A robust approach to estimate relative phytoplankton cell abundances from metagenomes
Ontology highlight
ABSTRACT:
ORGANISM(S): cyanobacteria algae land plants
SUBMITTER:
PROVIDER: S-BSST761 | biostudies-other |
REPOSITORIES: biostudies-other
Similar Datasets
Project description:Phytoplankton account for >45% of global primary production, and have an enormous impact on aquatic food webs and on the entire Earth System. Their members are found among prokaryotes (cyanobacteria) and multiple eukaryotic lineages containing chloroplasts. Genetic surveys of phytoplankton communities generally consist of PCR amplification of bacterial (16S), nuclear (18S) and/or chloroplastic (16S) rRNA marker genes from DNA extracted from environmental samples. However, our appreciation of phytoplankton abundance or biomass is limited by PCR-amplification biases, rRNA gene copy number variations across taxa, and the fact that rRNA genes do not provide insights into metabolic traits such as photosynthesis. Here, we targeted the photosynthetic gene psbO from metagenomes to circumvent these limitations: the method is PCR-free, and the gene is universally and exclusively present in photosynthetic prokaryotes and eukaryotes, mainly in one copy per genome. We applied and validated this new strategy with the size-fractionated marine samples collected by Tara Oceans, and showed improved correlations with flow cytometry and microscopy than when based on rRNA genes. Furthermore, we revealed unexpected features of the ecology of these ecosystems, such as the high abundance of picocyanobacterial aggregates and symbionts in the ocean, and the decrease in relative abundance of phototrophs towards the larger size classes of marine dinoflagellates. To facilitate the incorporation of psbO in molecular-based surveys, we compiled a curated database of >18,000 unique sequences. Overall, psbO appears to be a promising new gene marker for molecular-based evaluations of entire phytoplankton communities.
| S-EPMC10078663 | biostudies-literature
Project description:BackgroundMetagenomic profiling algorithms commonly rely on genomic differences between lineages, strains, or species to infer the relative abundances of sequences present in a sample. This observation plays an important role in the analysis of diverse microbial communities, where targeted sequencing of 16S and 18S rRNA, both well-known hypervariable genomic regions, have led to insights into microbial diversity and the discovery of novel organisms. However, the variable nature of discriminatory regions can also act as a double-edged sword, as the sought-after variability can make it difficult to design primers for their amplification through PCR. Moreover, the most variable regions are not necessarily the most informative regions for the purpose of differentiation; one should focus on regions that maximize the number of lineages that can be distinguished.ResultsHere we present AmpliDiff, a computational tool that simultaneously finds highly discriminatory genomic regions in viral genomes of a single species, as well as primers allowing for the amplification of these regions. We show that regions and primers found by AmpliDiff can be used to accurately estimate relative abundances of SARS-CoV-2 lineages, for example in wastewater sequencing data. We obtain errors that are comparable with using whole genome information to estimate relative abundances. Furthermore, our results show that AmpliDiff is robust against incomplete input data and that primers designed by AmpliDiff also bind to genomes sampled months after the primers were selected.ConclusionsWith AmpliDiff we provide an effective, cost-efficient alternative to whole genome sequencing for estimating lineage abundances in viral metagenomes.
| S-EPMC10960382 | biostudies-literature
Project description:SUMMARY:We present an R package called iq to enable accurate protein quantification for label-free data-independent acquisition (DIA) mass spectrometry-based proteomics, a recently developed global approach with superior quantitative consistency. We implement the popular maximal peptide ratio extraction module of the MaxLFQ algorithm, so far only applicable to data-dependent acquisition mode using the software suite MaxQuant. Moreover, our implementation shows, for each protein separately, the validity of quantification over all samples. Hence, iq exports a state-of-the-art protein quantification algorithm to the emerging DIA mode in an open-source implementation. AVAILABILITY AND IMPLEMENTATION:The open-source R package is available on CRAN, https://github.com/tvpham/iq/releases and oncoproteomics.nl/iq. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.
