Whole-Genome-Based Phylogeny and Taxonomy for Prokaryotes

A faithful prokaryotic phylogeny should be inferred from genomic data and phylogeny determines taxonomy. The ever-growing amount of sequenced genomes makes this approach feasible and practical. Whole-genome phylogeny must be based on alignment-free methodology and should be verified by direct comparison with taxonomy at all ranks from domains down to species. When the number of genomes goes into tens of thousands, the realization of the above program also presents technical challenges. The power of a long-tested Web Server named Composition Vector Tree (CVTree) will be demonstrated on examples from mega-classification of bacteria to high resolution at and below the species level

A fungal phylogeny based on 82 complete genomes using the composition vector method

<p>Abstract</p> <p>Background</p> <p>Molecular phylogenetics and phylogenomics have greatly revised and enriched the fungal systematics in the last two decades. Most of the analyses have been performed by comparing single or multiple orthologous gene regions. Sequence alignment has always been an essential element in tree construction. These alignment-based methods (to be called the standard methods hereafter) need independent verification in order to put the fungal Tree of Life (TOL) on a secure footing. The ever-increasing number of sequenced fungal genomes and the recent success of our newly proposed alignment-free composition vector tree (CVTree, see Methods) approach have made the verification feasible.</p> <p>Results</p> <p>In all, 82 fungal genomes covering 5 phyla were obtained from the relevant genome sequencing centers. An unscaled phylogenetic tree with 3 outgroup species was constructed by using the CVTree method. Overall, the resultant phylogeny infers all major groups in accordance with standard methods. Furthermore, the CVTree provides information on the placement of several currently unsettled groups. Within the sub-phylum Pezizomycotina, our phylogeny places the Dothideomycetes and Eurotiomycetes as sister taxa. Within the Sordariomycetes, it infers that <it>Magnaporthe grisea </it>and the Plectosphaerellaceae are closely related to the Sordariales and Hypocreales, respectively. Within the Eurotiales, it supports that <it>Aspergillus nidulans </it>is the early-branching species among the 8 aspergilli. Within the Onygenales, it groups <it>Histoplasma </it>and <it>Paracoccidioides </it>together, supporting that the Ajellomycetaceae is a distinct clade from Onygenaceae. Within the sub-phylum Saccharomycotina, the CVTree clearly resolves two clades: (1) species that translate CTG as serine instead of leucine (the CTG clade) and (2) species that have undergone whole-genome duplication (the WGD clade). It places <it>Candida glabrata </it>at the base of the WGD clade.</p> <p>Conclusion</p> <p>Using different input data and methodology, the CVTree approach is a good complement to the standard methods. The remarkable consistency between them has brought about more confidence to the current understanding of the fungal branch of TOL.</p

Metagenomic analyses reveal phylogenetic diversity of carboxypeptidase gene sequences in activated sludge of a wastewater treatment plant in Shanghai, China

Activated sludge of wastewater treatment plants carries a diverse microflora. However, up to 80–90 % of microorganisms in activated sludge cannot be cultured by current laboratory techniques, leaving an enzyme reservoir largely unexplored. In this study, we investigated carboxypeptidase diversity in activated sludge of a wastewater treatment plant in Shanghai, China, by a culture-independent metagenomic approach. Three sets of consensus degenerate hybrid oligonucleotide primers (CODEHOPs) targeting conserved domains of public carboxypeptidases have been designed to amplify carboxypeptidase gene sequences in the metagenomic DNA of activated sludge by PCR. The desired amplicons were evaluated by carboxypeptidase sequence clone libraries and phylogenetic analyses. We uncovered a significant diversity of carboxypeptidases present in the activated sludge. Deduced carboxypeptidase amino acid sequences (127–208 amino acids) were classified into three distinct clusters, α, β, and γ. Sequences belonging to clusters α and β shared 58–97 % identity to known carboxypeptidase sequences from diverse species, whereas sequences in the cluster γ were remarkably less related to public carboxypeptidase homologous in the GenBank database, strongly suggesting that novel carboxypeptidase families or microbial niches exist in the activated sludge. We also observed numerous carboxypeptidase sequences that were much closer to those from representative strains present in industrial and sewage treatment and bioremediation. Thermostable and halotolerant carboxypeptidase sequences were also detected in clusters α and β. Coexistence of various carboxypeptidases is evidence of a diverse microflora in the activated sludge, a feature suggesting a valuable gene resource to be further explored for biotechnology application

