Title: ESPRIT : estimating species richness using large collections of 16S rRNA pyrosequences
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Title: ESPRIT : estimating species richness using large collections of 16S rRNA pyrosequences
Physical Description: Book
Creator: Sun, Yijun
Cai, Yunpeng
Liu, Li
Yu, Fahong
Farrell, Michael L.
McKendree, William
Farmerie, William
Affiliation: United States Air Force -- Patrick Air Force Base -- Air Force Technical Applications Center
Publisher: Sun, Yijun
Place of Publication: Gainesville, Fla.
Publication Date: April 15, 2009
Copyright Date: 2009
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Published online 5 May 2009


Nucleic Acids Research, 2009, Vol. 37, No. 10 e76
doi:10.1093/nar/ ij ':'\


ESPRIT: estimating species richness using large

collections of 16S rRNA pyrosequences

Yijun Sun1'2'*, Yunpeng Cai2, Li Liu', Fahong Yu', Michael L. Farrell3, William McKendree3
and William Farmerie'

'Interdisciplinary Center for Biotechnology Research, 2Department of Electrical and Computer Engineering,
University of Florida, Gainesville, FL 32610-3622 and 3Materials Technology Directorate, Air Force Technical
Applications Center, 1030 S. Highway A1A, Patrick AFB, FL 32925-3002, USA

Received January 28, 2009; Revised April 14, 2009; Accepted April 15, 2009


ABSTRACT
Recent metagenomics studies of environmental
samples suggested that microbial communities are
much more diverse than previously reported, and
deep sequencing will significantly increase the esti-
mate of total species diversity. Massively parallel
pyrosequencing technology enables ultra-deep
sequencing of complex microbial populations rap-
idly and inexpensively. However, computational
methods for analyzing large collections of 16S ribo-
somal sequences are limited. We proposed a new
algorithm, referred to as ESPRIT, which addresses
several computational issues with prior methods.
We developed two versions of ESPRIT, one for per-
sonal computers (PCs) and one for computer clus-
ters (CCs). The PC version is used for small- and
medium-scale data sets and can process several
tens of thousands of sequences within a few min-
utes, while the CC version is for large-scale pro-
blems and is able to analyze several hundreds of
thousands of reads within one day. Large-scale
experiments are presented that clearly demon-
strate the effectiveness of the newly proposed algo-
rithm. The source code and user guide are freely
available at http://www.biotech.ufl.edu/people/sun/
esprit.html.


INTRODUCTION
The latest development of massively parallel pyrosequenc-
ing technology enables ultra-deep sequencing of complex
microbial populations rapidly and inexpensively (1,2).
For example, 454 Life Sciences GS FLX systems (Bran-
ford, CT, USA) (3) can finish a full pyrosequencing
run with more than 400K sequences within one day

*To whom correspondence should be addressed. Tel: + 352-273-8065; Fax:


of operation (http://www.454.com/products-solutions/
system-features.asp). It allows researchers to study genetic
materials recovered directly from environmental samples,
bypassing the needs for isolation and lab cultivation of
individual species, and thus opens a new window to
probe the hidden world of microbial communities. This
technique has been successfully used in several 16S
rRNA-based metagenomics analyses of various environ-
ments. For example, Sogin et al. (4) provided one of the
first global indepth descriptions of microbial diversities
and their relative abundance in the ocean, and Keijser et
al. (5) were among the first to study oral microbial popu-
lations. It has been shown that the microbial diversities are
at least one order of magnitude larger than previously
reported. These estimation results, however, were com-
puted through extrapolation. In order to obtain more
accurate estimates, surveys that are several orders of mag-
nitude larger than those reported in the literature may be
required to uncover sequences from minor components
(4,5). However, analyzing large collections of 16S riboso-
mal sequences poses a serious computational challenge for
existing algorithms.
In this article, we focus on taxonomy independent
analysis where sequences are classified into operational
taxonomic units (OTUs) of specified sequence variations,
based on which various ecological metrics are estimated.
Typically, sequences with <3% dissimilarity are assigned
to the same species, while those with <5% dissimilarity
are assigned to the same genus, although these distinctions
are controversial (6,7,8). One outstanding challenge of
taxonomy independent analysis is the alignment of
sequences of a sample for the calculation of sequence var-
iations. One commonly used method in the literature is
multiple sequence alignment (MSA) [see, for example,
(4,5,9)]. While significant improvement has been made in
the last decade to reduce the computational complexity
of MSA [e.g. MUSCLE (10) and MAFFT (11)], it is
still computationally intractable to align hundreds of


