On plotting species abundance distributions

1. There has been a revival of interest in species abundance distribution (SAD) models, stimulated by the claim that the log-normal distribution gave an underestimate of the observed numbers of rare species in species-rich assemblages. This led to the development of the neutral Zero Sum Multinomial distribution (ZSM) to better fit the observed data. 2. Yet plots of SADs, purportedly of the same data, showed differences in frequencies of species and of statistical fits to the ZSM and log-normal models due to the use of different binning methods. 3. We plot six different binning methods for the Barro Colorado Island (BCI) tropical tree data. The appearances of the curves are very different for the different binning methods. Consequently, the fits to different models may vary depending on the binning system used. 4. There is no agreed binning method for SAD plots. Our analysis suggests that a simple doubling of the number of individuals per species in each bin is perhaps the most practical one for illustrative purposes. Alternatively rank-abundance plots should be used. 5. For fitting and testing models exact methods have been developed and application of these does not require binning of data. Errors are introduced unnecessarily if data are binned before testing goodness-of-fit to models.     

‘Candidatus Dichloromethanomonas elyunquensis’ gen. nov., sp. nov., a dichloromethane-degrading anaerobe of the Peptococcaceae family

Taxonomic assignments of anaerobic dichloromethane (DCM)-degrading bacteria remain poorly constrained but are important for understanding the microbial diversity of organisms contributing to DCM turnover in environmental systems. We describe the taxonomic classification of a novel DCM degrader in consortium RM obtained from pristine Rio Mameyes sediment. Phylogenetic analysis of full-length 16S rRNA gene sequences demonstrated that the DCM degrader was most closely related to members of the genera Dehalobacter and Syntrophobotulus, but sequence similarities did not exceed 94% and 93%, respectively. Genome-aggregate average amino acid identities against Peptococcaceae members did not exceed 66%, suggesting that the DCM degrader does not affiliate with any described genus. Phylogenetic analysis of conserved single-copy functional genes supported that the DCM degrader represents a novel clade. Growth strictly depended on the presence of DCM, which was consumed at a rate of 160 ± 3 μmol L^?1 d^?1. The DCM degrader attained 5.25 × 10⁷ ± 1.0 × 10⁷ cells per μmol DCM consumed. Fluorescence in situ hybridization revealed rod-shaped cells 4 ± 0.8 μm long and 0.4 ± 0.1 μm wide. Based on the unique phylogenetic, genomic, and physiological characteristics, we propose that the DCM degrader represents a new genus and species, ‘Candidatus Dichloromethanomonas elyunquensis’.     

Expanding the ProteOn XPR36 biosensor into a 36-ligand array expedites protein interaction analysis

Here we demonstrate methods to expand the throughput of the ProteOn XPR36 biosensor allowing for the simultaneous kinetic characterization of several multiplexed formats, such as 36 disparate antibodies targeting the same antigen, and facilitating detailed epitope binning and mapping studies. The kinetic rate constants determined by these methods correlated with those obtained on Biacore 2000 and the absolute parameter values obtained on the ProteOn’s alginate-based GLC chip agreed closer with those from Biacore’s flat C1 chip than Biacore’s dextran-based CM4 chip. Pairwise epitope binning data from the ProteOn 36-ligand array format and those generated on an orthogonal array-based biosensor, the Octet QK384, gave similar results. In an epitope mapping study using biotinylated peptides, all three biosensor platforms were similar in their ability to identify antibodies that bound to linear epitopes. We apply alternative formats of the ProteOn array that enable a significantly higher number of assays to be conducted simultaneously than previously anticipated on this platform.     

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.     

Broad epitope coverage of a human in vitro antibody library

Successful discovery of therapeutic antibodies hinges on the identification of appropriate affinity binders targeting a diversity of molecular epitopes presented by the antigen. Antibody campaigns that yield such broad “epitope coverage” increase the likelihood of identifying candidates with the desired biological functions. Accordingly, epitope binning assays are employed in the early discovery stages to partition antibodies into epitope families or “bins” and prioritize leads for further characterization and optimization. The collaborative program described here, which used hen egg white lysozyme (HEL) as a model antigen, combined 3 key capabilities: 1) access to a diverse panel of antibodies selected from a human in vitro antibody library; 2) application of state-of-the-art high-throughput epitope binning; and 3) analysis and interpretation of the epitope binning data with reference to an exhaustive set of published antibody:HEL co-crystal structures. Binning experiments on a large merged panel of antibodies containing clones from the library and the literature revealed that the inferred epitopes for the library clones overlapped with, and extended beyond, the known structural epitopes. Our analysis revealed that nearly the entire solvent-exposed surface of HEL is antigenic, as has been proposed for protein antigens in general. The data further demonstrated that synthetic antibody repertoires provide as wide epitope coverage as those obtained from animal immunizations. The work highlights molecular insights contributed by increasingly higher-throughput binning methods and their broad utility to guide the discovery of therapeutic antibodies representing a diverse set of functional epitopes.     

