454 pyrosequencing to describe microbial eukaryotic community composition, diversity and relative abundance: a test for marine haptophytes.
ABSTRACT: Next generation sequencing of ribosomal DNA is increasingly used to assess the diversity and structure of microbial communities. Here we test the ability of 454 pyrosequencing to detect the number of species present, and assess the relative abundance in terms of cell numbers and biomass of protists in the phylum Haptophyta. We used a mock community consisting of equal number of cells of 11 haptophyte species and compared targeting DNA and RNA/cDNA, and two different V4 SSU rDNA haptophyte-biased primer pairs. Further, we tested four different bioinformatic filtering methods to reduce errors in the resulting sequence dataset. With sequencing depth of 11000-20000 reads and targeting cDNA with Haptophyta specific primers Hap454 we detected all 11 species. A rarefaction analysis of expected number of species recovered as a function of sampling depth suggested that minimum 1400 reads were required here to recover all species in the mock community. Relative read abundance did not correlate to relative cell numbers. Although the species represented with the largest biomass was also proportionally most abundant among the reads, there was generally a weak correlation between proportional read abundance and proportional biomass of the different species, both with DNA and cDNA as template. The 454 sequencing generated considerable spurious diversity, and more with cDNA than DNA as template. With initial filtering based only on match with barcode and primer we observed 100-fold more operational taxonomic units (OTUs) at 99% similarity than the number of species present in the mock community. Filtering based on quality scores, or denoising with PyroNoise resulted in ten times more OTU99% than the number of species. Denoising with AmpliconNoise reduced the number of OTU99% to match the number of species present in the mock community. Based on our analyses, we propose a strategy to more accurately depict haptophyte diversity using 454 pyrosequencing.
Project description:Microalgae in the division Haptophyta play key roles in the marine ecosystem and in global biogeochemical processes. Despite their ecological importance, knowledge on seasonal dynamics, community composition and abundance at the species level is limited due to their small cell size and few morphological features visible under the light microscope. Here, we present unique data on haptophyte seasonal diversity and dynamics from two annual cycles, with the taxonomic resolution and sampling depth obtained with high-throughput sequencing. From outer Oslofjorden, S Norway, nano- and picoplanktonic samples were collected monthly for 2 years, and the haptophytes targeted by amplification of RNA/cDNA with Haptophyta-specific 18S rDNA V4 primers. We obtained 156 operational taxonomic units (OTUs), from c. 400.000 454 pyrosequencing reads, after rigorous bioinformatic filtering and clustering at 99.5%. Most OTUs represented uncultured and/or not yet 18S rDNA-sequenced species. Haptophyte OTU richness and community composition exhibited high temporal variation and significant yearly periodicity. Richness was highest in September-October (autumn) and lowest in April-May (spring). Some taxa were detected all year, such as Chrysochromulina simplex, Emiliania huxleyi and Phaeocystis cordata, whereas most calcifying coccolithophores only appeared from summer to early winter. We also revealed the seasonal dynamics of OTUs representing putative novel classes (clades HAP-3-5) or orders (clades D, E, F). Season, light and temperature accounted for 29% of the variation in OTU composition. Residual variation may be related to biotic factors, such as competition and viral infection. This study provides new, in-depth knowledge on seasonal diversity and dynamics of haptophytes in North Atlantic coastal waters.
Project description:Microalgae in the division Haptophyta may be difficult to identify to species by microscopy because they are small and fragile. Here, we used high-throughput sequencing to explore the diversity of haptophytes in outer Oslofjorden, Skagerrak, and supplemented this with electron microscopy. Nano- and picoplanktonic subsurface samples were collected monthly for 2 yr, and the haptophytes were targeted by amplification of RNA/cDNA with Haptophyta-specific 18S ribosomal DNA V4 primers. Pyrosequencing revealed higher species richness of haptophytes than previously observed in the Skagerrak by microscopy. From ca. 400,000 reads we obtained 156 haptophyte operational taxonomic units (OTUs) after rigorous filtering and 99.5% clustering. The majority (84%) of the OTUs matched environmental sequences not linked to a morphological species, most of which were affiliated with the order Prymnesiales. Phylogenetic analyses including Oslofjorden OTUs and available cultured and environmental haptophyte sequences showed that several of the OTUs matched sequences forming deep-branching lineages, potentially representing novel haptophyte classes. Pyrosequencing also retrieved cultured species not previously reported by microscopy in the Skagerrak. Electron microscopy revealed species not yet genetically characterised and some potentially novel taxa. This study contributes to linking genotype to phenotype within this ubiquitous and ecologically important protist group, and reveals great, unknown diversity.