+ 352-273-8070; Em ail: i, I., 1-.,...... ,,I l ... ,,I


The author wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors.
C 2009 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License .. ...
by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.







e76 Nucleic Acids Research, 2009, Vol. 37, No. 10


thousands of sequences. Moreover, the use of MSA for
aligning hypervariable regions of 16S rRNA gene has not
been well justified in the literature. MSA is used to infer
homologous segments of input sequences. An underlying
assumption is that input sequences should share some
similarities, which may not be valid for 16S rRNA-based
studies that target on hypervariable regions of rRNA
genes (e.g. V6 and V3 regions). We experimentally found
through a benchmark study that the use of MSA leads to
an inflated estimate of genetic distances and microbial
diversities (see Results Section and Section 1S in the Sup-
plementary Data for a detailed discussion). Another major
issue of taxonomy-independent analysis is the high
computational complexity and memory requirement asso-
ciated with assigning sequences into OTUs. DOTUR (8) is
a commonly used algorithm for this purpose. It conveni-
ently integrates the tasks of performing clustering and sta-
tistical interference of species richness into one program,
which frees researchers from manually manipulating a
large distance matrix. However, DOTUR does not scale
well to handle extremely large data sets. The recently
released MOTHUR significantly improved the computa-
tional performance of DOTUR. As with DOTUR,
MOTHUR loads a distance matrix into memory before
proceeding to perform clustering. Hence, it does not fun-
damentally address the computational issue associated
with processing massive pyrosequencing data. Given a
full run of 454 data, a full distance matrix can be as large
as 1000 GB. Even if we remove duplicated sequences and
sequence pairs that have a pairwise distance larger than a
cutoff value (say 0.1), the resulting distance matrix in a
sparse format can be 20 GB in size, which is too big to be
directly loaded into the memory in most computers. There
exist a few algorithms that used various strategies to over-
come the above mentioned computational issues. Two typ-
ical methods are FastGroupII (12) and RDP-Pyro (13). By
submitting data to their web applications and through per-
sonal communications, we found that these methods can
work efficiently only for small- or medium-scale data sets.
In this article, we proposed a new algorithm, referred to
as ESPRIT, which addresses several computational limita-
tions with prior work by using parallel computing.
ESPRIT uses the Needleman-Wunsch algorithm (14) to
optimally align each pair of sequences. Through a bench-
mark study, we demonstrated that global pairwise align-
ment can provide a much more accurate estimate of
microbial richness than multiple alignment. A more
important reason, however, is that pairwise alignment
allows for parallel computing, while most MSA algo-
rithms can only be used in a single computer. We also
developed a new cluster algorithm, referred to as
Hcluster within the ESPRIT framework, to handle large-
scale clustering problems. Unlike a brute-force method,
Hcluster groups sequences into OTUs on-the-fly, while
keeping track of linkage information, to overcome
memory limitations. By assigning a computational task
to hundreds of nodes, ESPRIT is not computationally
constrained by the number of sequences to be analyzed,
but by the capacity of a computer cluster. Hence, ESPRIT
is very suitable for applications such as global ocean


PAGE 2 OF 8


survey of microbial populations that may require the ana-
lysis of data collected from thousands of locations.
We conducted large-scale experiments to demonstrate
the effectiveness of the proposed algorithm. We first per-
formed a simulation study by using a data set generated by
sequencing PCR amplicon libraries of known 16S rRNA
genes. To our knowledge, this is probably the first bench-
mark study reported in the literature to assess the estima-
tion accuracy of an algorithm for taxonomy-independent
analysis. Our experimental results justified the use of
global pairwise alignment for the purpose of estimating
microbial community compositions, and showed that the
commonly used MUSCLE + DOTUR pipeline may over-
estimate biodiversity. We then applied ESPRIT to eight
seawater samples. The results are very consistent with that
of the benchmark study. We finally used ESPRIT to ana-
lyze a recently collected air sample consisting of about
350K short reads. It is stated in (15) that microbial com-
munities may be too diversified to be practically tested by
amplification and sequencing of 16S rRNA genes. Our
results suggest that at least for air samples, it is computa-
tionally practical to use the current sequencing technology
to conduct 16S rRNA-based biodiversity surveys.


