TaxMan: a server to trim rRNA reference databases
and inspect taxonomic coverage
Bernd W. Brandt1,*, Marc J. Bonder1,2, Susan M. Huse3and Egija Zaura1
1Department of Preventive Dentistry, Academic Centre for Dentistry Amsterdam (ACTA), University of
Amsterdam and VU University Amsterdam, Amsterdam, The Netherlands,2Centre for Integrative Bioinformatics
(IBIVU), VU University Amsterdam, Amsterdam, The Netherlands and3Josephine Bay Paul Center for
Comparative Molecular Biology and Evolution, Marine Biological Laboratory, Woods Hole, MA, USA
Received February 12, 2012; Revised April 16, 2012; Accepted April 23, 2012
Amplicon sequencing of the hypervariable regions of
the small subunit ribosomal RNA gene is a widely
accepted method for identifying the members of
complex bacterial communities. Several rRNA gene
taxonomic names to the sequencing reads using
BLAST, USEARCH, GAST or the RDP classifier.
ample reads, but they are short, currently ?100–
450nt (depending on the technology), as compared
to the full rRNA gene of ?1550nt. It is important,
therefore, to select the right rRNA gene region for
sequencing. The primers should amplify the species
of interest and the hypervariable regions should
differentiate their taxonomy. Here, we introduce
TaxMan: a web-based tool that trims reference se-
quences based on user-selected primer pairs and
returns an assessment of the primer specificity by
taxa. It allows interactive plotting of taxa, both
amplified and missed in silico by the primers used.
Additionally, using the trimmed sequences improves
the speed of sequence matching algorithms. The
smaller database greatly improves run times (up to
98%) and memory usage, not only of similarity
searching (BLAST), but also of chimera checking
(UCHIME) and of clustering the reads (UCLUST).
The bacterial small subunit of the ribosomal gene, the 16S
rRNA gene, is the most common housekeeping genetic
marker used in bacterial phylogeny and taxonomy. The
reasons for this are its presence in almost all bacteria,
relative stability over time and its size that is large
enough for informatics purposes (1). Cloning of the
(nearly complete) 16S rRNA gene in Escherichia coli and
sequencing, although highly elaborate and costly, became
a standard method in determining microbial community
composition (2,3). With the advent of high throughput
cloning bias could be circumvented and the costs per nu-
cleotide substantially reduced. Now, the standard method
of assessing the taxonomic composition of microbial
communities is to sequence the 16S rRNA gene, using
PCR amplification and NGS technology. The bacterial
interspersed with variable sequences that include nine
hypervariable regions (4). These regions are flanked by
conserved parts of the 16S rRNA gene, which are used
in primer designs to target as diverse a bacterial commu-
nity as possible. The sequences of the hypervariable
regions themselves are used to discriminate among bacter-
Different hypervariable regions evolve at different rates
and different species of the same genus (or e.g. genera of
the same family) may be similar in some hypervariable
regions and more divergent in others (5,6). Primer bias
occurs when the selected primers do not anneal to the
DNA from all members of the community equally, but
preferentially amplify certain taxonomic groups. For
instance, Verrucomicrobia, a bacterial phylum previously
thought to occur in soil at a low abundance, was shown to
be highly abundant in different soil samples by simply
replacing commonly used primer set 27F/338R (V1–V2),
obviously biased against Verrucomicrobia, by the primer
set 515F/806R targeting hypervariable region V4 (7).
Assessing the nature and extent of primer bias is an
important first step whenever primers are selected.
In silico testing for the most effective regions for discern-
ing taxa from a particular environment or for finer reso-
lution of particular taxa would have a large impact on
experimental costs and outcomes. This has recently been
(NGS) technology, the
*To whom correspondence should be addressed. Tel: +31 20 5980401; Email: firstname.lastname@example.org
Nucleic Acids Research, 2012, Vol. 40, Web Server issuePublished online 22 May 2012
? The Author(s) 2012. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/
by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
demonstrated within the Human Microbiome Project (8),
where both the V1–V3 and the V3–V5 sections of the
rRNA gene were sequenced, trimmed and clustered into
3% operational taxonomic units (OTUs) (9). The V1–V3
data showed three dominant Lactobacillus OTUs, which
appear to differentiate L. crispatus, L. iners and L. gasseri
(10). These OTUs correspond to the three primary vaginal
biome types identified by Zhou et al. (11) and Ravel et al.