Modeling the Alignment Profile of Satellite Galaxies in Clusters

Analyzing the halo and galaxy catalogs from the Millennium simulations at redshifts $z=0,\ 0.5,\ 1$, we determine the alignment profiles of cluster galaxies by measuring the average alignments between the major axes of the pseudo inertia tensors from all satellites within cluster's virial radius and from only those satellites within some smaller radius as a function of the top-hat scale difference. The alignment profiles quantify how well the satellite galaxies retain the memory of the external tidal fields after merging into their host clusters and how fast they lose the initial alignment tendency as the cluster's relaxation proceeds. It is found that the alignment profile drops faster at higher redshifts and on smaller mass scales. This result is consistent with the picture that the faster merging of the satellites and earlier onset of the nonlinear effect inside clusters tend to break the preferential alignments of the satellites with the external tidal fields. Modeling the alignment profile of cluster galaxies as a power-law of the density correlation coefficient that is independent of the power spectrum normalization ($\sigma_{8}$) and demonstrating that the density correlation coefficient varies sensitively with the density parameter ($\Omega_{m}$) and neutrino mass fraction ($f_{\nu}$), we suggest that the alignment profile of cluster galaxies might be useful for breaking the $\Omega_{m}$-$\sigma_{8}$ and $f_{\nu}$-$\sigma_{8}$ degeneracies.Comment: accepted for publication in ApJ, Introduction and Conclusion sections improved, mistakes in plotting the figures corrected, detailed explanations for the dependence of the alignment profiles on the mass and redshift provided, 7 figures, 3 table

Predicting Internet of Things Data Traffic Through LSTM and Autoregressive Spectrum Analysis

The rapid increase of Internet of Things (IoT) applications and services has led to massive amounts of heterogeneous data. Hence, we need to re-think how IoT data influences the network. In this paper, we study the characteristics of IoT data traffic in the context of smart cities. Aiming at analyzing the influence of IoT data traffic on the access and core network, we generate various IoT data traffic according to the characteristics of different IoT applications. Based on the analysis of the inherent features of the aggregated IoT data traffic, we propose a Long Short-Term Memory (LSTM) model combined with autoregressive spectrum analysis to predict the IoT data traffic. In this model, the autoregressive spectrum analysis is used to estimate the minimum length of the historical data needed for predicting the traffic in the future, which alleviates LSTM's performance deterioration with the increase of sequence length. A sliding window enables predicting the long-term tendency of IoT data traffic while keeping the inherent features of the data traffic. The evaluation results show that the proposed model converges quickly and can predict the variations of IoT traffic more accurately than other methods and the general LSTM model.Peer reviewe

Predicting Internet of Things Data Traffic Through LSTM and Autoregressive Spectrum Analysis

The rapid increase of Internet of Things (IoT) applications and services has led to massive amounts of heterogeneous data. Hence, we need to re-think how IoT data influences the network. In this paper, we study the characteristics of IoT data traffic in the context of smart cities. Aiming at analyzing the influence of IoT data traffic on the access and core network, we generate various IoT data traffic according to the characteristics of different IoT applications. Based on the analysis of the inherent features of the aggregated IoT data traffic, we propose a Long Short-Term Memory (LSTM) model combined with autoregressive spectrum analysis to predict the IoT data traffic. In this model, the autoregressive spectrum analysis is used to estimate the minimum length of the historical data needed for predicting the traffic in the future, which alleviates LSTM's performance deterioration with the increase of sequence length. A sliding window enables predicting the long-term tendency of IoT data traffic while keeping the inherent features of the data traffic. The evaluation results show that the proposed model converges quickly and can predict the variations of IoT traffic more accurately than other methods and the general LSTM model.Peer reviewe

3D face reconstruction with geometry details from a single image

3D face reconstruction from a single image is a classical and challenging problem with wide applications in many areas. Inspired by recent works in face animation from RGB-D or monocular video inputs, we develop a novel method for reconstructing 3D faces from unconstrained 2D images using a coarse-to-fine optimization strategy. First, a smooth coarse 3D face is generated from an example-based bilinear face model by aligning the projection of 3D face landmarks with 2D landmarks detected from the input image. Afterward, using local corrective deformation fields, the coarse 3D face is refined using photometric consistency constraints, resulting in a medium face shape. Finally, a shape-from-shading method is applied on the medium face to recover fine geometric details. Our method outperforms the state-of-the-art approaches in terms of accuracy and detail recovery, which is demonstrated in extensive experiments using real-world models and publicly available data sets