Project description:BACKGROUND: Reducing the effects of sequencing errors and PCR artifacts has emerged as an essential component in amplicon-based metagenomic studies. Denoising algorithms have been designed that can reduce error rates in mock community data, but they change the sequence data in a manner that can be inconsistent with the process of removing errors in studies of real communities. In addition, they are limited by the size of the dataset and the sequencing technology used. RESULTS: FlowClus uses a systematic approach to filter and denoise reads efficiently. When denoising real datasets, FlowClus provides feedback about the process that can be used as the basis to adjust the parameters of the algorithm to suit the particular dataset. When used to analyze a mock community dataset, FlowClus produced a lower error rate compared to other denoising algorithms, while retaining significantly more sequence information. Among its other attributes, FlowClus can analyze longer reads being generated from all stages of 454 sequencing technology, as well as from Ion Torrent. It has processed a large dataset of 2.2 million GS-FLX Titanium reads in twelve hours; using its more efficient (but less precise) trie analysis option, this time was further reduced, to seven minutes. CONCLUSIONS: Many of the amplicon-based metagenomics datasets generated over the last several years have been processed through a denoising pipeline that likely caused deleterious effects on the raw data. By using FlowClus, one can avoid such negative outcomes while maintaining control over the filtering and denoising processes. Because of its efficiency, FlowClus can be used to re-analyze multiple large datasets together, thereby leading to more standardized conclusions. FlowClus is freely available on GitHub (jsh58/FlowClus); it is written in C and supported on Linux.
Project description:Early marker-based metagenomic studies were performed without properly accounting for the effects of noise (sequencing errors, PCR single-base errors, and PCR chimeras). Denoising algorithms have been developed, but they were validated using data derived from mock communities, in which the true sequences were known. Since the algorithms were designed to be used in real community studies, it is important to evaluate the results in such cases. With this goal in mind, we processed a real 16S rRNA metagenomic dataset through five denoising pipelines. By reconstituting the sequence reads at each stage of the pipelines, we determined how the reads were being altered. In one denoising pipeline, AmpliconNoise, we found that the algorithm that was designed to remove pyrosequencing errors changed the reads in a manner inconsistent with the known spectrum of these errors, until one of the parameters was increased substantially from its default value. Additionally, because the longest read was picked as the representative for each cluster, sequences were added to the 3' ends of shorter reads that were often dissimilar from what had been removed by the truncations of the previous filtering step. In QIIME, the denoising algorithm caused a much larger number of changes to the reads unless the parameters were changed from their defaults. The denoising pipeline in mothur avoided some of these negative side-effects because of its strict default filtering criteria, but these criteria also greatly limited the sequence information produced at the end of the pipeline. We recommend that those using these denoising pipelines be cognizant of these issues and examine how their reads are being transformed by the denoising process as a component of their analysis.
Project description:Haptophyta encompasses more than 300 species of mostly marine pico- and nanoplanktonic flagellates. Our aims were to investigate the Oslofjorden haptophyte diversity and vertical distribution by metabarcoding, and to improve the approach to study haptophyte community composition, richness and proportional abundance by comparing two rRNA markers and scanning electron microscopy (SEM). Samples were collected in August 2013 at the Outer Oslofjorden, Norway. Total RNA/cDNA was amplified by haptophyte-specific primers targeting the V4 region of the 18S, and the D1-D2 region of the 28S rRNA. Taxonomy was assigned using curated haptophyte reference databases and phylogenetic analyses. Both marker genes showed Chrysochromulinaceae and Prymnesiaceae to be the families with highest number of Operational Taxonomic Units (OTUs), as well as proportional abundance. The 18S rRNA data set also contained OTUs assigned to eight supported and defined clades consisting of environmental sequences only, possibly representing novel lineages from family to class. We also recorded new species for the area. Comparing coccolithophores by SEM with metabarcoding shows a good correspondence with the 18S rRNA gene proportional abundances. Our results contribute to link morphological and molecular data and 28S to 18S rRNA gene sequences of haptophytes without cultured representatives, and to improve metabarcoding methodology.
Project description:BACKGROUND: The popularity of new sequencing technologies has led to an explosion of possible applications, including new approaches in biodiversity studies. However each of these sequencing technologies suffers from sequencing errors originating from different factors. For 16S rRNA metagenomics studies, the 454 pyrosequencing technology is one of the most frequently used platforms, but sequencing errors still lead to important data analysis issues (e.g. in clustering in taxonomic units and biodiversity estimation). Moreover, retaining a higher portion of the sequencing data by preserving as much of the read length as possible while maintaining the error rate within an acceptable range, will have important consequences at the level of taxonomic precision. RESULTS: The new error correction algorithm proposed in this work - NoDe (Noise Detector) - is trained to identify those positions in 454 sequencing reads that are likely to have an error, and subsequently clusters those error-prone reads with correct reads resulting in error-free representative read. A benchmarking study with other denoising algorithms shows that NoDe can detect up to 75% more errors in a large scale mock community dataset, and this with a low computational cost compared to the second best algorithm considered in this study. The positive effect of NoDe in 16S rRNA studies was confirmed by the beneficial effect on the precision of the clustering of pyrosequencing reads in operational taxonomic units. CONCLUSIONS: NoDe was shown to be a computational efficient denoising algorithm for pyrosequencing reads, producing the lowest error rates in an extensive benchmarking study with other denoising algorithms.
