Input files

CLOTU requires three input files from the user: 1) one or several sequence files, in FASTA format [23] compressed ZIP file (hereafter referred to as SEQUENCE file), 2) a text file, that specifies sequences used as tags, primers and adaptors (hereafter referred to as TPA file) and 3) a text file, containing the FASTA file names and file identifiers (hereafter referred to as METADATA file) to be added to each sample.

Step1: Filtering and trimming

CLOTU provides different options for filtering low quality reads. 454 reads in the SEQUENCE file can be removed by the user if: 1) the tag and primer sequences does not match the sequences in the TPA file, 2) sequences have incompatible end tag combinations, 3) one or more ambiguous nucleotides (e.g. Ns) are present [24, 25] and 4) sequences are shorter than the user-defined minimum length.

These options can be optimized by the user and implemented either in combination or independently. It is also possible to accept mismatches in tags and primers. A Perl module included in the pipeline implements the Needleman-Wunsch algorithm [26] and considers indels for pairwise alignment of tags and primers before filtering out low quality sequences. The user can also define the threshold for minimal sequence length (default length is 150).

Sequencing by 454 pyrosequencing often results in ambiguous homopolymers. CLOTU provides an option where homopolymers above a certain length can be collapsed to a user-defined length, e.g. all homopolymers of length greater than six can be reduced to length six.

CLOTU allows trimming of tags, primers, and adapters (see additional file 1). One of these options, the 'Trim adaptor' option, removes exact and/or partial adaptor sequences found at the end of the reads. In order to reduce redundancy in the dataset before clustering, CLOTU also includes an option to remove all identical sequences. If this option is selected, CLOTU keeps track of all duplicate sequences and includes them in sequence abundance tallies for each cluster.

The filtering and trimming step produces four or five output files depending on the chosen parameters: 1) summary.txt summarizes the statistics of accepted sequences in tabular format for each basic step of the pipeline, 2) accepted.fas contains all accepted sequences in FASTA format, 3) rejected.fas contains all rejected sequence in FASTA format and 4) stats_log.txt lists the number of sequences in each sequence file compressed with ZIP. This file includes the parameters selected for the analysis, and detailed statistics regarding the number of accepted and rejected sequences for each of the activated filter and trim parameters. All invalid parameter settings and errors encountered are appended to this file. If the user has activated the 'collapse homopolymer' option, a fifth output file named homopolymers.html is also produced. The homopolymers.html allows visual verification of all sequences with homopolymers (see additional file 2).

Step 2: Clustering of sequence reads

For clustering of sequence reads, CLOTU uses the single-linkage clustering method as implemented in the BLASTCLUST program. This clusters DNA sequences based on pairwise matches using a BLAST algorithm [27]. The pipeline also provides the option to cluster DNA sequences using the CD-HIT package, an implementation of a greedy incremental clustering algorithm [28]. The user can define the minimum degree of pairwise sequence overlap as well as the sequence similarity threshold for clustering in both algorithms. The output file accepted.fas, containing all accepted sequences, is used as an input file for either clustering program. A typical BLASTCLUST output file consists of a sorted list of clusters of sequences separated by a newline character. The list is sorted first by cluster size and then alphabetically. Sequence identifiers within a cluster are space-separated and sorted, first by sequence length and then alphabetically. The longest sequence in each cluster is used as a representative sequence of that cluster. A Perl script creates a ready-to-use FASTA file from the raw BLASTCLUST output. CD-HIT produces ready-to-use FASTA files and the longest sequence from each cluster is considered as the representative sequence. A Perl script numbers the obtained clusters based on their abundance in the complete dataset. The clustering step also provides an option to exclude singletons, frequently used for reducing the impact of PCR and sequencing errors [29].

The clustering step produces five new output files: 1) cluster_out.fas contains the representative sequence of each cluster, 2) cluster_info.txt lists brief statistics about the number of sequences in each cluster and in the whole dataset, 3) matrix_table_1.xls lists the unique and identical (duplicate) sequence count from each tags used in the study, 4) singleton.fas lists all singletons in the FASTA format and 5) SeqInEachCluster.zip which includes separate ready-to-use FASTA formatted files of all sequences in each of the clusters, obtained for further assessment with various multiple sequence analyses or bioinformatics applications. The file stats_log.txt from step 1 is appended with brief statistics on the clusters obtained.