ESPRIT ALGORITHM
The algorithm consists of four modules: (i) removes low-
quality reads using various criteria, (ii) computes pairwise
distances of reads, (iii) groups reads into OTUs at different
dissimilarity levels and (iv) performs statistical inference to
estimate species richness. We developed two versions of
ESPRIT, one for personal computers (PC) and one for
computer clusters (CC). The PC version can process sev-
eral tens of thousands sequences within a few minutes,
while the CC version is able to analyze several hundreds
of thousands of reads within one day. The source code and
user guide are freely available at http://www.biotech.u-
fl.edu/people/sun/esprit.html. The readers who have no
access to a CC to analyze their data may contact the
corresponding author.

Removing low quality reads
The error rate of 454 Life Sciences GS FLX systems is
estimated to be five errors per kilobase (http://www.454.
com/products-solutions/system-features.asp). However,
only a small number of reads account for most of the
errors (16), and these outlier reads may be classified at
certain dissimilarity levels as rarely occurrent OTUs (i.e.
clusters containing only a few members). This will lead to
an overestimate of microbial richness in subsequent ana-
lyses, since many ecological metrics are computed based
on the numbers of rarely occurrent OTUs. To minimize
the effects of random sequencing errors, we remove the
reads that contain ambiguous nucleotides (N), and those
with more than one mismatch with the PCR primer at the
beginning of a read. Also, we eliminate the sequences with
atypical lengths. The default setting of the algorithm is to
retain the reads with a length within 1 SD from the mean
length. In order to reduce computational complexity, by
following the strategy used in (17), if two sequences are







Nucleic Acids Research, 2009, Vol. 37, No. 10 e76


(a) 0.35


(b) 3000


---- ------ ----- --- ------

S:









C b *


S 0.2 0.4 0.6 0.8 1


Kmer distance Pairwise distance
Figure 1. (a) k-mer distances are highly correlated with genetic distances. (b) Removing sequence pairs with k-mer distances larger than the default
threshold has a negligible impact on the estimation accuracy for the distance levels of interest. The experiment was performed on the 53R seawater
sample (see Results Section).


identical or one sequence is a subset of the other, only the
longer sequence is retained, and the number of occurrence
of each retained sequence is recorded and used in the sta-
tistical inference. A similar trimming procedure was also
used in (16). ESPRIT allows users to bypass this module
to use a sample filtered by a customized trimming
procedure.

Computing pairwise distances
We use the Needleman-Wunsch algorithm to optimally
align each pair of sequences in a sample, and the quickdist
algorithm (4) to compute pairwise distances. More speci-
fically, each pairwise distance equals mismatches, includ-
ing indels, divided by a sequence length. To avoid
overestimating distances between sequences from rapidly
diverging variable regions, end gaps are ignored and gaps
of any length are treated as a single evolutionary event or
mismatch.
The naive application of the Needleman-Wunsch algo-
rithm to an environmental sample, however, is computa-
tionally too expensive. For example, given one full run of
454 data with >400K reads, it is estimated that it would
take about 3 years to align all 80 billion pairs of sequences,
and need about 1000 GB to store the resulting distance
matrix in the PHYLIP format. However, we notice that
for the purpose of estimating biodiversity, only the
sequence pairs with distances <0.10 are of most interest,
which only account for a small fraction of all possible
pairs (about 1-5%, Figure 1S). This means possibly
20-100 folds of speedup. Moreover, by removing
unwanted pairs, distance information can be stored in a
sparse matrix that requires much less memory. In order to
rapidly identify the sequence pairs of interest, we compute
the k-mer distance of each pair of sequences. We give
below a brief description of how it works. A k-mer, also
known as k-tuple, is a sub-sequence consisting of k possi-
ble nucleotides. By specifying the value of k, a complete
alphabet Q of k-mer is constructed. The number of occur-
rence of each k-mer in alphabet 2, also called genomics


profile (18,19), is computed for each sequence. Then, the
k-mer distance of a pair of sequences is derived as


