ral
ssBioMed CentBMC Bioinformatics
Open AcceSoftware
WebCARMA: a web application for the functional and taxonomic
classification of unassembled metagenomic reads
Wolfgang Gerlach*1,2, Sebastian Jünemann2,3, Felix Tille2,
Alexander Goesmann2 and Jens Stoye1,2
Address: 1Faculty of Technology, Bielefeld University, Bielefeld, Germany, 2Center for Biotechnology (CeBiTec), Bielefeld University, Bielefeld,
Germany and 3Department of Periodontology, University Hospital Münster, Münster, Germany
Email: Wolfgang Gerlach* - wolfgang.gerlach@cebitec.uni-bielefeld.de; Sebastian Jünemann - jueneman@cebitec.uni-bielefeld.de;
Felix Tille - felix.tille@cebitec.uni-bielefeld.de; Alexander Goesmann - alexander.goesmann@cebitec.uni-bielefeld.de;
Jens Stoye - stoye@techfak.uni-bielefeld.de
* Corresponding author
Abstract
Background: Metagenomics is a new field of research on natural microbial communities. High-
throughput sequencing techniques like 454 or Solexa-Illumina promise new possibilities as they are
able to produce huge amounts of data in much shorter time and with less efforts and costs than
the traditional Sanger technique. But the data produced comes in even shorter reads (35-100
basepairs with Illumina, 100-500 basepairs with 454-sequencing). CARMA is a new software
pipeline for the characterisation of species composition and the genetic potential of microbial
samples using short, unassembled reads.
Results: In this paper, we introduce WebCARMA, a refined version of CARMA available as a web
application for the taxonomic and functional classification of unassembled (ultra-)short reads from
metagenomic communities. In addition, we have analysed the applicability of ultra-short reads in
metagenomics.
Conclusions: We show that unassembled reads as short as 35 bp can be used for the taxonomic
classification of a metagenome. The web application is freely available at http://
webcarma.cebitec.uni-bielefeld.de.
Background
Metagenomics is a new field of research on natural micro-
bial communities strongly enhanced by the new high-
throughput sequencing (HTS) technologies like Roche's
454-sequencing, ABI's SOLiD or Illumina's Genome Ana-
lyzer. In traditional genomics, the sequencing of new
microbial genomes was limited to those microbes that can
be cultivated in a monoculture, which constitute less than
about the functional and taxonomic composition of the
community they originate from [1].
With decreasing sequencing costs, shotgun metagenom-
ics, i.e. sequencing of random genomic DNA fragments
from natural microbial communities, has become feasible
[2-4]. Comparing such metagenomic sequences with
sequences of known function makes it possible to analyse
Published: 18 December 2009
BMC Bioinformatics 2009, 10:430 doi:10.1186/1471-2105-10-430
Received: 15 September 2009
Accepted: 18 December 2009
This article is available from: http://www.biomedcentral.com/1471-2105/10/430
© 2009 Gerlach et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Page 1 of 10
(page number not for citation purposes)
1 percent of all microbes. To explore the other 99 percent,
metagenomics makes it possible to retrieve information
the biological diversity and the underlying metabolic
pathways in microbial communities.

BMC Bioinformatics 2009, 10:430 http://www.biomedcentral.com/1471-2105/10/430To infer the taxonomic origin of metagenomic reads, two
kinds of methods, composition-based and comparison-
based, can be distinguished. The composition-based
methods extract sequence features, like GC content or k-
mer frequencies, and compare them with features of refer-
ence sequences with known taxonomic classifications [5-
7]. In detail, different techniques, like the calculation of
correlation coefficients between oligonucleotide patterns
[8], self-organizing maps (SOMs) [9], or support vector
machines (SVMs) [10] can be used to classify the metage-
nomic fragments. The comparison-based methods rely on
homology information obtained by database searches,
e.g. using search tools like BLAST[11,12]. An additional
challenge is the fact that the new sequencing techniques
produce short (100-500 bp with 454) and ultra-short (35
bp with SOLiD, 35-100 bp with Illumina, 30-35 bp with
Helicos [13]) reads. New bioinformatic tools have to be
developed that can cope with both, the huge amount of
data and the short read lengths. Especially the short read
lengths have been considered the main bottleneck for the
usage of ultra-short reads in metagenomics [14].
