Gclust: A Parallel Clustering Tool for Microbial Genomic Data.
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ABSTRACT:
SUBMITTER: Li R
PROVIDER: S-EPMC7056916 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND:Flow cytometry (FCM) is a powerful single-cell based measurement method to ascertain multidimensional optical properties of millions of cells. FCM is widely used in medical diagnostics and health research. There is also a broad range of applications in the analysis of complex microbial communities. The main concern in microbial community analyses is to track the dynamics of microbial subcommunities. So far, this can be achieved with the help of time-consuming manual clustering procedures that require extensive user-dependent input. In addition, several tools have recently been developed by using different approaches which, however, focus mainly on the clustering of medical FCM data or of microbial samples with a well-known background, while much less work has been done on high-throughput, online algorithms for two-channel FCM. RESULTS:We bridge this gap with flowEMMi, a model-based clustering tool based on multivariate Gaussian mixture models with subsampling and foreground/background separation. These extensions provide a fast and accurate identification of cell clusters in FCM data, in particular for microbial community FCM data that are often affected by irrelevant information like technical noise, beads or cell debris. flowEMMi outperforms other available tools with regard to running time and information content of the clustering results and provides near-online results and optional heuristics to reduce the running-time further. CONCLUSIONS:flowEMMi is a useful tool for the automated cluster analysis of microbial FCM data. It overcomes the user-dependent and time-consuming manual clustering procedure and provides consistent results with ancillary information and statistical proof.
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Project description:BACKGROUNDS: Recent explosion of biological data brings a great challenge for the traditional clustering algorithms. With increasing scale of data sets, much larger memory and longer runtime are required for the cluster identification problems. The affinity propagation algorithm outperforms many other classical clustering algorithms and is widely applied into the biological researches. However, the time and space complexity become a great bottleneck when handling the large-scale data sets. Moreover, the similarity matrix, whose constructing procedure takes long runtime, is required before running the affinity propagation algorithm, since the algorithm clusters data sets based on the similarities between data pairs. METHODS: Two types of parallel architectures are proposed in this paper to accelerate the similarity matrix constructing procedure and the affinity propagation algorithm. The memory-shared architecture is used to construct the similarity matrix, and the distributed system is taken for the affinity propagation algorithm, because of its large memory size and great computing capacity. An appropriate way of data partition and reduction is designed in our method, in order to minimize the global communication cost among processes. RESULT: A speedup of 100 is gained with 128 cores. The runtime is reduced from serval hours to a few seconds, which indicates that parallel algorithm is capable of handling large-scale data sets effectively. The parallel affinity propagation also achieves a good performance when clustering large-scale gene data (microarray) and detecting families in large protein superfamilies.
| S-EPMC3976248 | biostudies-literature
Project description:In recent years, the advent of high-throughput assays, coupled with their diminishing cost, has facilitated a systems approach to biology. As a consequence, massive amounts of data are currently being generated, requiring efficient methodology aimed at the reduction of scale. Whole-genome transcriptional profiling is a standard component of systems-level analyses, and to reduce scale and improve inference clustering genes is common. Since clustering is often the first step toward generating hypotheses, cluster quality is critical. Conversely, because the validation of cluster-driven hypotheses is indirect, it is critical that quality clusters not be obtained by subjective means. In this paper, we present a new objective-based clustering method and demonstrate that it yields high-quality results. Our method, modulated modularity clustering (MMC), seeks community structure in graphical data. MMC modulates the connection strengths of edges in a weighted graph to maximize an objective function (called modularity) that quantifies community structure. The result of this maximization is a clustering through which tightly-connected groups of vertices emerge. Our application is to systems genetics, and we quantitatively compare MMC both to the hierarchical clustering method most commonly employed and to three popular spectral clustering approaches. We further validate MMC through analyses of human and Drosophila melanogaster expression data, demonstrating that the clusters we obtain are biologically meaningful. We show MMC to be effective and suitable to applications of large scale. In light of these features, we advocate MMC as a standard tool for exploration and hypothesis generation.