| S-EPMC7178409 | biostudies-literature
Project description:Metagenomic and metatranscriptomic time-series data covering a 52-day period in the fall of 2016 provide an inventory of bacterial and archaeal community genes, transcripts, and taxonomy during an intense dinoflagellate bloom in Monterey Bay, CA, USA. The dataset comprises 84 metagenomes (0.8 terabases), 82 metatranscriptomes (1.1 terabases), and 88 16S rRNA amplicon libraries from samples collected on 41 dates. The dataset also includes 88 18S rRNA amplicon libraries, characterizing the taxonomy of the eukaryotic community during the bloom. Accompanying the sequence data are chemical and biological measurements associated with each sample. These datasets will facilitate studies of the structure and function of marine bacterial communities during episodic phytoplankton blooms.
| S-EPMC6646334 | biostudies-literature
Project description:BackgroundSpectral counting methods provide an easy means of identifying proteins with differing abundances between complex mixtures using shotgun proteomics data. The crux spectral-counts command, implemented as part of the Crux software toolkit, implements four previously reported spectral counting methods, the spectral index (SI(N)), the exponentially modified protein abundance index (emPAI), the normalized spectral abundance factor (NSAF), and the distributed normalized spectral abundance factor (dNSAF).ResultsWe compared the reproducibility and the linearity relative to each protein's abundance of the four spectral counting metrics. Our analysis suggests that NSAF yields the most reproducible counts across technical and biological replicates, and both SI(N) and NSAF achieve the best linearity.ConclusionsWith the crux spectral-counts command, Crux provides open-source modular methods to analyze mass spectrometry data for identifying and now quantifying peptides and proteins. The C++ source code, compiled binaries, spectra and sequence databases are available at http://noble.gs.washington.edu/proj/crux-spectral-counts.
| S-EPMC3599300 | biostudies-literature
Project description:Phytoplankton accessory pigments are commonly used to estimate phytoplankton size classes, particularly during development and validation of biogeochemical models and satellite ocean color-based algorithms. The diagnostic pigment analysis (DPA) is based on bulk measurements of pigment concentrations and relies on assumptions regarding the presence of specific pigments in different phytoplankton taxonomic groups. Three size classes are defined by the DPA: picoplankton, nanoplankton, and microplankton. Until now, the DPA has not been evaluated against an independent approach that provides phytoplankton size calculated on a per-cell basis. Automated quantitative cell imagery of microplankton and some nanoplankton, used in combination with conventional flow cytometry for enumeration of picoplankton and nanoplankton, provide a novel opportunity to perform an independent evaluation of the DPA. Here, we use a data set from the North Atlantic Ocean that encompasses all seasons and a wide range of chlorophyll concentrations (0.18-5.14 mg m-3). Results show that the DPA overestimates microplankton and picoplankton when compared to cytometry data, and subsequently underestimates the contribution of nanoplankton to total biomass. In contrast to the assumption made by the DPA that the microplankton size class is largely made up of diatoms and dinoflagellates, imaging-in-flow cytometry shows significant presence of diatoms and dinoflagellates in the nanoplankton size class. Additionally, chlorophyll b is commonly attributed solely to picoplankton by the DPA, but Chl b-containing phytoplankton are observed with imaging in both nanoplankton and microplankton size classes. We suggest revisions to the DPA equations and application of uncertainties when calculating size classes from diagnostic pigments.