In 2008, we developed CARMA [15], a new software pipe-
line for the characterisation of species composition and
the genetic potential of microbial samples using short
reads. In contrast to the traditional 16S-rRNA approach
for taxonomical classification [16-18], CARMA uses reads
that encode for known proteins. By assigning the taxo-
nomic origins to each read, a profile is constructed which
characterises the taxonomic composition of the corre-
sponding community. Krause et al. [15] showed on a syn-
thetic metagenome that even with reads as short as
around 100 bp, high accuracy predictions with an average
false positive rate of 0.1 to 2.5 percent are possible. The
CARMA pipeline has already been successfully applied to
454-sequenced communities including the characterisa-
tion of a plasmid sample isolated from a wastewater treat-
ment plant and other communities [19-22].
Here we introduce WebCARMA, a refined version of
CARMA available as a web application for the taxonomic
and functional classification of unassembled (ultra-)short
reads from metagenomic communities. The web applica-
tion is freely available at http://webcarma.cebitec.uni-
bielefeld.de. Furthermore, we examined the applicability
of CARMA for simulated ultra-short reads (≥ 35 bp) by
comparing the results with earlier ones obtained by short
reads (≈230 bp) using samples from a biogas plant [21].
Implementation
WebCARMA is based on a new CARMA version 2.1 with
some improvements over the last published version 1.2
[15]. We first review the basic concepts behind CARMA,
CARMA
Next-generation sequencing technologies in metagenom-
ics produce millions of DNA reads, from which CARMA
detects those that encode for known proteins. These reads
are called environmental gene tags (EGTs) [23]. In a second
step they are assigned to taxa from six taxonomical ranks:
superkingdom, phylum, class, order, genus and species.
The set of classified EGTs provides a taxonomical profile
for the microbial community.
In detail, BLASTX [24,25] is used to search within the set
of reads for candidate EGTs that encode for protein
sequences contained in the Pfam database [26]. A rather
relaxed E-value of 10 and frameshift option -w 15 are
used. Each read that has a match to a protein family mem-
ber is translated according to BLASTX reading frame and
frameshift predictions. The final determination of EGTs is
done by matching the candidate EGTs against their
matching protein families with the corresponding Pfam
Hidden Markov Models [27]. For this purpose mmpfam
from the HMMER package [28] is used. Only candidate
EGTs with an hmmpfam E-value match of 0.01 or lower
are accepted as EGTs.
After the EGTs are identified, they are taxonomically clas-
sified. Therefore, each EGT is aligned against the multiple
alignment of its family with hmmalign (also contained in
the HMMER package). From this new alignment, the pair-
wise sequence distance is computed for all pairs of
sequences, based on the fraction of identical amino acids.
This produces a pairwise distance matrix which then is
used to compute a phylogenetic tree with the neighbour-
joining clustering method [29]. The EGT is then classified
depending on its position within this tree. If the EGT is
localised within a subtree of family members all sharing
the same taxon, then the EGT is classified with the same
taxon. For example, if the EGT is localised in a subtree
with the three members Bacteria Cyanobacteria Synechococ-
cales Prochlorococcus, Bacteria Cyanobacteria Chroococcales
Synechococcus and Bacteria Cyanobacteria Nostocales Nostoc,
the EGT is classified as Bacteria Cyanobacteria. For a more
formal definition and further details see [15].
