Binning Approach Research Articles

Functional binning: improving convergence of Eulerian statistics from Lagrangian particle tracking

In the analysis of Lagrangian particle tracking data, ensemble averaging with spatial bins is used to generate Eulerian flow statistics. Due to the scattered nature of the particles over independent snapshots, the possible spatial resolution is directly dependent on the measured particle position accuracy and the amount of available data. This requires a balance between convergence of the underlying statistic and the bin resolution. Current binning approaches use the velocity information of the particle positions at single time steps directly and do not exploit the additional information available from the temporal filtering of the tracking process. We present a novel functional approach to the binning procedure that extracts all available information from the particle tracks and improves convergence speed. For a given experiment this allows for higher resolution of flow statistics than classical approaches or alternatively to reduce the necessary amount of data required for a given resolution. Furthermore, uncertainty measures from the particles position, velocity and acceleration can be propagated directly by weighting coefficients.

Identification of a super-functional Tfh-like subpopulation in murine lupus by pattern perception.

Dysregulated cytokine expression by T cells plays a pivotal role in the pathogenesis of autoimmune diseases. However, the identification of the corresponding pathogenic subpopulations is a challenge, since a distinction between physiological variation and a new quality in the expression of protein markers requires combinatorial evaluation. Here, we were able to identify a super-functional follicular helper T cell (Tfh)-like subpopulation in lupus-prone NZBxW mice with our binning approach "pattern recognition of immune cells (PRI)". PRI uncovered a subpopulation of IL-21+ IFN-γhigh PD-1low CD40Lhigh CXCR5- Bcl-6- T cells specifically expanded in diseased mice. In addition, these cells express high levels of TNF-α and IL-2, and provide B cell help for IgG production in an IL-21 and CD40L dependent manner. This super-functional T cell subset might be a superior driver of autoimmune processes due to a polyfunctional and high cytokine expression combined with Tfh-like properties.

Continuous chromosome-scale haplotypes assembled from a single interspecies F1 hybrid of yak and cattle.

BackgroundThe development of trio binning as an approach for assembling diploid genomes has enabled the creation of fully haplotype-resolved reference genomes. Unlike other methods of assembly for diploid genomes, this approach is enhanced, rather than hindered, by the heterozygosity of the individual sequenced. To maximize heterozygosity and simultaneously assemble reference genomes for 2 species, we applied trio binning to an interspecies F1 hybrid of yak (Bos grunniens) and cattle (Bos taurus), 2 species that diverged nearly 5 million years ago. The genomes of both of these species are composed of acrocentric autosomes.ResultsWe produced the most continuous haplotype-resolved assemblies for a diploid animal yet reported. Both the maternal (yak) and paternal (cattle) assemblies have the largest 2 chromosomes in single haplotigs, and more than one-third of the autosomes similarly lack gaps. The maximum length haplotig produced was 153 Mb without any scaffolding or gap-filling steps and represents the longest haplotig reported for any species. The assemblies are also more complete and accurate than those reported for most other vertebrates, with 97% of mammalian universal single-copy orthologs present.ConclusionsThe high heterozygosity inherent to interspecies crosses maximizes the effectiveness of the trio binning method. The interspecies trio binning approach we describe is likely to provide the highest-quality assemblies for any pair of species that can interbreed to produce hybrid offspring that develop to sufficient cell numbers for DNA extraction.

HIPPIE2: a method for fine-scale identification of physically interacting chromatin regions.

