Multi-k-mer de novo transcriptome assembly and assembly of assemblies using 454 and illumina data.

1/12/13
K-INBRE Bioinformatics Core Training
and Education Resource
1
Leveraging multiple sequencing technologies,
assembly algorithms, and assembly parameters
to create a de novo transcript libraries for four
non-models.
Jennifer Shelton
Bioinformatics Core Outreach Coordinator
Kansas State University

Outline
1/12/13
2
I.  Goals
II.  Metrics
III.  Background (multi-k assembly)
IV.  Workflow
V.  Results
VI.  Conclusions

Goals
1/12/13
3
Create a high quality reference transcriptomes
of non-model plants in order to:
- annotate lipid synthesis pathways
- compare expression profiles

Outline
1/12/13
4
I.  Goals
II.  Metrics
IV.  Workflow
V.  Results
VI.  Conclusions

Quality metrics
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5
1)  Cumulative lengths of contigs
2) Number of contigs
3) N25, N50, N75: Order contigs
smallest to largest report shortest
contig representing 25, 50 or 75% of
the cumulative contig length
4) Ortholog Hit Ratio: length of the
putative coding region (High Scoring
Pairs (HSP)) by the length of the
protein

‘Ideal’ quality metrics
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1)  Cumulative lengths of contigs:
small
2) Number of contigs: 20-60 k
3) N25, N50, N75: Order contigs
smallest to largest report shortest
contig representing 25, 50 or 75% of
the cumulative contig length: large
4) Ortholog Hit Ratio: length of the
putative coding region (High Scoring
Pairs (HSP)) by the length of the
protein:1 ‘full length’

Recently reported N50
5/22/13
7
Schliesky, Simon, et al. "RNA-seq assembly–are we there yet?." Frontiers in plant
science 3 (2012).
Reference" Year of publication" N50"
Bräutigam et al." 2011" 596 and 521"
Lu et al." 2012" 884"
Meyer et al. " 2012" 1308"
Garg et al. " 2011" 1671"
Mutasa-Göttgens" 2012"
1185 (1573 for loci
above 0.5kb)"
Xia et al." 2011" 485"
Chibalina and Filatov" 2011" 1321"
Wong" 2011" 948 and 938"
Shi et al. " 2011" 506"
Hyun et al. " 2012" 450"
Hao et al." 2012" 408"
Huang et al. " 2012" 887"
Zhang et al. " 2012" 823 (616-664)"

Ortholog hit ratio in recent literature
1/12/13
8
62% ≥0.5 and 35% ≥0.8 for Daphnia pulex
64% ≥0.5 and 35% ≥0.9 for salt marsh beetle
64% ≥0.5 and 40% ≥0.8 for Gryllus bimaculatus
58% ≥0.5 and 41% ≥0.8 for Oncopeltus fasciatus
Zeng V, et al. BMC Genomics. (2011) 12:581, Van Belleghem, Steven M., et al. PloS one 7.8
(2012): e42605, Zeng., et al. PLoS ONE (2013).

Outline
1/12/13
9
I.  Goals
II.  Metrics
IV.  Workflow
V.  Results
VI.  Conclusions

k-mers
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http://homolog.us/Tutorials/index.php?p=3.4&s=1
A k-mer is a substring within the larger string (the read)

Assembly details: exploring the parameter
space
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Low expression:
assembles best with
low values of k
High expression:
assembles best with
high values of k
All levels of
expression: assemble
to full-length with a
merged assembly of a
range of values of k
MANUSCRIPT CATEGORY: ORIGINAL PAPER
Oases de novo RNA-seq assembly
f Velvet and Oases assemblies on the human RNA-
>100 bp Sens. (%) Spec. (%) Full Lgth. 80% lgth.
789 12.45 83.58 42 78
319 17.23 92.55 828 7437
042 16.13 89.62 92 516
504 14.97 93.0 754 6882
986 12.78 93.16 213 1986
878 10.55 94.63 429 3751
507 7.9 94.81 107 1660
012 6.67 95.99 196 1885
rags longer that 100 bp (Tfrags), nucleotide sensitivity and
umber of full length or 80% length reconstructed Ensembl
ers, we tested an array of parameters and
ose datasets, namely n = 10, c = 3 and ABYSS
(Supplementary Material).
08-20) was run with the default parameters. In
length of 25 could not be modiﬁed.
tails after assembly were removed using the
om the EMBOSS package (Rice et al., 2000)
20 40 60 80 100
0200400600800
Expression Quantiles
Reconstructedtoatleast80%
Merged 19 35
k=19
k=21
k=27
k=31
k=35
Fig. 2. Comparison of single k-mer Oases assemblies and the merged
assembly from kMIN=19 to kMAX=35 by Oases-M, on the human dataset.
The total number of Ensembl transcripts assembled to 80% of their length is
provided by RPKM gene expression quantiles of 1464 genes each.
performed by Oases, it is possible to observe that this parameter
http://bioinformatics.oDownloadedfrom
Schulz M, et al. Bioinformatics (2012) 28:8 1086-1092.