d = 1 min(fi(i),f2(i))/(min(Li, L2) k + 1), 1
i=1

where fJ(i) and f2(i) are the numbers of the occurrence of
the i-th k-mer of two sequences, respectively, and L1 and
L2 are the lengths of two sequences, respectively. It has
been shown that k-mer distances are highly correlated
with genetic distances i2 'i. The concept of k-mer counting
has been used for various applications, including sequence
alignment (10), phylogenetic analysis (21,22) and detection
of horizontal gene transfer (23). We used this technique to
remove unwanted sequence pairs, and found that by using
a k-mer distance of 0.4, most of the sequence pairs of
interest can be rapidly identified (Figure la). The default
setting in ESPRIT is 0.5. Figure lb shows that removing
sequence pairs with k-mer distances larger than the default
parameter has a negligible impact on the estimation accu-
racy for the distance levels <0.1. It should be noted that
though the default k-mer threshold works well for all of
the 10 data sets tested in this article, it is possible that the
parameter may vary for different applications. Hence, we
provide an auxiliary program in the software package that
allows users to determine the parameter by comparing
k-mer distances against genetic distances using a small
subset of their samples.
In addition to a PC version, we also developed a CC
version for calculating both k-mer and genetic distances
based on globally aligned sequences. Given one full run of
454 sequences, by using 100 computer nodes, it takes only
3-5 h to finish this module, depending on the availability
of computer nodes.

Assigning sequences into OTUs
After a distance matrix is computed, complete-link hier-
archical clustering (24) is performed to assign sequences


0 0.05 0.1 0.15 0.2


0.3

0.25


0.2
0A
a 0.15

0.1

0.05

0


PAGE 3 OF 8


k-







e76 Nucleic Acids Research, 2009, Vol. 37, No. 10


into OTUs of defined sequence variations. Although
ESPRIT uses a sparse distance matrix, given a few runs
of 454 reads, the size of the matrix can still be quite large.
We spent considerable efforts on algorithm development
in order to overcome the memory issue, and devised a new
clustering algorithm, referred to as Hcluster. The basic
idea of the algorithm is to first sort pairwise distances in
an ascending order, and then process the distance infor-
mation on-the-fly. At each step, we classify the clusters
being analyzed into 'active' or 'inactive' clusters. Active
clusters are those with known distance information to
other clusters, but the information is not enough to
decide whether to merge them with other clusters.
Inactive clusters, on the other hand, are those with no
information at all, or those already merged with other
clusters. We only need to maintain the linkage informa-
tion for active clusters, which is updated at the time when
new distance information is processed. We have con-
ducted a large-scale experiment that demonstrated that
Hcluster performed very well in the presence of several
hundreds of thousands of reads. The accuracy of
Hcluster has been benchmarked against DOTUR and
MOTHUR. The three methods yielded the exactly same
results (Figure 6S). A numerical example is presented in
the Supplementary Data to illustrate how Hcluster works.
Clustering is a common problem in bioinformatics.
We provide Hcluster as a standalone algorithm in the soft-
ware package. This algorithm may be useful for other
applications where large-scale clustering is needed [e.g.
taxonomy-dependent analysis (13)].

Statistical inference of species richness
ESPRIT supports three richness estimators: rarefaction
analysis (25), Chaol (26) and ACE (27,28). Rarefaction
allows the calculation of the species richness for a given
number of sampled individuals and constructs so-called
rarefaction curves. The curve is the number of observed
OTUs as a function of the number of sampled sequences.
In case of a steep slope, it means that a large fraction of
the species diversity is not sampled yet, and more exhaus-
tive sampling will yield a significant number of additional
species. Chaol and ACE are two abundance-based cover-
age estimators that predict the species richness based on
the number of rarely occurrent OTUs. The cluster infor-
mation generated by ESPRIT allows users to compute
other ecological metrics, to derive a consensus sequence
of each cluster, and to align the sequences of rarely occur-
rent OTUs against a database, which may lead to the
identification of new organisms.


RESULTS
We conducted large-scale experiments to demonstrate the
effectiveness of the newly proposed ESPRIT algorithm.
When the PC version of ESPRIT was used, the experiment
was performed on a server with eight E5345 Xeon CPUs
and 16 GB memory operated on Linux 5.2 system. When
the CC version was used, the experiment was performed
on a CC administrated by the High-Performance Comput-
ing Center at the University of Florida. The detailed


PAGE 4 OF 8


computer configuration can be found at http://wiki.hpc.
ufl.edu/index.php/Operating_Environment.

