Metabarcoding free‐living marine nematodes using curated 18S and CO1 reference sequence databases for species‐level taxonomic assignments

AbstractExploring the genetic diversity of microbes within the environment through metagenomic sequencing first requires classifying these reads into taxonomic groups. Current methods compare these sequencing data with existing biased and limited reference databases. Several recent evaluation studies demonstrate that current methods either lack sufficient sensitivity for species-level assignments or suffer from false positives, overestimating the number of species in the metagenome. Both are especially problematic for the identification of low-abundance microbial species, e. g. detecting pathogens in ancient metagenomic samples. We present a new method, SPARSE, which improves taxonomic assignments of metagenomic reads. SPARSE balances existing biased reference databases by grouping reference genomes into similarity-based hierarchical clusters, implemented as an efficient incremental data structure. SPARSE assigns reads to these clusters using a probabilistic model, which specifically penalizes non-specific mappings of reads from unknown sources and hence reduces false-positive assignments. Our evaluation on simulated datasets from two recent evaluation studies demonstrated the improved precision of SPARSE in comparison to other methods for species-level classification. In a third simulation, our method successfully differentiated multiple co-existing Escherichia coli strains from the same sample. In real archaeological datasets, SPARSE identified ancient pathogens with ≤ 0.02% abundance, consistent with published findings that required additional sequencing data. In these datasets, other methods either missed targeted pathogens or reported non-existent ones. SPARSE and all evaluation scripts are available at https://github.com/zheminzhou/SPARSE.

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An iterative and automated computational pipeline for untargeted strain-level identification using MS/MS spectra from pathogenic samples

10.1101/812313 ◽

2019 ◽

Author(s):

Mathias Kuhring ◽

Joerg Doellinger ◽

Andreas Nitsche ◽

Thilo Muth ◽

Bernhard Y. Renard

Keyword(s):

Statistical Power ◽

Sequence Data ◽

A Priori ◽

Search Space ◽

Strain Level ◽

Reference Sequence ◽

Viral Origin ◽

Identification Of Species ◽

Taxonomic Assignments

AbstractUntargeted accurate strain-level classification of a priori unidentified organisms using tandem mass spectrometry is a challenging task. Reference databases often lack taxonomic depth, limiting peptide assignments to the species level. However, the extension with detailed strain information increases runtime and decreases statistical power. In addition, larger databases contain a higher number of similar proteomes.We present TaxIt, an iterative workflow to address the increasing search space required for MS/MS-based strain-level classification of samples with unknown taxonomic origin. TaxIt first applies reference sequence data for initial identification of species candidates, followed by automated acquisition of relevant strain sequences for low level classification. Furthermore, proteome similarities resulting in ambiguous taxonomic assignments are addressed with an abundance weighting strategy to improve candidate confidence.We apply our iterative workflow on several samples of bacterial and viral origin. In comparison to non-iterative approaches using unique peptides or advanced abundance correction, TaxIt identifies microbial strains correctly in all examples presented (with one tie), thereby demonstrating the potential for untargeted and deeper taxonomic classification. TaxIt makes extensive use of public, unrestricted and continuously growing sequence resources such as the NCBI databases and is available under open-source license at https://gitlab.com/rki_bioinformatics.

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Free‐living marine nematodes of the deseado river estuary (Siphonolaimoidea, Siphonolaimidae, Linhomoeidae) Santa Cruz, Argentina. 6

Studies on Neotropical Fauna and Environment ◽

10.1080/01650528909360794 ◽

1989 ◽

Vol 24 (4) ◽

pp. 231-247 ◽

Cited By ~ 3

Author(s):

Catalina T. Pastor de Ward

Keyword(s):

River Estuary ◽

Marine Nematodes ◽

Santa Cruz ◽

Free Living

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Differences in microhabitat, abundance, biomass and body size between oxybiotic and thiobiotic free-living marine nematodes

Deep Sea Research Part B Oceanographic Literature Review ◽

10.1016/0198-0254(87)90291-3 ◽

1987 ◽

Vol 34 (9) ◽

pp. 776

Keyword(s):

Body Size ◽

Marine Nematodes ◽

Free Living

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Migratory behavior of free-living marine nematodes surrounded by sediments experimentally contaminated by mixtures of polycyclic aromatic hydrocarbons

Journal of King Saud University - Science ◽

10.1016/j.jksus.2019.11.025 ◽

2020 ◽

Vol 32 (2) ◽

pp. 1339-1345 ◽

Cited By ~ 2

Author(s):

Mohamed Allouche ◽

Ahmed Nasri ◽

Abdel Halim Harrath ◽

Lamjed Mansour ◽

Hamouda Beyrem ◽

...

Keyword(s):

Polycyclic Aromatic Hydrocarbons ◽

Aromatic Hydrocarbons ◽

Migratory Behavior ◽

Marine Nematodes ◽

Free Living ◽

Polycyclic Aromatic

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Spatial aggregation patterns of free-living marine nematodes in contrasting sandy beach micro-habitats

Journal of the Marine Biological Association of the United Kingdom ◽

10.1017/s0025315410001128 ◽

2010 ◽

Vol 91 (3) ◽

pp. 615-622 ◽

Cited By ~ 22

Author(s):

Ruth Gingold ◽

Silvia E. Ibarra-Obando ◽

Axayácatl Rocha-Olivares

Keyword(s):

Negative Correlation ◽

Sandy Beach ◽

Distribution Patterns ◽

Significant Negative Correlation ◽

Small Scale ◽

Spatial Distributions ◽

Biological Processes ◽

Marine Nematodes ◽

Free Living ◽

Biological Similarity

In the absence of chemical or physical gradients, random displacement of organisms can result in unpredictable distribution patterns. In spite of a limited locomotive capability, marine nematodes may choose where to settle after re-suspension and may maintain their position in the sediment under calm conditions, leading to small-scale (<1 m) spatial variability. However, in more energetic environments, nematodes become re-suspended with sediments and re-distributed at distances dependent on prevalent hydrodynamic regimes, from metre- to decametre-scale or more. In this study, we tested the hypothesis that micro-habitats (i.e. runnels and sandbars) in a macrotidal sandy beach influence the distribution patterns of free-living marine nematodes by exhibiting contrasting hydrodynamic regimes. Specifically, we predicted patchier distributions in the calmer environment (runnel). We sampled nematodes in each habitat from <1 m to decametre scales. Our results show more heterogeneous spatial distributions in the runnel, presumably owing to a predominance of active displacement under calmer conditions and sediment cohesion by algal films. Biological similarity among runnel replicates was low, whereas replicates from the sandbar exhibited higher similarity, presumably because of homogenization of the sediment and inhabiting fauna by tidal currents. A significant negative correlation between biological similarity and sampling distance was found in the runnel, but not in the sandbar. The most similar samples were the closest in the runnel and the most distant in the sandbar. More patchily distributed taxa were found in the runnel and a larger fraction of homogeneously or randomly distributed taxa in the sandbar. We conclude that different hydrodynamic regimes in contrasting intertidal micro-habitats significantly influenced the nematofaunal distribution, resulting in different spatial patterns next to one another in the same beach. This has significant implications for sampling and monitoring designs and begs the need for detailed studies about the physical and biological processes governing meiobenthic communities.

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