Step 3: Taxonomic annotation of sequences using BLAST

Taxonomic annotations are done by database searches using BLAST n against either user-defined databases or a downloaded version of the NCBInr database, maintained and updated on the Bioportal server [30]. User-defined databases can be made available for a defined group of users or to all users of the CLOTU pipeline through the Bioportal infrastructure. BLAST searches are done with user-specified settings of E-value threshold, number of score descriptions to report and number of pairwise alignments (see additional file 1). As the NCBInr database contains sequences derived from environmental surveys lacking taxonomical information, the user can choose to remove such hits from the BLAST output files (Perl scripts).

As a rough evaluation of the obtained clusters, CLOTU provides statistics about the degree to which the different clusters have best BLAST hits against the same database sequences. If many clusters have their best hit against the same reference sequences this may indicate that strict clustering parameters have been used, although this might not be universal for other sequences, including ITS.

The BLAST step produces five output files: 1) blastout.txt contains the results from BLAST searches in text format, 2) output_bp.html contains the parsed BLAST search results in color and tabular form, for easy visualization, 3) outfile_bp.txt contains parsed BLAST search results in text format, 4) outfile_bp.xls is a BLAST search result parsed file in Microsoft Excel (tab delimited) format and 5) matrix_table_2.xls is the same as matrix_table_1.xls produced in the previous step with the addition of an extra column for the top BLAST hit (see additional file 3). All significant BLAST search hits reported are summarized and appended in stats_log.txt file. Each of the parsed BLAST output files (outfile_bp html, outfile_bp.txt, and outfile_bp.xls) also report all significant hits, along with the top hit that passed the BLAST parsing criteria, as well as brief statistics about the total numbers of hits and number of uncultured sequences reported.

Implementation

CLOTU is written in Perl v5.8 and PHP 4.3 and implemented on the Bioportal at the University of Oslo. Bioportal is a web-based bioinformatics service and currently the largest high performance-computing environment for bioinformatics in Norway. Bioportal is freely available to academic users at the following URL: http://www.bioportal.uio.no/. The available computer resources are 593 cores on a TITAN cluster [31] at University of Oslo. In addition, Bioportal has access to all free or idle TITAN cores if needed (approximately 4000 at present). The TITAN cluster has Linux nodes with 16 gigabytes of memory and 2× quadcore CPUs or 2× dual-core CPUs. The CLOTU and Bioportal tutorials are available at the Bioportal website [32].

Analysed dataset

A dataset including 12,486 fungal ITS1 rDNA sequences generated by 454 sequencing of eight environmental samples from four plant roots is used here to demonstrate the utility of the CLOTU pipeline. The fungal ITS1 amplicons were obtained through a nested PCR approach using the fungal-specific primer ITS1-F [33] in combination with the primer ITS4 [34] in PCR1 and fusion primers (i.e. including tags and adaptors) based on ITS5 and ITS2 [34] in PCR2. The raw ITS1 sequences consisted of tags, forward primer, target sequence, reverse primer, reverse complement of tags used and adaptor (Figure 1). Tags were used on both ends to be able to control for sequences with incompatible end tag combinations generated during sample pooling for emulsion PCR. Although mainly overseen, such sequences with incompatible tag combinations have been reported as a serious problem in other publications [35, 36]. The ITS1 dataset has been submitted to GenBank (short read archive) [SRA: SRP006413].

Parameters selected for the analysed dataset

We did two separate analyses of the ITS1 dataset, each with two different settings, to evaluate and illustrate the different options available in CLOTU. In the first analyses (I) we searched for both the forward (ITS5) and reverse (ITS2) primers within the sequences, in order to filter out those that had not been fully sequenced. We did two separate runs of this analysis: one allowing no errors (mismatches) in the primers, and one allowing for two errors in each primer. In the second analysis (II) we only searched for the forward primer (ITS5), to also retain partially sequenced ITS1 fragments. Again, we did two separate runs in this analysis allowing zero or two mismatches in the forward primer. The four different filtering parameter settings were each used with the two different clustering methods BLASTCLUST and CD-HIT. The parameters for BLASTCLUST and CD-HIT were 95%, 96%, 97%, 98% and 99% identity and 50% sequence coverage.