Benchmark study
The purpose of the simulation study is 2-fold: (i) to inves-
tigate which strategy, pairwise or MSA, is more suitable
for taxonomy-independent analysis, and (ii) to assess the
estimation accuracy of the ESPRIT algorithm. Although
MUSCLE+DOTUR has been used in several metage-
nomics studies, its performance has never been bench-
marked in the literature. This is in part due to the fact
that under current technologies, one may never know pre-
cisely the ground truth information of the compositions of
a microbial community. The benchmark information,
however, is critical to evaluate the performance of an algo-
rithm performed on real-world data and to make a mean-
ingful comparison of taxonomic distributions of different
environments.
The simulation data consisted of about 340K short
sequences, generated by pyrosequencing two PCR ampli-
con libraries of 43 known 16S rRNA gene fragments using
the Roche GS20 system. It was originally used in (16) to
study the prebased error rate of the system. We applied
both ESPRIT and MUSCLE+DOTUR to the 43 refer-
ence gene sequences to estimate the numbers of OTUs
defined at various distance levels, also known as lineage-
through-time curve in the literature (8). Parameters
-maxiters 1 -diags 1 and -sv were used in MUSCLE. The
two so-obtained curves served as the ground truth to
benchmark the performance of the two algorithms. In
order to study how the two algorithms perform in the
presence of sequencing errors, we generated two data
sets by mapping each read to the 43 reference sequences
and retaining the reads that have less than 3% or 5%
mismatches with the closest reference sequence. To elimi-
nate statistical variations, each algorithm was run 10 times
for each data set. In each run, 10K reads were randomly
sampled from each data set. The lineage-through-time
curves, averaged over 10 runs, are plotted in Figure 2.
Since the ground truth curves generated by ESPRIT and
MUSCLE+DOTUR are very similar (Figure 7S pre-
sented in the Supplementary Data), for ease of presenta-
tion, only the curve of ESPRIT is plotted. From the figure,
we observe that while both algorithms always lead to an
overestimate of the number of OTUs due to the presence
of sequencing errors, pairwise alignment can provide a
more accurate estimate of microbial richness than multiple
alignment. For example, for the first data set, at the 0.05
distance level, the numbers of OTUs estimated by
ESPRIT and MUSCLE+DOTUR are 44 (95% CI:
43-45) and 92 (95% CI: 82-102), respectively, while the
ground truth is 42 (Figure 2a).
One may wonder whether the disparity between the
results of the two methods is due to the compromise on
the parameter settings of MUSCLE. To answer this
question, we repeated the experiment by using the default
parameter of MUSCLE (-maxiters 16) (29). The results
are reported in Figure 3. Though computationally
expensive, using the default parameter results in a
much better alignment in terms of the sum of pairwise







Nucleic Acids Research, 2009, Vol. 37, No. 10 e76


(a) 500


0 '1 i
0.02 0.04 0.06 0.08 0.1 0.12
Pairwise distance


Figure 2. Lineage-through-time curves generated by using ESPRIT and MUSCLE
read containing up to (a) 3% and (b) 5% sequencing errors.


(b) 500


400


= 300
I-
0

E 200
z

100


0.02 0.04 0.06 0.08 0.1 0.12
Pairwise distance


Pairwise distance
DOTUR algorithms performed on simulation data with each


01
0.02


Figure 3. Lineage-through-time curves generated by MUSCLE+ DOTUR using simple
each read containing up to (a) 3% and (b) 5% sequencing errors.


alignment scores. Interestingly, we observe that the esti-
mate obtained by using the default parameter is much
worse than that obtained by using the simple parameter.
This can be explained by the fact that MSA aims to min-
imize the sum of pairwise alignment scores, ignoring the
fact that a large proportion of sequence pairs originate
from distantly related OTUs. Since many existing methods
used MSA for taxonomy-independent analysis, this issue
merits further investigation, which is presented in the
Supplementary Data (Section 1S). We also conducted a
benchmark study comparing the prediction performance
of ESPRIT with those of NAST (30) and RDP-Pyro (13)
in Section 2S.