WebCARMA
We have reimplemented large parts of CARMA, including
a faster construction of the phylogenetic trees by caching
the pairwise distances between Pfam family members. The
CARMA results now include for each EGT the correspond-
ing hmmpfam E-values and a list of GO-Ids (Gene Ontol-
ogy Identifiers) [30] associated with the corresponding
Pfam family. The Gene Ontology provides a controlled
vocabulary for gene products, distinguishing between
their associated biological processes, cellular componentsPage 2 of 10
(page number not for citation purposes)
before we present our new WebCARMA. and molecular functions, and can therefore be used to cre-

BMC Bioinformatics 2009, 10:430 http://www.biomedcentral.com/1471-2105/10/430
ate a functional profile of the metagenome. A major mod-
ification of CARMA is the usage of the NCBI taxonomy
database [31,32] instead of the Pfam nomenclature. The
NCBI taxonomy database currently indexes over 200,000
species [33], which are classified in a hierarchical tree
structure. Each taxon from the taxonomy is represented as
a node in the tree with a unique identifier (tax_id) and
its taxonomic rank ranging from superkingdom to sub-
species. For compatibility with other applications and
databases, the output files of CARMA contain, along with
the prettyprint taxa classifications, the corresponding
tax_id.
The WebCARMA website is built upon an Apache Web
Server using Perl and CGI. The CARMA pipeline is exe-
cuted on the compute cluster of the Bielefeld University
Bioinformatics Resource Facility at the Center for Biotech-
nology (CeBiTec) using Sun Grid Engine http://griden
gine.sunsource.net/. In order to use WebCARMA and to
upload metagenomic sequences, the users have to register
with their e-mail address. After the uploads are finished,
CARMA starts with the search for EGTs and the taxonom-
ical classification. By the time the jobs are completed, the
user receives an e-mail with a download address pointing
to the results.
We provide several pre-computed data formats that allow
the user to explore the results in different ways:
 The translated EGTs, with additional information
about the name of the original metagenomic
sequence, reading frame, Pfam ID, HMMER E-value
and a list of GO-Ids associated with that correspond-
ing Pfam ID.
 A GO-term profile in two variants, as a text data file
and visualised as a histogram in PDF format.
 The taxonomical classification results as a text data
file.
 A taxonomic profile, once as a text data file and once
visualised by histograms for each taxonomic rank in
PDF format.
The profile data files as well as the classification results are
provided in TSV-format (Tab Separated Values), which
makes it easy to import the data into other programs (e.g.
spreadsheet) for different visualisation types or any other
further processing.
We provide Perl scripts for download that can easily be
used as templates for own data processing pipelines. A
Results and Discussion
The WebCARMA Web Application
A local installation of CARMA [15] requires the Pfam
MySQL database and several bioinformatics tools, like
BLASTX, the HMMER package, the PHYLIP package [34]
and several Perl packages. Most strikingly, it has high
computational demands which make the usage of a high-
performance grid inevitable. Therefore, we introduce
WebCARMA, our new platform-independent web appli-
cation which makes CARMA easily accessible to the scien-
tific community (see Figure 1).
WebCARMA produces functional (using GO-Terms) and
taxonomic profiles (using NCBI taxonomy) of metagen-
omic sequences, available in text format and as histo-
grams.
An example of a functional profile of a complete metage-
nomic 454 data set (described in the use case study
below) produced with WebCARMA is depicted in Figure
2. Examples of comparative taxonomic profiles are dis-
cussed in the use case study.
To classify a metagenomic data set as used in the use case
study below (600 000 reads and average 230 bp read
length) takes about 1 800 CPU hours (each CPU with 2.5
GHz) on our compute cluster. Because the neighbour-
joining algorithm has quadratic runtime, cluster jobs
which process bigger Pfam families will take much more
time to compute than other. Therefore, using 100 CPUs, it
takes for this data set about 40 hours until the last job has
finished.