(12). The V3–V5 sequence data, however, was dominated
by only one OTU, which included over six different
Lactobacillus species. Conversely, the V3–V5 sequence
data identified a Bifidobacteriaceae OTU that was not
detected as such with the V1–V3 sequences.
The data resulting from PCR amplification and NGS
sequencing requires processing through a bioinformatics
pipeline. This pipeline should assure that low quality se-
quences are discarded and meaningful groups or clusters
of sequences, OTUs, are created. The representative
sequence of each OTU is then compared with sequences
found in publicly available 16S rRNA gene databases
and, when possible, a consensus taxonomic lineage
(genus, family or higher taxon) is given to the OTU.
For these downstream analyses of the sequences, only
the amplified part of the 16S rRNA gene is required.
The use of the short amplicon sequences instead of the
full-length rRNA gene as reference sets in computational
pipelines, reduces the run times considerably. Some
programs such as GAST (13), used to assign taxonomy
based on the best match in a Global Alignment for
Sequence Taxonomy, require a trimmed database that
matches the length of the amplicons. An additional ad-
vantage of using a trimmed database is that it can serve
as a quality check for accurate trimming of (the
Programs already exist that test which sequences match
a given oligonucleotide probe. For the different rRNA
gene databases, these are SILVA’s TestProbe (14),
Greengenes’ Probes (15) or RDP’s Probe Match (16).
Probes can be designed using stand-alone software, such
as Primrose (17) and PrimerProspector (18). The latter
provides a probe/primer design pipeline that supports de
novo barcoded primer design and includes command-line
scripts to analyze taxonomic coverage. Most programs,
however, do not return trimmed reference sequences
matching the probes.
Wehave developed TaxMan,
web-tool, to trim the reference sequences of several
rRNA gene databases to the hypervariable regions used,
based on pre-selected primers, and to interactively analyze
taxonomic coverage. We show that the use of the provided
trimmed sequences in computations increases analysis
speed. Additionally, by assessing the ability of amplifica-
tion products to differentiate specific taxa from a particu-
lar environment, thus by analyzing the taxonomic
coverage using several rRNA gene databases, before per-
forming the sequencing, researchers will be able to better
target their experiments to resolve the taxa of greatest
interest to their research question. To this end, TaxMan
also provides graphical analysis of the taxa that are
selected for or against with the selected primer set(s).
MATERIALS AND METHODS
Several rRNA gene databases are provided, including
two oral microbiome-specific databases: CORE (16S
rDNA database of the core human oral microbiome) (19)
and HOMD (Human Oral Microbiome Database) (20), the
vaginal 16S reference package (21), as well as more inclusive
databases such as Greengenes (15) and the SILVA compre-
hensive ribosomal RNA databases (small subunit, small
subunit with human skin and mouse wound microbiome,
and large subunit) (14). Other databases can be added
upon user’s request.
the taxonomic lineage as FASTA description. The different
taxonomic categories are separated by a semi-colon. For
The taxonomy is taken from the source databases and is
not changed. For all databases, missing categories in the
taxonomic lineage are represented by an ‘empty string’.
This can occur if, for example, no order or family, but a
genus was supplied by the respective taxonomy. The
‘empty string’ is replaced with ‘noname’ in the tree. If
the database has classified a sequence as unclassified
explicitly, this will remain as such.
All databases are made non-redundant. The databases,
apart from SILVA, have been preprocessed to include the
lineage in the FASTA records.
The Excel file was downloaded [http://microbiome.osu
concatenated. The CORE accession id and the lineage
form the FASTA header line.
The 16S rRNA RefSeq and taxon table [http://www
.homd.org/ (20)] data were combined based on the HOT
identifier. The constructed FASTA headers start with the
HOT id merged with the strain synonym followed by the
The Greengenes PROKMSA_id and GenBank accession in
the Greengenes FASTA file [current_GREENGENES_
weremerged withan underscore
(Greengenes/Hugenholtz) format was changed.
Files were downloaded from http://www.arb-silva.de/
Vaginal 16S reference
The sequences were taken from the alignment file and gaps
were removed (vaginal_aln.fasta; http://microbiome.fhcrc.
org/apps/refpkg/). The lineages, based on the taxtable.txt
file, contain the following levels: species, genus, family,
order, class, phylum and superkingdom. The word
‘unclassified’ was appended to the lineage at the level
from which all sub-classifications are absent.
