Group Title: BMC Genomics
Title: Comparison of next generation sequencing technologies for transcriptome characterization
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 Material Information
Title: Comparison of next generation sequencing technologies for transcriptome characterization
Physical Description: Book
Language: English
Creator: Wall, P. Kerr
Leebens-Mack, Jim
Chanderbali, André
Barakat, Abdelali
Wolcott, Erik
Liang, Haiying
Landherr, Lena
Tomsho, Lynn
Hu, Yi
Carlson, John
Ma, Hong
Schuster, Stephan
Soltis, Douglas
Soltis, Pamela
Altman, Naomi
dePamphilis, Claude
Publisher: BMC Genomics
Publication Date: 2009
Abstract: BACKGROUND:We have developed a simulation approach to help determine the optimal mixture of sequencing methods for most complete and cost effective transcriptome sequencing. We compared simulation results for traditional capillary sequencing with "Next Generation" (NG) ultra high-throughput technologies. The simulation model was parameterized using mappings of 130,000 cDNA sequence reads to the Arabidopsis genome (NCBI Accession SRA008180.19). We also generated 454-GS20 sequences and de novo assemblies for the basal eudicot California poppy (Eschscholzia californica) and the magnoliid avocado (Persea americana) using a variety of methods for cDNA synthesis.RESULTS:The Arabidopsis reads tagged more than 15,000 genes, including new splice variants and extended UTR regions. Of the total 134,791 reads (13.8 MB), 119,518 (88.7%) mapped exactly to known exons, while 1,117 (0.8%) mapped to introns, 11,524 (8.6%) spanned annotated intron/exon boundaries, and 3,066 (2.3%) extended beyond the end of annotated UTRs. Sequence-based inference of relative gene expression levels correlated significantly with microarray data. As expected, NG sequencing of normalized libraries tagged more genes than non-normalized libraries, although non-normalized libraries yielded more full-length cDNA sequences. The Arabidopsis data were used to simulate additional rounds of NG and traditional EST sequencing, and various combinations of each. Our simulations suggest a combination of FLX and Solexa sequencing for optimal transcriptome coverage at modest cost. We have also developed ESTcalc, an online webtool, which allows users to explore the results of this study by specifying individualized costs and sequencing characteristics.CONCLUSION:NG sequencing technologies are a highly flexible set of platforms that can be scaled to suit different project goals. In terms of sequence coverage alone, the NG sequencing is a dramatic advance over capillary-based sequencing, but NG sequencing also presents significant challenges in assembly and sequence accuracy due to short read lengths, method-specific sequencing errors, and the absence of physical clones. These problems may be overcome by hybrid sequencing strategies using a mixture of sequencing methodologies, by new assemblers, and by sequencing more deeply. Sequencing and microarray outcomes from multiple experiments suggest that our simulator will be useful for guiding NG transcriptome sequencing projects in a wide range of organisms.
General Note: Periodical Abbreviation:BMC Genomics
General Note: Start page 347
General Note: M3: 10.1186/1471-2164-10-347
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Volume ID: VID00001
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Holding Location: University of Florida
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Methodology article

Comparison of next generation sequencing technologies for
transcriptome characterization
P Kerr Wall', Jim Leebens-Mack2, Andre S Chanderbali3, Abdelali Barakat4,
Erik Wolcott', Haiying Liang4, Lena Landherr', Lynn P Tomsho5, Yi Hu1,
John E Carlson4, Hong Mal, Stephan C Schuster5, Douglas E Soltis3,
Pamela S Soltis6, Naomi Altman7 and Claude W dePamphilis*1

Address: 'Department of Biology, Institute of Molecular Evolutionary Genetics, and The Huck Institutes of the Life Sciences, The Pennsylvania State
University, University Park, PA 16802, USA, 2Department of Plant Biology, University of Georgia, Athens, GA 30602, USA, 3Department of
Biology, University of Florida, PO Box 118526, Gainesville, FL, 32611, USA, 4The School of Forest Resources, Department of Horticulture, and
Huck Institutes of the Life Sciences, Pennsylvania State University, 323 Forest Resources Building, University Park, PA 16802, USA, 5Center for
Comparative Genomics, Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA 16802, USA, 6Florida
Museum of Natural History, University of Florida, P.O. Box 117800, Gainesville, FL, 32611, USA and 7Department of Statistics and The Huck
Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA 16802, USA
Email: P Kerr Wall; Jim Leebens-Mack; Andre S Chanderbali;
Abdelali Barakat; Erik Wolcott; Haiying Liang; Lena Landherr;
Lynn P Tomsho; Yi Hu; John E Carlson; Hong Ma;
Stephan C Schuster; Douglas E Soltis; Pamela S Soltis;
Naomi Altman; Claude W dePamphilis*
* Corresponding author

Published: I August 2009 Received: I August 2008
BMC Genomics 2009, 10:347 doi: 10.1 186/1471-2164-10-347 Accepted: I August 2009
This article is available from:
2009 Wall et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.ore/licenses/by/2.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Background: We have developed a simulation approach to help determine the optimal mixture
of sequencing methods for most complete and cost effective transcriptome sequencing. We
compared simulation results for traditional capillary sequencing with "Next Generation" (NG) ultra
high-throughput technologies. The simulation model was parameterized using mappings of 130,000
cDNA sequence reads to the Arabidopsis genome (NCBI Accession SRA008180.19). We also
generated 454-GS20 sequences and de novo assemblies for the basal eudicot California poppy
(Eschscholzia californica) and the magnoliid avocado (Persea americana) using a variety of methods
for cDNA synthesis.
Results: The Arabidopsis reads tagged more than 15,000 genes, including new splice variants and
extended UTR regions. Of the total 134,791 reads (13.8 MB), I 19,518 (88.7%) mapped exactly to
known exons, while 1, I 117 (0.8%) mapped to introns, I 1,524 (8.6%) spanned annotated intron/exon
boundaries, and 3,066 (2.3%) extended beyond the end of annotated UTRs. Sequence-based
inference of relative gene expression levels correlated significantly with microarray data. As
expected, NG sequencing of normalized libraries tagged more genes than non-normalized libraries,
although non-normalized libraries yielded more full-length cDNA sequences. The Arabidopsis data
were used to simulate additional rounds of NG and traditional EST sequencing, and various
combinations of each. Our simulations suggest a combination of FLX and Solexa sequencing for
optimal transcriptome coverage at modest cost. We have also developed ESTcalc http://

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http://www. Sims/, an online webtool, which allows users to explore the results
of this study by specifying individualized costs and sequencing characteristics.
Conclusion: NG sequencing technologies are a highly flexible set of platforms that can be scaled
to suit different project goals. In terms of sequence coverage alone, the NG sequencing is a
dramatic advance over capillary-based sequencing, but NG sequencing also presents significant
challenges in assembly and sequence accuracy due to short read lengths, method-specific
sequencing errors, and the absence of physical clones. These problems may be overcome by hybrid
sequencing strategies using a mixture of sequencing methodologies, by new assemblers, and by
sequencing more deeply. Sequencing and microarray outcomes from multiple experiments suggest
that our simulator will be useful for guiding NG transcriptome sequencing projects in a wide range
of organisms.

Sequencing technology has made great advances over the
last 30 years since the development of chain-terminating
inhibitor-based technologies [1]. Traditional sequencing
approaches require cloning of DNA fragments into bacte-
rial vectors for amplification and sequencing of individual
templates using vector-based primers. This approach was
adapted for cDNA libraries [2] and, with the advent of
capillary sequencing, became suitable for high-through-
put sequencing of large samples of transcripts, termed
Expressed Sequence Tags (ESTs). ESTs have become an
invaluable resource for gene discovery, genome annota-
tion, alternative splicing, SNP discovery, molecular mark-
ers for population analysis, and expression analysis in
animal, plant, and microbial species [3]. Other
approaches for analyzing transcriptomes include serial
analysis of gene expression (SAGE) [4], massively parallel
signature sequencing (MPSS) [5], and microarrays [6,7].
These approaches, which involve the sequencing or
hybridizing of small concatamers of cDNA derived from
mRNA by reverse transcription, have been used success-
fully in analyzing the expression of genomes (transcrip-
tomes) at a very large scale, usually from species with a
sequenced genome or an existing and extensive EST data
set. Although several alternatives have been described
since the emergence of EST sequencing projects, none has
yet totally supplanted the use of bacterial vectors and
Sanger sequencing.

In 2005, two new sequencing technologies were intro-
duced both based on sequencing by synthesis, which
promised to replace or enhance traditional sequencing
methods. The 454 system, using
pyrosequencing technology [8], and the Solexa system, which detects fluorescence sig-
nals [9]. Both execute millions of sequencing reactions in
parallel, producing data at ultrahigh rates [10]. Although
read lengths are much shorter with these new methods
than with capillary sequencing (averaging 100-230 bp
and 300-400 bp for 454FLX and 454Titanium, respec-

tively, and 35 to up to 76 b for Illumina Solexa platforms),
respectively, both platforms generate sufficient data to
completely re-sequence bacterial genomes in a single run
[8,11-13]. In the past year, Applied Biosystems has intro-
duced their SOLiD sequencer http://www3. appliedbiosys
tems.comr another short-read 20-35 bp platform, with
read lengths anticipated to be 50 bp in the upcoming
SOLiD3 release. The three platforms offer a variety of
experimental approaches for characterizing a transcrip-
tome, including single-end and paired-end cDNA
sequencing, tag profiling (3' end sequencing especially
appropriate to estimating expression level), methylation
assays, small RNA sequencing, sample tagging ("barcod-
ing") to permit small subsample identification, and splice
variant analyses. Several challenges face investigators hop-
ing to use these methods, including the relatively large
cost of most NG experiments and intense demands for
data storage and analysis on the scale required for NG
datasets, and rapidly evolving technologies. Initial studies
reported success with 454 sequencing of chloroplast
genomes [14,15], small RNAs [16-19], and transcrip-
tomes of organisms with [20-22] or without [23] exten-
sive genomic sequence information.

