DNA barcoding and metabarcoding of standardized samples reveal patterns of marine benthic diversity

Documenting the diversity of marine life is challenging because many species are cryptic, small, and rare, and belong to poorly known groups. New sequencing technologies, especially when combined with standardized sampling, promise to make comprehensive biodiversity assessments and monitoring feasible on a large scale. We used this approach to characterize patterns of diversity on oyster reefs across a range of geographic scales comprising a temperate location [Virginia (VA)] and a subtropical location [Florida (FL)]. Eukaryotic organisms that colonized multilayered settlement surfaces (autonomous reef monitoring structures) over a 6-mo period were identified by cytochrome c oxidase subunit I barcoding (>2-mm mobile organisms) and metabarcoding (sessile and smaller mobile organisms). In a total area of ∼15.64 m2 and volume of ∼0.09 m3, 2,179 operational taxonomic units (OTUs) were recorded from 983,056 sequences. However, only 10.9% could be matched to reference barcodes in public databases, with only 8.2% matching barcodes with both genus and species names. Taxonomic coverage was broad, particularly for animals (22 phyla recorded), but 35.6% of OTUs detected via metabarcoding could not be confidently assigned to a taxonomic group. The smallest size fraction (500 to 106 μm) was the most diverse (more than two-thirds of OTUs). There was little taxonomic overlap between VA and FL, and samples separated by ∼2 m were significantly more similar than samples separated by ∼100 m. Ground-truthing with independent assessments of taxonomic composition indicated that both presence–absence information and relative abundance information are captured by metabarcoding data, suggesting considerable potential for ecological studies and environmental monitoring.

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Assessment of Sustainability of Bio Treated Lignocellulose-Based Oleogels

Polymers ◽

10.3390/polym13020267 ◽

2021 ◽

Vol 13 (2) ◽

pp. 267

Author(s):

Carmen Fajardo ◽

Alba Blánquez ◽

Gabriela Domínguez ◽

Antonio M. Borrero-López ◽

Concepción Valencia ◽

...

Keyword(s):

Large Scale ◽

Structural Changes ◽

Raw Materials ◽

Agricultural Residues ◽

Molecular Techniques ◽

Added Value ◽

Renewable Raw Materials ◽

Sequencing Technologies ◽

Phylogenetic Composition ◽

Eukaryotic Organisms

The development of biological strategies to obtain new high-added value biopolymers from lignocellulosic biomass is a current challenge for scientific community. This study evaluates the biodegradability and ecotoxicity of new formulated oleogels obtained from fermented agricultural residues with Streptomyces, previously reported to show improved rheological and tribological characteristics compared to commercial mineral lubricants. Both new oleogels exhibited higher biodegradation rates than the commercial grease. Classical ecotoxicological bioassays using eukaryotic organisms (Lactuca sativa, Caenorhabditis elegans) showed that the toxic impact of the produced bio-lubricants was almost negligible and comparable to the commercial grease for the target organisms. In addition, high throughput molecular techniques using emerging next-generation DNA-sequencing technologies (NGS) were applied to study the structural changes of lubricant-exposed microbial populations of a standard soil. Results obtained showed that disposal of biomass-based lubricants in the soil environment did not substantially modify the structure and phylogenetic composition of the microbiome. These findings point out the feasibility and sustainability, in terms of biodegradability and eco-safety, of the new bio-lubricants in comparison with commercial mineral greases. This technology entails a promising biological strategy to replace fossil and non-renewable raw materials as well as to obtain useful biopolymers from agricultural residues with potential for large-scale applications.

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MitoZ: A toolkit for mitochondrial genome assembly, annotation and visualization

10.1101/489955 ◽

2018 ◽

Cited By ~ 1

Author(s):

Guanliang Meng ◽

Yiyuan Li ◽

Chentao Yang ◽

Shanlin Liu

Keyword(s):

Mitochondrial Genome ◽

Open Source Software ◽

Large Scale ◽

High Throughput Sequencing ◽

De Novo ◽

Ecological Studies ◽

Free Open Source Software ◽

Sequencing Technologies ◽

Evolutionary Studies ◽

Free Open Source

AbstractMitochondrial genome (mitogenome) plays important roles in evolutionary and ecological studies. It becomes routine to utilize multiple genes on mitogenome or the entire mitogenomes to investigate phylogeny and biodiversity of focal groups with the onset of High Throughput Sequencing technologies. We developed a mitogenome toolkit MitoZ, consisting of independent modules ofde novoassembly, findMitoScaf, annotation and visualization, that can generate mitogenome assembly together with annotation and visualization results from HTS raw reads. We evaluated its performance using a total of 50 samples of which mitogenomes are publicly available. The results showed that MitoZ can recover more full-length mitogenomes with higher accuracy compared to the other available mitogenome assemblers. Overall, MitoZ provides a one-click solution to construct the annotated mitogenome from HTS raw data and will facilitate large scale ecological and evolutionary studies. MitoZ is free open source software distributed under GPLv3 license and available athttps://github.com/linzhi2013/MitoZ.