Nucleic AcidsResearch, 2012,Vol. 40,Web Server issueW83
The web site takes forward and reverse PCR primer
sequence(s) as input. Primers may contain ambiguity
codes. The reverse primer needs to be in the reverse com-
plement orientation, as is common for PCR primers. The
user can further select a target rRNA reference database.
Options include setting a mismatch percentage for the
primers, removing forward and/or reverse primer(s)
from the amplicons and two options related to treatment
of (redundant) lineages (cf. online documentation).
The FASTA sequences of the rRNA gene databases have
been preprocessed to contain the taxonomic lineage in the
FASTA header. In silico PCR is performed with an adapted
version of primersearch from EMBOSS (v6.4.0) (22) to find
the positions of the primers in the sequences and with Perl
code to extract the corresponding sub-sequences. The
adaptation of primersearch changes the expansion of the
IUPAC ambiguity codes. For example, R now expands to
GAR instead of GA. In cases where more amplicons are
produced for a single reference sequence, the longest
amplicon is kept. Then, the set of produced amplicon
sequences is made non-redundant. Next, a taxonomic tree
is built of the amplicons and combined with the tree of
the original reference sequences. In cases where different
species have identical amplicons, the taxonomic lineages
are optionally summarized to the first non-common
level, similar to microarray probes, for example, Bacteria;
Bacteroidetes;(Sphingobacteria/Flavobacteria). This tree
data is used for the HTML Tree viewer, pie-chart plotting
(using jqPlot, an open source project by Chris Leonello;
http://www.jqplot.com/) and for the FASTA headers in
the downloadable file.
RESULTS AND DISCUSSION
For NGS amplicon sequencing of bacterial communities,
hypervariable regions of the rRNA genes are amplified
with PCR. The TaxMan server provides in silico PCR
against several rRNA reference databases and interactive
analysis of the resulting taxonomic coverage. If more than
one forward and reverse primer is provided, a multiplex
PCR is performed: all forward primers are combined with
all reverse primers. The ambiguity codes, possibly present
in a primer, are expanded to include subsets of ambiguity
codes, since the rRNA reference sequences can themselves
contain ambiguity codes.
The selection of a (few) hypervariable region(s) of the
rRNA gene, resulting in shorter sequences, has two
(i) the reference database can be trimmed to corres-
pond with the used rRNA gene region. This can
increase the analysis speed considerably both by
reducing the length of the sequences to search
against and because shorter sequences can be more
redundant, the number of non-redundant sequences
to search against is also reduced and
(ii) the ability to differentiate taxa is reduced, because
the targeted hypervariable region(s) can have iden-
tical sequences for different species.
Improvements in speed and memory usage
The difference in run time between using the trimmed
versus the original reference data set was assessed for
several programs. We measured the run times of BLAST
(23) to find the taxonomy of the reads, of UCLUST (24)
to cluster the reads (using default and reference optimal)
and of UCHIME (25) to chimera check the reads. The test
data consisted of reads from pyrosequenced amplicons
from oral samples (V5–V7 region, Kraneveld,E.A. et al.,
submitted for publication). This data was either only
denoised (722943 reads) for UCHIME chimera checking
or denoised and chimera-checked (644797 reads) for
BLAST and UCLUST clustering. For BLAST, the
non-redundant, leaving 2806 reads.
Table 1 shows the computer run times, memory usage
and improvements therein when the different programs
were run with the original 16S rRNA gene reference data
as compared to the trimmed 16S rRNA gene sequence data
(primers removed). As can be seen from Table 1, the use of
these trimmed sequences that correspond with the
amplicons can result in considerable improvements in
both run time and memory usage of 25% up to 98%.