These Next Generation (NG) sequencing methods prom-
ise a cost-effective means of either deeply sampling or
fully sequencing an organism's transcriptome, with even
small experiments tagging a very large number of
expressed genes. However, prior transcriptome sequenc-
ing studies have been largely exploratory, only hinting at
the potential for NG transcriptome sequencing at differ-
ent scales. There is a great need for quantitative studies
and analysis tools that help investigators optimally design
NG sequencing experiments to address specific goals.

A complete solution to this problem would involve realis-
tic models for each technology, accounting for the cost of
library generation and data collection, the characteristics
of cDNA libraries, transcript abundance distributions,
read length distributions, and the error rates in sequence

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BMC Genomics 2009,10:347

BMC Genomics 2009, 10:347

generation and assembly. The present study focuses on
the first four of these issues to provide estimates of theo-
retical coverage of complex transcriptomes with varying
scales and types of DNA sequencing experiments. In ear-
lier publications [24,25], we developed a robust simula-
tion approach to model traditional capillary
transcriptome sequencing, which incorporates distribu-
tions of the relative start site of cDNA sequences as a func-
tion of cDNA length, the read length distribution, and the
transcript abundance distribution. We have now adapted
this simulation approach to model the specific character-
istics of NG sequencing. The results from this study
should help researchers working with these new and excit-
ing technologies.

The present study has several goals. First, we report empir-
ical comparisons of 454 pyrosequencing and capillary-
based transcriptome sequencing from the model plant,
Arabidopsis thaliana, and two non-model plant species, the
basal eudicot Eschscholzia californica (California poppy)
and the magnoliid Persea americana (avocado). We use
these results to examine the effects of library preparation
procedures, specifically, normalized versus non-normal-
ized and random versus oligo-dT primed libraries. We
then introduce a simulation approach, based on the GS20
sequencing results, to predict the outcome of additional
GS20 transcriptome sequencing experiments while
accounting for critical features in cDNA library construc-
tion. We then use the GS20 simulation results to extrapo-
late results for 454FLX and Solexa platforms, in order to
estimate technology-specific sequencing characteristics.
Finally, we report on simulated experiments aimed at
characterizing the optimal mixture of methods for most
complete and cost-effective transcriptome sequencing
with one or more sequencing technologies.
Table I: Sequencing statistics of analyzed libraries.


Next Generation Transcriptome sequencing of
Arabidopsis floral tissue
A half plate of GS20 sequencing from an Arabidopsis ran-
dom-primed cDNA library generated 134,791 reads total-
ling 13.8 MB with an average length of 102.2 bp. The
reads were assembled into 82,281 unigenes, which
included 8,188 contigs with an average length of 147 bp
and 74,093 singleton reads (Table 1). We mapped
122,344 (90.8%) reads to the TAIR 7 Arabidopsis genome
annotation (Table 2 and see Methods). Of the total
mapped reads, 88.7% were located within 15,539 genic
regions and 2.1% were located in intergenic regions.
Within the genic regions, 119,518 (88.7%) reads mapped
exactly to known exons, while 1,117 (0.8%) and 11,524
(8.6%) reads mapped to introns and intron/exon bound-
aries, respectively. Also, 3,066 (2.3%) of the reads
included in the genic regions extended current boundaries
of known genes while 302 reads combined two annotated
genes or marked areas of the genome with overlapping
genes. There were 12,447 (6.7%) reads that did not have
a significant BLASTn match to any location within the
genome. There were 1,085 genes that had more than 20
reads per locus, and the 10 most highly expressed genes
(Table 3), included two subunits of the photosynthetic
protein RuBisCo, as well as TASTY, TGG1, and PDF1.
These "top ten" transcripts had read counts ranging from
190 to 586 reads with the RuBisCO small subunit 1A
being most highly represented. At this shallow sequencing
depth, 2 non-overlapping contigs, with lengths of 357 and
240 bp, mapped to the RuBisCO small subunit 1A gene.

Despite low overall transcriptome coverage, one-half
plate ofArabidopsis GS20 sequence data returned 27 fully
sequenced cDNAs, as well as 292, 628, and 1008 genes at

Ath (random)

Pam (normal)

Eca (oligo-dT)

Eca (random)

Eca (combined)

134,791 102.2 269,057 85.9 251,716 98.9 307,836 98.2 559,552 98.6


147.0 22,303 107.3 18,339 148.5 14,242 146.9 30,603

Singletons 74,093 101.6 211,882 85.0 64,931 99.9 61,031 99.1 89,982



106.1 234,185 90.6 83,270 107.7 75,273

105.1 120,585 106.9


Read, Contig, Singleton, and Unigene Counts (n), mean sequence lengths ( x ), and total amount of sequence data (MB) for 454 GS20 libraries
analyzed. Species codes are Ath (Arabidopsis thaliana), Pam (Persea americana, avocado), Eca (Eschscholzia californica, California poppy). cDNA library
production method indicated in parentheses. Read lengths based on number of Q20 equivalent bases produced, after trimming and cleaning with
the program seqclean; normalized library original read mean length was 100.1 prior to trimming
normalization adapter.

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BMC Genomics 2009, 10:347

Table 2: Arabidopsis 454 reads mapped to the annotated

Sequence Type





Extended UTR

Overlapped Genes


No Hit


Reads Genes Total (%)

119,518 15,539 88.7

103,509 14,754 76.8

1,117 877 0.8

11,524 5,973 8.6

3,066 1,635 2.3

302 177 0.2

2,826 2,096 2.1





All 454 reads were mapped (BLAST-n, default parameters) to the
genome. TAIR XML files were parsed to obtain exon structure and
location within the genome. Percentages were calculated for each
class of sequence type. The number of genes does not equal the
summation of gene components because there are some genes that
are hit by multiple reads in different sections of the gene. The percent
for each gene component is the percent of total reads.

90%, 80%, and 70% coverage, respectively. These results
demonstrate that nominal amounts of 454 sequencing
can generate complete or nearly complete sequences for
an appreciable number of genes, especially those that are
small and highly expressed. Another very promising result
is the improved annotation of genes for both model and
non-model species. For example, although the Arabidop-
sis genome has been largely sequenced since 2000 [26],
the half plate of GS20 extended the untranslated regions
(UTRs) of roughly 3,066 genes and mapped new tran-
script boundaries of 8,662 genic regions. These regions are
possibly new splice variants of previously annotated
genes. Finally, 2,826 transcripts were mapped to 2,096
unique intergenic regions. These transcripts might repre-
sent un-annotated protein-coding genes or non-coding
RNA sequences that have not previously been sampled in
traditional cDNA libraries.

Transcriptome sequencing of Eschscholzia californica
using oligo-dT and random-primed libraries
Two full plates (over 559,000 total reads) of GS20
sequencing was performed on the emerging model basal
eudicot, Eschscholzia californica [27,28], including one
plate from a 454 library of oligo-dT primed cDNA and
one plate from a 454 library of random hexamer-primed
cDNA. The library of oligo-dT primed cDNA generated
251,716 reads totalling 24.9 MB with an average length of


98.9 bp. The reads assembled into 83,270 unigenes,
including 18,339 contigs with an average length of 148.5
bp and 64,931 singletons (Table 1). The library of ran-
dom-primed cDNA generated 307,836 reads totalling
30.2 MB with an average length of 98.2 bp. The reads
assembled into 75,273 unigenes, including 14,242 con-
tigs with an average length of 146.9 bp and 61,031 single-
ton reads (Table 1). Finally, we assembled both plates,
which resulted in 120,585 unigenes, including 30,603
contigs with an average length of 157.0 bp and 89,892
singleton reads (Table 1).

As expected, the most obvious difference between the
oligo-dT and random-primed cDNA sequences was the
representation of rRNA genes. Additional rounds of
mRNA purification, however, could have reduced the
level of rRNA "contamination". We also examined the rel-
ative start positions of the reads from each library by map-
ping the reads to the proteome of Arabidopsis (Figure 1A).
The relative start positions are defined as the start position
of the best Arabidopsis HSP divided by the length of the
best protein match. As expected, the oligo-dT library had
a greater 3' bias than the random primed library. The uni-
genes from both libraries mapped to 6,498 unique Arabi-
dopsis genes, with 4,066 of the transcripts found in both.
The level of redundancy observed between these two
plates (just 62.6%) suggests that many more genes would
be discovered with additional sequencing.

Transcriptome sequencing in a normalized library of
Persea americana
One plate of GS20 sequencing was performed on a nor-
malized library for Persea americana, an emerging model
for the magnoliids [29]. The plate generated 298,055
reads totalling 29.8 MB with an average length of 100.1
bp. We then trimmed the adaptors used in the normaliza-
tion step, which reduced the total number of reads to
269,057 with an average sequence length of 85.9 bp.
Trimming the adaptors reduced the total amount of
sequence by more than 6 MB, bringing the total to 23.1
MB. The reads assembled into 234,185 unigenes, includ-
ing 22,303 contigs with an average length of 107.3 bp and
211,882 singleton reads (Table 1).

