementary Approaches
Max Output: 95 Gigabases
Max. paired end reads 640 million
Max Output: 300 Gigabases
Max. paired end reads 3 billion
1
Effectively haploid
Sequencing Strategy
Today
Megagametophyte Whole Genome Shotgun (MWGS)
• Not enough haploid DNA in a megagametophyte to
implement a complete list of WGS ingredients.
• Compromise: Obtain DNA for longer
insert linking libraries (> 1kbp) from
diploid needle tissue.
• Prepare only short insert Illumina
libraries from megagametophye
tissue.
P. taeda 2011 crop
N
mean
st. dev.
min
max
54
1361 ng
675 ng
580 ng
3560 ng
M-WGS Short Insert Libraries
Preliminary QC and Size Selection
Each DNA sample is then run on an Agilent
Bioanalyzer to determine a preliminary estimate
of insert size and coefficient of variation.
If within spec, selected DNA samples are
converted into Illumina libraries
M-WGS Short Insert Libraries
Library QC and Titration
• Libraries are subsequently QCed on the
Illumina MiSeq
A k-mer Genome Size Estimate
How deep to sequence the libraries?
Experimentally – hybridization
Computationally (WGS) – choose substring of the reads of length k
P. taeda genome size ≅ total k-mers in genome
total k-mers in P. taeda genome ≅
total k-mers in P. taeda reads
expected number of times a
genomically unique k-mer is observed in the reads
k-mer Genome Size Estimates
Loblolly pine Pinus taeda:
31-mers total: 3.736 x 1011
Expected k-mer depth: 18.11
Estimated genome size: 20.63 GB
Sugar pine Pinus lambertiana:
31-mers total: 2.776 x 1011
Expected k-mer depth: 8.12
Estimated genome size: 34.19 GB
High Copy 31-mers
1.09% of distinct 31-mers
33% of all 31-mers
High Copy 31-mers
0.35% of distinct 31-mers
33% of all 31-mers
24-mers total: : 4.092 x 1011
Expected k-mer depth: 19.79
Estimated genome size: 20.68 GB
24-mers total: 3.031 x 1011
Expected k-mer depth: 8.89
Estimated genome size: 33.98 GB
truly large genomes
P. taeda Version 0.9 Library Statistics
• Haploid short insert libraries
– 10 short insert libraries 200 - 640bp
– 1.4Tbp GA2x, HiSeq, MiSeq sequence
– 65 fold coverage
• Diploid jumping libraries
– 47 jumping libraries 1300 – 5500bp
– 280Gbp GA2x sequence
– 12 fold coverage
• 13 Fosmid DiTag Libraries
Elements of the Conifer Genome Sequencing Project
•
65X coverage in paired ends from a single
seed
• 1/3 in GAIIx, 160-bp overlapping pairs
• 2/3 in HiSeq, 100-bp pairs
• 1.7 billion reads from “jumping” libraries
from pine needles, diploid DNA
Collect jumping reads from same haplotype
1.7 billion jumping
reads (4 Kbp)
93 million Di-Tag
reads (36 Kbp)
Keep only pairs
where both reads
match haploid
DNA
Filter: both reads had to be
covered by 52-mers from
megagametophyte data
How to get all these reads into a single
assembly run?