Most regulatory chromatin interactions are mediated by various transcription factors (TFs) and involve physically interacting elements such as enhancers, insulators or promoters. To map these elements and interactions at a fine scale, we developed HIPPIE2 that analyzes raw reads from high-throughput chromosome conformation (Hi-C) experiments to identify precise loci of DNA physically interacting regions (PIRs). Unlike standard genome binning approaches (e.g. 10-kb to 1-Mb bins), HIPPIE2 dynamically infers the physical locations of PIRs using the distribution of restriction sites to increase analysis precision and resolution. We applied HIPPIE2 to in situ Hi-C datasets across six human cell lines (GM12878, IMR90, K562, HMEC, HUVEC, NHEK) with matched ENCODE/Roadmap functional genomic data. HIPPIE2 detected 1042 738 distinct PIRs, with high resolution (average PIR length of 1006 bp) and high reproducibility (92.3% in GM12878). PIRs are enriched for epigenetic marks (H3K27ac, H3K4me1) and open chromatin, suggesting active regulatory roles. HIPPIE2 identified 2.8 million significant PIR–PIR interactions, 27.2% of which were enriched for TF binding sites. 50 608 interactions were enhancer–promoter interactions and were enriched for 33 TFs, including known DNA looping/long-range mediators. These findings demonstrate that the novel dynamic approach of HIPPIE2 (https://bitbucket.com/wanglab-upenn/HIPPIE2) enables the characterization of chromatin and regulatory interactions with high resolution and reproducibility.

Fatigue design load calculations of the offshore NREL 5 MW benchmark turbine using quadrature rule techniques

AbstractA novel approach is proposed to reduce, compared with the conventional binning approach, the large number of aeroelastic code evaluations that are necessary to obtain equivalent loads acting on wind turbines. These loads describe the effect of long‐term environmental variability on the fatigue loads of a horizontal‐axis wind turbine. In particular, Design Load Case 1.2, as standardized by IEC, is considered. The approach is based on numerical integration techniques and, more specifically, quadrature rules. The quadrature rule used in this work is a recently proposed “implicit” quadrature rule, which has the main advantage that it can be constructed directly using measurements of the environment. It is demonstrated that the proposed approach yields accurate estimations of the equivalent loads using a significantly reduced number of aeroelastic model evaluations (compared with binning). Moreover, the error introduced by the seeds (introduced by averaging over random wind fields and sea states) is incorporated in the quadrature framework, yielding an even further reduction in the number of aeroelastic code evaluations. The reduction in computational time is demonstrated by assessing the fatigue loads on the NREL 5 MW reference offshore wind turbine in conjunction with measurement data obtained at the North Sea, for both a simplified and a full load case.

Impacts of Binning Methods on High‐Latitude Electrodynamic Forcing: Static Versus Boundary‐Oriented Binning Methods

AbstractAn outstanding issue in the general circulation model simulations for Earth's upper atmosphere is the inaccurate estimation of Joule heating, which could be associated with the inaccuracy of empirical models for high‐latitude electrodynamic forcing. The binning methods used to develop those empirical models may contribute to the inaccuracy. Traditionally, data are binned through a static binning approach by using fixed geomagnetic coordinates, in which the dynamic nature of the forcing is not considered and therefore the forcing patterns may be significantly smeared. To avoid the smoothing issue, data can be binned according to some physically important boundaries in the high‐latitude forcing, that is, through a boundary‐oriented binning approach. In this study, we have investigated the sensitivity of high‐latitude forcing patterns to the binning methods by applying both static and boundary‐oriented binning approaches to the electron precipitation and electric potential data from the Defense Meteorological Satellite Program satellites. For this initial study, we have focused on the moderately strong and dominantly southward interplanetary magnetic field conditions. As compared with the static binning results, the boundary‐oriented binning approach can provide a more confined and intense electron precipitation pattern. In addition, the magnitudes of the electric potential and electric field in the boundary‐oriented binning results increase near the convection reversal boundary, leading to a ~11% enhancement of the cross polar cap potential. The forcing patterns obtained from both binning approaches are used to drive the Global Ionosphere and Thermosphere Model to assess the impacts on Joule heating by using different binning patterns. It is found that the hemispheric‐integrated Joule heating in the simulation driven by the boundary‐oriented binning patterns is 18% higher than that driven by the static binning patterns.