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Nebula: ultra-efficient mapping-free structural variant genotyper

Nucleic Acids Research ◽

10.1093/nar/gkab025 ◽

2021 ◽

Author(s):

Parsoa Khorsand ◽

Fereydoun Hormozdiari

Keyword(s):

Large Scale ◽

Structural Variants ◽

Sequencing Technologies ◽

Generic Framework ◽

Common Genetic Variants ◽

Order Of Magnitude ◽

Complex Events ◽

Comparable Accuracy ◽

Using Data ◽

Computational Resources

Abstract Large scale catalogs of common genetic variants (including indels and structural variants) are being created using data from second and third generation whole-genome sequencing technologies. However, the genotyping of these variants in newly sequenced samples is a nontrivial task that requires extensive computational resources. Furthermore, current approaches are mostly limited to only specific types of variants and are generally prone to various errors and ambiguities when genotyping complex events. We are proposing an ultra-efficient approach for genotyping any type of structural variation that is not limited by the shortcomings and complexities of current mapping-based approaches. Our method Nebula utilizes the changes in the count of k-mers to predict the genotype of structural variants. We have shown that not only Nebula is an order of magnitude faster than mapping based approaches for genotyping structural variants, but also has comparable accuracy to state-of-the-art approaches. Furthermore, Nebula is a generic framework not limited to any specific type of event. Nebula is publicly available at https://github.com/Parsoa/Nebula.

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General Unified Microbiome Profiling Pipeline (GUMPP) for Large Scale, Streamlined and Reproducible Analysis of Bacterial 16S rRNA Data to Predicted Microbial Metagenomes, Enzymatic Reactions and Metabolic Pathways

Metabolites ◽

10.3390/metabo11060336 ◽

2021 ◽

Vol 11 (6) ◽

pp. 336

Author(s):

Boštjan Murovec ◽

Leon Deutsch ◽

Blaž Stres

Keyword(s):

16S Rrna ◽

Metabolic Pathways ◽

Large Scale ◽

Enzymatic Reactions ◽

Operational Taxonomic Units ◽

Biochemical Pathways ◽

Meaningful Information ◽

Novel Biomarkers ◽

Reproducible Analysis ◽

Microbiome Profiling

General Unified Microbiome Profiling Pipeline (GUMPP) was developed for large scale, streamlined and reproducible analysis of bacterial 16S rRNA data and prediction of microbial metagenomes, enzymatic reactions and metabolic pathways from amplicon data. GUMPP workflow introduces reproducible data analyses at each of the three levels of resolution (genus; operational taxonomic units (OTUs); amplicon sequence variants (ASVs)). The ability to support reproducible analyses enables production of datasets that ultimately identify the biochemical pathways characteristic of disease pathology. These datasets coupled to biostatistics and mathematical approaches of machine learning can play a significant role in extraction of truly significant and meaningful information from a wide set of 16S rRNA datasets. The adoption of GUMPP in the gut-microbiota related research enables focusing on the generation of novel biomarkers that can lead to the development of mechanistic hypotheses applicable to the development of novel therapies in personalized medicine.

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Can Large-Scale Climatic Models Be Linked with Multiscale Ecological Studies?*

Conservation Biology ◽

10.1046/j.1523-1739.1993.07020256.x ◽

1993 ◽

Vol 7 (2) ◽

pp. 256-270 ◽

Cited By ~ 62

Author(s):

Terry L. Root ◽

Stephen H. Schneider

Keyword(s):

Large Scale ◽

Ecological Studies

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The challenges and potential utility of phenotypic specimen-level phylogeny based on maximum parsimony

Earth and Environmental Science Transactions of the Royal Society of Edinburgh ◽

10.1017/s1755691018000877 ◽

2018 ◽

Vol 109 (1-2) ◽

pp. 301-323 ◽

Cited By ~ 2

Author(s):