Recent Assemblers for Illumina Data
• MSR-CA (Aleksey Zimin, UMD)
– Based on Celera assembler
– 454, Illumina, and Sanger reads
•
•
•
•
•
•
Allpaths-LG
SOAPdenovo
Velvet
ABySS
Contrail
SGA
Two Classes of Assembly Algorithms
• Overlap-Layout-Consensus (OLC)
– Used by most assemblers for previous generation (Sanger)
sequencing
– Celera Assembler, PCAP, Phusion, Arachne, etc
• De Bruijn Graph
– Used by most assemblers for Illumina data
– SOAPdenovo, Allpaths-LG, Velvet, Abyss, etc
• We use a combined approach that combines the
benefits of both OLC and the De Bruijn Graph in our
MSR-CA assembler
Combine Benefits of OLC and De Bruijn Graph
• Benefits of OLC
– Can deal with variable
length reads and reads from
different sequencing
platforms
– Overlaps can be long and
thus more reliable
– Overlaps do not have to be
exact
– Can resolve repeats of up to
read size
• Drawbacks of OLC
– Computationally intensive,
number of overlaps grows
quickly with the number of
reads and coverage
• Benefits of DeBruijn Graph
– Computationally faster
• Drawbacks of DeBruijn Graph
– Errors in the reads create
spurious branches in the graph
requiring error correction
• Max. size of k-mer is limited
by the shortest read size
• All overlaps in the graph are
exact overlaps of k-1 bases
• Repeats of longer than k
bases cannot be resolved
– Without space consuming side
information
Super reads
GOAL: Reduce the amount of input data without losing information
• Consider a read
CGACTGACCAGATGACCATGACAGATACATGGT stop
extend 5 GACTGACCAG
ATACATGGTA 10 stop
extend 3 CGACTGACCA
ATACATGGTC 2
• Typically Illumina sequencing projects generate data with high
coverage (>50x). With 100bp reads this implies that a new read
starts on average at least every other base:
read R extended to super read S
super read S (red)
the other reads extend to
the S as well
Super-Reads Compress the Data
• 100-fold compression
• 50% of sequence is in super reads
> 500 bp
• Super-read total: 52 Gbp
MaSuRCA assembler performance
• 64-core computer with 1 Terabyte of RAM
• Time/memory to assemble:
• QuORUM error correction: 10 days / 800 GB
• Super-reads construction plus filtering: 11 days /
400 GB
• Contig and scaffold construction: 60+ days / 450 Gb
• uses CABOG assembler
• Gap filling with super-reads: 8 days / 300 Gb
MSR-CA Output
Contigs: contiguous sequences that do not appear to be repetitive
(may contain internal repeats). These end up in scaffolds.
Scaffolds: ordered and oriented collections of contigs, built using
mate pair data. A scaffold can consist of just one contig (a "singlecontig" scaffold).
Degenerate contigs: contigs that appeared to be repeats according
to the coverage statistics. Only placed in scaffolds when linked to
contigs via mate pairs. Most of them will end up being placed in
more than one location, but many will not appear in any scaffold.
P. taeda WGS V0.6 (June 2012)
• Approximately 35X coverage
– 7 billion reads (50 million jumping library reads)
– Compressed to 377 million Super-reads
• Total Sequence: 18,321,727,393 bp
• Total contig sequence: 14,606,783,345 bp
• N50 1,199bp (9.16 Gbp is contained in contigs of 1199 bp or longer)
• Total scaffold sequence (with imputed gaps): 18,428,460,141bp
• N50 1,230bp (9.21 Gbp is contained in scaffolds of 1230 bp or longer)
• Degenerate contig sequence 3.8Gb
P. taeda WGS V0.8 (January 2013)
• Approximately 65X coverage
– 16 billion reads (1.7 billion jumping library reads)
– Compressed to 150 million Super-reads
• Total Sequence: 22,518,572,092 bp
• N50 Contig: 7,083bp
• N50 Scaffold: 15,885 bp
P. taeda WGS V0.9 (March 2013)
• Total Sequence: 20.1 Gbp
• Total contig sequence: 2.3 Gbp
• N50 8,200bp (11.6 million)
• Total scaffold sequence (with imputed gaps): 17.8 Gbp
• N50 30,700bp (4.8 million)
Ongoing Efforts
• Improve MSR-CA scaffolding
• Transcriptome + WGS assembly
• Fosmid pool sequencing and assembly
• GBS to anchor and orient scaffolds
• Sugar pine genome: 35 Gigabases!
Elements of the Conifer Genome Sequencing Project
Sequencing Strategy
Molecular approach to complexity reduction
End of
summer 2013
Fosmid Pooling:
Genome partitioning for reduced assembly complexity
• The immense and complex diploid pine genome can be
economically and efficiently partitioned into smaller, functionally
haploid, pieces using pools of fosmid clones.
• Fosmids in a pool should have a combined insert size far less than
a haploid genome size; to ensure haploid genome
representation.
• The sequence data obtained from a single fosmid pool may be up
to 80 X deep.
• The sequence data obtained from a pool must be screened for
vector and E. coli contamination
• Ideally: larger clones (BACs) are more desirable, more likely to
span repeats
Fosmid Sequence Components
fosmid
end seq.
long
insert
short
insert
fosmid
(haploid)
• Haploid fosmids with vector tagged ends
• Primary coverage from short insert libraries
• Additional coverage from long insert libraries
from equi-molar pool of pools.