Binning Approach based on Classical Clustering for Type 2 Diabetes Diagnosis

In recent years, numerous studies have been fo-cusing on metagenomic data to improve the ability of human disease prediction. Although we face the complexity of disease, some proposed frameworks reveal promising performances in using metagenomic data to predict disease. Type 2 diabetes (T2D) diagnosis by metagenomic data is one of the challenging tasks compared to other diseases. The prediction performances for T2D usually reveal poor results which are around 65% in accuracy in state-of-the-art. In this study, we propose a method com-bining K-means clustering algorithm and unsupervised binning approaches to improve the performance in metagenome-based disease prediction. We illustrate by experiments on metagenomic datasets related to Type 2 Diabetes that the proposed method embedded clusters generated by K-means allows to increase the performance in prediction accuracy reaching approximately or more than 70%.

Assessment of yield gaps on global grazed‐only permanent pasture using climate binning

To meet rising demands for agricultural products, existing agricultural lands must either produce more or expand in area. Yield gaps (YGs)-the difference between current and potential yield of agricultural systems-indicate the ability to increase output while holding land area constant. Here, we assess YGs in global grazed-only permanent pasture lands using a climate binning approach. We create a snapshot of circa 2000 empirical yields for meat and milk production from cattle, sheep, and goats by sorting pastures into climate bins defined by total annual precipitation and growing degree-days. We then estimate YGs from intra-bin yield comparisons. We evaluate YG patterns across three FAO definitions of grazed livestock agroecosystems (arid, humid, and temperate), and groups of animal production systems that vary in animal types and animal products. For all subcategories of grazed-only permanent pasture assessed, we find potential to increase productivity several-fold over current levels. However, because productivity of grazed pasture systems is generally low, even large relative increases in yield translated to small absolute gains in global protein production. In our dataset, milk-focused production systems were found to be seven times as productive as meat-focused production systems regardless of animal type, while cattle were four times as productive as sheep and goats regardless of animal output type. Sustainable intensification of pasture is most promising for local development, where large relative increases in production can substantially increase incomes or "spare" large amounts of land for other uses. Our results motivate the need for further studies to target agroecological and economic limitations on productivity to improve YG estimates and identify sustainable pathways toward intensification.

MetaCon: unsupervised clustering of metagenomic contigs with probabilistic k-mers statistics and coverage

MotivationSequencing technologies allow the sequencing of microbial communities directly from the environment without prior culturing. Because assembly typically produces only genome fragments, also known as contigs, it is crucial to group them into putative species for further taxonomic profiling and down-streaming functional analysis. Taxonomic analysis of microbial communities requires contig clustering, a process referred to as binning, that is still one of the most challenging tasks when analyzing metagenomic data. The major problems are the lack of taxonomically related genomes in existing reference databases, the uneven abundance ratio of species, sequencing errors, and the limitations due to binning contig of different lengths.ResultsIn this context we present MetaCon a novel tool for unsupervised metagenomic contig binning based on probabilistic k-mers statistics and coverage. MetaCon uses a signature based on k-mers statistics that accounts for the different probability of appearance of a k-mer in different species, also contigs of different length are clustered in two separate phases. The effectiveness of MetaCon is demonstrated in both simulated and real datasets in comparison with state-of-art binning approaches such as CONCOCT, MaxBin and MetaBAT.

Rapid evaluation of immobilized immunoglobulins using automated mass-segmented ToF-SIMS