Server output files, taxonomic coverage and visualization
In addition to producing trimmed versions of a reference
database, TaxMan can be used to analyze the taxonomic
coverage of the trimmed sequences (amplicons), and the
original reference database sequences. We illustrate the
use of TaxMan with a primer set used in our previous
studies on the oral microbiota of children and oral
health (26). The primers target the V5–V6 hypervariable
region of the 16S rRNA gene. This example is present on
The output provides an overview of the run: the number
of non-redundant sequences in the selected database and
number of total and non-redundant sequences that the
primers formed. In addition, the percentage of sequences
(based on the number of total or non-redundant
amplicons) in the entire reference database targeted by
the primers is stated. Not all database sequences are
full-length rRNA gene sequences. Therefore, especially
when primers target the ends of the rRNA gene
sequence, the coverage may appear to be lower than
expected. Last, links are shown to three different
sections of the output page: the download, tree and pie
Under ‘Download amplicon and lineage data’, three
files can be downloaded: the taxonomic lineage coverage
and two FASTA files with the amplicon sequences. The
lineage file contains counts for all taxa in the amplicon set
and in the reference database. The FASTA files contain
the same sequence data, but with different headers for
W84Nucleic Acids Research, 2012,Vol. 40,Web Server issue
redundant sequences: either the taxonomic lineage is
summarized (to the identical part or to the first
non-common level) or all original FASTA headers are
The tree and especially the pie chart sections provide
interactive analysis and visualization. The tree is expand-
able and searchable (Figure 1). The pie charts provide a
different view on the taxonomic coverage to facilitate the
analysis of taxonomic distributions (Figure 2). By clicking
on a slice of the Root pie, a pie for the next taxonomic
level is plotted. For this plot, a percentage threshold can
be applied. This threshold filters the data that is plotted at
the percentage that the respective taxa occur in their
taxonomic parent level. For example, a threshold of
14% for Bacteria will only show those bacterial phyla
that occur at least 14% (relative to their counts in the
Firmicutes in this example.
For the amplicon sequences, the differences between
the taxa targeted by the amplicons compared with the
reference can also be plotted. Now, the size of the pie
slice relates to the number of sequences missing for this
taxonomic level. The percentage threshold here filters
on the percentage of missing sequences at the selected
taxonomic level. This offers a detailed view on what
taxa are absent. For example, with the threshold set
to ?70%, the pie only shows taxa for which at least
70% of the reference sequences are missing. At this
threshold, relatively most sequences, not targeted by
the primers, are from the phylum Fusobacteria (51
out of 67 in this example). Clearly, these numbers
depend on the selected database. However, replacing
the V5–V6 primers with V5–V7 primers provided
better coverage of the Fusobacteria occurring in the
The pie charts are highly flexible: each pie can be set to
plot the differences and each pie can have a different
threshold. The selected thresholds are shown in the pie
and a pink header indicates the ‘difference plots’.
wouldbe the phylum
Figure 1. Partial tree view of the amplicons based on the CORE
database. For each node, it shows the number of sequences targeted
by the given primers, followed by number in the original reference as
well as the percentage. The data used for the tree (except the percent-
ages) is downloadable as the tab-delimited lineage file.
Table 1. Data on CPU time, run time (hr:mm:ss format), physical memory (mem) and virtual memory (vmem) usage (in kb) as reported by the
cluster software (PBS). BLAST was run on eight cores, the other programs on one core. Percentage improvement is calculated as the relative dif-
ProgramMeasure Original set Trimmed set % Improvement Fold improvement
BLAST CPU time
UCLUST ref optb
aUCLUST reference mode.
bUCLUST reference optimal mode.
cThe concordance is 93.5%.
The fold improvement is the ratio (original/trimmed)
Nucleic AcidsResearch, 2012,Vol. 40,Web Server issue W85
The Taxman server provides a user-friendly way to carry
out (multiplex) in silico PCR to produce trimmed versions
of rRNA gene reference databases. Both the trimmed
sequences and the distribution of targeted taxa can be
downloaded for local use. TaxMan also supports inter-
active analysis of the taxonomic coverage including pie
charts which can quickly illustrate, with taxonomic trees,
which taxa, according to the selected rRNA database, are
targeted by the primer set(s) and which are not. The use of
the trimmed sequences instead of the full-length rRNA
gene sequences in computational pipelines results in
significant improvements in the use of computational
We would like to thank the teams who produce the
databases used in TaxMan. We are also thankful to the
SILVA team for providing the multiple sequence align-
ment of the SILVA SSU Ref NR data set.
University of Amsterdam under the research priority area
‘Oral Infections and Inflammation’ (to B.W.B.); National
Science Foundation [NSF/BDI 0960626 to S.M.H.]; the
European Union Seventh Framework Programme (FP7/
2007-2013) under ANTIRESDEV grant agreement no
241446 (to E.Z.). Funding for open access charge:
Conflict of interest statement. None declared.