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Project description:After a decade of digitization and technological advancements, we have an abundance of usable genomic data, which provide unique insights into our well-being. However, such datasets are large in volume, and retrieving meaningful information from them is often challenging. Hence, different indexing techniques and data structures have been proposed to handle such a massive scale of data. We utilize one such technique: Generalized Suffix Tree (GST). In this paper, we introduce an efficient parallel generalized suffix tree construction algorithm that is scalable for arbitrary genomic datasets. Our construction mechanism employs shared and distributed memory architecture collectively while not posing any fixed, prior memory requirement as it uses external memory (disks). Our experimental results show that our proposed architecture offers around 4-times speedup with respect to the sequential algorithm with only 16 parallel processors. The experiments on different datasets and parameters also exhibit the scalability of the execution time. In addition, we utilize different string queries and demonstrate their execution time on such tree structure, illustrating the efficacy and usability of GST for genomic data.
| S-EPMC7197101 | biostudies-literature
Project description:The rapid development of sequencing technology has led to an explosive accumulation of genomic sequence data. Clustering is often the first step to perform in sequence analysis, and hierarchical clustering is one of the most commonly used approaches for this purpose. However, it is currently computationally expensive to perform hierarchical clustering of extremely large sequence datasets due to its quadratic time and space complexities. In this paper we developed a new algorithm called ESPRIT-Forest for parallel hierarchical clustering of sequences. The algorithm achieves subquadratic time and space complexity and maintains a high clustering accuracy comparable to the standard method. The basic idea is to organize sequences into a pseudo-metric based partitioning tree for sub-linear time searching of nearest neighbors, and then use a new multiple-pair merging criterion to construct clusters in parallel using multiple threads. The new algorithm was tested on the human microbiome project (HMP) dataset, currently one of the largest published microbial 16S rRNA sequence dataset. Our experiment demonstrated that with the power of parallel computing it is now compu- tationally feasible to perform hierarchical clustering analysis of tens of millions of sequences. The software is available at http://www.acsu.buffalo.edu/∼yijunsun/lab/ESPRIT-Forest.html.
| S-EPMC5421816 | biostudies-literature
Project description:Structural heterogeneity in single-particle cryo-electron microscopy (cryo-EM) data represents a major challenge for high-resolution structure determination. Unsupervised classification may serve as the first step in the assessment of structural heterogeneity. However, traditional algorithms for unsupervised classification, such as K-means clustering and maximum likelihood optimization, may classify images into wrong classes with decreasing signal-to-noise-ratio (SNR) in the image data, yet demand increased computational costs. Overcoming these limitations requires further development of clustering algorithms for high-performance cryo-EM data processing. Here we introduce an unsupervised single-particle clustering algorithm derived from a statistical manifold learning framework called generative topographic mapping (GTM). We show that unsupervised GTM clustering improves classification accuracy by about 40% in the absence of input references for data with lower SNRs. Applications to several experimental datasets suggest that our algorithm can detect subtle structural differences among classes via a hierarchical clustering strategy. After code optimization over a high-performance computing (HPC) environment, our software implementation was able to generate thousands of reference-free class averages within hours in a massively parallel fashion, which allows a significant improvement on ab initio 3D reconstruction and assists in the computational purification of homogeneous datasets for high-resolution visualization.
| S-EPMC5546606 | biostudies-literature
Project description:BackgroundThe non-conserved accessory genome of bacteria can be associated with important adaptive characteristics that can contribute to niche specificity or pathogenicity of strains. High degrees of structural and compositional diversity in genomic islands and other elements of the accessory genome can complicate characterization of accessory genome contents among populations of strains. Methods for easily and effectively defining the distributions of discrete elements of the accessory genome among bacterial strains in a population are needed to explore the relationships between the flexible genome and bacterial adaptive traits.ResultsWe have developed the open-source software package ClustAGE. This program, written in Perl, uses BLAST to cluster nucleotide accessory genomic elements from the genomes of multiple bacterial strains and to identify their distribution within the study population. The program output can be used in combination with strain phenotype data or other characteristics to detect associations. Optional graphical output is available for visualizing accessory genome gene content and distribution patterns. The capabilities of the software are demonstrated on a collection of 14 Pseudomonas aeruginosa genome sequences.ConclusionsThe ClustAGE software and utilities are effective for identifying characteristics and distributions of accessory genomic elements among groups of bacterial genomes. The ability to easily and effectively characterize the accessory genome of a sequence collection may provide a better understanding of the accessory genome's contribution to a species' adaptation and pathogenesis. The ClustAGE source code can be downloaded from https://clustage.sourceforge.io and a limited web-based implementation is available at http://vfsmspineagent.fsm.northwestern.edu/cgi-bin/clustage.cgi .