To determine the success of the normalization step, we
plotted the relative frequency of the number of reads per
gene, using Arabidopsis as a reference (Figure 1B). Com-
pared to the other library methods used in this study, the
normalized Persea library (solid blue line) contained the
largest number of genes with fewer than five reads per
gene and the fewest number of genes with more than 5
reads per gene. The gene with the highest number of
mapped reads was a protein phosphatase with 37 reads. In
contrast, the most highly represented genes in the poppy
non-normalized libraries had over 1000 reads mapping to

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Table 3: Top 10 most frequently detected unigenes in 454 cDNA libraries of Arabidopsis, Eschscholzia, and Persea.

Library Contig Len Reads Cov AGI Len Evalue Annotation

Ath-rand 08061 357 586 34.8 ATIG67090




























1025 0.0 RuBisCO small subunit IA (RBCS-IA) (ATSIA)

00035 1326

08724 1653

08295 1175

08670 310

00011 240

00660 640

07960 927

04760 1157

08550 373

19682 387

19707 2089

18128 151

19695 308

19793 940

18734 849

00048 2823

18697 144

19623 2638

19622 120

15341 109

15345 162

15162 315

15258 606

14312 182

15290 2020

15208 1052

14424 2660

96.8 ATIG54040

90.0 AT5G26000

94.6 AT2G42840

31.5 AT5G38410

23.4 ATIG67090

76.9 AT2G21660

52.6 AT5G60390

82.3 AT3G12145

100 ATCG00220

83.2 AT5G39170

100 ATIG70370

10.0 AT3G47550

100 AT5G52160

100 AT2G36830

100 AT3G 16640

80.0 AT5G35750

24.7 AT4G06746

81.4 AT2G01830

6.4 ATIG23800

6.9 AT4G03930

12.0 AT3G59430

19.7 AT5G26670

53.3 AT3G 12340

56.2 ATMG00030

100 ATIG70370

100 AT2G36830

75.4 AT5G34750

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2e- 162




RuBisCO small subunit 3B (RBCS-3B) (ATS3B)

RuBisCO small subunit IA (RBCS-IA) (ATSIA)

ATGRP7 (Cold, Circadian Rhythm, RNA Binding 2)

elongation factor I -alpha/EF-I -alpha

FLRI (FLOR I); enzyme inhibitor

PSBM, PSII low MW protein

Unknown protein

BURP domain-containing protein/polygalacturonase

C3HC4-type RING finger family protein

protease inhibitor/seed storage/lipid transfer protein

GAMMA-TIP (Tonoplast intrinsic protein gamma)

TCTP (Translationally Controlled Tumor Protein)

AHK2 (Arabidopsis Histidine Kinsase 2)

RAP2.9 (related to AP2 9); transcription factor


ALDH2B7 (Aldehyde dehydrogenase 2B7)


Unknown protein

Pectinacetylesterase, putative

FK506 binding/peptidyl-prolyl cis-trans isomerase


BURP domain-containing protein/polygalacturonase

GAMMA-TIP (Tonoplast intrinsic protein gamma)


BMC Genomics 2009,10:347


Table 3: Top 10 most frequently detected unigenes in 454 cDNA libraries of Arabidopsis, Eschscholzia, and Persea. (Continued)
Eca-rand 15320 1146 437 48.3 AT5GO2500 2373 0 HSC70-1 (heat shock cognate 70 kDa protein I)

Eca-rand 15269 304 417 5.8 AT2G47410 5221 0.2

Pam-norm 15603 133 37 9.8 ATIG59830 1357 0.005

Pam-norm 18074 139 32 10.4 ATI G14270 1343 0.3

Pam-norm 8473 176 27 10.4 AT4G 17890 1688 0.1

Pam-norm 14132 213 26 7.3 AT2G40820 2907 1.9

Pam-norm 15140 237 26 48.5 AT2G41430 489 2e-13

Pam-norm 4395 144 25 6.4 ATI G45545 2259 0.08

Pam-norm 15762 102 24 3.5 ATI GO 1950 2901 0.2

Pam-norm 10833 112 20 6.4 AT3G03640 1747 0.001

Pam-norm 18760 253 19 59.0 AT4G 14270 429 2e-04

Pam-norm 18306 208 18 48.5 AT4G 14270 429 8e-05

Nucleotide binding

PP2A-1 (protein phosphatase 2A-2)

CAAX amino terminal protease family protein

AGD8, UBP20 (Ubiquitin-specific Protease 20)

Proline-rich family protein

ERDI5 (Early Responsive To Dehydration 15)

Similar to unknown protein

Armadillo/beta-catenin repeat family protein

GLUC (Beta-glucosidase homolog)

Protein containing PAM2 motif

Protein containing PAM2 motif

Unigenes from each library, Arabidopsis flower bud random-primed (Ath-rand), Eschscholzia flower bud oligo-dT (Eca-oligo) and random-primed
(Eca-rand), and Persea americana normalized flower bud (Pam-norm), were mapped to the annotated TAIR cDNA and protein datasets using
BLASTx (e-5 cutoff). Column headers are contig name (Contig), contig length (Len), number of reads per contig (Reads), percent coverage (Cov),
Arabidopsis best hit gene identifier (AGI), annotation (Annotation), and E-value. Ribosomal RNA and contaminants such as putative endophytes
removed from this list. Refer to Additional file I for detailed BLAST results.

specific Arabidopsis genes. Hence, the normalization step
was successful. Note that the Persea library, constructed
using the Trimmer-Direct Kit (Evrogen) with amplifica-
tion of full-length cDNAs (Clontech's SMART technol-
ogy), also has the least amount of 3' bias in read start
positions (Figure 1A).

Correlation of observed Arabidopsis transcript
frequencies with microarray data
Of the 21,707 genes included on the Arabidopsis Affyme-
trix (AFFY) microarray, 13,790 had at least one read
mapped to its cDNA sequence. For these genes, we used
AFFY microarray expression values generated from inflo-
rescence tissue in the same A. thaliana ecotype [30] to
compare with the number of 454 reads for each gene. The
comparison revealed that 1,907 genes that were detected
above normalized expression level 50 with the AFFY chip
were not detected in the 454 sequences, while 1,375 genes
were detected in 454 reads, but were below expression
level 50 with AFFY data (a common cutoff for reliable
detection with the AFFY system). An additional 1,717
genes detected by 454 reads were not included as probes
on the AFFY gene chip. A moderate correlation was
observed between microarray expression values and
number of 454 reads (Figure 2 with r = 0.67, r2 = 0.444, p
< 0.0001).

Next Generation transcriptome simulation study
A primary goal of large-scale transcriptome sequencing is
to identify and obtain full-length sequences of all of the
expressed genes in an organism or tissue. A researcher will
typically begin with RNAs isolated from a tissue of interest
or a collection of tissues from the entire organism. The
researcher may use tissue from a particular developmental
stage or assay gene expression under a range of experimen-
tal conditions (e.g., light/temperature/water/nutrient
stress, gene knock out). Each of the new NG technologies
(e.g., 454-GS20/FLX, Solexa) produces data with charac-
teristics that can be evaluated and compared to each other
and traditional capillary sequencing.

In order to predict the expected outcomes of varied
amounts of sequencing effort using a blend of technolo-
gies, we developed a predictive model based on the simu-
lation engine of ESTstat [24,25]. Inputs to the model
include four distribution profiles that reflect information
about the cDNA library or sequencing technology: 1) the
transcript abundance profile, a transcriptome-specific fre-
quency distribution of the number of tags of different
genes in the entire transcriptome, 2) the distribution of
cDNA lengths 3) the distribution of sequencing start sites,
and 4) the distribution of read lengths after removal of
vector and low quality data. The first three of these reflect

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BMC Genomics 2009,10:347



w 0.010
A O .

0.002 .




Number of Reads per Unigene

Figure I
Distributions of relative start sites and number of
reads per gene. A. Start site distributions of 454 sequences
for each species in this study including random, oligo-dT, and
normalized oligo-dT libraries. Sequencing start sites are cal-
culated as the start position, defined by BLASTn (Arabidopsis)
or BLASTx (Eschscholzia, Persea) hit divided by the cDNA or
protein length and expressed as percentage of the gene
length. B. Distribution of the number of reads from each
library mapped to an Arabidopsis gene, defined by best
BLASTn or BLASTx hit of each read to the TAIR genes. Spe-
cies abbreviations are ATH (Arabidopsis thaliana), ECA
(Eschscholzia californica), and PAM (Persea americana).

library specific features, while the fourth is mostly
dependent upon the sequencing technology. The ESTstat
simulation model has been tested under a variety of situ-
ations and found to robustly predict the outcomes of
future sequencing experiments. Although ESTstat can esti-
mate and correct assembly errors in silico without refer-
ence to a known genome sequence, we were able to map
each read to its known location on the Arabidopsis genome
to assess and correct assembly error.