Experiments on eight seawater samples
We applied ESPRIT to reanalyze eight seawater samples
downloaded from (4). The DNA materials within environ-
mental samples were collected from eight different


locations
as a par
microbial


0.04 0.06 0.08 0.1 0.12
Pairwise distance


or default parameters performed on simulation data with



in the Atlantic and Pacific Oceans, respectively,
t of efforts to develop a global description of
diversities in the ocean. A total of 118000


PCR amplicons were sequenced that covered the V6
hyperviable region of rRNAs. The 454 reads have under-
gone a systematic trimming process. Thus, we bypassed
the trimming procedure. The number of sequences for
each sample ranges from 5000-17 666 (see Table 1). The
interested reader may refer to (4) for a detailed description
of the data and sample preparations.
We reran MUSCLE + DOTUR by using the parameters
provided by (4). The default parameters of ESPRIT were
used. Due to space limitations, only the results of sample
FS396 are presented in the main text. FS396 contains
17 666 reads after trimming, and is the largest data set in
size among the eight samples. However, the results of the
other seven seawater samples, presented in the
Supplementary Data (Figures S8 and S9), are very


PAGE 5 OF 8








e76 Nucleic Acids Research, 2009, Vol. 37, No. 10


Table 1. Running time of the PC version of ESPRIT performed on eight seawater samples

Data sets

112R 115R 137 138 53R 55R FS312 FS396

Number of reads 9282 11005 13097 14374 5000 13902 4835 17666
CPU time 2m 54s 4m 13s 4m 28s 6m 19s 59s 7m 31s 49s 6m 34s


FS396


. . . .. . . . . . . . ... . . . . . . . . . . ..

V.-




0.02 0.04 0.06 0.08 0.1
Pairwise Distance


(b)
3


2.5


2
0

1.5
z

1

n c


0.04 0.06 0.08 0.1
Pairwise Distance


(c)


2.5


x 104


FS396/Chaol


I-MUSCLE+Dotur
-,K,, ---ESPRIT__







. .. .....................




0.04 0.06 0.08 0.1
Pairwise Distance


Figure 4. (a) Lineage-through-time curves, (b) ACE and (c) Chaol estimates generated by using ESPRIT and MUSCLE+DOTUR algorithms
performed on the FS396 data. Error bars of Chaol estimates represent the 95% confidence interval.


MUSCLE+DOTUR


ESPRIT


0.5 1 1.5
Number of sequences sampled


2
x 104


0.5 1 1.5
Number of sequences sampled x 104


Figure 5. Rarefaction curves generated by using (a) MUSCLE+ DOTUR and (b) ESPRIT performed on the FS396 data.


consistent with that of FS396. The lineage-through-time
curves, ACE and Chaol estimates at three different dis-
tance levels are depicted in Figure 4. We observe that
ESPRIT gives a much lower estimate of species richness
than the competing method, which is consistent with the
result of the simulation study described above. For exam-
ple, at the distance of 0.05, the number of observed OTUs
and the ACE and Chaol estimates obtained by ESPRIT


are only about 50-70% of those obtained by
MUSCLE+DOTUR. This becomes more evident in
Figure 5, where the results of the rarefaction analysis of
FS396 are presented. Though the existing pipeline over-
estimates the diversity of microbes, the conclusion of (4)
still holds that the microbial diversity of seawater samples
is much larger than previously reported, and the steep
slopes of the rarefaction curves, even at relatively large


PAGE 6 OF 8


.......... ........i......... .........I







PAGE 7 OF 8


x 104
9 | -i
-0.01
8 -0.03

7
-0.05

6 -
o
5 -
O
S4

E
z3 2

1

0
0 0.5 1 1.5 2 2.5
Number of sequences sampled


3 3.5
x 105


Figure 6. Rarefaction analysis of an air sample. Rarefaction curves
are shown for OTUs with sequence variations that do not exceed
1, 3, 5 or 10%.



genetic distances (e.g. 0.1), suggest that a large fraction of
species have not been sampled yet. We notice that the
disparity between the results of the two methods is much
larger than that in the simulation study (Figures 2, 4, S8
and S9). One possible reason is that the sequences in the
seawater samples are more diverse than those in the sim-
ulation data (see Section 1S for a detailed discussion).
ESPRIT is computationally very efficient. As can be
seen from Table 1 where the CPU times of the PC version
of ESPRIT performed on the eight seawater samples are
recorded, ESPRIT can process several tens of thousands
of sequences within a few minutes. As both the results of
(4) and our reanalysis of their samples suggest deeper
sequencing is required for accurate estimation of micro-
bial richness of environmental samples, the CC version of
ESPRIT is expected to be computationally more efficient
than the PC version, as we will see in the next section.

