Dissimilarity-Overlap analysis of replicate enrichment communities.
Ontology highlight
ABSTRACT:
SUBMITTER: Vila JCC
PROVIDER: S-EPMC7490387 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Ecological studies require key decisions regarding the appropriate size and number of sampling units. No methods currently exist to measure precision for multivariate assemblage data when dissimilarity-based analyses are intended to follow. Here, we propose a pseudo multivariate dissimilarity-based standard error (MultSE) as a useful quantity for assessing sample-size adequacy in studies of ecological communities. Based on sums of squared dissimilarities, MultSE measures variability in the position of the centroid in the space of a chosen dissimilarity measure under repeated sampling for a given sample size. We describe a novel double resampling method to quantify uncertainty in MultSE values with increasing sample size. For more complex designs, values of MultSE can be calculated from the pseudo residual mean square of a permanova model, with the double resampling done within appropriate cells in the design. R code functions for implementing these techniques, along with ecological examples, are provided.
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Project description:The majority of microbial communities consist of hundreds to thousands of species, creating a massive network of organisms competing for available resources within an ecosystem. In natural microbial communities, it appears that many microbial species have highly redundant metabolisms and seemingly are capable of utilizing the same substrates. This is paradoxical, as theory indicates that species requiring a common resource should outcompete one another. To better understand why microbial species can coexist, we developed metabolic overlap (MO) as a new metric to survey the functional redundancy of microbial communities at the genome scale across a wide variety of ecosystems. Using metagenome-assembled genomes, we surveyed nearly 1,000 studies across nine ecosystem types. We found the highest MO in extreme (i.e., low pH/high temperature) and aquatic environments, while the lowest MO was observed in communities associated with animal hosts, the built/engineered environment, and soil. In addition, different metabolism subcategories were explored for their degree of MO. For instance, overlap in nitrogen metabolism was among the lowest in animal and engineered ecosystems, while species from the built environment had the highest overlap. Together, we present a metric that utilizes whole genome information to explore overlapping niches of microbes. This provides a detailed picture of potential metabolic competition and cooperation between species present in an ecosystem, indicates the main substrate types sustaining the community, and serves as a valuable tool to generate hypotheses for future research.
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Project description:BackgroundDNA methylation is an important epigenetic mark and dysregulation of DNA methylation is associated with many diseases including cancer. Advances in next-generation sequencing now allow unbiased methylome profiling of entire patient cohorts, greatly facilitating biomarker discovery and presenting new opportunities to understand the biological mechanisms by which changes in methylation contribute to disease. Enrichment-based sequencing assays such as MethylCap-seq are a cost effective solution for genome-wide determination of methylation status, but the technical reliability of methylation reconstruction from raw sequencing data has not been well characterized.MethodsWe analyze three MethylCap-seq data sets and perform two different analyses to assess data quality. First, we investigate how data quality is affected by excluding samples that do not meet quality control cutoff requirements. Second, we consider the effect of additional reads on enrichment score, saturation, and coverage. Lastly, we verify a method for the determination of the global amount of methylation from MethylCap-seq data by comparing to a spiked-in control DNA of known methylation status.ResultsWe show that rejection of samples based on our quality control parameters leads to a significant improvement of methylation calling. Additional reads beyond ~13 million unique aligned reads improved coverage, modestly improved saturation, and did not impact enrichment score. Lastly, we find that a global methylation indicator calculated from MethylCap-seq data correlates well with the global methylation level of a sample as obtained from a spike-in DNA of known methylation level.ConclusionsWe show that with appropriate quality control MethylCap-seq is a reliable tool, suitable for cohorts of hundreds of patients, that provides reproducible methylation information on a feature by feature basis as well as information about the global level of methylation.