| S-EPMC5910555 | biostudies-literature
Project description:The prevailing paradigm for the analysis of biological data involves comparing groups of replicates from different conditions (e.g. control and treatment) to statistically infer features that discriminate them (e.g. differentially expressed genes). However, many situations in modern genomics such as single-cell omics experiments do not fit well into this paradigm because they lack true replicates. In such instances, spectral techniques could be used to rank features according to their degree of consistency with an underlying metric structure without the need to cluster samples. Here, we extend spectral methods for feature selection to abstract simplicial complexes and present a general framework for clustering-independent analysis. Combinatorial Laplacian scores take into account the topology spanned by the data and reduce to the ordinary Laplacian score when restricted to graphs. We demonstrate the utility of this framework with several applications to the analysis of gene expression and multi-modal genomic data. Specifically, we perform differential expression analysis in situations where samples cannot be grouped into distinct classes, and we disaggregate differentially expressed genes according to the topology of the expression space (e.g. alternative paths of differentiation). We also apply this formalism to identify genes with spatial patterns of expression using fluorescence in-situ hybridization data and to establish associations between genetic alterations and global expression patterns in large cross-sectional studies. Our results provide a unifying perspective on topological data analysis and manifold learning approaches to the analysis of large-scale biological datasets.
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Project description:Presumptive identification of different Enterobacteriaceae species is routinely achieved based on biochemical properties. Traditional practice includes manual comparison of each biochemical property of the unknown sample with known reference samples and inference of its identity based on the maximum similarity pattern with the known samples. This process is labor-intensive, time-consuming, error-prone, and subjective. Therefore, automation of sorting and similarity in calculation would be advantageous. Here we present a MATLAB-based graphical user interface (GUI) tool named BioCluster. This tool was designed for automated clustering and identification of Enterobacteriaceae based on biochemical test results. In this tool, we used two types of algorithms, i.e., traditional hierarchical clustering (HC) and the Improved Hierarchical Clustering (IHC), a modified algorithm that was developed specifically for the clustering and identification of Enterobacteriaceae species. IHC takes into account the variability in result of 1-47 biochemical tests within this Enterobacteriaceae family. This tool also provides different options to optimize the clustering in a user-friendly way. Using computer-generated synthetic data and some real data, we have demonstrated that BioCluster has high accuracy in clustering and identifying enterobacterial species based on biochemical test data. This tool can be freely downloaded at http://microbialgen.du.ac.bd/biocluster/.
| S-EPMC4563349 | biostudies-literature
Project description:Summary:The ever-increasing growth of high-throughput sequencing technologies has led to a great acceleration of medical and biological research and discovery. As these platforms advance, the amount of information for diverse genomes increases at unprecedented rates. Confidentiality, integrity and authenticity of such genomic information should be ensured due to its extremely sensitive nature. In this paper, we propose Cryfa, a fast secure encryption tool for genomic data, namely in Fasta, Fastq, VCF, SAM and BAM formats, which is also capable of reducing the storage size of Fasta and Fastq files. Cryfa uses advanced encryption standard (AES) encryption combined with a shuffling mechanism, which leads to a substantial enhancement of the security against low data complexity attacks. Compared to AES Crypt, a general-purpose encryption tool, Cryfa is an industry-oriented tool, which is able to provide confidentiality, integrity and authenticity of data at four times more speed; in addition, it can reduce the file sizes to 1/3. Due to the absence of a method similar to Cryfa, we have simulated its behavior with a combination of encryption and compression tools, for comparison purpose. For instance, our tool is nine times faster than its fastest competitor in Fasta files. Also, Cryfa has a very low memory usage (only a few megabytes), which makes it feasible to run on any computer. Availability and implementation:Source codes and binaries are available, under GPLv3, at https://github.com/pratas/cryfa. Supplementary information:Supplementary data are available at Bioinformatics online.
| S-EPMC6298042 | biostudies-literature