Surface interactions largely control how biomaterials interact with biology and how many other types of materials function in industrial applications. ToF-SIMS analysis is extremely useful for interrogating the surfaces of complex materials and shows great promise in analyzing biological samples. Previously, the authors demonstrated that segmentation (between 1 and 0.005 m/z mass bins) of the mass spectral axis can be used to differentiate between polymeric materials with both very similar and dissimilar molecular compositions. Here, the same approach is applied for the analysis of proteins on surfaces, focusing on the effect of binding and orientation of an antibody on the resulting ToF-SIMS spectrum. Due to the complex nature of the samples that contain combinations of only 20 amino acids differing in sequence, it is enormously challenging and prohibitively time-consuming to distinguish the minute variances presented in each dataset through manual analysis alone. Herein, the authors describe how to apply the newly developed rapid data analysis workflow to previously published ToF-SIMS data for complex biological materials, immobilized antibodies. This automated method reduced the analysis time by two orders of magnitudes while enhancing data quality and allows the removal of any user bias. The authors used mass segmentation at 0.005 m/z over a 1-300 mass range to generate 60 000 variables. In contrast to the previous manual binning approach, this method captures the entire mass range of the spectrum resulting in an information-rich dataset rather than specifically selected mass spectral peaks. This work constitutes an additional proof of concept that rapid and automated data analyses involving mass-segmented ToF-SIMS spectra can efficiently and robustly analyze a broader range of complex materials, ranging from generic polymers to complicated biological samples. This automated analysis method is also ideally positioned to provide data to train machine learning models of surface-property relationships that can greatly enhance the understanding of how the surface interacts with biology and provides more accurate and robust quantitative predictions of the biological properties of new materials.

Dispersion of stratified dust layers by a moving shock wave

Simulations of a shock passing over stratified dust layers containing two different types of particles were conducted to explore the effect of particle size and dust layer thickness on particle dispersion with application to coal mine explosion conditions. The simulations solve the Euler equations of fluid dynamics and granular flow, and accounts for different particle types using a binning approach. Test cases with a 2.7-mm layer consisting of particles with a smaller diameter (ds,2=10, 20, and 40 µm) placed on top of a 10-mm layer consisting particles with a larger diameter (ds,1=80 µm) were performed to study the effect of particle size on dust dispersion. The results indicate that larger particles from the lower layer can be lifted higher than the smaller particles from the upper layer if the two types of particles have a large size difference. In addition, dust dispersion of a shock passing over a rock-dust layer on top of a coal-dust layer were also investigated. Here the coal dust has a diameter of 30 µm and a density of 1330 kg/m3, and the rock dust has a diameter of 15 µm and a density of 2680 kg/m3. The rock-layer thickness of 1, 2, and 3 mm were considered, and the coal-dust layer underneath had a thickness of 4 mm. The results suggest that the rock particles work more effectively on suppressing the coal dust from lifting with increasing rock-layer thickness. Placing another layer of coal dust on top of the rock-coal layer shows that the rock dust underneath fails to suppress the coal particles above from being lifted, and this suggests the need to simultaneously or regularly apply rock dust during the mining operation.

Measuring the Interaction of Transcription Factor Nrf2 with Its Negative Regulator Keap1 in Single Live Cells by an Improved FRET/FLIM Analysis.

Transcription factor NF-E2 p45-related factor 2 (Nrf2) and its principal negative regulator, Kelch-like ECH-associated protein 1 (Keap1), comprise a molecular effector and sensor system that robustly responds to perturbations of the cellular redox homeostasis by orchestrating a comprehensive cytoprotective program. Under homeostatic conditions, Nrf2 is a short-lived protein, which is targeted for ubiquitination and proteasomal degradation. Upon encounter of electrophiles, oxidants, or pro-inflammatory stimuli, the cysteine sensors in Keap1 are chemically modified, rendering Keap1 unable to target Nrf2 for degradation, and consequently leading to accumulation of the transcription factor and enhanced transcription of cytoprotective genes. A detailed understanding of the protein-protein interactions between Nrf2 and Keap1 has been achieved by use of various in vitro systems, but few assays are available to assess these interactions in the context of the living cell. We previously developed an imaging-based FLIM/FRET methodology to visualize and measure the interaction between Nrf2 and Keap1 in single cells. Here, our goal was to improve this methodology in order to increase throughput and precision, and decrease cell-to-cell variability. To eliminate the possibility of orientation bias, we incorporated a flexible linker between Keap1 and the FRET acceptor fluorescent protein tag. To ensure the correct image capture of Nrf2 fused to the FRET donor fluorescent protein tag, we matched the maturation time of the fluorescent tag to the half-life of the endogenous Nrf2, by using sfGFP as the FRET donor. Using a global binning approach increased the assay throughput, whereas including the measured instrument response function in the analysis improved precision. The application of this methodology revealed a strong covariation of the results with the expression level of the acceptor. Taking the acceptor level into account circumvented cell-to-cell variability and enhanced sensitivity of the measurements of the Keap1-Nrf2 interaction in live cells.