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Project description:The transition towards a sustainable society involves the utilization of lignocellulosic biomass as a renewable feedstock for materials, fuel, and base chemicals. Lignocellulose consists of cellulose, hemicellulose, and lignin, forming a complex, recalcitrant matrix where efficient enzymatic saccharification is pivotal for accessing its valuable components. This study investigated microbial communities from brackish Lauwersmeer Lake, in The Netherlands, as a potential source of xylan-degrading enzymes. Environmental sediment samples were enriched with wheat arabinoxylan (WAX) and beechwood glucuronoxylan (BEX), with enrichment on WAX showing higher bacterial growth and complete xylan degradation compared to BEX. Metagenomic sequencing revealed communities consisting almost entirely of bacteria (>99%) and substantial shifts in composition during the enrichment. The first generation of seven-day enrichments on both xylans led to a high accumulation of Gammaproteobacteria (49% WAX, 84% BEX), which were largely replaced by Alphaproteobacteria (42% WAX, 69% BEX) in the fourth generation. Analysis of the protein function within the sequenced genomes showed elevated levels of genes associated with the carbohydrate catabolic process, specifically targeting arabinose, xylose, and xylan, indicating an adaptation to the primary monosaccharides present in the carbon source. The data open up the possibility of discovering novel xylan-degrading proteins from other sources aside from the thoroughly studied Bacteroidota.
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Project description:We present the first large-scale methylome-wide association studies (MWAS) for major depressive disorder (MDD) to identify sites of potential importance for MDD etiology. Using a sequencing-based approach that provides near-complete coverage of all 28 million common CpGs in the human genome, we assay methylation in MDD cases and controls from both blood (N = 1132) and postmortem brain tissues (N = 61 samples from Brodmann Area 10, BA10). The MWAS for blood identified several loci with P ranging from 1.91 × 10-8 to 4.39 × 10-8 and a resampling approach showed that the cumulative association was significant (P = 4.03 × 10-10) with the signal coming from the top 25,000 MWAS markers. Furthermore, a permutation-based analysis showed significant overlap (P = 5.4 × 10-3) between the MWAS findings in blood and brain (BA10). This overlap was significantly enriched for a number of features including being in eQTLs in blood and the frontal cortex, CpG islands and shores, and exons. The overlapping sites were also enriched for active chromatin states in brain including genic enhancers and active transcription start sites. Furthermore, three loci located in GABBR2, RUFY3, and in an intergenic region on chromosome 2 replicated with the same direction of effect in the second brain tissue (BA25, N = 60) from the same individuals and in two independent brain collections (BA10, N = 81 and 64). GABBR2 inhibits neuronal activity through G protein-coupled second-messenger systems and RUFY3 is implicated in the establishment of neuronal polarity and axon elongation. In conclusion, we identified and replicated methylated loci associated with MDD that are involved in biological functions of likely importance to MDD etiology.
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Project description:A primary challenge in the analysis of RNA-seq data is to identify differentially expressed genes or transcripts while controlling for technical biases. Ideally, a statistical testing procedure should incorporate the inherent uncertainty of the abundance estimates arising from the quantification step. Most popular methods for RNA-seq differential expression analysis fit a parametric model to the counts for each gene or transcript, and a subset of methods can incorporate uncertainty. Previous work has shown that nonparametric models for RNA-seq differential expression may have better control of the false discovery rate, and adapt well to new data types without requiring reformulation of a parametric model. Existing nonparametric models do not take into account inferential uncertainty, leading to an inflated false discovery rate, in particular at the transcript level. We propose a nonparametric model for differential expression analysis using inferential replicate counts, extending the existing SAMseq method to account for inferential uncertainty. We compare our method, Swish, with popular differential expression analysis methods. Swish has improved control of the false discovery rate, in particular for transcripts with high inferential uncertainty. We apply Swish to a single-cell RNA-seq dataset, assessing differential expression between sub-populations of cells, and compare its performance to the Wilcoxon test.