Emanuel TSCHOPP ◽

Paul UPCHURCH

Keyword(s):

Phylogenetic Analysis ◽

Maximum Parsimony ◽

Large Scale ◽

Intraspecific Variability ◽

Species Level ◽

A Posteriori ◽

Operational Taxonomic Units ◽

Vertebrate Palaeontology ◽

Tree Topologies ◽

Parsimony Criterion

ABSTRACTSpecimen-level phylogenetic approaches are widely used in molecular biology for taxonomic and systematic purposes. However, they have been largely ignored in analyses based on morphological traits, where phylogeneticists mostly resort to species-level analyses. Recently, a number of specimen-level studies have been published in vertebrate palaeontology. These studies indicate that specimen-level phylogeny may be a very useful tool for systematic reassessments at low taxonomic levels. Herein, we review the challenges when working with individual organisms as operational taxonomic units in a palaeontological context, and propose guidelines of how best to perform a specimen-level phylogenetic analysis using the maximum parsimony criterion. Given that no single methodology appears to be perfectly suited to resolve relationships among individuals, and that different taxa probably require different approaches to assess their systematics, we advocate the use of a number of methodologies. In particular, we recommend the inclusion of as many specimens and characters as feasible, and the analysis of relationships using an extended implied weighting approach with different downweighting functions. Resulting polytomies should be explored using a posteriori pruning of unstable specimens, and conflicting tree topologies between different iterations of the analysis should be evaluated by a combination of support values such as jackknifing and symmetric resampling. Species delimitation should be consistent among the ingroup and based on a reproducible approach. Although time-consuming and methodologically challenging, specimen-level phylogenetic analysis is a highly useful tool to assess intraspecific variability and provide the basis for a more informed and accurate creation of species-level operational taxonomic units in large-scale systematic studies. It also has the potential to inform us about past speciation processes, morphological trait evolution, and their potential intrinsic and extrinsic drivers in pre-eminent detail.

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SOMDE: A scalable method for identifying spatially variable genes with self-organizing map

10.1101/2020.12.10.419549 ◽

2020 ◽

Author(s):

Minsheng Hao ◽

Kui Hua ◽

Xuegong Zhang

Keyword(s):

Gene Expression ◽

Large Scale ◽

Expression Patterns ◽

Self Organizing Map ◽

Expression Data ◽

Spatial Expression ◽

Variable Expression ◽

Sequencing Technologies ◽

Physical Context ◽

Variable Genes

AbstractRecent developments of spatial transcriptomic sequencing technologies provide powerful tools for understanding cells in the physical context of tissue micro-environments. A fundamental task in spatial gene expression analysis is to identify genes with spatially variable expression patterns, or spatially variable genes (SVgenes). Several computational methods have been developed for this task. Their high computational complexity limited their scalability to the latest and future large-scale spatial expression data.We present SOMDE, an efficient method for identifying SVgenes in large-scale spatial expression data. SOMDE uses selforganizing map (SOM) to cluster neighboring cells into nodes, and then uses a Gaussian Process to fit the node-level spatial gene expression to identify SVgenes. Experiments show that SOMDE is about 5-50 times faster than existing methods with comparable results. The adjustable resolution of SOMDE makes it the only method that can give results in ~5 minutes in large datasets of more than 20,000 sequencing sites. SOMDE is available as a python package on PyPI at https://pypi.org/project/somde.

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Utilizing the VirIdAl Pipeline to Search for Viruses in the Metagenomic Data of Bat Samples

Viruses ◽

10.3390/v13102006 ◽

2021 ◽

Vol 13 (10) ◽

pp. 2006

Author(s):

Anna Y Budkina ◽

Elena V Korneenko ◽

Ivan A Kotov ◽

Daniil A Kiselev ◽

Ilya V Artyushin ◽

...