We used the results from our GS20 sequencing to simulate
different levels of sequencing coverage for each of the NG
and capillary technologies. For each technology, we con-
sidered both non-normalized and perfectly normalized
libraries, in which the expression level of every gene is

0 1 0 0 0 O00 o mW OGEO (D@ 0
0 1 5 10 50 100 500
GS20 Reads per Gene

Figure 2
Correlation of gene expression with number of tran-
scripts. Linear Regression comparing number of 454 reads
with Affymetrix (AFFY) gene chip expression values for Arabi-
dopsis young inflorescence. Each symbol represents a single
gene, with many genes having overlapping counts. Correla-
tion between the two measures of gene expression is highly
significant (r = 0.67, r2 = 0.444, p < 0.0001).

made identical. Actual normalization experiments should
therefore fall somewhere between non-normalized and
perfectly normalized, depending on the normalization
method, RNA quality, and success of the normalization
procedure (see Materials and Methods for more detail).
We used the following parameters to help evaluate the dif-
ferent sequencing platforms: transcriptome coverage, per-
centage of all expressed genes that were tagged, percentage
of singletons, number of unigenes, mean unigene length,
and the percentage of all expressed genes that were
sequenced completely (i.e. 100% covered; Figures 3A, 3B,
3C, 3D, 3E, and 3F).

Transcriptome coverage (Figure 3A) is a direct indicator of
the sequencing depth and breadth of sequence data rela-
tive to the sample transcriptome. We define the transcrip-
tome coverage as the total non-redundant number of
bases from sampled genes that are included in at least one
EST, divided by the sum of cDNA lengths for all expressed
genes (including both detected and undetected genes in
the transcriptome). In this study, the 15,276 detected
genes and randomly sampled 3,007 undetected genes
(estimated using ESTstat, see Materials and Methods) sum
to 18,283 genes, with an expected total cDNA length of
29.8 MB. The transcriptome coverage, as a function of the
total number of sequenced bases (MB), differs only
slightly for all technologies. However, when the amount

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S0 0


0 20 40 60 (bp/Length*100)
Relative Start Site (bp/Length100)

BMC Genomics 2009,10:347




200- /

0 5 10 50 500 5000 0.2 0.5 1 2 5 10 50 200

Total Sequence 100B) Price C$1000)

(page number not for citation purposes)

4-- -


5 10 50 500 5000 0.2 0.5 1 2 5 10 50 200
Total Sequence (MB) Price ($1000)

Figure 3
Simulation results for different Next Generation sequencing technologies. Simulation results illustrating predicted
outcomes for different transcriptome sequencing technologies with a complex library expressing ca. 18,000 genes. Left column
illustrates predicted outcomes as a function of MB of sequence; right column gives predicted outcomes as a function of esti-
mated sequencing cost (see text for cost assumptions, which do not include varied costs for RNA isolation and library prepa-
ration). Each simulated data set was used to calculate: A) percent of transcriptome sequenced with at least one read and not
necessarily in one contiguous sequence, B) number of genes tagged, C) number of unigenes obtained, D) mean unigene length
(bp), E) percent of reads that are singleton sequences, and F) the number of genes with 100% coverage. Each technology is rep-
resented by a different line color, with solid lines indicating non-normalized libraries and dashed lines indicating theoretically
perfectly normalized libraries. ESTS = 5' capillary sequence (black); GS20 = 454 GS20 (green); GSFLX = 454 GSFLX (blue);
SOL = Solexa (red). The following prices (per MB) were used in the calculations: ESTS ($1330), GS20 ($240), GSFLX ($90),
and SOL ($4). For several of the measures, the Solexa result is hidden under the topmost line. Additional details provided in

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BMC Genomics 2009, 10:347

of sequence is low (1-500 MB), the transcriptome cover-
age is greater in the normalized libraries (dashed lines)
compared to the non-normalized libraries (solid lines) for
each technology. Theoretically, perfect normalization will
equalize the level of expression for all genes, without any
other impact on library quality, and thus will increase the
coverage of genes that are randomly sampled. Using the
distributions of cDNA length, read length, and sequenc-
ing start sites obtained in these experiments, we estimate
that traditional 5' capillary sequencing of a non-normal-
ized library will cover approximately 14%, 52%, and 82%
of the transcriptome with 6.25, 50, and 200 MB of
sequencing, respectively. For a normalized library, the
percentage increases to 18%, 69%, and 95% with the
same amounts of sequence. The same pattern was
observed for the NG technologies but with higher levels of
transcriptome coverage. For example, the GS20 technol-
ogy is estimated to cover 15%, 54%, and 88% of the tran-
scriptome for a non-normalized library and 18.2%, 72%,
and 98% of the transcriptome for a normalized library at
6.25, 50, and 200 MB of sequencing. The lower coverage
of capillary-based EST sequencing given the same number
of sequenced bases is attributed to biases implicit in the
cDNA cloning process. The FLX is estimated to cover 15%,
54%, and 88% for the non-normalized library and 18%,
72%, 98% for a normalized library at the same intervals.
Finally, the Solexa platform is estimated to cover 55% and
87% for the non-normalized library and 75% and 98%
for the normalized library for 50 and 200 MB, respec-
tively. Given that one plate of sequence data from the Sol-
exa platform is estimated at 1,000 MB, we chose 50 MB
(1/20 of a plate) as the first interval to be simulated, and
we excluded all intervals less than 50 MB.

Transcriptome coverage differs substantially among the
various technologies at the same cost. However, the cost
used in this analysis refers only to the actual sequencing
costs and not the pre-processing costs such as library prep-
aration and normalization. The Solexa platform rapidly
approaches 100% coverage primarily because the cost of
sequencing is substantially smaller per MB (simulations
for Solexa were based on $4000/plate at 1,000 MB/plate).
Solexa is followed by GS20, FLX, and conventional EST
sequences. It is estimated that traditional capillary
sequencing would reach 100% transcriptome coverage at
more than 200 MB and at a cost of over $200,000. While
Solexa sequencing is the most economical technology for
deep coverage of transcriptomes, de novo assembly of
short Solexa sequences for non-model species remains an
unresolved challenge.

A second indicator of the depth of transcriptome sequenc-
ing is the percentage of genes tagged (Figure 3B). A gene is
considered tagged if it has been sampled with at least one
read. The percentage of genes tagged increases with both


amount of sequencing and price. For a non-normalized
traditional library, we estimate that 27%, 75%, and 96%
of the genes will be tagged in our sample transcriptome
with 6.25, 50, and 200 MB of sequencing. For a normal-
ized library, the percentage increases to 39%, 98%, and
100% with the same amounts of sequence. As expected,
this percentage increases when the sequencing is done
with any of the NG technologies. The cost of gene tagging
also differs substantially among the various sequencing
technologies. The Solexa platform tags essentially 100%
of the expressed genes with less than one plate of
sequence ($4000). Solexa is followed by GS20, FLX, and
conventional EST sequences. Capillary sequencing would
approach 100% genes tagged at more than 200 MB and
over $200,000.

The number of unigenes (Figure 3C) including singletons
and contigs has typically been used to estimate the
number of transcribed genes in a tissue. With small
amounts of sequencing, the number of unigenes is similar
to the number of sequences, but with more sequencing
multiple reads are observed for each gene (increasing
redundancy), and the rate of discovery for new genes falls
off. At a particular point in the sequencing process (peaks
in Figure 3C), the number of unigenes will begin to
decrease as disconnected reads coalesce into contigs cov-
ering entire genes, and eventually the unigene number
approaches the number of genes expressed in the library.
The rate at which multiple reads for a gene coalesce into a
single contig is a function of read length. With the capil-
lary technology, each read is large compared to the NG
reads. With a non-normalized library similar to the model
library, we will reach the peak unigene number at more
than 200 MB of sequencing. With a normalized library,
we reach the peak at approximately 100 MB and decrease
gradually with an additional 100 MB of sequence. How-
ever, we still do not reach the estimated 18,000 genes
expressed in the Arabidopsis floral library. For the FLX tech-
nology, the maximum number of unigenes occurs at
roughly 100 MB and 50 MB for the non-normalized and
normalized libraries, respectively. However, because the
FLX sequences are two to three times shorter than the tra-
ditional sequences, the peak is reached with roughly dou-
ble the number of unigenes (38,000 and 46,000,
respectively). For the GS20 platform, the peaks occur at
nearly the same levels (approximately 100 MB) as the FLX
platform, but since these reads are half as long as FLX
reads, the GS20 produces more than twice the number of
unigenes (92,000 and 115,000) for both library types. The
Solexa platform produces many more unigenes at all lev-
els of sequencing and the peak occurs at approximately
200 MB for both library types (1.3 and 1.7 million reads).

The mean unigene I,. ,rli (Figure 3D) is an important statis-
tic if the goal of the transcriptome sequences is to perform

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BMC Genomics 2009, 10:347

multi-gene phylogenetic or molecular evolutionary analy-
ses. In this case, researchers would like full-length
sequences for many expressed genes, not just small frag-
ments of expressed genes. In the Arabidopsis genome, the
average transcript length is approximately 1,500 bp
(1,436 for all transcripts and 1,628 bp for only the tran-
scripts predicted to be expressed in this library). Therefore,
a researcher would like to sequence enough of a library to
produce contiguous sequences with average lengths of all
genes in the library. We calculated the unigene length in
two different ways. First, we used the mean length of all
unigenes, although this estimate lowers the mean length
for the shorter sequences in the NG technologies. Second,
we calculated the mean length of only the longest uni-
genes for each gene (Figure 3D). All NG technology and
library type combinations require greater depth of
sequencing to reach the same level as its traditional coun-
terpart. When we examine the mean unigene length in
relation to price, the traditional sequencing produces the
longest unigenes until approximately $5,000 worth of
sequencing. This is approximately 4-5 MB of capillary
sequencing and 6,000-8,000 reads. At this point, the NG
technologies begin to generate enough sequences to
assemble longer unigenes at a lower cost.