Overview WebCARMAFigure 1
Overview WebCARMA. The web application Web-
Functional
EGTs (FASTA)
GO Profile (TSV)
Visualization (PDF)
Visualization (PDF)
Taxonomic profile (TSV)
Classifications (TSV)
Taxonomic
results.tgz
WebCARMA
Metagenomic Reads
download
uploadPage 3 of 10
(page number not for citation purposes)
manual with further explanations can be found on the
WebCARMA site.
CARMA.

BMC Bioinformatics 2009, 10:430 http://www.biomedcentral.com/1471-2105/10/430Comparison of full-length 454 and simulated ultra-short
reads
In order to evaluate the applicability of short and ultra-
short reads in metagenomics, we used a real-world data
set and several realistic simulated data sets. The real-world
data set consisted of 600 000 reads with an average read
length of 230 bp of a microbial community from an agri-
cultural biogas reactor [35], sequenced with the 454
Genome Sequencer FLX system (Roche Applied Science).
Instead of using real ultra-short reads, we decided to sim-
ulate ultra-short reads by clipping off suffixes of the 454-
reads to get the desired read lengths. We generated nine
data sets, each consisting of reads of one of the lengths 35
bp, 40 bp, 50 bp, 60 bp, 70 bp, 100 bp, 150 bp, 200 bp,
and 250 bp, respectively.
It is known that some sequencing techniques exhibit cor-
relations between read coverage and GC content [36-38].
By using simulated reads instead of real ultra-short reads
we can be sure that any differences we see in the classifica-
tion results between the data sets are only due to the dif-
ferent read lengths. If there is a bias in the 454 data, then
we also have the same bias in the simulated data sets and
our comparison should not be much affected by this.
First, we analyse the number and lengths of the EGTs
obtained for each data set, then we compare the taxo-
nomic classification results for the different read lengths.
As shown in Table 1, the number of reads in each data set
decreases with increasing read length. This is because the
454 data set contains reads of different lengths and some
Functional Profileigure 2
Functional Profile. Example of a functional profile: 40 most abundant GO-terms in the metagenome of an agricultural biogas
reactor.
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
ATP binding (function)
DNA binding (function)
m
embrane (component)
intracellular (component)
translation (process)
electron transport (process)
structural constituent of ribosome (function)
ribosome (component)
carbohydrate metabolic process (process)
proteolysis (process)
integral to membrane (component)
oxidoreductase activity (function)
cytoplasm (component)
m
etabolic process (process)
catalytic activity (function)
transposition, DNA-mediated (process)
transposase activity (function)
DNA replication (process)
hydrolase activity, hydrolyzing O-glycosyl compounds (function)
regulation of transcription, DNA-dependent (process)
RNA binding (function)
tRNA processing (process)
DNA repair (process)
DNA-directed DNA polymerase activity (function)
NAD or NADH binding (function)
glycolysis (process)
DNA methylation (process)
transcription factor activity (function)
zinc ion binding (function)
kinase activity (function)
transport (process)
GTP binding (function)
biosynthetic process (process)
hydrolase activity (function)
N-methyltransferase activity (function)
m
etal ion binding (function)
phosphorylation (process)
nucleus (component)
lyase activity (function)
m
etalloendopeptidase activity (function)
Co
un
t o
f s
up
po
rti
ng
E
G
Ts
Functional Profile
Table 1: Number of reads in each data set.
Length 35 bp 40 bp 50 bp 60 bp 70 bp 100 bp 150 bp 200 bp 250 bp original
Reads 616 069 616 031 613 943 606 760 598 811 584 168 550 945 492 305 297 852 616 072
EGTs 886 7 836 29 999 48 472 62 112 92 000 119 674 130 544 89 979 172 461
Unique 886 7 827 29 923 48 218 61 687 90 854 116 743 125 624 85 565 164 444
Yield 0.14% 1.27% 4.87% 7.95% 10.30% 15.55% 21.19% 25.52% 28.73% 26.69%
Number of reads and EGT yield for each data set. Some metagenomic reads have matches to more than one Pfam family and therefore are
translated into more than one EGT. The column "Unique" denotes the total number of EGTs where EGTs from the same read are counted only Page 4 of 10
(page number not for citation purposes)
once. The column "Yield" denotes the fraction of (unique) EGTs that could be obtained from the corresponding data set.