1. Janda,J.M. and Abbott,S.L. (2007) 16S rRNA gene sequencing
for bacterial identification in the diagnostic laboratory: pluses,
perils, and pitfalls. J. Clin. Microbiol., 45, 2761–2764.
Figure 2. An example of pie plots for the amplicons (CORE database). The distribution of sub-categories within three taxonomic levels, shown as
the chart titles, is plotted. The percentage threshold is 0 for all plots. The top panel series is obtained by clicking on Bacteria (Root pie) and
Actinobacteria (Bacteria pie). Clicking a pie slice or legend label will produce the next chart and hide the legend of the previous one (except the
legend of the Root pie). The bottom panel series of charts is similar, but for the phylum Actinobacteria a plot of differences, indicated by the pink
header, is shown. Here, the data refers to the number of sequences missed by the amplicons as compared with the reference data. For the class
Actinobacteridae, 46 out of 110 sequences are missing (see legend). The ‘100%’ in the Actinobacteridae pie slice illustrates that all missed sequences
in the phylum Actinobacteria belong to the Actinobacteridae class. For Coriobacteridae, no sequences are missing (indicated by 0/9 in the legend).
When hovering over a ‘legend’ label, always the number of sequences that are targeted is displayed in the pie (Actinobacteridae; cnt: 64/110).
Therefore, this information is the same for both types of pies for Actinobacteria.
W86 Nucleic Acids Research, 2012,Vol. 40,Web Server issue
2. Ro ¨ ling,W.F.M. and Head,I.M. (2005) Prokaryotic systematics: Download full-text
PCR and sequence analysis of amplified 16S rRNA genes.
In: Osborn,A.M. and Smith,C.J. (eds), Molecular Microbial
Ecology. Taylor & Francis Group, New York, pp. 25–56.
3. Clarridge,J.E. III (2004) Impact of 16S rRNA gene sequence
analysis for identification of bacteria on clinical microbiology and
infectious diseases. Clin. Microbiol. Rev., 17, 840–862.
4. Petrosino,J.F., Highlander,S., Luna,R.A., Gibbs,R.A. and
Versalovic,J. (2009) Metagenomic pyrosequencing and microbial
identification. Clin. Chem., 55, 856–866.
5. Schloss,P.D. (2010) The effects of alignment quality, distance
calculation method, sequence filtering, and region on the analysis
of 16S rRNA gene-based studies. PLoS Comput. Biol., 6,
6. Youssef,N., Sheik,C.S., Krumholz,L.R., Najar,F.Z., Roe,B.A. and
Elshahed,M.S. (2009) Comparison of species richness estimates
obtained using nearly complete fragments and simulated
pyrosequencing-generated fragments in 16S rRNA gene-based
environmental surveys. Appl. Environ. Microbiol., 75, 5227–5236.
7. Bergmann,G.T., Bates,S.T., Eilers,K.G., Lauber,C.L.,
Caporaso,J.G., Walters,W.A., Knight,R. and Fierer,N. (2011) The
under-recognized dominance of Verrucomicrobia in soil bacterial
communities. Soil. Biol. Biochem., 43, 1450–1455.
8. NIH HMP Working Group. Peterson,J., Garges,S., Giovanni,M.,
McInnes,P., Wang,L., Schloss,J.A., Bonazzi,V., McEwen,J.E.,
Wetterstrand,K.A., Deal,C. et al. (2009) The NIH Human
Microbiome Project. Genome Res., 19, 2317–2323.
9. Schloss,P.D., Gevers,D. and Westcott,S.L. (2011) Reducing the
effects of PCR amplification and sequencing artifacts on 16S
rRNA-based studies. PLoS ONE, 6, e27310.
10. Huse,S.M., Ye,Y., Zhou,Y. and Fodor,A.A. (2012) A Core
human microbiome as viewed through 16S rRNA sequence
clusters. PLoS ONE, 7, e34242.
11. Zhou,X., Brotman,R.M., Gajer,P., Abdo,Z., Schu ¨ ette,U., Ma,S.,
Ravel,J. and Forney,L.J. (2010) Recent advances in
understanding the microbiology of the female reproductive tract
and the causes of premature birth. Infect. Dis. Obstet. Gynecol.,
12. Ravel,J., Gajer,P., Abdo,Z., Schneider,G.M., Koenig,S.S.K.,
McCulle,S.L., Karlebach,S., Gorle,R., Russell,J., Tacket,C.O.
et al. (2011) Vaginal microbiome of reproductive-age women.