The percentage of singleton reads (Figure 3E) reflects
sequencing depth and the likelihood that a given read will
assemble to form a contig with other reads. A singleton is
defined as a single read that does not contain enough
overlap in length to be combined with other reads from
the same transcribed gene. The percentage of singletons is
also inversely proportional to the levels of redundancy in
the library. Therefore, additional sequencing usually
reduces the percentage of singletons. This is the case for
capillary sequencing, where the percentages of singletons
are 73%, 40%, and 16% for non-normalized and 81%,
23%, and 4% for normalized libraries at the 6.25, 50, and
200 MB levels, respectively. For the GS20, these values
change to 76%, 48%, and 25% for non-normalized librar-
ies and 80%, 34%, and 7% for normalized libraries at the
same levels. For the FLX, the percentage of singletons
changes to 74%, 44%, and 22% for non-normalized and
to 78%, 29%, and 5% for normalized libraries at the same
levels. Finally, for Solexa, the percentage of singletons is
predicted to be around 68%, 47%, and 25% for non-nor-
malized and 67%, 32%, and 7% for normalized libraries
at the 50, 200, and 1000 MB sequence intervals, respec-

The final parameter used to evaluate and compare the
technologies is the percentage of genes with 100% coverage
(Figure 3F). As with mean unigene length, gene coverage
can be calculated using all of the unigenes per gene, or by
using only the longest unigene. The smaller reads from the
NG technologies might cover all the regions within a


gene. However, many of the reads for a gene will not have
sufficient overlap to assemble into a contiguous sequence.
Although we calculated both estimates, we use the per-
centage of gene coverage based on the longest unigene for
comparisons to other platforms. In relation to amount of
sequencing (MB), the capillary, GS20, and FLX technolo-
gies have similar percentages. The Solexa platform
requires more data (MB of sequencing) to fully sequence
a similar number of genes. For example, the FLX generates
unigenes that completely cover roughly 18% and 58% of
the total genes with 200 MB and 1000 MB of sequence
data. The same amounts of Solexa sequencing would fully
sequence 4% and 25% of the genes. However, the FLX
experiment would cost approximately $18,000 and
$90,000, whereas the Solexa data could be generated for
roughly $800 and $4,000. Finally, with capillary sequenc-
ing, 200 MB would need to be sequenced at $250K to fully
cover 25% of the genes.

Combinations of traditional and NG sequencing
Analyses of genome sequencing projects suggest that opti-
mal genome assemblies can be obtained through a com-
bination of traditional and NG technologies [ 11 ]. In order
to investigate the combination of these new technologies
for transcriptome sequencing, we examined the addition
of NG sequences to traditional capillary sequences (Fig-
ures 3A, 3B, and 3C) and the combinations of NG
sequences alone (Figures 3D, 3E, and 3F). All of the indi-
cators from the previous section dramatically improved
with the addition of small amounts of NG sequences.
Among the various combinations of technologies, there is
little difference in most of the indicators used in the pre-
vious section. For example, the percentage of genes tagged
approaches 100% with very small amounts of NG
sequences. Therefore, to evaluate the various combina-
tions of technologies, we compared three of the statistics
described above: mean unigene length, transcriptome
coverage, and percent of genes 100% covered.

The addition of NG sequences to traditional capillary
sequences increased each of these three indicators at most
sequence increments (Figures 4A, 4B, and 4C). Only the
addition of one plate of Solexa and all GS20 plate incre-
ments decreased the mean unigene length (Figure 4A).
The addition of four plates of FLX increased the mean uni-
gene length to 1327 and 1380 bp with 3.25 and 50 MB
and of traditional sequences, respectively. At these same
increments, transcriptome coverage would increase from
94% to 95% (Figure 4B), while the percent of genes 100%
covered would increase from 33% to 38% (Figure 4C).
The addition of this amount of FLX would increase the
total cost of sequencing from $40K to $102,000. How-
ever, sequencing only four plates of FLX, assuming perfect
assembly, could in theory generate 1323-bp unigenes at
under $40,000, with approximately 94% transcriptome

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BMC Genomics 2009, 10:347

coverage and covering 37% of the genes 100% covered.
Adding four plates of Solexa to four plates of FLX would
generate 1466 bp unigenes at just over $50,000 (Figure
4D). This amount of sequencing would cover 100% of the
transcriptome (Figure 4E) and fully sequence 84% of the
genes (Figure 4F). Under these conditions, the primary
advantage of including Sanger sequences would be the
improvement of assembly through the inclusion of long
individual reads, and simplification of downstream exper-
iments with physical clones.

ESTcalc: A simulation calculator for NG transcriptome
sequencing experiments Sims/
With sequencing technologies rapidly advancing,
researchers will wish to predict the cost and potential out-
comes of diverse transcriptome sequencing projects under
a wide range of initial assumptions. We have constructed
ESTcalc, an online webtool, which allows users to explore
the results of this study by specifying individualized costs
and sequencing characteristics. Users can choose a single
sequencing method (5' Sanger sequencing, GS20, GSFLX,
or Solexa), perfectly normalized or non-normalized
libraries, and varied amounts of sequencing and read
lengths to predict many of the same parameters used in
this study. User-defined costs can include both fixed (e.g.,
cost of obtaining libraries) and per unit sequencing costs,
with default costs the same as used in our study. ESTcalc
will extrapolate from the closest treatments examined in
our simulation study, and give outcomes such as the
project cost, predicted number of unigenes, unigene
length, transcriptome coverage, and related statistics pre-
sented earlier in this study. Combinations of sequencing
strategies, such as normalized plus non-normalized
libraries, or combinations of different technologies, can
also be examined under the same range of combinations
used in our study. Additional combinations, including
parameter sets for SOLiD sequencing, will be added to
ESTcalc as they are obtained in ongoing data analyses.

Next Generation transcriptome sequencing
Next Generation sequencing has great potential for accu-
rate transcriptome characterization because of the large
amount of data obtained at considerably lower costs com-
pared to traditional methods. Although the cost of tradi-
tional sequencing (over $1000/MB) has continued to
decrease over the last decade, the lower cost of NG
sequencing ($250/MB for GS20, $90/MB for FLX, and $5/
MB for Solexa) will dramatically improve transcriptome
sequencing in future research. The overall yield and value
of NG sequencing is evident in the amount of sequence
data obtained in each run. We identified a large number
of uniquely tagged gene sequences in each of our three
cDNA libraries (Arabidopsis, poppy, and Persea). With only


a small amount of sequencing (one-half plate on the
GS20) in Arabidopsis, we tagged more than 15,000 genes
and completely or nearly completely sequenced several
hundred of the highly expressed genes. Even with a very
modest amount of data by NG sequencing standards,
many of these sequences extended the annotated untrans-
lated regions (UTRs) and redefined intron/exon bounda-
ries, including evidence of alternative splicing. We also
identified more than 2,000 transcripts that were not pre-
viously annotated in the Arabidopsis genome. These may
define new genes or transcribed non-coding regions such
as miRNA or other small RNA. In any event, these results
illustrate the utility of NG transcriptome sequencing for
genome annotation.

Our data were limited to Arabidopsis inflorescence, and
there are likely to be differences in experimental outcomes
using different organisms and tissue. To assess the similar-
ity of the Arabidopsis inflorescence transcriptome with
other transcriptomes, we considered the distribution of
intensities of the perfect match probes from several
Affymetrix experiments involving various tissues and
organisms. We examined data from Arabidopsis inflores-
cence, leaf, and root on ATH1 arrays [30], human skeletal
muscle [31] on the hgu95av2 array, Caenorhabditis elegans
(whole worm) [32] on the C. elegans array, Drosophila mel-
anogaster (whole fly) [33], and Saccharomyces cerevisiae
(yeast) on ammonium sulfite nitrogen source [34]. Small
differences are observed in the expression profiles, con-
sistent with some samples having different proportions of
genes expressed more or less highly, but overall, the distri-
bution of expression intensities is very similar for all of
the samples (Figure 5A). These differences among samples
are on the same scale, and sometimes smaller than, the
variations seen among replicate samples from Arabidopsis
inflorescence. Because the tissue-specific expression pro-
file is the one method-independent input to the simula-
tion model, we can expect similar predictions for
transcriptomes from different sources.