Understanding antimicrobial discovery and resistance from a metagenomic and metatranscriptomic perspective: advances and applications.

Our inability to cultivate most microorganisms, specifically bacteria, in the laboratory has for many years restricted our view and understanding of the bacterial meta-resistome in all living and nonliving environments. As a result, reservoirs, sources and distribution of antibiotic resistance genes (ARGS) and antibiotic-producers, as well as the effects of human activity and antibiotics on the selection and dissemination of ARGs were not well comprehended. With the advances made in the fields of metagenomics and metatranscriptomics, many of the hitherto little-understood concepts are becoming clearer. Further, the discovery of antibiotics such as lugdinin and lactocillin from the human microbiota, buttressed the importance of these new fields. Metagenomics and metatranscriptomics are becoming important clinical diagnostic tools for screening and detecting pathogens and ARGs, assessing the effects of antibiotics, other xenobiotics and human activity on the environment, characterizing the microbiome and the environmental resistome with lesser turnaround time and decreasing cost, as well as discovering antibiotic-producers. However, challenges with accurate binning, skewed ARGs databases, detection of less abundant and allelic variants of ARGs and efficient mobilome characterization remain. Ongoing efforts in long-read, phased- and single-cell sequencing, strain-resolved binning, chromosomal-conformation capture, DNA-methylation binning and deep-learning bioinformatic approaches offer promising prospects in reconstructing complete strain-level genomes and mobilomes from metagenomes.

Novel 2D binning approach for advanced LIMS depth profiling analysis

Novel layer-by-layer ablation protocol for sensitive chemical depth profiling measurements of micrometer-sized samples.

Assessment of urban microbiome assemblies with the help of targeted in silico gold standards

BackgroundMicrobial communities play a crucial role in our environment and may influence human health tremendously. Despite being the place where human interaction is most abundant we still know little about the urban microbiome. This is highlighted by the large amount of unclassified DNA reads found in urban metagenome samples. The only in silico approach that allows us to find unknown species, is the assembly and classification of draft genomes from a metagenomic dataset. In this study we (1) investigate the applicability of an assembly and binning approach for urban metagenome datasets, and (2) develop a new method for the generation of in silico gold standards to better understand the specific challenges of such datasets and provide a guide in the selection of available software.ResultsWe applied combinations of three assembly (Megahit, SPAdes and MetaSPAdes) and three binning tools (MaxBin, MetaBAT and CONCOCT) to whole genome shotgun datasets from the CAMDA 2017 Challenge. Complex in silico gold standards with a simulated bacterial fraction were generated for representative samples of each surface type and city. Using these gold standards, we found the combination of SPAdes and MetaBAT to be optimal for urban metagenome datasets by providing the best trade-off between the number of high-quality genome draft bins (MIMAG standards) retrieved, the least amount of misassemblies and contamination. The assembled draft genomes included known species like Propionibacterium acnes but also novel species according to respective ANI values.ConclusionsIn our work, we showed that, even for datasets with high diversity and low sequencing depth from urban environments, assembly and binning-based methods can provide high-quality genome drafts. Of vital importance to retrieve high-quality genome drafts is sequence depth but even more so a high proportion of the bacterial sequence fraction too achieve high coverage for bacterial genomes. In contrast to read-based methods relying on database knowledge, genome-centric methods as applied in this study can provide valuable information about unknown species and strains as well as functional contributions of single community members within a sample. Furthermore, we present a method for the generation of sample-specific highly complex in silico gold standards.ReviewersThis article was reviewed by Craig Herbold, Serghei Mangul and Yana Bromberg.