Experiments on an air sample
We applied ESPRIT to an air sample recently collected
from Iraq. The small subunit rRNA gene fragments that
cover the V6 hyperviable region of rRNAs were amplified,
cloned and pyrosequenced using 454 Life Sciences sys-
tems. A total of 348 952 sequences of an average of
275 nt in length were obtained. This is one of the largest
16s rRNA-based biodiversity surveys conducted using air
samples. The number of sequences is one order of magni-
tude larger than those of the seawater samples we consid-
ered in the previous section. Interested reader may refer to
the Supplementary Data for a detailed description on how
the air sample was prepared.
The CC version of ESPRIT with the default parameters
was used. After the trimming procedure, 40 928 (about
13%) reads were removed as low-quality reads. Figure 6
presents the results of the rarefaction analysis of the OTUs
defined at four different sequence variations, ranging from


Nucleic Acids Research, 2009, Vol. 37, No. 10 e76


Table 2. The number of observed OTUs, the ACE and Chaol
estimates of an air sample at four different distance levels

Pairwise distance

0.01 0.03 0.05 0.1

OTUs 80238 18686 8344 2109
ACE 147266 23894 9664 2262
Chaol 138376 23921 9748 2293
Upper 139911 24302 9932 2362
Lower 136881 23566 9585 2242

The 95% CIs of the Chaol estimates are also provided.


0.01 to 0.1. We observe that, in contrast with the results of
the seawater samples, the rarefaction curves saturate even
at a relatively small genetic distance of 0.03, indicating
that additional sampling may not lead to significantly
increased estimates of total species diversity. The results
of ACE and Chaol estimators are reported in Table 2. At
the distance levels of 0.05 and 0.1, the numbers of
observed OTUs are already very close to the ACE and
Chaol estimates. These experimental results suggest
that, at least for air samples, the current sequencing tech-
nology is sufficient to conduct 16S rRNA-based biodiver-
sity surveys. The experiment was performed on a small CC
consisting of 100 nodes, and it took about 10 h to finish
the entire analysis.


DISCUSSIONS
Equipped with next-generation sequencing technology,
researchers now start to sequence many millions of
sequences for applications such as global ocean surveys
and epidemiological studies with many patients. Parallel
computing that distributes computation to hundreds or
even thousands of nodes seems at this time to be the
only viable approach. We have demonstrated that the
newly proposed ESPRIT algorithm can process several
hundreds of thousands of sequences within 10h by using
a relatively small CC. To our knowledge, no existing
methods can efficiently handle such large data. The two
key components of ESPRIT are the use of pairwise instead
of multiple sequence alignment, which allows for parallel
computing, and the development of Hcluster that per-
forms hierarchical clustering using online learning to
address the memory issue. We have demonstrated that
MSA is not only computationally expensive, but also
tends to overestimate microbial diversity (see Section
1S). In order to process many millions of reads, research-
ers may need to collaborate with a supercomputing center
(e.g. IBM Roadrunner which has about 20000 nodes).
Our ESPRIT algorithm can easily be modified to work
on any cluster. The computational complexity of
ESPRIT is quadratic with respect to the number of
reads. At Roadrunner's full capacity, we estimate that it
will take 1 s, 10 h and 40 h to process 1, 50 and 100 million
reads, respectively. Of course, this is an overoptimistic
estimation, since it focuses only on computational com-
plexity and assumes that the resources of the entire








e76 Nucleic Acids Research, 2009, Vol. 37, No. 10



supercomputing center are available for this singular task.
ESPRIT, however, does provide a promising direction
for analyzing large collection of 16S rRNA data. We
will continue to optimize the performance of ESPRIT
and release the software to the community. We believe
that ESPRIT will evolve to be a powerful tool for meta-
genomics study.


SUPPLEMENTARY DATA

Supplementary Data are available at NAR Online.


ACKNOWLEDGEMENTS

We thank the editor and three reviewers for their valuable
comments to improve the quality of the article. The simu-
lation study presented in Section 1S was inspired by one of
the reviewers' comment.

Conflict of interest statement. None declared.


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