Proc. Natl. Acad. Sci. USA, 108(Suppl. 1), 4680–4687.
13. Huse,S.M., Dethlefsen,L., Huber,J.A., Mark Welch,D.,
Relman,D.A. and Sogin,M.L. (2008) Exploring microbial diversity
and taxonomy using SSU rRNA hypervariable tag sequencing.
PLoS Genet., 4, e1000255.
14. Pruesse,E., Quast,C., Knittel,K., Fuchs,B.M., Ludwig,W.,
Peplies,J. and Glo ¨ ckner,F.O. (2007) SILVA: a comprehensive
online resource for quality checked and aligned ribosomal RNA
sequence data compatible with ARB. Nucleic Acids Res., 35,
15. DeSantis,T.Z., Hugenholtz,P., Larsen,N., Rojas,M., Brodie,E.L.,
Keller,K., Huber,T., Dalevi,D., Hu,P. and Andersen,G.L. (2006)
Greengenes, a chimera-checked 16S rRNA gene database and
workbench compatible with ARB. Appl. Environ. Microbiol., 72,
16. Cole,J.R., Chai,B., Farris,R.J., Wang,Q., Kulam,S.A.,
McGarrell,D.M., Garrity,G.M. and Tiedje,J.M. (2005) The
Ribosomal Database Project (RDP-II): sequences and tools for
high-throughput rRNA analysis. Nucleic Acids Res., 33,
17. Ashelford,K.E., Weightman,A.J. and Fry,J.C. (2002) PRIMROSE:
a computer program for generating and estimating the
phylogenetic range of 16S rRNA oligonucleotide probes and
primers in conjunction with the RDP-II database. Nucleic Acids
Res., 30, 3481–3489.
18. Walters,W.A., Caporaso,J.G., Lauber,C.L., Berg-Lyons,D.,
Fierer,N. and Knight,R. (2011) PrimerProspector: de novo design
and taxonomic analysis of barcoded polymerase chain reaction
primers. Bioinformatics, 27, 1159–1161.
19. Griffen,A.L., Beall,C.J., Firestone,N.D., Gross,E.L.,
DiFranco,J.M., Hardman,J.H., Vriesendorp,B., Faust,R.A.,
Janies,D.A. and Leys,E.J. (2011) CORE: a
phylogenetically-curated 16S rDNA database of the core oral
microbiome. PLoS ONE, 6, e19051.
20. Chen,T., Yu,W.H., Izard,J., Baranova,O.V., Lakshmanan,A. and
Dewhirst,F.E. (2010) The Human Oral Microbiome Database: a
web accessible resource for investigating oral microbe taxonomic
and genomic information. Database, 2010, baq013.
21. Srinivasan,S., Hoffman,N.G., Morgan,M.T., Matsen,F.A.,
Fiedler,T.L., Ross,F.J., McCoy,C.O., Hall,R.W., Bumgarner,R.,
Marrazzo,J.M. et al. (2012) Bacterial communities in women
with bacterial vaginosis: high resolution phylogenetic analyses
reveal relationships of microbiota to clinical criteria. PLoS ONE,
22. Rice,P., Longden,I. and Bleasby,A. (2000) EMBOSS: the
European Molecular Biology Open Software Suite. Trends Genet.,
23. Altschul,S.F., Madden,T.L., Scha ¨ ffer,A.A., Zhang,J., Zhang,Z.,
Miller,W. and Lipman,D.J. (1997) Gapped BLAST and
PSI-BLAST: a new generation of protein database search
programs. Nucleic Acids Res., 25, 3389–3402.
24. Edgar,R.C. (2010) Search and clustering orders of magnitude
faster than BLAST. Bioinformatics, 26, 2460–2461.
25. Edgar,R.C., Haas,B.J., Clemente,J.C., Quince,C. and Knight,R.
(2011) UCHIME improves sensitivity and speed of chimera
detection. Bioinformatics, 27, 2194–2200.
26. Crielaard,W., Zaura,E., Schuller,A.A., Huse,S.M., Montijn,R.C.
and Keijser,B.J.F. (2011) Exploring the oral microbiota of
children at various developmental stages of their dentition in the
relation to their oral health. BMC Med. Genomics, 4, 22.
Nucleic AcidsResearch, 2012,Vol. 40,Web Server issue W87