Performance evaluation and first clinical experience with the Varian RGSC module for breath detection of 15 lung cancer patients

The University Hospital of Düsseldorf, Germany (UKD) recently installed the Respiratory Gating for Scanners module (RGSC) (Varian Medical Systems, Palo Alto, USA). The aim of this article is to report on the commissioning and clinical implementation of the RGSC system. The steps encompassed the validation of the manufacturer's specifications including functionality tests using a commercial and in-house developed breathing phantom, to establish calibration procedures, and clinical workflow analysis involving breath acquisition and patient data evaluation. In this context also the RGSC signal without motion was performed to assess the calibration procedure. Reproducibility test were conducted as well with breathing phantoms. Fifteen clinical breath curves were examined in order to assess the impact of treatment related uncertainties such as noises of the CT, patient positioning, movement of the CT table, unintended patient motion. Finally, different binning approaches were assessed and the effect on the CT reconstructions and methodic advantages were investigated. All technical specifications of the manufacturer were confirmed. A baseline drift of 1.83mm of the measured breath curve occurred during longitudinal movement of the CT table. This drift is smaller if the direction of table motion coincides precisely with the level of calibration. If the calibration is carried out on extensions for patient positioning we measured a baseline drift up to 6mm. It was found that especially for a combination of a ceiling mounted IR-camera and amplitude based 4D-CT reconstructions, precise calibration is prerequisite. The evaluations of patient breath curves and corresponding CT reconstructions revealed patient specific aspects and variations, respectively. Consequently patient selection criteria need to be established in parallel with the technical implementation and validation phase of respiratory gating.

MetaWRAP\u2014a flexible pipeline for genome-resolved metagenomic data analysis

BackgroundThe study of microbiomes using whole-metagenome shotgun sequencing enables the analysis of uncultivated microbial populations that may have important roles in their environments. Extracting individual draft genomes (bins) facilitates metagenomic analysis at the single genome level. Software and pipelines for such analysis have become diverse and sophisticated, resulting in a significant burden for biologists to access and use them. Furthermore, while bin extraction algorithms are rapidly improving, there is still a lack of tools for their evaluation and visualization.ResultsTo address these challenges, we present metaWRAP, a modular pipeline software for shotgun metagenomic data analysis. MetaWRAP deploys state-of-the-art software to handle metagenomic data processing starting from raw sequencing reads and ending in metagenomic bins and their analysis. MetaWRAP is flexible enough to give investigators control over the analysis, while still being easy-to-install and easy-to-use. It includes hybrid algorithms that leverage the strengths of a variety of software to extract and refine high-quality bins from metagenomic data through bin consolidation and reassembly. MetaWRAP’s hybrid bin extraction algorithm outperforms individual binning approaches and other bin consolidation programs in both synthetic and real data sets. Finally, metaWRAP comes with numerous modules for the analysis of metagenomic bins, including taxonomy assignment, abundance estimation, functional annotation, and visualization.ConclusionsMetaWRAP is an easy-to-use modular pipeline that automates the core tasks in metagenomic analysis, while contributing significant improvements to the extraction and interpretation of high-quality metagenomic bins. The bin refinement and reassembly modules of metaWRAP consistently outperform other binning approaches. Each module of metaWRAP is also a standalone component, making it a flexible and versatile tool for tackling metagenomic shotgun sequencing data. MetaWRAP is open-source software available at https://github.com/bxlab/metaWRAP.

North Atlantic subpolar gyre along predetermined ship tracks since 1993: a monthly data set of surface temperature, salinity, and density

Abstract. We present a binned product of sea surface temperature, sea surface salinity, and sea surface density data in the North Atlantic subpolar gyre from 1993 to 2017 that resolves seasonal variability along specific ship routes (https://doi.org/10.6096/SSS-BIN-NASG). The characteristics of this product are described and validated through comparisons to other monthly products. Data presented in this work were collected in regions crossed by two predetermined ship transects, between Denmark and western Greenland (AX01) and between Iceland, Newfoundland, and the northeastern USA (AX02). The data were binned along a selected usable transect. The analysis and the strong correlation between successive seasons indicate that in large parts of the subpolar gyre, the binning approach is robust and resolves the seasonal timescales, in particular after 1997 and in regions away from the continental shelf. Prior to 2002, there was no winter sampling over the West Greenland Shelf. Variability in sea surface salinity increases towards Newfoundland south of 54∘ N, as well as in the western Iceland Basin along 59∘ N. Variability in sea surface temperature presents less spatial structure with an increase westward and towards Newfoundland. The contribution of temperature variability to density dominates in the eastern part of the gyre, whereas the contribution of salinity variability dominates in the southwestern part along AX02.