| S-EPMC7589370 | biostudies-literature
Project description:Relative error estimation has been recently used in regression analysis. A crucial issue of the existing relative error estimation procedures is that they are sensitive to outliers. To address this issue, we employ the γ -likelihood function, which is constructed through γ -cross entropy with keeping the original statistical model in use. The estimating equation has a redescending property, a desirable property in robust statistics, for a broad class of noise distributions. To find a minimizer of the negative γ -likelihood function, a majorize-minimization (MM) algorithm is constructed. The proposed algorithm is guaranteed to decrease the negative γ -likelihood function at each iteration. We also derive asymptotic normality of the corresponding estimator together with a simple consistent estimator of the asymptotic covariance matrix, so that we can readily construct approximate confidence sets. Monte Carlo simulation is conducted to investigate the effectiveness of the proposed procedure. Real data analysis illustrates the usefulness of our proposed procedure.
| S-EPMC7513150 | biostudies-literature
Project description:Electrospray ionization (ESI) is widely used as an ionization source for the analysis of complex mixtures by mass spectrometry. However, different compounds ionize more or less effectively in the ESI source, meaning instrument responses can vary by orders of magnitude, often in hard-to-predict ways. This precludes the use of ESI for quantitative analysis where authentic standards are not available. Relative ionization efficiency (RIE) scales have been proposed as a route to predict the response of compounds in ESI. In this work, a scale of RIEs was constructed for 51 carboxylic acids, spanning a wide range of additional functionalities, to produce a model for predicting the RIE of unknown compounds. While using a limited number of compounds, we explore the usefulness of building a predictor using popular supervised regression techniques, encoding the compounds as combinations of different structural features using a range of common "fingerprints". It was found that Bayesian ridge regression gives the best predictive model, encoding compounds using features designed for activity coefficient models. This produced a predictive model with an R 2 score of 0.62 and a root-mean-square error (RMSE) of 0.362. Such scores are comparable to those obtained in previous studies but without the requirement to first measure or predict the physical properties of the compounds, potentially reducing the time required to make predictions.
| S-EPMC7191837 | biostudies-literature
Project description:Understanding the abundance change of certain bacterial taxa is quite important for the study of soil microbiology. However, the observed differences of relative abundances by high-throughput techniques may not accurately reflect those of the actual taxon abundances. This study investigated whether soil microbial abundances coupling with microbial quantities can be more informative in describing the microbial population distribution under different locations. We analyzed relative abundances of the major species in soil microbial communities from Beijing and Tibet grasslands by using 16 S rRNA high-throughput sequencing technique, and quantified the absolute bacterial cell numbers directly or indirectly by multiple culture-independent measurements, including adenosine tri-phosphate (ATP), flow cytometry (FCM), quantitative real-time PCR (qPCR), phospholipid fatty acids (PLFA) and microbial biomass Carbon (MBC). By comparison of the relative abundance and the estimated absolute abundances (EAA) of the major components in soil microbial communities, several dominant phyla, including Actinobacteria, Bacteroidetes, Verrucomicrobia, Chloroflexi, Gemmatimonates and Planctomycetes, showed significantly different trends. These results indicated that the change in EAA might be more informative in describing the dynamics of a population in a community. Further studies of soil microbes should combine the quantification and relative abundances of the microbial communities for the comparisons among various locations.
| S-EPMC5500469 | biostudies-literature
Project description:Ecologists often analyze relative abundances, which are an example of compositional data. However, they have made surprisingly little use of recent advances in the field of compositional data analysis. Compositions form a vector space in which addition and scalar multiplication are replaced by operations known as perturbation and powering. This algebraic structure makes it easy to understand how relative abundances change along environmental gradients. We illustrate this with an analysis of changes in hard-substrate marine communities along a depth gradient. We fit a quadratic multivariate regression model with multinomial observations to point count data obtained from video transects. As well as being an appropriate observation model in this case, the multinomial deals with the problem of zeros, which often makes compositional data analysis difficult. We show how the algebra of compositions can be used to understand patterns in dissimilarity. We use the calculus of simplex-valued functions to estimate rates of change, and to summarize the structure of the community over a vertical slice. We discuss the benefits of the compositional approach in the interpretation and visualization of relative abundance data.
| S-EPMC6164550 | biostudies-literature