1. Sequencing and Assembly of the WheatD Genome using BAC Pools A Preliminary Study Daniela Puiu Sept 23rd 2013

2. Outline Wheat genomes Previous assemblies Study Goals
Dataset 1 evaluation
Dataset 2 evaluation
Future work

3. Backgroud
8-10K years ago
Emmer/Ancient Wheat + Goat grass = Bread/Common Wheat
Triticum dicoccoides + Aegilops tauschii = Triticum aestivum
AABB + DD = AABBDD

4. Wheat WGS Assemblies
Last 12 months: 3 Wheat assembly papers published in Nature !
WheatABD: hexapoild & very repetitive: HARD TO ASSEMBLE
WheatD: easier? “the assembly represents 83% of the genome, of which 65% comprised transposable elements.“ .
publication
group
technology
cvg
assembler
wheatABD
Nature,
UK,454,CSH
454 5
Newbler
wheatA
Nature,
BGI
HiSeq 90
SOAPdenovo2
wheatD
GAIIx/HiSeq
60?
SOAPdenovo
itt, hierarchical .
#scf M
Max Kb
N50
AssemblySize Gb
GenomeSize
wheatABD
5.32 21
3.8
17.33
wheatA
7.98
1,066
63.69
4.67
4.93
wheatD
0.43
606
57.6 
3.31
4.36

5. WheatD WGS Data 42X cvg of fragment reads
25% of the reads shorter than 44bp
Max insert size of 20K .
type
#libs
#readsM
rdLen
insLen
cvg
Illumina pe 46
1,524
43-149 39
mp_short 22
595
43-89
2,000-2,500 8
mp_med 15
372
5,000-10,000 5
mp_long 1 77 48
20000 84
2,716
167-20,000 56
454 se 28 27
44-2,049 3
Total
132
2,743
43-2,049
0-20,000 60

6. Study Goals Improve the WheatD genome assembly!
Collaboration with UCDavis (JAN DVORAK) & USDA
Better genomics=> better Wheat
Instead of the WGS use the BAC Pool strategy
A BAC Pool consists of a collection of BACs organized as a Minimum Tiling Path; the BACs cover the whole genome; position on the BACs on the chromosomes is known.
Questions:
Q1.How many BACs/Pool? 8?
Q2.Overlapping vs Non-Overlapping BACs?
Q3.Which technology : 454 vs MiSeq? Cost vs Quality?
Q4.Which assembler?
Q5: Can we use the WGS reads for additional scaffolding?

7. Q2: Nonoverlapping vs Overlapping?
Example: 5 BACs Pools
Nonoverlapping BACs: 1,3,5,8,10
Overlapping BACs: ,2,3,4,5
Issues:
NonOvl: Assignment of contigs to the BACs
Ovl: Coverage doubles in the overlapping regions
=> possible assembly duplications

8. Dataset 1 Q2: Nonoverlapping vs overlapping BACs?
4 Pool Sets, 8 BACs/pool, 454_se
1 BAC = 170K
8 nonOvl BACs= 1.36M % of the genome
8 ovl BACs = 1.06M % of the genome
320 BACs/pool, 454_pe (~4K inserts) % of the genome
Read length statistics:
type
pool#
BACs/
pool
readType
#reads K
max
mea
Sum Mb
cvg
nonovl
102 8 se 79
1,623
640 51
37.5 98
128
1,126
642 82
60.61 .
320 pe
1,284
968
221
283
5.2
ovl
295 81
1,094
553 44
42.3
1540
105
1,106
509 53
50.53
1,340
935
195
261
4.81

9. Dataset 1 Assembly Ideal assembly results:
nonoverlapping pools: 8 contigs
overlapping pools: 1 contig
A4: Newber generated longer scaffolds than Celera, Abyss ...
454_pe selection: alignment based
1 in 40 mates should align to the pool
Alignment metrics: mapping score, length, %id?
Top 2.5%: rdLen=381 alignLen=302 %id=97.5 score=180 : 0 mates align
Mated bwasw alignments: 1 in 3 mates align
Used mated bwasw alignments >= 75bp+ match, 96% identity
poolType
pool#
#ctg2K+
ctgSum Mb
ctgN50 Kp
#scf2K+
scfSum
scfN50 Kb
nonovl
102 44
1.25
46.13 35
1.26
51.37 98 49
1.17
38.39 34
1.23
65.88
ovl
295 65
1.00
28.65 15
1.03
556.87
1540 41
0.64
8.36 20
0.65
85.78

10. Dataset 1 Assembly (cont)
A2: Overlapping pools seem to work best

11. Dataset 2
Q3: 454 vs MiSeq?
Q5: Can we use the WGS reads for additional scaffolding?
4 Pool Sets, 8 BACs/pool, both 454 & MiSeq reads
454_se , 454_pe (320 BACs/pool, 4Kbp inserts)
MiSeq_pe (625bp ins)
Read length statistics:
pool
type
pool#
#BACs/
454
#reads K
rdMea
rdSum M
cvg
MiSeq
ovl
240 8 50
580 29 27
737
251
182
171
168 58
533 31
1,075
265
250 ?
145 85
545 46 44
1,615
399
376
192
108
615 66 63
932
230
217

12. Dataset 2 (cont) Kmer histograms show large variation in coverage
Multiple peaks: expected cvg, overlapping regions, other long repeats ?

13. BAC Pool 145 Assembly Assembler N50 nucmer -l 127
#scf
2K+
Max Kb
Sum Mb
N50
NEWBLER 51 99
1.21 40
NEWBLER.pe 16
244
1.23
122
SOAPdenovo2 K127 22
173
134
ABYSS-pe
k191 38
138
1.27 83
MaSuRCA 75
129
1.32 42
SPAdes
k127 60
127
1.19 33
Velvet
108 71
1.17 15
Discovar
141 78
2.06 28
nucmer -l 127
Newbler.pe vs SOAPdenovo2 scaffolds
A4: Newbler (v2.8) & SOAPdenovo2(r240,K=127) => longest scaffolds

14. Dataset 2 Assembly
Using additional WGS libraries for scaffolding (2-10K ins,rdLen=89 & 20K ins)
pool
Assembler/Dataset
#scf(2Kb+)
max(Kb)
sum(Kb)
n50(Kb)
240
NEWBLER 37 90
833 29
NEWBLER.pe 20
157
840
117
SOAPdenovo2 25
288
875
100
SOAPdenovo2.wgs.2K 23
350
903
157*
168 50
115
869
150
893 67
181
932
120 10
306
937
156*
145 51 99
1,207 40 16
244
1,226
122 22
173
134 11
336
1,234
148
SOAPdenovo2.wgs.5K 13
184
137
SOAPdenovo2.wgs.10K
279
1,257
SOAPdenovo2.wgs.20K 15
180
1,250
SOAPdenovo2.wgs.2-20K
290
1,254
202*
192
169
921 48
202
929
140 18
203
943
102 12
230
950
188*

15. Dataset 2 Assembly (cont)
A3: MiSeq seem to work better than 454
A5: Using WGS reads => longer scaffolds

16. Prolamine Gene Locus : 2.2Mb
Begin1(Mb)
End1(Mb)
Begin2(Mb)
End2(Mb)
Len(Kb)
Dist(Mb)
Longest Repeat
1.467
1.477
1.565
1.574
9.6
0.1

17. Future Work
“Re-assemble the 454 data for the 2.2 Mb prolamin gene locus, assembled and validated with the BioNano mapping system.”
Evaluate other scaffolding & finishing tools
Optimal coverage?
How can we further improve the assemblies?
Suggestions?