Analyses of the ITS data

In analysis I the CD-HIT clustering approach yielded almost the same number of clusters as BLASTCLUST irrespective of allowing zero or two bp primer mismatch, except when very stringent parameter settings (98% and 99% identity) were used. In analysis II CD-HIT yielded more clusters than the BLASTCLUST approach, even when allowing up to 5% sequence divergence (see additional file 4).

Singletons, i.e. clusters including only one sequence, are to some extent considered a result of PCR and sequencing errors and often omitted from further analysis [29]. The CLOTU pipeline provides a separate FASTA formatted file with all singletons, which enables a separate comparison to the reference sequence database (e.g. NCBInr database) using BLAST. It is noteworthy that most of the top hits were to taxa not covered by the non-singleton clusters. This may reflect poor read quality of the sequences giving rise to random ITS sequences as the best matches [29]. Alternatively, it may indicate the presence of many rare taxa within the samples being studied (see additional file 3: matrix_table_2.xls for the singleton BLAST hit), and that removal of singleton clusters without further assessment in environmental sequencing studies may lead to the loss of valuable information [37]. In CLOTU, the 'remove singleton' option can be deactivated to include the BLAST top hits for even these clusters.

In both analyses I and II, using 98% and 99% sequence identity, far more clusters appeared among the sequences when two base pair mismatches were allowed in primers. This may indicate that a higher proportion of low quality sequences have been included when allowing for two base pair errors in primers, resulting in additional clusters. To further evaluate the two clustering methods, BLAST searches were performed on the representative sequences from all clusters obtained using 95% to 99% of sequence identity and 40% to 80% sequence coverage. The BLAST results showed that stringent clustering parameters (above 50% coverage and 98%-99% identity) had an impact on the number of clusters obtained in BLASTCLUST. CD-HIT was found to be less sensitive in this respect (see Table 2 for details).

In CLOTU, our example dataset with 12,486 sequences took 202 seconds (~ 3 minutes) for analysis I when the CD-HIT clustering program was selected, and 590 seconds (~10 minutes) when BLASTCLUST was used. The total time for calculation with either CD-HIT or BLASTCLUST was below 20 seconds without BLAST searches.

CLOTU compared to other bioinformatics tools

Compared to other pipelines, CLOTU provides a broad range of filtering options, with many unique functionalities, like filtering based on the presence of one or both primers and sequences with non-congruent tags. Although mainly ignored, it has been shown that sequences with incompatible tag combination can be prevalent in some datasets [35, 36]. CLOTU also allows the inclusion of a certain number of mismatches in primers as well as tags. The trimming options provided in CLOTU include trimming of only tags or both tags and primers. Furthermore, CLOTU can detect partial adaptors at the end of the sequence when the amplicons are not sequenced completely.

In 454 sequencing, most sequencing errors arise from homopolymer stretches. CLOTU provides the option to collapse homopolymers with user specified settings. As far as we know, among the mentioned pipelines only CLOTU includes this functionality.

CLOTU provides two different clustering methods. BLAST searches with representative sequences from each cluster showed that the two clustering approaches mostly identified the same hits, with a few unique hits for some of clusters obtained using CD-HIT.

PANGEA performs taxonomic annotation of reads and splits the dataset into classified and unclassified reads based on taxonomic affiliation before clustering. We would argue that such a procedure, relying on e.g. GenBank matches, is problematic and may influence the clustering. It seems a better option to cluster sequences prior to taxonomic annotation. CANGS and SEQ TRIM do not provide clustering options. In the RDP pipeline, alignment is required before clustering, something that is highly problematic when working with more variable sequences than 16S.

One of the other useful features of CLOTU is that BLAST is integrated in the pipeline, making it unnecessary for the users to download databases for BLAST searches. In other pipelines such as QIIME, PANGEA, CANGS and PANGEA, the user needs to set up the database and BLAST program on their local computer for assigning taxonomic affiliation to the 454 reads.

In contrast to other pipelines, CLOTU provides several output files at every analytical step, allowing the user to explore their data more deeply in addition to obtaining high quality sequence files. CLOTU is available on Bioportal, where output files can be used in several other bioinformatics applications already installed, maintained and routinely updated (see list of applications at http://www.bioportal.uio.no/appinfo/).