BMC Bioinformatics 2009, 10:430 http://www.biomedcentral.com/1471-2105/10/430
of the reads are already too short to serve as a template for
all simulated data sets. The relative amount of EGTs that
is found in each data set increases with increasing read
length.
Figure 3 shows the EGT length distribution in each data
set as a function of read length. Shown are the minimum,
25% quantile, median, 75% quantile and the maximum.
Our results show that the median EGT length does not
scale linearly with the read length. The length of Pfam
families and domains poses an upper bound on the pos-
sible length of local alignments between translated reads
and Pfam families. The longer a read is, the higher is the
probability that parts of the reads lie outside of the match-
ing gene and can not contribute to the EGT.
In rare cases, it happens that an EGT is one amino acid
longer than its read length divided by three. For example,
in the set of EGTs produced from the 150 bp-reads data
set, the longest EGTs are 51 amino acids long. This can
occur when blastx predicts two frameshifts in one read.
For our analysis of the applicability of ultra-short reads
with CARMA, we considered seven different taxonomic
ranks: superkingdom, phylum, class, order, family, genus
and species. A first relative abundance for each taxon is
obtained by dividing the absolute number of EGTs that
predict this taxon by the total number of EGTs at the same
taxonomic rank. The latter do not include EGTs which
predict nothing ("unknown"). We consider taxa with a
relative abundance below the threshold 0.015 in all data
sets, to be false positives. Therefore they are classified as
"other".
After applying the threshold we recompute the relative
abundances for each taxon, this time subtracting both,
"unknown" and "other" from the total number of EGTs at
the same taxonomic rank.
EGT lengths distributionFigur 3
EGT lengths distribution. EGT length distribution in each data set as a function of read length. Shown are the minimum,
0
10
20
30
40
50
60
70
80
90
0 50 100 150 200 250
E
G
T
Le
ng
th
(a
m
in
o
ac
id
s)
Read Length (base pairs)Page 5 of 10
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25% quantile, median, 75% quantile and maximum.

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With this, we have normalised the relative abundances for
the taxa such that they sum up to 1 and therefore ensured
comparability between the data sets.
For scaling reasons, the fractions of "unknown" and
"other" EGTs are not shown in the histograms (except
"unknown" on superkingdom level). This data is given in
Tables 2 and 3.
Even though the taxonomic predictions on lower taxo-
nomic ranks (order, family, genus and species) are known
to be imprecise, we included them in our experiment in
order to study the effect of using (ultra-)short reads com-
pared to longer ones at all taxonomic ranks.
Figures 4, 5 and 6 show the results for superkingdom,
order and species. The complete set of figures for the eval-
uation at all taxonomic ranks can be found in Additional
Files 1, 2, 3, 4, 5, 6 and 7.
The results show that CARMA predicts for all data sets and
all taxonomic ranks the same taxa. For higher taxonomic
ranks, even the relative abundance levels are similar
between the different data sets. Deviations of 35 bp-reads
for example can be seen on the level of order, where sig-
nificantly more of Thermotogales and Haemosporida,
and less of Thermoanaerobacterales are predicted. The 40
bp data set does not show these differences. Even more
deviations can be found on lower taxonomic ranks, for
example species.
Furthermore, as expected, the rate of EGTs which are not
classified increases for lower taxonomic ranks for all data
sets (Table 2). Interestingly, the rate of unclassified EGTs
is smaller for shorter reads than for longer reads. This
might be due to the circumstance that shorter EGTs need
to have more sequence similarity to the Pfam families
than longer EGTs, in order to achieve the same E-value
threshold.