Keyword(s):

Large Scale ◽

High Throughput Sequencing ◽

Metagenomic Data ◽

Sequencing Data ◽

Viral Pathogens ◽

Genomic Databases ◽

Bioinformatic Pipeline ◽

Viral Genomes ◽

Sequencing Technologies ◽

Viral Screening

According to various estimates, only a small percentage of existing viruses have been discovered, naturally much less being represented in the genomic databases. High-throughput sequencing technologies develop rapidly, empowering large-scale screening of various biological samples for the presence of pathogen-associated nucleotide sequences, but many organisms are yet to be attributed specific loci for identification. This problem particularly impedes viral screening, due to vast heterogeneity in viral genomes. In this paper, we present a new bioinformatic pipeline, VirIdAl, for detecting and identifying viral pathogens in sequencing data. We also demonstrate the utility of the new software by applying it to viral screening of the feces of bats collected in the Moscow region, which revealed a significant variety of viruses associated with bats, insects, plants, and protozoa. The presence of alpha and beta coronavirus reads, including the MERS-like bat virus, deserves a special mention, as it once again indicates that bats are indeed reservoirs for many viral pathogens. In addition, it was shown that alignment-based methods were unable to identify the taxon for a large proportion of reads, and we additionally applied other approaches, showing that they can further reveal the presence of viral agents in sequencing data. However, the incompleteness of viral databases remains a significant problem in the studies of viral diversity, and therefore necessitates the use of combined approaches, including those based on machine learning methods.

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Assessing species coverage and assembly quality of rapidly accumulating sequenced genomes

10.1101/2021.10.15.464561 ◽

2021 ◽

Author(s):

Romain Feron ◽

Robert Michael Waterhouse

Keyword(s):

Large Scale ◽

Automated Analysis ◽

Single Copy ◽

The United States ◽

Community Resource ◽

Assembly Quality ◽

Sequencing Technologies ◽

Analysis Workflow ◽

Genome Assemblies

Ambitious initiatives to coordinate genome sequencing of Earth's biodiversity mean that the accumulation of genomic data is growing rapidly. In addition to cataloguing biodiversity, these data provide the basis for understanding biological function and evolution. Accurate and complete genome assemblies offer a comprehensive and reliable foundation upon which to advance our understanding of organismal biology at genetic, species, and ecosystem levels. However, ever-changing sequencing technologies and analysis methods mean that available data are often heterogeneous in quality. In order to guide forthcoming genome generation efforts and promote efficient prioritisation of resources, it is thus essential to define and monitor taxonomic coverage and quality of the data. Here we present an automated analysis workflow that surveys genome assemblies from the United States National Center for Biotechnology Information (NCBI), assesses their completeness using the relevant Benchmarking Universal Single-Copy Orthologue (BUSCO) datasets, and collates the results into an interactively browsable resource. We apply our workflow to produce a community resource of available assemblies from the phylum Arthropoda, the Arthropoda Assembly Assessment Catalogue. Using this resource, we survey current taxonomic coverage and assembly quality at the NCBI, we examine how key assembly metrics relate to gene content completeness, and we compare results from using different BUSCO lineage datasets. These results demonstrate how the workflow can be used to build a community resource that enables large-scale assessments to survey species coverage and data quality of available genome assemblies, and to guide prioritisations for ongoing and future sampling, sequencing, and genome generation initiatives.

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OncoThreads: Visualization of Large Scale Longitudinal Cancer Molecular Data

10.31219/osf.io/b3n4u ◽

2021 ◽

Author(s):

Theresa A Harbig ◽

Sabrina Nusrat ◽

Tali Mazor ◽

Qianwen Wang ◽

Alexander Thomson ◽

...

Keyword(s):

Longitudinal Data ◽

Large Scale ◽

Cancer Genomics ◽

Temporal Patterns ◽

Molecular Data ◽

Liquid Biopsies ◽

Sequencing Technologies ◽

Molecular Features ◽

Wide Range ◽

The Impact

Molecular profiling of patient tumors and liquid biopsies over time with next-generation sequencing technologies and new immuno-profile assays are becoming part of standard research and clinical practice. With the wealth of new longitudinal data, there is a critical need for visualizations for cancer researchers to explore and interpret temporal patterns not just in a single patient but across cohorts. To address this need we developed OncoThreads, a tool for the visualization of longitudinal clinical and cancer genomics and other molecular data in patient cohorts. The tool visualizes patient cohorts as temporal heatmaps and Sankey diagrams that support the interactive exploration and ranking of a wide range of clinical and molecular features. This allows analysts to discover temporal patterns in longitudinal data, such as the impact of mutations on response to a treatment, e.g. emergence of resistant clones. We demonstrate the functionality of OncoThreads using a cohort of 23 glioma patients sampled at 2-4 timepoints. OncoThreads is freely available at http://oncothreads.gehlenborglab.org and implemented in Javascript using the cBioPortal web API as a backend.

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