• Fosmid end sequences (diTags) link ends of the assembly
and count fosmids in a pool
Fosmid Pools
Determining the Best Assembler for the Job
Assembly results for a relatively large pool of approximately 600 P. taeda fosmids
Assembler
Allpaths-LG
Stat
N50
Sum
987
2499
7781 30271
26298
14 x 106
ctg
1524
2355
6031 12509
10324
14 x 106
33595 35682 38361
30114
9 x 106
248
scf
2162
506
1375
9224
14753
15 x 106
ctg
3519
503
1339
5000
6826
14 x 106
32603 35087 38119
30147
5 x 106
scf30K+
SOAP
Q1
scf
scf30K+
MSR-CA
Count
quartiles
Q2
Q3
136
scf
3251
123
185
495
33389
15 x 106
ctg
23873
76
175
348
1515
15 x 106
33907 35766 38683
33389
12 x 106
scf30K+
322
Use Cases for Fosmid Pools
• Assembler Evaluation
• Repeat Library Construction
• SNP Identification
Genomic Sequence
Pinus taeda BACs and Fosmids
Pinus taeda BACs
Pinus taeda Fosmids
Total number of sequences
103
90,973
Average sequence length
115,130
2,918
Median sequence length
118,782
475
N50 sequence length (bp)
127,167
16,204
Shortest sequence length
1,392
201
Longest sequence length
235,088
75,791
Total length (bp)
11,858,447
265,511,345
GC %
37.98%
38.09%
A : C : T : G%
31.27 : 18.79 : 31.32 : 18.62
30.94:19.07:30.97:19.03
Combined sequence resource represents roughly 1% of the estimated 22 GB genome
Similarity and De Novo Repeat
Identification
Tandem Repeat Finder (TRF)
Homology (Censor against RepBase)
Summary of Repbase v17.07
• Number of entries: 28,155
• Number of species represented: 715
• Number of repeat families: 280
• Angiosperm entries: 131
• Gymnosperm entries (conifer):15
De Novo (REPET/TEannot)
• Self-alignment (all vs all) with BLAST to find
HSPs is followed by clustering with Grouper,
Recon, and Piler
• 3 sets of clusters are aligned with a MSA
(MAP) to derive a consensus sequence
• Structural search runs simultaneously
(LTR Harvest) to detect highly diverged LTRs
• Final Blastclust to cluster potential sequences
Tandem Repeats
Comparison across sequenced angiosperms and other gymnosperms (partial)
Number of Microsatellite loci/Mbp
70.00
Total tandem
content: 2.6%
3.31% of BACs
2.59% of fosmids
60.00
50.00
Pinus taeda
40.00
Taxus mairei
Picea glauca
30.00
Cucumis sativus
20.00
Populus trichocarpa
10.00
Arabidopsis thaliana
Vitis vinifera
0.00
Pinus taeda (BAC + Fosmid)
Picea glauca (BAC)
Taxus mairei (Fosmid)
Micro
Mini
Sat
Micro
Mini
Sat
Micro
Mini
Sat
2
21
123
2
27
122
2
24
230
Cumulative length
126,254
216,194
154,835
876
843
389
1,411
587
1,598
Num. of loci
64,740
10,508
1,258
11
10
1
32
19
2
Most frequent period
0.05%
0.08%
0.06%
0.33%
0.32%
0.15%
0.09%
0.04%
0.10%
241,822
4,323,361
2,650,740
925
6,603
1,864
3,024
15,875
5,871
Most frequent period
(%)
Total cumulative length
(bp)
Total (%)
Homology Search Results
Censor (BLAST-style) comparisons against Repbase
Partial and Full-length Interspersed Alignments (compared across species)
Vitis vinifera
Populus trichocarpa
Picea glauca
Class I: LTR: Copia
Pinus taeda
Class I: LTR: Gypsy
Arabidopsis thaliana
Class II: DNA Transposon
Taxus mairei
Class I: Non-LTR: LINE
Cucumis sativus
0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00%
Percent of sequence sets
Full-length Alignments Only
Pinus taeda (BACs) by homology
Full Length Sequences
80-80-80 Rule (Wicker et al. 2007)
•
•
•
80 bp in length
80% identity
80% coverage
Pinus taeda (fosmids) by homology
Pinus taeda (BACs) by de novo
Pinus taeda (fosmids) by de novo
0.00% 1.00% 2.00% 3.00% 4.00% 5.00% 6.00% 7.00% 8.00%
Summary of Combined Homology and
De Novo Approach
• 88% repetitive (partial and full-length)