NG sequencing simulation studies and comparisons of
Simulation studies help researchers predict outcomes of
expensive or time consuming experiments that cannot be
readily performed in the near term. For transcriptome
sequencing, simulation studies have allowed researchers
to conduct in silico experiments of systems that would be
costly and time-consuming to do in the lab. We have
developed a simulation approach to understand the
advantages of each of the NG technologies in comparison
with traditional capillary sequencing. Although all tech-
nologies eventually converge at similar points with regard
to unigene length, transcriptome coverage, and percent-
age of genes fully sequenced (1,444 bp, 18,283 genes, and
100% for Arabidopsis), the NG technologies offer huge

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60- ___________





.... A

100 200 300
Total Sequence (MB)

20 40 60 80 100 120
Price ($1000)

400 10 20 30 40 50
Price ($1000)

I 5' EST
I +n SOL
* n=1 plate
A n=2 plates
* n=4 plates

Figure 4
Simulation results for combinations of Next Generation sequencing technologies. Illustrating predicted outcomes
from combined sequencing technologies with a complex library expressing ca. 18,000 genes. A-C) Combinations include 3.125,
12.5, or 50 MB 5' Sanger sequencing plus 0 to 4 plates of GSFLX and/or Solexa sequence. Each technology or combination of
technologies is represented by a different line color, with black indicating Sanger alone, and blue, red, and green lines indicating
the addition of GSFLX, Solexa, and GSFLX+Solexa, respectively. The square (n = I), triangle (n = 2), and diamond (n = 4)
shaped points on each line indicate the number of plates added for each technology. Results shown for non-normalized librar-
ies. A) Mean length of longest Unigene per gene (bp), B) Transcriptome Coverage (%), and C) Number of Genes with 100%
coverage as a function of total sequence (MB, left panel) and estimated sequencing cost ($ 1000, right panel) for the different
technologies and combinations of technologies. Abbreviations and cost functions are as described in Figures 3D, 3E, and 3F)
Combinations include 100, 200, or 400 MB of GSFLX non-normalized or normalized sequencing plus 0 to 4 plates of Solexa
sequence. Each technology or combination of technologies is represented by a different line color, with black and red lines indi-
cating GSFLX alone with non-normalized and normalized libraries, respectively. Green and pink lines indicate the addition of
Solexa non-normalized and normalized sequences, respectively. The square (n = 1), triangle (n = 2), and diamond (n = 4)
shaped points on each line indicate the number of plates added for each technology. D) Mean length of longest Unigene per
gene (bp), E) Transcriptome Coverage (%), and F) Number of Genes with 100% coverage as a function of total sequence (MB,
left panel) and estimated sequencing cost ($ 1000, right panel) for the different technologies.

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10 20 30 40 50
Total Sequence (MB)

- GSFLX (norm)
i +n SOL
-+n SOL (norm)
* n=1 plate
A n=2 plates
* n=4 plates

"IL ...........

'Z' ;-'; P -

BMC Genomics 2009, 10:347

advances, most notably in the amount of sequence gener-
ated at considerably lower costs. Even though small NG
experiments will tag a very large fraction of the transcrip-
tome, it will commonly be in the form of thousands of
disconnected fragments of genes, with relatively few full-
length cDNAs. Thus, NG technologies are very effective for
tagging sequences from fully sequenced species. However,
researchers sequencing transcripts from novel species with
few genomic resources or from species that are evolution-
arily distant from a sequenced model organism might face
several challenges when evaluating the data. The prob-
lems might become amplified with 25-30 bp reads gener-
ated by the Solexa system or other short-read platforms.
The benefits of normalization are most evident in tradi-
tional sequencing, although some benefits, which include
longer unigenes, are apparent in the NG technologies.
However, the cost of normalization, and the potential for
loss of closely related genes from the dataset, might out-
weigh the potential benefits.

Although NG sequencing does outperform traditional
sequencing in many areas, the problems in assembly can-
not be underestimated. Solexa and SOLiD sequences, cur-
rently less than 40 bp, will pose problems in assembly of
unigenes, especially for short segments of genes that may
be present in several genes. For example, only 81.7% of
15-mers are unique in the Populus transcriptome (PTR,
Figure 5B). This leaves nearly 7 million 15-mers not
unique to the Populus transcriptome, including 43,069 15-
mers that are present in at least 10 different genes (results
not shown). Until methods are developed to deal with
this large fraction of sequence fragments that might lead
to mis-assembled unigenes, researchers will not be able to
use the Solexa or SOLiD technologies alone for transcrip-
tome sequencing in non-model species. Research into
genome assembly strategies with these short sequences
with and without a reference genome is currently under
investigation [35-37] and will hopefully become part of
transcriptome assembly. The addition of Solexa sequences
to longer NG sequences or traditional capillary sequences
should help assemble larger unigenes. These longer
sequences will have a much higher confidence in their
uniqueness. The combination of technologies for tran-
scriptome sequencing is analogous to genome shotgun
sequencing which uses varying sizes of clones.

In order to evaluate the robustness of our simulator to
both different organisms and tissues, we compared our
simulated results against the poppy and avocado tran-
scriptomes generated for this study and against two
recently published 454 transcriptomes [22,23]. Vera et al.
(2007) performed de novo assembly of 454-transcrip-
tome sequences in the butterfly Melitaea cinxia. The RNAs
were isolated from a genetically diverse pool of larvae,
pupae, and adults. The authors sequenced two plates of


GS20, and after trimming and cleaning, there were
518,079 reads (approximately 50 MB) with an average
length of 110 bp. The reads assembled into 108,297 uni-
genes that included 59,945 singleton reads (55.4% single-
tons). The mean unigene length of 149 bp is the
summation of 197 bp for all contigs plus the 110 bp aver-
age for the singleton reads. From our simulation results,
we would predict roughly 91,000 unigenes, 48% single-
tons, and an average unigene length of 177 bp. Weber et.
al sequenced 2 GS20 plates of cDNA derived from above-
ground tissues of 8-day old light-grown Arabidopsis seed-
lings. The reads tagged an estimated 17,499 cDNAs, which
is nearly identical to what our simulations would predict
for that amount of GS20 data. For poppy, each of the
observed assembly characteristics (Table 1) are very close
to the predicted values for this amount of GS20 sequence.
For example, the average unigene numbers for each of the
poppy libraries are 83,370 and 75,273, which are very
close to the estimated 76,000 unigenes from the simula-
tion. For avocado, the observed number of unigenes and
percent singletons (234,000 and 90%) is considerably
larger than predicted for a normalized library sequenced
to this depth. We do not know if this unexpected large
number of unigenes in avocado is due to a larger underly-
ing transcriptome size, sequence error causing false misas-
sembly [25], or some other unknown factor. For each of
these comparisons, we applied the distributions of read
lengths, sequence start sites, and transcript abundance fre-
quencies previously observed from Arabidopsis. There-
fore, although we have not actually fine-tuned these
specific outcomes with the true (and unknown) transcript
distribution profiles for each of species and tissue, the
observed outcomes are very close to the model predic-
tions. This is particularly true when considering the above
uncertainties associated with de novo assembly, differ-
ences in tissue sources, and technical variation that may
be expected from run to run.

Analysis of gene expression by NG sequencing
NG sequencing is potentially a direct and cost-effective
way to obtain genome scale expression information from
organisms that lack a genome sequence and comprehen-
sive microarray platform. Digital expression data
obtained by direct sequencing is not dependent on gene
models, comprehensive genome data, or understanding
of alternative splice forms. The half plate of GS20
sequencing in Arabidopsis showed a moderate correlation
(r = 0.67, r2 = 0.444, p < 0.0001) between the number of
reads and microarray expression values generated from
the same tissue and ecotype. Solexa and SOLiD have the
potential to increase this correlation, since with millions
of sequence reads per experiment, they will ultimately
have a large dynamic range similar to traditional microar-
ray experiments. Correct mapping to specific genes may
be problematic for short Solexa or SOLiD reads (Figure

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BMC Genomics 2009, 10:347

8 10 12
Probe Expression (log2)


B -


15 20 25 30
xmer size (bp)

Figure 5
Probe expression distributions and relati'
ness of varying x-mer sizes. A. Probe expre
tion for different tissues of Arabidopsis and var
species, as indicated. Data shown are the smoo
grams of the log2 (perfect match expression) va
sample taken from microarray datasets (see te
are adjusted to the same fifth percentile. B. Rel
ness of varying x-mer sizes in full cDNA collect
eral sequenced genomes. For each species, spe
different colored line, we determined all DNA
ious sizes (x = I5, 20, 25, 30, 35, or 40 bp). Rel
uniqueness is number of x-mers unique to only
divided by the total number of x-mers in the tr
The level of uniqueness increases with size of x
ies with the organism.

5B), when no genome sequence is available.
3' UTRs, however, should improve assign
and increase efficiency of massively parallel
for assessing gene expression levels [38,39]. I
an organism without a sequenced genom
added expression evidence could be very imp


early stages of developing its transcriptome, an advantage
for GS20 or FLX NG technologies. Even in organisms that
have comprehensive microarrays, the probe designs are
usually dependent on and built with information from
early genome assemblies. For example, the current Arabi-
dopsis Affymetrix AThI array [40,41] contains probes for
approximately 22,000 genes, approximately 5000-6000
genes fewer than the current annotation.

human NG sequencing can be scaled to suit different project goals
NG sequencing technologies are a highly flexible set of
platforms that can be used alone or in combination to
best suit the research at hand. Small-scale experiments
(e.g., 1/16 1 plate) provide a wealth of information
14 including the tagging of many or most expressed genes,
microsatellite markers, full-length sequencing of highly
expressed genes, and modest expression level informa-
tion. Since there can be multiple lanes in NG plates (up to
16), and multiple bar-coded libraries can be sequenced on
a single plate, researchers might fully sequence a small
number of highly expressed genes with very little cost or
time investment. Low-copy, highly expressed genes might
be quite useful for phylogenetic analysis or markers for
population level studies. Even small-scale experiments
will tag a large fraction of genes in a transcriptome. These
- arabidopsis
- rice tags can be used for building microarray probes [23] and
Syeapopulus enhancing microarray design. Small studies might
- worm sequence the highly expressed genes from many different
human tissues in the same sequencing runs without the need for
r1 5 bar-coding. Small experiments might also be sufficient to
provide rich information for genome annotations in pre-
draft and early draft forms. For example, a single run plate
will tag nearly every transcribed gene and help identify
ve unique- UTR and intron/exon boundaries. However, experiments
ssion distribu- on this scale sample only a small fraction of the actual
ious other transcriptome and the assembly is often in many small
thed histo- pieces. Moderate transcriptome studies (e.g., 2-5 plates)
values for each have the potential to sequence more than 50% of the tran-
t). Dat ase s scriptome. They will provide small annotation datasets,
active unique-
ions from sev- identify new genes in an organism, further extend genic
cified by a regions, and help with alternative splicing, especially in
x-mers of var- sequenced genomes. With deeper sequencing (e.g. 6-20
ative x-mer plates), researchers attain a level of transcriptome that has
one gene never been possible before due to the higher cost of earlier
anscriptome. technologies. Not only will these studies sequence more
-mer, and var- than 90% of the transcriptome, the coverage per gene will
approach traditional sequencing. This should allow
researchers to use these genes to identify pathways, deter-
mine tissue-specific expression for lowly expressed genes,
Enriching for and will be critical for genome annotation.