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Project description:A rapidly increasing flow of genomic data requires the development of efficient methods for obtaining its compact representation. Feature extraction facilitates classification, clustering and model analysis for testing and refining biological hypotheses. "Shotgun" metagenome is an analytically challenging type of genomic data - containing sequences of all genes from the totality of a complex microbial community. Recently, researchers started to analyze metagenomes using reference-free methods based on the analysis of oligonucleotides (k-mers) frequency spectrum previously applied to isolated genomes. However, little is known about their correlation with the existing approaches for metagenomic feature extraction, as well as the limits of applicability. Here we evaluated a metagenomic pairwise dissimilarity measure based on short k-mer spectrum using the example of human gut microbiota, a biomedically significant object of study.We developed a method for calculating pairwise dissimilarity (beta-diversity) of "shotgun" metagenomes based on short k-mer spectra (5 ? k ? 11). The method was validated on simulated metagenomes and further applied to a large collection of human gut metagenomes from the populations of the world (n=281). The k-mer spectrum-based measure was found to behave similarly to one based on mapping to a reference gene catalog, but different from one using a genome catalog. This difference turned out to be associated with a significant presence of viral reads in a number of metagenomes. Simulations showed limited impact of bacterial genetic variability as well as sequencing errors on k-mer spectra. Specific differences between the datasets from individual populations were identified.Our approach allows rapid estimation of pairwise dissimilarity between metagenomes. Though we applied this technique to gut microbiota, it should be useful for arbitrary metagenomes, even metagenomes with novel microbiota. Dissimilarity measure based on k-mer spectrum provides a wider perspective in comparison with the ones based on the alignment against reference sequence sets. It helps not to miss possible outstanding features of metagenomic composition, particularly related to the presence of an unknown bacteria, virus or eukaryote, as well as to technical artifacts (sample contamination, reads of non-biological origin, etc.) at the early stages of bioinformatic analysis. Our method is complementary to reference-based approaches and can be easily integrated into metagenomic analysis pipelines.
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Project description:Deciphering relevant biological insights from epigenomic data can be a challenging task. One commonly used approach is to perform enrichment analysis. However, finding, downloading and using the publicly available functional annotations require time, programming skills and IT infrastructure. Here we describe the online tool EpiAnnotator for performing enrichment analyses on epigenomic data in a fast and user-friendly way.EpiAnnotator is an R Package accompanied by a web interface. It contains regularly updated annotations from 4 public databases: Blueprint, RoadMap, GENCODE and the UCSC Genome Browser. Annotations are hosted locally or in a server environment and automatically updated by scripts of our own design. Thousands of tracks are available, reflecting data on a variety of tissues, cell types and cell lines from the human and mouse genomes. Users need to upload sets of selected and background regions. Results are displayed in customizable and easily interpretable figures.The R package and Shiny app are open source and available under the GPL v3 license. EpiAnnotator's web interface is accessible at http://computational-epigenomics.com/en/epiannotator.epiannotator@computational-epigenomics.com.
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Project description:High resolution imaging spectroscopy data have been recognised as a valuable data resource for augmenting detailed material inventories that serve as input for various urban applications. Image-specific urban spectral libraries are successfully used in urban imaging spectroscopy studies. However, the regional- and sensor-specific transferability of such libraries is limited due to the wide range of different surface materials. With the developed methodology, incomplete urban spectral libraries can be utilised by assuming that unknown surface material spectra are dissimilar to the known spectra in a basic spectral library (BSL). The similarity measure SID-SCA (Spectral Information Divergence-Spectral Correlation Angle) is applied to detect image-specific unknown urban surfaces while avoiding spectral mixtures. These detected unknown materials are categorised into distinct and identifiable material classes based on their spectral and spatial metrics. Experimental results demonstrate a successful redetection of material classes that had been previously erased in order to simulate an incomplete BSL. Additionally, completely new materials e.g., solar panels were identified in the data. It is further shown that the level of incompleteness of the BSL and the defined dissimilarity threshold are decisive for the detection of unknown material classes and the degree of spectral intra-class variability. A detailed accuracy assessment of the pre-classification results, aiming to separate natural and artificial materials, demonstrates spectral confusions between spectrally similar materials utilizing SID-SCA. However, most spectral confusions occur between natural or artificial materials which are not affecting the overall aim. The dissimilarity analysis overcomes the limitations of working with incomplete urban spectral libraries and enables the generation of image-specific training databases.
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