Distance Matrix Methods Research Articles

Genetic diversity and population structure of Uganda's yam (Dioscorea spp.) genetic resource based on DArTseq.

Assessing the genetic diversity of yam germplasm from different geographical origins for cultivation and breeding purposes is an essential step for crop genetic resource conservation and genetic improvement, especially where the crop faces minimal attention. This study aimed to classify the population structure, and assess the extent of genetic diversity in 207 Dioscorea rotundata genotypes sourced from three different geographical origins. A total of 4,957 (16.2%) single nucleotide polymorphism markers were used to assess genetic diversity. The SNP markers were informative, with polymorphic information content ranging from 0.238 to 0.288 and a mean of 0.260 across all the genotypes. The observed and expected heterozygosity was 0.12 and 0.23, respectively while the minor allele frequency ranged from 0.093 to 0.124 with a mean of 0.109. The principal coordinate analysis, model-based structure and discriminant analysis of principal components, and the Euclidean distance matrix method grouped 207 yam genotypes into three main clusters. Genotypes from West Africa (Ghana and Nigeria) had significant similarities with those from Uganda. Analysis of molecular variance revealed that within-population variation across three different geographical origins accounted for 93% of the observed variation. This study, therefore, showed that yam improvement in Uganda is possible, and the outcome will constitute a foundation for the genetic improvement of yams in Uganda.

OTU Delimitation with Earthworm DNA Barcodes: A Comparison of Methods

Although DNA barcodes-based operational taxonomic units (OTUs) are increasingly used in earthworm research, the relative efficiency of the different methods available to delimit them has not yet been tested on a comprehensive dataset. For this study, we used three datasets containing 651, 2304 and 4773 COI barcodes of earthworms from French Guiana, respectively, to compare five of these methods: two phylogenetic methods—namely Poisson Tree Processes (PTP) and General Mixed Yule Coalescence (GMYC)—and three distance matrix methods—namely Refined Single Linkage (RESL, used for assigning Barcode Index Numbers in the Barcode of Life Data systems), Automatic Barcode Gap Discovery (ABGD), and Assemble Species by Automatic Partitioning (ASAP). We found that phylogenetic approaches are less suitable for delineating OTUs from DNA barcodes in earthworms, especially for large sets of sequences. The computation times are unreasonable, they often fail to converge, and they also show a strong tendency to oversplit species. Among distance-based methods, RESL also has a clear tendency to oversplitting, while ABGD and ASAP are less prone to mismatches and have short computation times. ASAP requires less a priori knowledge for model parameterisation than AGBD, provides efficient graphical outputs, and has a much lower tendency to generate mismatches.

CProtMEDIAS: clustering of amino acid sequences encoded by gene families by MErging and DIgitizing Aligned Sequences.

Protein phylogenetic analysis focuses on the evolutionary relationships among related protein sequences and can help researchers infer protein functions and developmental trajectories. With the advent of the big data era, the existing protein phylogenetic methods, including distance matrix and character-based methods, are facing challenges in both running time and application scope. Here, we developed an R package that we call CProtMEDIAS that is useful for protein phylogenetic analysis. In contrast to existing phylogenetic analysis methods, CProtMEDIAS utilizes dimensionality reduction algorithms to digitize multiple sequence alignments and quickly conduct phylogenetic analysis with a large number of amino acid sequences from similarly distant protein families and species. We used CProtMEDIAS to perform a dimensionality reduction, clustering, pseudotime, specific residue and evolutionary trajectory analysis of the plant homeobox superfamily. We found that CProtMEDIAS delivers consistent clustering, fast running and elegant presentation and thus provides powerful new tools and methods for protein clustering and evolutionary analysis.

Stochastic Optimal Operation Framework of an Integrated Methane-Based Zero-CO2 Energy Hub in Energy Markets

Energy hubs (EHs) are couplers between different energy carriers in smart grids. The optimal participation of these actors in energy markets (EMs) as active and helpful actors is essential. This paper presents a new structure of methane-based EH considering biomass fuel to participate in the EMs of electricity, heat, and natural gas (NG). For this purpose, we propose an optimal bidding framework for the EH as a MILP stochastic optimization problem. The EH does not inject any CO2 pollution into the air (zero-CO2) and converts it into valuable methane (CH4) fuel using the CH4 production unit. To model uncertain parameters, electricity market price, wind speed, and solar radiation, an LSTM-based model of deep learning is proposed for scenario generation. Moreover, the Kantorovich distance matrix method reduces the generated scenarios. Since the proposed EH structure is compatible with Finland's infrastructure, simulation studies using actual data of this country are performed on selected days. The results show that in addition to profitable operation, high flexibility, environmental friendliness, and high accuracy of uncertainty modeling, the EH has no dependence on the purchase of energy carriers.

