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BinSanity: unsupervised clustering of environmental microbial assemblies using coverage and affinity propagation.

ABSTRACT: Metagenomics has become an integral part of defining microbial diversity in various environments. Many ecosystems have characteristically low biomass and few cultured representatives. Linking potential metabolisms to phylogeny in environmental microorganisms is important for interpreting microbial community functions and the impacts these communities have on geochemical cycles. However, with metagenomic studies there is the computational hurdle of 'binning' contigs into phylogenetically related units or putative genomes. Binning methods have been implemented with varying approaches such as k-means clustering, Gaussian mixture models, hierarchical clustering, neural networks, and two-way clustering; however, many of these suffer from biases against low coverage/abundance organisms and closely related taxa/strains. We are introducing a new binning method, BinSanity, that utilizes the clustering algorithm affinity propagation (AP), to cluster assemblies using coverage with compositional based refinement (tetranucleotide frequency and percent GC content) to optimize bins containing multiple source organisms. This separation of composition and coverage based clustering reduces bias for closely related taxa. BinSanity was developed and tested on artificial metagenomes varying in size and complexity. Results indicate that BinSanity has a higher precision, recall, and Adjusted Rand Index compared to five commonly implemented methods. When tested on a previously published environmental metagenome, BinSanity generated high completion and low redundancy bins corresponding with the published metagenome-assembled genomes.

SUBMITTER: Graham ED

PROVIDER: S-EPMC5345454 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:By combining a reference-independent SNP analysis and average nucleotide identity (ANI) with affinity propagation clustering (APC), we developed a significantly improved methodology allowing resolving phylogenetic relationships, based on objective criteria. These bioinformatics tools can be used as a general ruler to determine phylogenetic relationships and clustering of bacteria, exemplary done with Francisella (F.) tularensis. Molecular epidemiology of F. tularensis is currently assessed mostly based on laboratory methods and molecular analysis. The high evolutionary stability and the clonal nature makes Francisella ideal for subtyping with single nucleotide polymorphisms (SNPs). Sequencing and real-time PCR can be used to validate the SNP analysis. We investigate whole-genome sequences of 155 F. tularensis subsp. holarctica isolates. Phylogenetic testing was based on SNPs and average nucleotide identity (ANI) as reference independent, alignment-free methods taking small-scale and large-scale differences within the genomes into account. Especially the whole genome SNP analysis with kSNP3.0 allowed deciphering quite subtle signals of systematic differences in molecular variation. Affinity propagation clustering (APC) resulted in three clusters showing the known clades B.4, B.6, and B.12. These data correlated with the results of real-time PCR assays targeting canSNPs loci. Additionally, we detected two subtle sub-clusters. SplitsTree was used with standard-setting using the aligned SNPs from Parsnps. Together APC, HierBAPS, and SplitsTree enabled us to generate hypotheses about epidemiologic relationships between bacterial clusters and describing the distribution of isolates. Our data indicate that the choice of the typing technique can increase our understanding of the pathogenesis and transmission of diseases with the eventual for prevention. This is opening perspectives to be applied to other bacterial species. The data provide evidence that Germany might be the collision zone where the clade B.12, also known as the East European clade, overlaps with the clade B.6, also known as the Iberian clade. Described methods allow generating a new, more detailed perspective for F. tularensis subsp. holarctica phylogeny. These results may encourage to determine phylogenetic relationships and clustering of other bacteria the same way.

Project description:A longstanding challenge in epilepsy research and practice is the need to classify synchronization patterns hidden in multivariate electroencephalography (EEG) data that is routinely superimposed with intensive noise. It is essential to select a suitable feature extraction method to achieve high recognition performance. A typical approach is to extract the mutual information (MI) between pairs of channels. This calculation, which considers the differences between the sequence pairs to build a reasonable partition, can improve the classification performance. On this basis, however, it is even more difficult to adaptively classify the synchronization patterns hidden in multivariate EEG data under circumstances of insufficient a priori knowledge of domain dependency, such as denoising, feature extraction on a special patient, etc. To address these problems by (1) effectively calculating the MI matrix (synchronization pattern) and (2) accurately classifying the seizure or non-seizure state, this study first accurately measures the synchronization between channel pairs in terms of affinity propagation clustering partition MI (APCPMI). The global synchronization measurement is then obtained by organizing APCPMIs of all channel pairs into a correlation matrix. Finally, a cross-layer fully connected net is designed to characterize the synchronization dynamics correlation matrices adaptively and identify seizure or non-seizure states automatically. Experiments are performed using the CHB-MIT scalp EEG dataset to evaluate the proposed approach. Seizure states are identified with an accuracy, sensitivity, and specificity of 0.9793 ± 0.002, 0.9942 ± 0.0005, and 0.9676 ± 0.003, respectively; the resulting performance is superior to those achieved by most existing methods over the same dataset. Furthermore, the approach alleviates the necessity for strictly preprocessing (denoising, removing interferences and artifacts) the EEG data using prior knowledge, which is usually required by existing approaches.

Project description:BACKGROUND: Genome scale data on protein interactions are generally represented as large networks, or graphs, where hundreds or thousands of proteins are linked to one another. Since proteins tend to function in groups, or complexes, an important goal has been to reliably identify protein complexes from these graphs. This task is commonly executed using clustering procedures, which aim at detecting densely connected regions within the interaction graphs. There exists a wealth of clustering algorithms, some of which have been applied to this problem. One of the most successful clustering procedures in this context has been the Markov Cluster algorithm (MCL), which was recently shown to outperform a number of other procedures, some of which were specifically designed for partitioning protein interactions graphs. A novel promising clustering procedure termed Affinity Propagation (AP) was recently shown to be particularly effective, and much faster than other methods for a variety of problems, but has not yet been applied to partition protein interaction graphs. RESULTS: In this work we compare the performance of the Affinity Propagation (AP) and Markov Clustering (MCL) procedures. To this end we derive an unweighted network of protein-protein interactions from a set of 408 protein complexes from S. cervisiae hand curated in-house, and evaluate the performance of the two clustering algorithms in recalling the annotated complexes. In doing so the parameter space of each algorithm is sampled in order to select optimal values for these parameters, and the robustness of the algorithms is assessed by quantifying the level of complex recall as interactions are randomly added or removed to the network to simulate noise. To evaluate the performance on a weighted protein interaction graph, we also apply the two algorithms to the consolidated protein interaction network of S. cerevisiae, derived from genome scale purification experiments and to versions of this network in which varying proportions of the links have been randomly shuffled. CONCLUSION: Our analysis shows that the MCL procedure is significantly more tolerant to noise and behaves more robustly than the AP algorithm. The advantage of MCL over AP is dramatic for unweighted protein interaction graphs, as AP displays severe convergence problems on the majority of the unweighted graph versions that we tested, whereas MCL continues to identify meaningful clusters, albeit fewer of them, as the level of noise in the graph increases. MCL thus remains the method of choice for identifying protein complexes from binary interaction networks.

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BinSanity: unsupervised clustering of environmental microbial assemblies using coverage and affinity propagation.

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