space
1/12/13
Gruenheit N, et al. BMC Genomics.
(2012) 13:92.
in assemblies that used one k-mer size. 392 of these
sequences were assembled using exactly one parameter
combination. Similarly, for P. cheesemanii the success of
cutoffs but only 18 with all 20 k-mer sizes. 445 genes
were only completely assembled with one coverage cutoff
and 495 genes were only completely assembled with one
20
19
18
17
16
15
14
13
12
11
10
9
8
7
6
5
4
3
2
100 200 300 400 500 600 700
25 53 55 57 59 61 6351494745434139373533312927
k-mer
coveragecutoff
number of complete coding sequences
Figure 1 Number of complete transcripts identified in different assemblies of P. fastigiatum reads. 380 different assemblies were made
using ABySS [25,26] and a combination of (i) coverage cutoffs between 2 and 20 and (ii) k-mer sizes between 25 and 63. Transcripts covering
the complete coding sequence of the homologue from A. lyrata or A. thaliana, respectively, were identified and counted. The maximum number
(741) of complete transcripts was identified for coverage cutoff seven and k-mer size 41 while the lowest (70) number of complete transcripts
was identified for coverage cutoff 19 and k-mer size 63.
Gruenheit et al. BMC Genomics 2012, 13:92
http://www.biomedcentral.com/1471-2164/13/92
Page 4 of 19
Number of contigs
assembled to full
length was found to
peak with k-mer
values ~ 41
(~82% of genes were
only assembled to
over 80% with one
k-mer)

space
1/12/13
Multi-k-mer assembly improves assembly to
full length and assembly of a broad range of
expression quantiles

Outline
1/12/13
14
I.  Goals
II.  Metrics
IV.  Workflow
V.  Results
VI.  Conclusions

Sand bluestem assembly- complete, currently preparing reads for mapping and mapping them back, also
running OHR for merged assembly.
Current workﬂow:
454 reads
A. geradii ssp. hallii
! and
A. gerardii ssp. gerardii
Illumina paired end
reads
A. geradii ssp. hallii
! and
A. gerardii ssp. gerardii
Tagcleaner to remove
PrimeSmart sequences
Prinseq to remove low
quality reads and tails,
reads <100bp, low
entropy, poly A/T/N
tails, remove identical
reads
MIRA
MIRA assembly of
454 reads
Merge
MIRA assembly
Oases-M
Velvet
assemblies
using multi
values of k
(k=23 - k=61)
Comparison of
assemblies
“Blind” metrics
highest N25, N50,
N75; cumulative
length of contigs;
number of contigs
Blastx
against
Phytozome
v9.0 S.
bicolor
protein
database
Ortholog hit ratio
((length of hit /3) /
length of ortholog)
Number of unique
blast hits; number of
putative paralog/
homeolog groups
Sickle to remove reads
with N, low quality,
reads <50bp
Prinseq to remove low
quality reads and tails,
poly A/T/N tails,
remove identical reads
Oases
assemblies
using multi
values of k
(k=23 - k=61)
Assembly overview: workflow
1/12/13
15
1) Stringently clean
2) Assemble Illumina reads with
a De Bruijn Graph assembler,
and 454 with an Overlap
Layout Consensus assembler
3) Merge with MIRA or CD-HIT
4) Compare assemblies with
metrics based on contiguity
and putative homology to
closest relative

Length and number of contigs with a range
of k-mers
1/12/13
16
60
145
230
315
400
23
25
27
29
31
33
35
37
39
41
43
45
47
49
51
53
55
57
59
61
MIRA(454)
MIRAcluster
0
75
150
225
300
375
450
525
600
Sand bluestem assembly length and number of contigs
Cumulativelengthofsequences(Mb)
Assembly k-mer value or name
Numberofsequences(k)
Cumulative length of sequences (Mb)
Number of sequences x 10^5
5.0
Sand bluestem N values
26,000 contigs after
clustering