Conclusions
CARMA is a software pipeline for the characterisation of
species composition and the genetic potential of micro-
bial samples using short reads that encode for known pro-
teins. The species composition can be described by
classifying the reads into taxonomic groups of organisms
they most likely stem from. CARMA has been successfully
evaluated on a synthetic metagenome [15] and has
already been used for the analysis of several microbial
communities [19,20].
Here we have presented our new web application Web-
CARMA, which makes metagenomic analyses with
CARMA easily accessible. WebCARMA provides func-
Table 3: Rate of "Other" EGTs.
Read Length Superkingdom Phylum Class Order Family Genus Species
35 0.0011 0.0671 0.1651 0.2816 0.4057 0.4554 0.6776
40 0.0011 0.0678 0.1691 0.2901 0.4388 0.5056 0.7340
50 0.0019 0.0667 0.1609 0.2954 0.4591 0.5292 0.7606
60 0.0024 0.0619 0.1552 0.2864 0.4637 0.5302 0.7663
70 0.0023 0.0617 0.1554 0.2954 0.4535 0.5221 0.7505
100 0.0035 0.0655 0.1539 0.2891 0.4456 0.4978 0.7172
150 0.0071 0.0692 0.1565 0.2964 0.4555 0.5006 0.6756
200 0.0100 0.0651 0.1467 0.2938 0.4500 0.4954 0.6658
250 0.0137 0.0542 0.1377 0.2849 0.4317 0.4693 0.6364
Table 2: Rate of "Unknown" EGTs. Rate of "Unknown" EGTs that could not be classified further from the complete set of EGTs.
Read Length Superkingdom Phylum Class Order Family Genus Species
35 0.09 0.31 0.38 0.45 0.52 0.53 0.59
40 0.09 0.26 0.37 0.43 0.51 0.52 0.57
50 0.09 0.27 0.38 0.43 0.51 0.52 0.58
60 0.09 0.28 0.39 0.45 0.53 0.54 0.61
70 0.09 0.29 0.4 0.46 0.54 0.56 0.63
100 0.1 0.32 0.43 0.49 0.58 0.6 0.68
150 0.11 0.33 0.44 0.52 0.6 0.62 0.71
200 0.11 0.34 0.45 0.52 0.61 0.63 0.73
250 0.11 0.32 0.44 0.51 0.6 0.63 0.73Page 6 of 10
(page number not for citation purposes)
"Other" are EGT's with a relative abundance below the threshold 0.015 and are not shown in the histograms. Here we show the rates of "Other"
EGTs relative to the total number of classified EGTs for each taxonomic rank and data set.

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Page 7 of 10
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superkingdomFigu e 4
superkingdom. Taxonomic results on the level of superkingdom.
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Bacteria
Archaea
Unknown
Eukaryota
R
el
at
iv
e
A
bu
nd
an
ce
Taxonomic Profile - Superkingdom
35bp
40bp
50bp
60bp
70bp
100bp
150bp
200bp
250bp
orderFigu e 5
order. Taxonomic results on the level of order. Only taxa with an abundance of 0.015 or higher are shown.
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
Clostridiales
Methanomicrobiales
Bacteroidales
Bacillales
Actinomycetales
Lactobacillales
Thermoanaerobacterales
Enterobacteriales
Flavobacteriales
Rhizobiales
Burkholderiales
Methanosarcinales
Myxococcales
Thermotogales
Desulfuromonadales
Chlorobiales
Haemosporida
R
el
at
iv
e
A
bu
nd
an
ce
Taxonomic Profile - Order
35bp
40bp
50bp
60bp
70bp
100bp
150bp
200bp
250bp

BMC Bioinformatics 2009, 10:430 http://www.biomedcentral.com/1471-2105/10/430tional and taxonomic classifications in common data for-
mats as well as basic visualisations of the profiles.
