ESPRIT-Tree: hierarchical clustering analysis of millions of 16S rRNA pyrosequences in quasilinear computational time

Cai, Yunpeng; Sun, Yijun

doi:10.1093/nar/gkr349

Cited by 139 publications

(136 citation statements)

References 33 publications

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“…Due to the superior performance of MtHc [10] than other four state-of-the-art clustering methods (MSClust [11], ESPRIT-Tree [12], CROP [13] and BEBaC [14]), we select MtHc method to generate the OTUs in this paper.…”

Section: Resultsmentioning

confidence: 99%

Exploring the interaction patterns among taxa and environments from marine metagenomic data

2016

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The sequencing revolution driven by high-throughput technologies has generated a huge amount of marine microbial sequences which hide the interaction patterns among microbial species and environment factors. Exploring these patterns is helpful for exploiting the marine resources. In this paper, we use the complex network approach to mine and analyze the interaction patterns of marine taxa and environments in spring, summer, fall and winter seasons. With the 16S rRNA pyrosequencing data of 76 time point taken monthly over 6 years, we first use our MtHc clustering algorithm to generate the operational taxonomic units (OTUs). Then, employ the k-means method to divide 76 time point samples into four seasonal groups, and utilize mutual information (MI) to construct the four correlation networks among microbial species and environment factors. Finally, we adopt the symmetrical non-negative matrix factorization method to detect the interaction patterns, and analysis the relationship between marine species and environment factors. The results show that the four seasonal microbial interaction networks have the characters of complex networks, and interaction patterns are related with the seasonal variability; the same environmental factor influences different species in the four seasons; the four environmental factors of day length, photosynthetically active radiation, NO 2 + NO 3 and silicate may have stronger influences on microbes than other environment factors.

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Section: Resultsmentioning

confidence: 99%

Exploring the interaction patterns among taxa and environments from marine metagenomic data

2016

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“…ESPRIT reduces computational complexity by generating only the lower part of a dendrogram. The approach of Mothur and ESPRIT is similar but instead of pairwise global alignment used by ESPRIT, Mothur uses multiple sequence alignment tool such as MUSCLE [14] to compute the pairwise distance matrix. It has been seen that pairwise alignment produces better clustering outcomes than multiple sequence alignments [7], [15].…”

Section: B Agglomerative Hierarchical Clustering (Ahc)mentioning

confidence: 99%

OTU Clustering A window to analyse uncultured microbial world

Bhat¹,

Prabhu²

2017

IJSRCSE

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-Clustering is the technique used to deal with higher amounts of data by partitioning the data into some groups based on some attributes. Clustering technique has many applications in different fields of science and technology. It is an important tool in genomics and metagenomics which performs taxonomic profiling of the microbial world by grouping 16S RDNA amplicon reads into clusters called as Operational Taxonomic Units (OTUs). With the help of Next Generation Sequencing (NGS) tools and clustering it has become easy for scientists to find the microbial diversities in different environments without culturing the microbes. Assignment of 16s rDNA sequences to the clusters called as OTUs is the main task in metagenomics algorithms and is also the main bottleneck for analysing microbial communities. Taxonomic profiling of 16S rDNA is an important step in Metagenomic pipeline analysis. There are several OTU clustering algorithms which clusters the amplicon reads of 16S rDNA into OTUs, each algorithm use a specific type of clustering technique to cluster the sequence reads. Some of the mostly used algorithms are Uclust, swarm, SUMACLUST, SortMeRNA, USEARCH. In this paper, we first give a brief overview of major clustering techniques and their types. Furthermore, we provide a comprehensive overview of OTU clustering algorithms.

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“…We employ ESPIRITTree [8] to cluster sequences into OTUs at various distance levels. To tackle the problem that ESPRIT-Tree uses a fixed distance threshold to define OTUs (operational taxonomy units) [7], which is inconsistent with genomic variations between taxa in real-world.…”

Section: ) Preprocessing Of Icomm Datamentioning

confidence: 99%

Intra- and inter-sparse multiple output regression with application on environmental microbial community study

Yang

Leung

Yiu

et al. 2013

2013 IEEE International Conference on Bioinformatics and Biomedicine

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Abstract-Feature selection is important for many biological studies, especially when the number of available samples is limited (in order of hundreds) while the number of input features is large (in order of millions), such as eQTL (expression quantitative trait loci) mapping, GWAS (genome wide association study) and environmental microbial community study. We study the problem of multiple output regression which leverages the underlying common relationship shared by multiple output features and propose an efficient and accurate approach for feature selection. Our approach considers both intra-and inter-group sparsities. The intergroup sparsity assumes that only small set of input features are related to the output features. The intragroup sparsity assumes that each input features may relate to multiple output features which should have different kinds of sparsity. Most existing methods do not model the intragroup sparsity well by either assuming uniform regularization on each group, i.e. each input feature relates to similar number of output features, or requiring prior knowledge of the relationship of input and output features. By modelling the regression coefficients as a mixture distributions of Laplacian and Gaussian, we can shrink group regression coefficients to be small adaptively and learn the intergroup, intragroup sparsity and shrinkage estimation patterns. Empirical studies on the synthetic and real environmental microbial community datasets show that our model has better predictions on test dataset than existing methods such as Lasso, Elastic Net, dirty model and rMTFL (robust multi-task feature learning). Moreover, by using least angle regression or coordinate descent and projected gradient descent techniques for optimization, we can obtain the optimal regression efficiently.

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ESPRIT-Tree: hierarchical clustering analysis of millions of 16S rRNA pyrosequences in quasilinear computational time

Cited by 139 publications

References 33 publications

Exploring the interaction patterns among taxa and environments from marine metagenomic data

Exploring the interaction patterns among taxa and environments from marine metagenomic data

OTU Clustering A window to analyse uncultured microbial world

Intra- and inter-sparse multiple output regression with application on environmental microbial community study

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