Training Data Selection by Categorical Variables for Better Rare Event Prediction in Multiple Products Production Line

Manufacturers are struggling to use data from multiple products production lines to predict rare events. Improving the quality of training data is a common way to improve the performance of algorithms. However, there is little research about how to select training data quantitatively. In this study, a training data selection method is proposed to improve the performance of deep learning models. The proposed method can represent different time length multivariate time series spilt by categorical variables and measure the (dis)similarities by the distance matrix and clustering method. The contributions are: (1) The proposed method can find the changes to the training data caused by categorical variables in a multivariate time series dataset; (2) according to the proposed method, the multivariate time series data from the production line can be clustered into many small training datasets; and (3) same structure but different parameters prediction models are built instead of one model which is different from the traditional way. In practice, the proposed method is applied in a real multiple products production line dataset and the result shows it can not only significantly improve the performance of the reconstruction model but it can also quantitively measure the (dis)similarities of the production behaviors.

Improved Accuracy of Animal Skin Pattern retrieval with CNN Model and Distance Metrics

This study describes the retrieval of animal skins with very diverse shapes and textures. With so many variations in each animal skin pattern, an appropriate and supported CNN model is needed as well as an appropriate distance matrix method to support retrieval performance. This research was conducted on 6 types of animal skin images. In experimenting to obtain this animal skin image, 4 CNN models were used, namely EfficientnetB7, Inception V3, MobilenetV2, and Resnet50 V2, as well as 2 distance metrics methods, namely Euclidean and Manhattan. Based on the experiment, the average with 2 measuring distances is more than 90%. The CNN model has the highest retrieval accuracy in Inception V3 with an average of 97.2% MobilenetV2 with an average of 92.9%, Resnet50 V2 with an average of 95.5%, and EfficientNetB7 with an average of 96%. In the skin animal image retrieval accuracy which is more than 70%, the highest number on CNN model Inception V2 is 41 images and EfficientNetb7 is 40 images with Manhattan Distance. Some animal skin image patterns have a retrieval accuracy of up to 100% using 4 CNN models, but some patterns have an accuracy below 50%, so this is part of the continuation of research on animal skin patterns. Keywords-- Manhattan, Euclidean, CNN, EfficientnetB7, InceptionV3, Mobilenet V2, Resnet50 V2

Analysis of Euclidean Distance and Manhattan Distance in the K-Means Algorithm for Variations Number of Centroid K

K-Means is a clustering algorithm based on a partition where the data only entered into one K cluster, the algorithm determines the number group in the beginning and defines the K centroid. The initial determination of the cluster center is very influential on the results of the clustering process in determining the quality of grouping. Better clustering results are often obtained after several attempts. The manhattan distance matrix method has better performance than the euclidean distance method. The author making the result of conducted testing with variations in the number of centroids (K) with a value of 2,3,4,5,6,7,8,9 and the authors having conclusions where the number of centroids 3 and 4 have a better iteration of values than the number of centroids that increasingly high and low based on the iris dataset.

Creating functional groups of marine fish from categorical traits.

BackgroundFunctional groups serve two important functions in ecology: they allow for simplification of ecosystem models and can aid in understanding diversity. Despite their important applications, there has not been a universally accepted method of how to define them. A common approach is to cluster species on a set of traits, validated through visual confirmation of resulting groups based primarily on expert opinion. The goal of this research is to determine a suitable procedure for creating and evaluating functional groups that arise from clustering nominal traits.MethodsTo do so, we produced a species by trait matrix of 22 traits from 116 fish species from Tasman Bay and Golden Bay, New Zealand. Data collected from photographs and published literature were predominantly nominal, and a small number of continuous traits were discretized. Some data were missing, so the benefit of imputing data was assessed using four approaches on data with known missing values. Hierarchical clustering is utilised to search for underlying data structure in the data that may represent functional groups. Within this clustering paradigm there are a number of distance matrices and linkage methods available, several combinations of which we test. The resulting clusters are evaluated using internal metrics developed specifically for nominal clustering. This revealed the choice of number of clusters, distance matrix and linkage method greatly affected the overall within- and between- cluster variability. We visualise the clustering in two dimensions and the stability of clusters is assessed through bootstrapping.ResultsMissing data imputation showed up to 90% accuracy using polytomous imputation, so was used to impute the real missing data. A division of the species information into three functional groups was the most separated, compact and stable result. Increasing the number of clusters increased the inconsistency of group membership, and selection of the appropriate distance matrix and linkage method improved the fit.DiscussionWe show that the commonly used methodologies used for the creation of functional groups are fraught with subjectivity, ultimately causing significant variation in the composition of resulting groups. Depending on the research goal dictates the appropriate strategy for selecting number of groups, distance matrix and clustering algorithm combination.