Previous metagenomic analysis relied on reads of length
100 bp or longer. We have shown that ultra-short reads as
short as 35 bp can be used for the taxonomic classification
of a metagenome. The biogas data set we have used in the
analysis is a low complexity data set with only a few prev-
alent species. Therefore, our results do not necessarily
apply to data sets of higher complexity. Still, we think we
have shown that ultra-short reads can indeed, in principle,
be used for reliable taxonomic classification of a micro-
bial community if the coverage is high enough. We have
found most differences between the different data sets in
the taxa of higher order, e.g. at the species level, and in
general for species with very low abundance.
Metagenomics with CARMA still leaves some room for
improvements. Proper statistics to assess the significance
of functional and taxonomic predictions based on short
Concentrations, unpublished manuscript) are still miss-
ing. The abundance levels of the classification results have
to be read with care. Species with larger genomes or more
genes than other species will be overrepresented in the
taxonomic profiles because more EGTs can be found.
Therefore, more accurate results might be achieved by
weighting EGTs using additional information like the
genome size of the closest known relative.
Availability and requirements
 Project name: WebCARMA
 Project home page: http://webcarma.cebitec.uni-
bielefeld.de
 Operating system(s): Platform independent (web-
service); Unix/Linux (stand-alone program)
 Programming language: Perl
speciesFigure 6
species. Taxonomic results on the level of species. Only taxa with an abundance of 0.015 or higher are shown.
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
Methanoculleus marisnigri
Desulfitobacterium hafniense
Syntrophomonas wolfei
Clostridium thermocellum
Clostridium leptum
Thermosinus carboxydivorans
Candidatus Methanoregula boonei
Oryza sativa
Symbiobacterium thermophilum
Methanospirillum hungatei
Clostridium cellulolyticum
Pelotomaculum thermopropionicum
Methanocorpusculum labreanum
Thermotoga lettingae
R
el
at
iv
e
A
bu
nd
an
ce
Taxonomic Profile - Species
35bp
40bp
50bp
60bp
70bp
100bp
150bp
200bp
250bpPage 8 of 10
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reads (Kowalczyk et al.: Significance Tests for Short Read  Other requirements: none

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 License: GNU GPL
 Any restrictions to use by non-academics: none
 Upload restriction: Maximum 100 MB of FASTA per
month
Authors' contributions
The new version of CARMA was implemented by WG. SJ
and FT contributed to the WebCARMA web application.
AG and JS supervised the development of the initial sys-
tem design and gave important suggestions to the manu-
script. All authors have read and approved the
manuscript.
Additional Files
This is the complete set of figures for the evaluation from
the use case study. Only those taxa are visualized, where
in at least one of both data sets the predicted relative fre-
quency was 0.015 or higher. Taxa that are not visualised
contribute to "Other".
Additional material
Acknowledgements
WG was supported by the DFG Graduiertenkolleg 635 Bioinformatik, SJ by
the BMBF PathoGeno-Mik Plus network project 0313801N and FT by the
BMBF ERA-NET PathoGenoMics project 0313939A.
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Additional file 1
Taxonomic results on the level of superkingdom. Only taxa with an
abundance of 0.015 or higher are shown.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471-
2105-10-430-S1.PDF]
Additional file 2
Taxonomic results on the level of phylum. Only taxa with an abundance
of 0.015 or higher are shown.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471-
2105-10-430-S2.PDF]
Additional file 3
Taxonomic results on the level of class. Only taxa with an abundance
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Click here for file
[http://www.biomedcentral.com/content/supplementary/1471-
2105-10-430-S3.PDF]
Additional file 4
Taxonomic results on the level of order. Only taxa with an abundance
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Click here for file
[http://www.biomedcentral.com/content/supplementary/1471-
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Additional file 5
Taxonomic results on the level of family. Only taxa with an abundance
of 0.015 or higher are shown.
Click here for file
Additional file 6
Taxonomic results on the level of genus. Only taxa with an abundance
of 0.015 or higher are shown.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471-
2105-10-430-S6.PDF]
Additional file 7
Taxonomic results on the level of species. Only taxa with an abundance
of 0.015 or higher are shown.
Click here for file
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