Project description:The development of high-throughput sequencing technologies has revolutionized the field of microbial ecology via the sequencing of phylogenetic marker genes (e.g. 16S rRNA gene amplicon sequencing). Denoising, the removal of sequencing errors, is an important step in preprocessing amplicon sequencing data. The increasing popularity of the Illumina MiSeq platform for these applications requires the development of appropriate denoising methods.The newly proposed denoising algorithm IPED includes a machine learning method which predicts potentially erroneous positions in sequencing reads based on a combination of quality metrics. Subsequently, this information is used to group those error-containing reads with correct reads, resulting in error-free consensus reads. This is achieved by masking potentially erroneous positions during this clustering step. Compared to the second best algorithm available, IPED detects double the amount of errors. Reducing the error rate had a positive effect on the clustering of reads in operational taxonomic units, with an almost perfect correspondence between the number of clusters and the theoretical number of species present in the mock communities.Our algorithm IPED is a powerful denoising tool for correcting sequencing errors in Illumina MiSeq 16S rRNA gene amplicon sequencing data. Apart from significantly reducing the error rate of the sequencing reads, it has also a beneficial effect on their clustering into operational taxonomic units. IPED is freely available at http://science.sckcen.be/en/Institutes/EHS/MCB/MIC/Bioinformatics/ .
Project description:High-throughput parallel sequencing is a powerful tool for the quantification of microbial diversity through the amplification of nuclear ribosomal gene regions. Recent work has extended this approach to the quantification of diversity within otherwise difficult-to-study metazoan groups. However, nuclear ribosomal genes present both analytical challenges and practical limitations that are a consequence of the mutational properties of nuclear ribosomal genes. Here we exploit useful properties of protein-coding genes for cross-species amplification and denoising of 454 flowgrams. We first use experimental mixtures of species from the class Collembola to amplify and pyrosequence the 5' region of the COI barcode, and we implement a new algorithm called PyroClean for the denoising of Roche GS FLX pyrosequences. Using parameter values from the analysis of experimental mixtures, we then analyse two communities sampled from field sites on the island of Tenerife. Cross-species amplification success of target mitochondrial sequences in experimental species mixtures is high; however, there is little relationship between template DNA concentrations and pyrosequencing read abundance. Homopolymer error correction and filtering against a consensus reference sequence reduced the volume of unique sequences to approximately 5% of the original unique raw reads. Filtering of remaining non-target sequences attributed to PCR error, sequencing error, or numts further reduced unique sequence volume to 0.8% of the original raw reads. PyroClean reduces or eliminates the need for an additional, time-consuming step to cluster reads into Operational Taxonomic Units, which facilitates the detection of intraspecific DNA sequence variation. PyroCleaned sequence data from field sites in Tenerife demonstrate the utility of our approach for quantifying evolutionary diversity and its spatial structure. Comparison of our sequence data to public databases reveals that we are able to successfully recover both interspecific and intraspecific sequence diversity.
Project description:Viruses influence the ecology and diversity of phytoplankton in the ocean. Most studies of phytoplankton host-virus interactions have focused on bloom-forming species like Emiliania huxleyi or Phaeocystis spp. The role of viruses infecting phytoplankton that do not form conspicuous blooms have received less attention. Here we explore the dynamics of phytoplankton and algal viruses over several sequential seasons, with a focus on the ubiquitous and diverse phytoplankton division Haptophyta, and their double-stranded DNA viruses, potentially with the capacity to infect the haptophytes. Viral and phytoplankton abundance and diversity showed recurrent seasonal changes, mainly explained by hydrographic conditions. By 454 tag-sequencing we revealed 93 unique haptophyte operational taxonomic units (OTUs), with seasonal changes in abundance. Sixty-one unique viral OTUs, representing Megaviridae and Phycodnaviridae, showed only distant relationship with currently isolated algal viruses. Haptophyte and virus community composition and diversity varied substantially throughout the year, but in an uncoordinated manner. A minority of the viral OTUs were highly abundant at specific time-points, indicating a boom-bust relationship with their host. Most of the viral OTUs were very persistent, which may represent viruses that coexist with their hosts, or able to exploit several host species.
Project description:Sequencing of 16S rRNA gene tags is a popular method for profiling and comparing microbial communities. The protocols and methods used, however, vary considerably with regard to amplification primers, sequencing primers, sequencing technologies; as well as quality filtering and clustering. How results are affected by these choices, and whether data produced with different protocols can be meaningfully compared, is often unknown. Here we compare results obtained using three different amplification primer sets (targeting V4, V6-V8, and V7-V8) and two sequencing technologies (454 pyrosequencing and Illumina MiSeq) using DNA from a mock community containing a known number of species as well as complex environmental samples whose PCR-independent profiles were estimated using shotgun sequencing. We find that paired-end MiSeq reads produce higher quality data and enabled the use of more aggressive quality control parameters over 454, resulting in a higher retention rate of high quality reads for downstream data analysis. While primer choice considerably influences quantitative abundance estimations, sequencing platform has relatively minor effects when matched primers are used. Beta diversity metrics are surprisingly robust to both primer and sequencing platform biases.