47
57
merge
CDH cluster
MIRA cluster
1.168 1.948 2.932 129.331497 1.07545
1.218 1.974 2.95 111.672465 0.90385
1.404 2.23 3.299 418.762352 2.77833
1.399 2.274 3.339 96.411479 0.70852 CDH cluster 1399 2274 3
1.825 2.676 3.856 123.666263 0.59598 MIRA cluster 1825 2676 3
100
200
300
400
500
27
37
47
57
merge
CDHcluster
MIRAcluster
0
0.75
1.5
2.25
3
Bittersweet assembly length and number of contigs
Cumulativelengthofsequences(Mb)
Numberofsequencesx10^5
1.1
1.8
2.6
3.3
4.0
27
37
47
57
merge
CDHcluster
MIRAcluster
Bittersweet N values
Contiglength(kb)
N75 (kb) N50 (kb)
N25 (kb)
of k-mers
1/12/13
17
60-70,000 contigs
after clustering

37
47
57
merge
CDH cluster
MIRA cluster
1.206 2.008 3.087 128.100083 1.1091 37 1.206 2.00
1.195 1.977 3.051 113.176134 0.93839 47 1.195 1.97
1.271 2.035 3.096 102.507455 0.82755 57 1.271 2.03
1.41 2.211 3.331 345.752982 2.31102 merge 1.41 2.21
1.44 2.27 3.422 84.202533 0.59174 CDH cluster 1440 2270
1.804 2.69 3.941 105.920843 0.50279 MIRA cluster 1804 2690
1.1
1.7
2.3
2.8
3.4
4.0
27
37
47
57
merge
CDHcluster
MIRAcluster
Balsam N values
Contiglength(kb)
N75 (kb) N50 (kb)
N25 (kb)
80
185
290
395
500
27
37
47
57
merge
CDHcluster
MIRAcluster
0
0.75
1.5
2.25
3
Balsam assembly length and number of contigsCumulativelengthofsequences(Mb)
Numberofsequencesx10^5
of k-mers
1/12/13
18
50-60,000 contigs
after clustering

N25, N50, N75 across a range of k-mer
values
1/12/13
19
Duan, Jialei, et al. BMC genomics 13.1 (2012): 392. Meyer E, et al. The
Plant Journal. (2012) 70: 879-890. Liu, Mingying, et al. PloS one (2012)
7.10. Chouvarine, Philippe, et al. PloS one 7.1 (2012): e29850.
MIRA
MIRAc
0.4
1.6
2.7
3.9
5.0
23
25
27
29
31
33
35
37
39
41
43
45
47
49
51
53
55
57
59
61
MIRA(454)
MIRAcluster
Sand bluestem N values
Contiglength(kb)
N75 (kb) N50 (kb)
N25 (kb)
Sand bluestem’s
N50 is 3.2 kb after clustering
Other published N50 values:
wheat 1.4 kb
Panicum hallii 1.3 kb
Ma Bamboo 1.1 kb
Miscanthus 0.7 kb

values
1/12/13
20
Bittersweet’s
N50 is 2.3-2.7 kb after
clustering
wheat 1.4 kb
Ma Bamboo 1.1 kb
Miscanthus 0.7 kb
0.59598 MIRA cluster 1825 2676 3856 123666263 59598Numberofsequencesx10^5
1.1
1.8
2.6
3.3
4.0
27
37
47
57
merge
CDHcluster
MIRAcluster
Bittersweet N values
Contiglength(kb)
N75 (kb) N50 (kb)
N25 (kb)

values
1/12/13
21
Bittersweet’s
N50 is 2.3-2.7 kb after
clustering
wheat 1.4 kb
Ma Bamboo 1.1 kb
Miscanthus 0.7 kb
2.31102 merge 1.41 2.211 3.331 345.752982 2.31102
0.59174 CDH cluster 1440 2270 3422 84202533 59174
0.50279 MIRA cluster 1804 2690 3941 105920843 50279
1.1
1.7
2.3
2.8
3.4
4.0
27
37
47
57
merge
CDHcluster
MIRAcluster
Balsam N values
Contiglength(kb)
N75 (kb) N50 (kb)
N25 (kb)