微生物宏基因组数据分析方法研究进展

随着测序技术的迅速发展,人们对宏基因组的研究逐渐深入。通过宏基因组学对微生物群落的测序和分析,以理解微生物组成与环境之间的相互作用。微生物宏基因组的分析摆脱了传统研究中微生物分离培养的技术限制,并获得了微生物群落的相对丰度和群落的功能等信息。用于微生物数据分析的工具和软件较多,对于研究者选择合适的分析方法具有一定困难。概述了微生物宏基因组分析方法的流程,总结了分析中常用的工具及软件,为研究者快速筛选分析方法,揭示数据背后的生物学意义提供参考。     

Practical application of self-organizing maps to interrelate biodiversity and functional data in NGS-based metagenomics

Next-generation sequencing (NGS) technologies have enabled the application of broad-scale sequencing in microbial biodiversity and metagenome studies. Biodiversity is usually targeted by classifying 16S ribosomal RNA genes, while metagenomic approaches target metabolic genes. However, both approaches remain isolated, as long as the taxonomic and functional information cannot be interrelated. Techniques like self-organizing maps (SOMs) have been applied to cluster metagenomes into taxon-specific bins in order to link biodiversity with functions, but have not been applied to broad-scale NGS-based metagenomics yet. Here, we provide a novel implementation, demonstrate its potential and practicability, and provide a web-based service for public usage. Evaluation with published data sets mimicking varyingly complex habitats resulted into classification specificities and sensitivities of close to 100% to above 90% from phylum to genus level for assemblies exceeding 8 kb for low and medium complexity data. When applied to five real-world metagenomes of medium complexity from direct pyrosequencing of marine subsurface waters, classifications of assemblies above 2.5 kb were in good agreement with fluorescence in situ hybridizations, indicating that biodiversity was mostly retained within the metagenomes, and confirming high classification specificities. This was validated by two protein-based classifications (PBCs) methods. SOMs were able to retrieve the relevant taxa down to the genus level, while surpassing PBCs in resolution. In order to make the approach accessible to a broad audience, we implemented a feature-rich web-based SOM application named TaxSOM, which is freely available at http://www.megx.net/toolbox/taxsom. TaxSOM can classify reads or assemblies exceeding 2.5 kb with high accuracy and thus assists in linking biodiversity and functions in metagenome studies, which is a precondition to study microbial ecology in a holistic fashion.     

宏基因组重叠群分箱方法研究综述

宏基因组学技术可以直接从环境中提取微生物的全部遗传物质,而不需要像传统方法一样在培养基上纯培养。这种技术的出现为科学家对微生物群落的结构和功能的认识提供了重要的方法,同时对疾病的诊治、环境的治理以及生命的认识具有重大的意义。从环境中提取出微生物全部遗传物质,对其进行测序从而得到它们的reads片段,通过reads组装工具可以进一步组装成重叠群片段。对重叠群片段进行分箱,可以从宏基因组样本中重建出更多完整的基因。分箱效果的好坏直接影响到后续的生物分析,因此如何将这些含有不同微生物基因混合的重叠群序列进行有效的分箱成为了宏基因组学研究的热点和难点。机器学习方法被广泛应用于宏基因组重叠群分箱,通常分为有监督重叠群分类方法和无监督重叠群聚类方法。该综述针对宏基因组重叠群分箱方法进行了较为全面的阐述,深入剖析了重叠群分类方法与聚类方法,发现其存在分类准确率较低、分箱时间较长、难以从复杂数据集中重建更多微生物基因等问题,并对未来重叠群分箱方法的研究和发展进行了展望。作者建议可以使用半监督学习、集成学习以及深度学习方法,并采用更有效的数据特征表示等途径来提高分箱效果。     

HiCORE: Hi-C Analysis for Identification of Core Chromatin Looping Regions with Higher Resolution

Genome-wide chromosome conformation capture (3C)-based high-throughput sequencing (Hi-C) has enabled identification of genome-wide chromatin loops. Because the Hi-C map with restriction fragment resolution is intrinsically associated with sparsity and stochastic noise, Hi-C data are usually binned at particular intervals; however, the binning method has limited reliability, especially at high resolution. Here, we describe a new method called HiCORE, which provides simple pipelines and algorithms to overcome the limitations of single-layered binning and predict core chromatin regions with three-dimensional physical interactions. In this approach, multiple layers of binning with slightly shifted genome coverage are generated, and interacting bins at each layer are integrated to infer narrower regions of chromatin interactions. HiCORE predicts chromatin looping regions with higher resolution, both in human and Arabidopsis genomes, and contributes to the identification of the precise positions of potential genomic elements in an unbiased manner.