• 29% repetitive (full-length only defined by 80-80-80)
– 87% of the full-length content is characterized as LTR retrotransposons
• Repeats are highly diverged
– Only 23% identified by homology for full and partial elements
– Repbase contains just 15 (+5) gymnosperm elements
– 6,270 novel families discovered with no homology
• 5,155 are single copy
• High copy elements are either Gypsy or Copia LTRs
• Nested repeats common in LTR retrotransposons
Novel Repeat Elements
Diverged LTRs are annotated as 6,270 novel families
Top 400 elements only cover 12% of the combined sequence sets
Repeat family
Full-Length Copies
Length (bp)
Percent of Sequence
Set
TPE1
159
1,077,598
0.39%
PtPiedmont (93122)
133
969,109
0.35%
IFG7
162
956,018
0.34%
PtOuachita (B4244)
47
576,871
0.21%
Corky
78
469,286
0.17%
PtCumberland (B4704)
67
431,492
0.16%
PtBastrop (82005)
38
378,631
0.14%
PtOzark (100900)
32
378,020
0.14%
PtAppalachian
67
367,653
0.13%
PtPineywoods (B6735)
68
322,632
0.12%
PtAngelina (217426)
24
309,248
0.11%
Gymny
24
291,479
0.11%
PtConagree (B3341)
50
285,850
0.10%
PtTalladega (215311)
33
274,826
0.10%
Total
982
7,088,713
2.56%
(212735)
Novel Repeat Elements
MSA with annotations of the novel Gypsy LTR - PtAppalachian
MSA with annotations of the novel Copia LTR -PtPineywoods
Elements of the Conifer Genome Sequencing Project
Loblolly transcriptome from 30 unique RNA
collections
Carol Loopstra (RNA) and Keithanne Mockaitis (sequen
Progressive Transcript Profiling
Build a useful transcriptome reference early in project:
 generate long reads for ease of assembly, scaffolding of existing shorter data
 integrate community data into assemblies
Early Development
seeds
young seedlings
Vegetative Organs
vegetative buds
candles
stems
needles
roots
Reproductive
Development
megastrobili
microstrobili
Early Stress
Signaling Responses
cold
heat
elevated UV
compression
Transcriptome Assembly
• Considerable variation in de novo
transcriptome assemblies
– Used a compare and compete methodology to
select the final transcripts
– Two Trinity versions and Velvet/Oasis (6 different
k-mer sizes)
– First analysis: Basic clustering methods with 454
and other protein evidence to determine optimal
full-length proteins
Coding transcripts, clustered outputs by assembler
Transcript Class
Trinity
2012.10.05
Trinity
2013.02.25
Velvet 1.2.08
Oases 0.2.08
complete CDS
58,707
115,353
395,370
complete CDS, UTR poor
8,023
10,033
39,833
complete CDS, UTR very short/absent
1,076
1,393
7,298
total complete protein (non-unique)
67,806
126,779
442,501
partial protein coding
196,252
total
264,058
404,722
531,501
2,041,836
2,484,337
Protein coding loci, estimated from transcript evidence alone (32 sets):
87,602 unique complete
64,610 mapped to the WGS assembly
preliminary results, Keithanne Mockaitis
Improving Transcriptome Assembly
• Improved transcript grouping with exon-aware clustering methods
Transcript Class
Total (Improved Clustering)
Mapped to genome v0.9
Unmapped
Primary, complete CDS
87,241
83,271
3970
Alternate, complete CDS
642,175
642,175
4092
Partial CDS
69,044
61,114
7930
Alternative partial CDS
617,248
607,490
9758
• Duplicates/Paralogs
• Pseudogenes
• Too much compression of Unigene set?
Mapping Occurrences
Complete transcripts mapped
1
9840
2
3574
3
2542
4
2624
>/=5
64690
Examining Gene Families
MyB Transcription Factors
Homeobox Transcription Factors
Improving the Assembly with Transcriptome
• Map WGS (v0.9) against the transcripts with
nucmer
• Iteratively compute alignments and merge
scaffolds.