lent accuracy
l sequencing
n research on
e, the value-
)ortant in the

NG technologies are revolutionizing EST sequencing and
applications that revolve around gene expression.
Another important consideration in NG transcriptome

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BMC Genomics 2009, 10:347

sequencing is the efficiency and flexibility in library con-
struction. NG library construction costs less, takes less
time, and does not produce physical clones that must be
stored. If the goal is sequencing full-length genes, non-
normalized libraries will yield a larger number of full-
length sequences in small sequencing experiments com-
pared to normalized libraries. As sequencing quantities
increase, this relationship reverses, and normalized librar-
ies will capture more full-length cDNA sequences. There is
also a trade-off in the cost of normalization versus the cost
of sequencing. We have shown the feasibility of using a
simulation approach to quantitatively evaluate the differ-
ent platforms and the various combinations of platforms.
Currently, the low per-base cost of Solexa sequencing sug-
gests that it may be the most efficient method of transcrip-
tome characterization for sequenced genomes (e.g. Figure
3), but in the absence of a reference genome, the problem
of de novo assembly of the short Solexa reads has not yet
been resolved. Under these circumstances, a blend of Sol-
exa and GS-FLX sequencing may be optimal (Figure 4).

This is the first simulation study to address some of the
technology-specific characteristics found in several NG
sequencing technologies. Our approach focuses on the
critical questions of data production and coverage, which
differ dramatically between methods and experimental
scales. By extrapolating the results of the GS20 simula-
tions, we are able to predict outcomes with various NG
methods and combinations. ESTcalc allows a variety of
assumptions to be explored that will be relevant to differ-
ent experimental designs with current and future tran-
scriptome sequencing technologies. Although ESTcalc and
the underlying simulations do not currently incorporate
explicit models of sequencing and assembly errors, the
results provide a null hypothesis of predicted outcomes
with theoretically perfect sequence data and no assembly
error. Deviations from these values in real transcriptome
datasets and assemblies will reflect the magnitude of these
errors and potential contamination of transcriptome
libraries with genomic DNA. These factors will tend to
inflate the number of singleton reads relative to the pre-
dicted numbers without such errors; their evaluation will
aid in sequence cleaning and assembly experiments. A
next step will be to develop realistic models of error in
sequencing and assembly, and to provide tools to allow
any sets of assumptions about read length and cost to be
examined. Future studies should be able to build upon
this first simulation study, while accounting for addi-
tional issues in transcriptome sequencing and assembly

RNA preparation
Arabidopsis thaliana (cv: Landsberg) plants used in this
study were grown in a culture chamber at 23 temp and


40% humidity with 18 hours light/6 hours dark. RNA iso-
lation from Arabidopsis plants was performed with the
RNA Aqueous-Midi kit (Ambion, Inc; catalog: #1911) fol-
lowing the manufacturer's recommendations with modi-
fications as previously reported [27]. California poppy
total RNA was prepared from pre-meiotic flower buds
using TRIzol reagent (Invitrogen) according to the manu-
facturer's recommendations. Stages of flower develop-
ment were defined as described previously [42]. RNA
quality and quantity were checked using a Bioanalyzer
(Agilent, inc). Persea americana pre-meiotic flower buds
(Stages 6-7), in which all floral organs are present but sta-
mens and carpels are immature [43], were collected from
a tree cultivated on the Gainesville campus of the Univer-
sity of Florida (Kim 1135; voucher deposited at FLAS).
Total RNA was isolated using a combination of the CTAB
DNA extraction protocol [44] and the RNeasy Plant Mini
kit (Qiagen) as previously described for basal
angiosperms [45]. RNA integrity was verified with a Bio-
analyzer (Agilent Inc.).

mRNA purification and 454 library construction for
Arabidopsis thaliana and Eschscholzia californica
Messenger RNA was extracted from total RNA using
Poly(A)Purist'M mRNA Purification Kit (Ambion, Inc., cat-
alog # 1916) according to the manufacturer's recommen-
dation. mRNA quality was checked with a Bioanalyzer
(Agilent, Inc). cDNAs were prepared using the ZAP-cDNA
Synthesis Kit (Stratagene) according to manufacturer's
instructions, except that 2 micrograms of mRNA was used
rather than the recommended 5 ug. For Arabidopsis, two
cDNAs were prepared, the first by oligo-dT priming and
the second using the random hexamer primers provided
in the kit. For California poppy, only random hexamer
priming was used to prepare ds cDNA. 454 libraries were
constructed from the cDNAs and sequenced using the
approach described by [8]. One half plate, one, and two
plates were sequenced from Arabidopsis, Persea, and Cal-
ifornia poppy, respectively, using the 454-G20 sequencer
according to manufacturers protocols (Roche, Inc).

Normalized cDNA library construction in Persea
Messenger RNA was isolated from 250 ug total RNA using
the Poly(A)Purist'M mRNA Purification Kit (Ambion, Inc.,
catalog # 1916) according to the manufacturer's protocol.
Approximately 1 ug high quality mRNA, verified through
Bioanalyzer as above, was used to construct a normalized
cDNA library using the Trimmer-Direct Kit (Evrogen),
which combines a modification of SMART cDNA prepara-
tion [46] with DSN-normalization technology [47]. Spe-
cifically, first strand cDNA was reverse transcribed using a
3' adapter (CDS-3M; Evrogen) that anneals to poly(A)
RNA tails and a second adaptor, BD SMART" Oligo IV
(Clontech), that anneals to the 5' dC tails created by

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MMLV reverse transcriptase, and serves as an extended
template for the first strand synthesis. Double-stranded
cDNA was synthesized and simultaneously amplified
with the BD SMARTM 5' PCR Primer (Clontech) that
anneals to both adaptors through 13 PCR cycles of 95 C
for 7 sec; 66C for 30 sec; 72 C for 6 min on a BioRad
thermocycler. Approximately 1.2 ug of double stranded
cDNA was purified with the Wizard PCR Purification Kit
(Promega) followed by ethanol precipitation, and nor-
malized according to the Trimmer-Direct protocol. Nor-
malized cDNA was subjected to two rounds of single
primer PCR amplifications exploiting the complementa-
rity of the cDNA ends (primer sites) to suppress short frag-
ment amplification [48] and enrich the cDNA pool with
full length transcripts. The first PCR amplification was
conducted for 18 cycles of 95 C for 7 sec; 65 C for 20 sec;
72 C for 6 min. First amplification products with efficient
normalization were diluted 10-fold and subjected to 12
PCR cycles of 95 C for 7 sec; 64 0C for 20 sec; 72 0C for 4
min. Approximately 20 ug of normalized amplified cDNA
were thus obtained.

Sequence analysis
All of the sff files have been deposited into the Short Read
Archive at NCBI with the accession SRA008180.19. 454
reads for all species were assembled using the 454 New-
bler Assembler [8]. Using the program seqclean httpz/l, we vector
trimmed and quality trimmed both the original read files
and the contig files generated by the Newbler assembler.
We parsed the 454 ReadStatus.txt file to determine the sin-
gleton reads, which did not assemble with any other
reads. For each library, we used then the contig and single-
ton files to generate a unigene file. Finally, we calculated
the number of sequences, the mean length of all
sequences, and the total MB for each of the 4 file types
(read, contig, singleton, and unigene). We also performed
an additional assembly step using cap3 with 95% identity
and 30 bp overlap (default for all other parameters).

Genome mappings for Arabidopsis were determined using
the best BLASTn [49] match of each individual read versus
the TAIR7 genome annotation. We wrote Perl scripts to
parse the TAIR xml files containing chromosome loca-
tions for all genes in the current annotation. We assigned
the following categories to each read: Exon, intron,
intron/exon, extended UTR, overlapping genes, inter-
genic, or no hit. All reads mapping to known gene loca-
tions were also mapped to the TAIR cDNA dataset. We
calculated the start and stop positions for all reads on each
cDNA and determined the reads/gene distribution based
on all Arabidopsis genes tagged.