Ortholog hit ratio with a range of k-mers
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Numberofcontigsx103
Ortholog hit ratio
0
8
16
24
32
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4 1.5
mira 23 25 27 29 31 33 35 37
39 41 43 45 47 49 51 53 55
57 59 61
** Note: This algorithm varies
slightly from the final OHR
alogorithm I used in slide 27

1/12/13
Gruenheit N, et al. BMC Genomics.
(2012) 13:92.
in assemblies that used one k-mer size. 392 of these
sequences were assembled using exactly one parameter
combination. Similarly, for P. cheesemanii the success of
cutoffs but only 18 with all 20 k-mer sizes. 445 genes
were only completely assembled with one coverage cutoff
and 495 genes were only completely assembled with one
20
19
18
17
16
15
14
13
12
11
10
9
8
7
6
5
4
3
2
100 200 300 400 500 600 700
25 53 55 57 59 61 6351494745434139373533312927
k-mer
coveragecutoff
number of complete coding sequences
Figure 1 Number of complete transcripts identified in different assemblies of P. fastigiatum reads. 380 different assemblies were made
using ABySS [25,26] and a combination of (i) coverage cutoffs between 2 and 20 and (ii) k-mer sizes between 25 and 63. Transcripts covering
the complete coding sequence of the homologue from A. lyrata or A. thaliana, respectively, were identified and counted. The maximum number
(741) of complete transcripts was identified for coverage cutoff seven and k-mer size 41 while the lowest (70) number of complete transcripts
was identified for coverage cutoff 19 and k-mer size 63.
Gruenheit et al. BMC Genomics 2012, 13:92
http://www.biomedcentral.com/1471-2164/13/92
Page 4 of 19
Number of contigs
assembled to full
length was found to
peak with k-mer
values ~ 41
(similar to our peak
at k = 47)

Ortholog hit ratio for bluestem
1/12/13
24
59
61
MIRA cluster
0.4768073818 0.2972410611
0.4619314293 0.2767591755
0.7127479439 0.5199564586
0
5000
10000
15000
20000
25000
30000
35000
40000
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4 1.5
OHR histogram
Numberofcontigs
OHR bins
23 25 27 29 31 33 35
37 39 41 43 45 47 49
51 53 55 57 59 61 MIRA cluster
The number
of contigs is
lower but the
OHR
histogram of
the clustered
assembly has
proportionally
fewer
fragmented
contigs

1.5
ter
0%
10%
20%
30%
40%
50%
60%
70%
80%
23
25
27
29
31
33
35
37
39
41
43
45
47
49
51
53
55
57
59
61
MIRAcluster
Percentage of > 50% and > 80% full length contigs with a blast hit
Contigswithablashitoverthreshold
Assembly
Greater than 50% Greater than 80%
Are the sequences with hits closer to full
length?
1/12/13
25

1/12/13
26
51% ≥0.5 and 33% ≥0.8. for bluestem k(39-45)
71% ≥0.5 and 52% ≥0.8. for bluestem
62% ≥0.5 and 35% ≥0.8 for Daphnia pulex
64% ≥0.5 and 35% ≥0.9 for salt marsh beetle
64% ≥0.5 and 40% ≥0.8 for Gryllus bimaculatus
58% ≥0.5 and 41% ≥0.8 for Oncopeltus fasciatus
Zeng V, et al. BMC Genomics. (2011) 12:581, Van Belleghem, Steven M., et al. PloS one 7.8
(2012): e42605, Zeng., et al. PLoS ONE (2013).

Conclusions
1/12/13
27
1) Metrics based on contiguity suggest that
many of the Illumina assemblies are highly
contiguous compared to recent de novo plant
transcriptomes
2) Metrics based on OHR suggest the assembly
is accurate and the multi-k-mer method and
clustering steps are improving the quality of the
assembly
3) 454 data appears to have been less cost
efficient than the Illumina data (in terms of all
metrics accept cumulative length of assembly)

Acknowledgments
1/12/13
28
Nic Herndon, Sanjay Chellapilla, Alina Akhunova,
Eduard Akhunov, Hanquan Liang, Loretta C Johnson,
Susan J. Brown

Questions?
1/12/13
29

Multi-k-mer de novo transcriptome assembly and assembly of assemblies using 454 and illumina data.

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Multi-k-mer de novo transcriptome assembly and assembly of assemblies using 454 and illumina data.