Broadscale Ecological Patterns Are Robust to Use of Exact Sequence Variants versus Operational Taxonomic Units.

Recent discussion focuses on the best method for delineating microbial taxa, based on either exact sequence variants (ESVs) or traditional operational taxonomic units (OTUs) of marker gene sequences. We sought to test if the binning approach (ESVs versus 97% OTUs) affected the ecological conclusions of a large field study. The data set included sequences targeting all bacteria (16S rRNA) and fungi (internal transcribed spacer [ITS]), across multiple environments diverging markedly in abiotic conditions, over three collection times. Despite quantitative differences in microbial richness, we found that all α and β diversity metrics were highly positively correlated (r > 0.90) between samples analyzed with both approaches. Moreover, the community composition of the dominant taxa did not vary between approaches. Consequently, statistical inferences were nearly indistinguishable. Furthermore, ESVs only moderately increased the genetic resolution of fungal and bacterial diversity (1.3 and 2.1 times OTU richness, respectively). We conclude that for broadscale (e.g., all bacteria or all fungi) α and β diversity analyses, ESV or OTU methods will often reveal similar ecological results. Thus, while there are good reasons to employ ESVs, we need not question the validity of results based on OTUs.IMPORTANCE Microbial ecologists have made exceptional improvements in our understanding of microbiomes in the last decade due to breakthroughs in sequencing technologies. These advances have wide-ranging implications for fields ranging from agriculture to human health. Due to limitations in databases, the majority of microbial ecology studies use a binning approach to approximate taxonomy based on DNA sequence similarity. There remains extensive debate on the best way to bin and approximate this taxonomy. Here we examine two popular approaches using a large field-based data set examining both bacteria and fungi and conclude that there are not major differences in the ecological outcomes. Thus, it appears that standard microbial community analyses are not overly sensitive to the particulars of binning approaches.

Unsupervised fuzzy binning of metagenomic sequence fragments on three-dimensional Barnes-Hut t-Stochastic Neighbor Embeddings.

Shotgun metagenomic studies attempt to reconstruct population genome sequences from complex microbial communities. In some traditional genome demarcation approaches, high-dimensional sequence data are embedded into two-dimensional spaces and subsequently binned into candidate genomic populations. One such approach uses a combination of the Barnes-Hut approximation and the $t -$Stochastic Neighbor Embedding (BH-SNE) algorithm for dimensionality reduction of DNA sequence data pentamer profiles; and demarcation of groups based on Gaussian mixture models within humanimposed boundaries. We found that genome demarcation from three-dimensional BH-SNE embeddings consistently results in more accurate binnings than 2-D embeddings. We further addressed the lack of a priori population number information by developing an unsupervised binning approach based on the Subtractive and Fuzzy c-means (FCM) clustering algorithms combined with internal clustering validity indices. Lastly, we addressed the subject of shared membership of individual data objects in a mixed community by assigning a degree of membership to individual objects using the FCM algorithm, and discriminated between confidently binned and uncertain sequence data objects from the community for subsequent biological interpretation. The binning of metagenome sequence fragments according to thresholds in the degree of membership opens the door for the identification of horizontally transferred elements and other genomic regions of uncertain assignment in which biologically meaningful information resides. The reported approach improves the unsupervised genome demarcation of populations within complex communities, increases the confidence in the coherence of the binned elements, and enables the identification of evolutionary processes ignored in hard-binning approaches in shotgun metagenomic studies.