In order to evaluate the sequences from the three libraries
without a sequenced genome, we used BLASTx and


BLASTn searches against the TAIR protein and cDNA data-
sets, respectively. We parsed the BLAST output to deter-
mine the location and e-value to the best Arabidopsis
gene. For each best hit, we also determined the length of
the protein and/or cDNA, as well as the annotation of the
gene. We used the length of the read, the start and stop
locations on the gene, and the length of the best hit to cal-
culate each Arabidopsis gene's percent coverage. Gene
coverage is defined as the number of bases or amino acids
covered by at least 1 read divided by the length of the
gene. We estimated the gene coverage using both relaxed
and strict definitions. For the relaxed definition, we con-
sidered all reads or contigs mapped to a gene, thus allow-
ing for a non-contiguous definition of the gene's coverage.
In the strict case, we only considered the longest unigene
or read mapped to an individual gene. Please see Addi-
tional file 1 for detailed blast results of each dataset used
in this study.

Simulation studies
Using a half plate of GS20 sequences, we developed an
approach to simulate additional rounds of sequencing in
silico as follows. We mapped 118,485 reads to 15,276
genes in the TAIR cDNA dataset using the similarity search
program BLASTn. We selected the best high scoring pair
(HSP) for each read and its corresponding cDNA. Since
there are multiple versions of loci for 3,156 genes, we used
the version for each gene with the largest number of reads
mapped to its sequence. Using this approach, we created
the following distributions: Read length, read count, and
cDNA start site distributions. The read length distribution
is comprised of all read lengths sampled. The read count
distribution contains the number of reads mapped to each
gene. For example, the distribution contained 3149, 2238,
and 1672 genes with 1, 2, and 3 reads/gene respectively.
The most highly expressed gene (AT1 G67090, see Table 3)
had 586 reads, or about 0.5% of total reads.

We then estimated 3,007 zero class genes by providing the
reads/gene profile to the program ESTstat [25]. Zero class
genes are defined as genes that have not been sampled
(sequenced) but are presumed to be present in the library,
but typically expressed at low levels. Failure to account for
these genes would bias many of the simulation estimates.
We then randomly sampled the zero class genes from the
remaining genes not originally sampled. The addition of
the zero class genes to the 15,276 transcribed genes
totalled 18,283 genes. For the simulation, we used the
number of reads per gene distribution to randomly select
reads from particular genes based on the number of times
they were sequenced. For example, genes that were
sequenced only once had a much lower chance of being
selected for a plate of simulated reads than genes repre-
sented by hundreds or thousands of reads. Finally, we col-
lected all the start sites for each read against its

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BMC Genomics 2009, 10:347

corresponding cDNA to generate a start site distribution.
Based on previous work [24,25], we assumed that start
sites are dependent on gene length. Based on the quartiles
of the Arabidopsis cDNA lengths, we grouped the start
sites into four groups: 1) 1-1000 bp, 2) 1001-1500 bp, 3)
1501-2000 bp, and 4) greater than 2000 bp. Using the
gene length, we created relative start site distributions
dependent on gene length.

The four libraries used in this study were all sequenced
using a GS20 machine. In order to compare the GS20
technology to other NG technologies, we also simulated
FLX, Solexa, and traditional capillary sequences. For FLX,
we used all of the same distributions for the GS20 simula-
tion, except the read length distribution. Since the average
read length in our GS20 runs was approximately 100 bp,
and the FLX has been reported to generate 250 bp reads,
we multiplied the read length of each randomly chosen
read by 2.5 for the FLX simulations. In the Solexa simula-
tions, we used a random start site distribution with a 25
bp read length average. We used the same gene expression
distribution as the GS20 for all technologies.

To simulate traditional capillary sequences, we down-
loaded 48,130 ESTs from four different Arabidopsis librar-
ies: flower buds, green siliques, roots, and above ground
organs 2-6 week old. We partitioned the ESTs into two
groups, 5' and 3' based on the annotations located in the
fasta header. We then randomly selected 5,000 ESTs from
both groups, mapped the transcripts to the Arabidopsis
cDNA dataset using BLASTn, and generated start site dis-
tribution based on cDNA length. We used a 750 bp read
length, except when the randomly chosen gene or the
length of the gene minus the randomly chosen start site
was less than 750 bp. In these cases, we used the gene
length or the distance from the start site to the end of the
gene as the read length.

In order to compare all technologies, we used conserva-
tive estimates (as of late 2008, not including variable costs
of library or sample preparation) of the amount (MB) and
price ($) of sequencing with each technology. For GS20,
an average plate costs $6000/plate and the plate generates
25 MB of data ($240/MB). Since NG machines can be par-
titioned into smaller segments, we simulated 1/16, 1/8, 1/
4, 1/2, 1, 2, 4, and 10 plates for all three technologies. For
the GS20, this came to 1.56, 3.12, 6.25, 12.5, 25, 50, 100,
250, and 500 MB increments. For FLX, the cost was calcu-
lated using $9000/plate and 100 MB of data ($90/MB),
with 6.25, 12.5, 25, 50, 100, 400, 1000, and 2000 MB
increments. For Solexa, we used $4000/plate and 1 GB of
data ($4/MB) with 50, 100, 200, 500, 1000, 2000, 4000,
10000, and 20000 MB increments. Finally, we converted
the cost of traditional capillary sequencing, which is nor-
mally calculated per EST (read), by using the conventional
$1/EST with 750 bp length ($1330/MB). This included


1.56, 3.12, 6.25, 12.5, 25, 50, 100, and 200 MB incre-

To examine the effects of normalization in next genera-
tion transcriptome sequencing, we simulated normalized
sequencing for each of the above technologies. We
assumed perfect normalization, and changed the gene
expression distribution to be equal for all genes. There-
fore, of the 18,261 genes estimated to be in the library,
each gene has the exact same probability of being chosen
as every other gene in the dataset. Although normaliza-
tion can be technically difficult and requires more labor to
generate, we excluded these costs and assumed that there
were no additional library costs with normalization

To compare the expected simulation results for each tech-
nology and combinations of technologies, we calculated
the following parameters: percent transcriptome, the
number of genes tagged, the total number of unigenes
(contigs plus singletons), the mean length of the longest
unigene per gene, and the number of genes covered by
reads of at least 90% of the length of the gene and in only
one unigene. Please see Additional file 2 for detailed sim-
ulation results used in this study.

Authors' contributions
PKW carried out the data analysis and simulation study.
ASC, AB, HL, LL, LT, YH generated the data for all GS20
experiments, including the tissue extraction, library con-
struction and optimization at Penn State (SCS, JEC, and
CWD labs) and at University of Florida (DES and PSS lab,
and the ICBR lab under the direction of William Far-
conceived of the study, and participated in its design.
PKW, JLM, ASC, AB, DES, PSS, NA, CWD coordinated and
helped to draft the manuscript. PKW, EW, CWD designed
and developed ESTcalc. All authors read and approved the
final manuscript.

Additional material

Additional file 1
Unigene blast results. This file contains the detailed blast results of all
unigenes. There are 5 worksheets that contain the blast information for
each of the GS20 unigene builds, Arabidopsis ('ath'), Eschscholzia
('eca random', 'eca oligo-dT', and 'eca combined'), and Persea
('pam -normalized'). Each worksheet has the: ..i..... columns: unigene
name (query), query unigene length (qlen), number of reads (reads), cov-
erage of best blast result (coy), the database of the best hit (db), the best
hit gene name (hit), the length of the hit (hlen), the value, bit score, and
description of the best hit (desc). See materials and methods for descrip-
tion of the ii. .. ...... ni .1 searches used in these analyses.
Click here for file

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Additional file 2
Detailed simulation summary report. This file contains the detailed sim-
ulation results used to generate many of the figures in this article. Simu-
lation results illiniaion,,g predicted outcomes for tii. .,, 1, "'.. ,I,......
sequencing technologies and combinations of sequencing technologies
with a complex library expressing ca. 18,000 genes. The first six fields
techh, mbl, tech, mb2, tech3, mb3) contain the technology or combi-
nations of technologies for each simulation along with the amount (mb)
of sequencing. For each simulated run, we calculated 16 tIl... param-
eters: price the estimated price (per MB) using the '-..'.... 1 prices for
each technology EST5 ($1330), GS20 ($240), GSFLX ($90), and SOL
($4); len -percent of transcriptome sequenced with at least one read and
not necessarily in one contiguous sequence; reads -the number of reads
in the simulation, sin percent of reads that are singleton sequences; uni
number of unigenes obtained; puni number of unigenes divided by the
number of estimated genes as a percentage, ulen mean unigene length
(bp)' lulen mean unigene length (bp) using only the longest unigene per
gene; genes the estimated number of genes tagged; pgenes the esti-
mated number of genes tagged as a percentage; c90 the number of genes
with more than 90% coverage; c90s the number of genes with more
than 90% coverage and in one contiguous sequence (s = strict); p90s -
the percentage of estimated genes with 90% coverage and in 1 contiguous
sequence; cl00 the number of genes with 100% coverage; clOOs the
number of genes with 100% coverage and in one contiguous sequence (s
strict); plOOs the percentage of estimated genes with 100% coverage
and in one contiguous sequence.
Click here for file
[ -

We thankthe Huck Institutes of the Life Sciences and the Eberly College of
Penn State for supporting the cost of 454 sequencing of Arabidopsis and
poppy, Richard Meisel for valuable discussion of Drosophila transcriptomes,
and Tony Omeis and the Biology Department Greenhouses (PSU) for cul-
tivating the poppy plants. We would also like to thank Webb Miller, Jiping
Wang, and Bruce Lindsay for ideas on transcript assembly and simulation
strategies. This research was supported in part by NSF Plant Genome
Awards DBI-0 115684 (The Floral Genome Project) and DEB 0638595 (The
Ancestral Angiosperm Genome Project).

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