• 12,000+ scaffolds merged during first pass
• . . . V1.0
Elements of the Conifer Genome Sequencing Project
Source: Jiao et al., Ancestral polyploidy in seed plants and angiosperms, Nature, Vol. 473, May 5, 2011
Mapping Full-Length Orthologous Proteins
Alignments: exonerate protein2genome, heuristic, 70% query coverage, 70% similarity
~220,000 query proteins total
• Physcomitrella patens: 2,761 out of 25,506 (10.8%)
• Selaginella moellendorffii: 2,025 out of 16,821 (12.0%)
• ‘Basal’ angiosperm:
– Amborella trichopoda: 4,076 out of 25,347 (16.1%)
• Angiosperms:
–
–
–
–
–
Arabidopsis thaliana: 4,777 out of 27,986 (17.1%)
Populus trichocarpa: 4,023 out of 18,588 (21.6%)
Sorghum bicolor: 3,368 out of 24,122 (18.1%)
Vitis vinifera: 3,833 out of 18,441 (20.8%)
Glycine max: 9,970 out of 52,178 (19.1%)
• Gymnosperms:
• Picea: 6,696/11,065 (60.5%)
– The majority of these are Picea sitchensis (Ralph et al., 2008)
• Pinus: 345/426 (81.0%)
Mapping Proteins
~220K full-length proteins and CEGMA analysis
• BLAT/Exonerate with ~220K proteins
– Requiring 70% similarity and 70% query coverage,
45,101 proteins aligned to 11,897 unique
scaffolds/contigs
• CEGMA
– Examines conserved eukaryotic core genes (KOGS)
– 240 full-length and 197 partial proteins (of 458)
– 113 full-length proteins of the 248 in the highly
conserved category
Training MAKER
Pinus taeda resources:
ADEPT2 Project Clusters
Exon Capture (Neves et al. 2013)
PineRefSeq Transcriptome
454 Transcriptome (Lorenz et al. 2012)
Pinus Resources:
TreeGenes UniGenes
Whitebark pine (RNASeq)
Sugar pine transcriptome (454 + RNASeq)
Limber pine transcriptome (RNASeq)
Lodgepole pine (454) (Parchman et al. 2010)
Longleaf pine (454) (Lorenz et al. 2012)
Picea Resources:
TreeGenes UniGenes
Sitka spruce (Sanger/454) (Ralph et al. 2008)
Norway spruce (454) (Chen et al. 2013)
Congenie transcriptome (Nysterdt et al. 2013)
Norway spruce (454) (Lorenz et al. 2013)
White spruce (454) (Rigault et al. 2011)
. . . Just finished at iPlant (TACC)
Running on 8,000 cores…
WebApollo on TreeGenes
Introns
•
•
•
•
Exon conservation highlighted
Supporting EST evidence
Intron 2: Size 131,138
Intron 3: Size 179,620
WebApollo on TreeGenes
Gene family conservation
An Example of: jcf7180063228536 4.97Mbp scaffold
WebApollo on TreeGenes
Conifer specific proteins
An Example of: jcf7180063228536
Elements of the Conifer Genome Sequencing Project
Dendrome Project
TreeGenes Database to Distribute Transcriptome and Genome
GENome Sequence Annotation Server (GenSAS)
Community Annotation
GENome Sequence Annotation Server (GenSAS)
Opening Screen
Sequence choices
from database or
uploaded file
List of available
programs to run with
optional
paramaterization
After the selected programs run, clicking on the green
icon will bring users to a map interface
GENome Sequence Annotation Server (GenSAS)
Map Interface
Quick
Navigation
Shortcuts
Click to
jump
positions
Task Name
Export
Layer and
Save
Click on features to
see more info
SS
AZ
JY
DP
PD David Neale (r), co-PD Jill Wegrzyn (c),
and (l to r) John Liechty, Ben Figueroa,
Patrick McGuire, Pedro J. MartinezGarcia, Hans Vasquez-Gross
UC Davis
(l to r) Co-PD Pieter de Jong, Ann
Holtz-Morris, Maxim Koriabine,
Boudewijn ten Hallers
CHORI BAC/PAC
Co-PD Chuck Langley (r) and (l to r)
Marc Crepeau, Kristian Stevens, and
Charis Cardeno UC Davis
The Maryland Genome Assembly Group featuring co-PD Steven Salzberg
and Daniela Puiu (Johns Hopkins U) and co-PD Jim Yorke and
Aleksey Zimin (U of Maryland)
Co-PD Keithanne Mockaitis
and Zach Smith Indiana U
Co-PD Carol Loopstra
and Jeff Puryear TAMU
Co-PD Dorrie Main
WSU