A distance matrix based algorithm for solving the traveling salesman problem

This paper presents a new algorithm for solving the well-known traveling salesman problem (TSP). This algorithm applies the Distance Matrix Method to the Greedy heuristic that is widely used in the TSP literature. In particular, it is shown that there exists a significant negative correlation between the variance of distance matrix and the performance of the Greedy heuristic, that is, the less the variance of distance matrix among the customer nodes is, the better solution the Greedy heuristic can provide. Thus the Distance Matrix Method can be used to improve the Greedy heuristic’s performance. Based on this observation, a method called Minimizing the Variance of Distance Matrix (MVODM) is proposed. This method can effectively improve the Greedy heuristic when applied. In order to further improve the efficiency, a heuristic that can quickly provide approximate solutions of the MVODM is developed. Finally, an algorithm combining this approximate MVODM method and Greedy heuristic is developed. Extensive computational experiments on a well-established test suite consisting of 82 benchmark instances with city numbers ranging from 1000 to 10,000,000 demonstrate that this algorithm not only improves the average tour quality by 40.1%, but also reduces the running time by 21.7%, comparing with the Greedy algorithm. More importantly, the performance of the proposed approach can beat the Savings heuristic, the best known construction heuristic in the TSP literature.

IDXL: Species Tree Inference Using Internode Distance and Excess Gene Leaf Count.

We propose an extension of the distance matrix methods NJst and ASTRID to infer species trees from incongruent gene trees having Incomplete Lineage Sorting. Both approaches consider the average internode distance (ID) between individual taxa pairs as the distance measure. The measure ID does not use the root of a tree, and thus may not always infer the relative position of a taxon with respect to the root. We define a novel distance measure excess gene leaf count (XL) between individual couplets. The XL measure is computed using the root of a tree. It is proved to be additive, and is shown to infer the relative order of divergence among individual couplets better. We propose a novel method IDXL which uses both the XL and ID measures for species tree construction. IDXL is shown to perform better than NJst and other distance matrix approaches for most of the biological and simulated datasets. Having the same computational complexity as NJst, IDXL can be applied for species tree inference on large-scale biological datasets.

Minimum Vertex-type Sequence Indexing for Clusters on Square Lattice

An effective indexing scheme for clusters that enables fast structure comparison and congruence check is desperately desirable in the field of mathematics, artificial intelligence, materials science, etc. Here we introduce the concept of minimum vertex-type sequence for the indexing of clusters on square lattice, which contains a series of integers each labeling the vertex type of an atom. The minimum vertex-type sequence is orientation independent, and it builds a one-to-one correspondence with the cluster. By using minimum vertex-type sequence for structural comparison and congruence check, only one type of data is involved, and the largest amount of data to be compared is n pairs, n is the cluster size. In comparison with traditional coordinate-based methods and distance-matrix methods, the minimum vertex-type sequence indexing scheme has many other remarkable advantages. Furthermore, this indexing scheme can be easily generalized to clusters on other high-symmetry lattices. Our work can facilitate cluster indexing and searching in various situations, it may inspire the search of other practical indexing schemes for handling clusters of large sizes.

MGUPGMA: A Fast UPGMA Algorithm With Multiple Graphics Processing Units Using NCCL.

A phylogenetic tree is a visual diagram of the relationship between a set of biological species. The scientists usually use it to analyze many characteristics of the species. The distance-matrix methods, such as Unweighted Pair Group Method with Arithmetic Mean and Neighbor Joining, construct a phylogenetic tree by calculating pairwise genetic distances between taxa. These methods have the computational performance issue. Although several new methods with high-performance hardware and frameworks have been proposed, the issue still exists. In this work, a novel parallel Unweighted Pair Group Method with Arithmetic Mean approach on multiple Graphics Processing Units is proposed to construct a phylogenetic tree from extremely large set of sequences. The experimental results present that the proposed approach on a DGX-1 server with 8 NVIDIA P100 graphic cards achieves approximately 3-fold to 7-fold speedup over the implementation of Unweighted Pair Group Method with Arithmetic Mean on a modern CPU and a single GPU, respectively.

Data-Driven Approach to Benthic Cover Type Classification Using Bathymetric LiDAR Waveform Analysis

A data-driven method for describing the benthic cover type based on full-waveform bathymetric LiDAR data analysis is presented. The waveform of the bathymetric LiDAR return pulse is first modeled as a sum of three functions: a Gaussian pulse representing the surface return, a function modeling the backscatter and another Gaussian pulse modeling the return from the bottom surface. Two sets of variables are formed: one containing features describing the bottom return and the other describing various conditions, such as water quality and the depth of the seabed. Regression analysis is used to eliminate the effect of the condition variables on the features, after which the features are mapped onto a cell lattice using a self-organizing map (SOM). The cells of the SOM are grouped into seven clusters using the neighborhood distance matrix method. The clustering result is evaluated using the seabed substrate map based on sonar measurements, as well as delineation of photic zones in the study area. High correspondence between the clusters and the substrate type/photic zone has been obtained indicating that the proposed clustering method adequately describes the benthic cover in the study area. The bottom return pulse waveforms corresponding to the clusters and a cluster map of the study area are also presented. The method can be used for clustering full waveform bathymetric LiDAR data acquired from large areas to discover the structure of benthic cover types and to focus the field studies accordingly.

Tutorial on phylogenetic inference — 2

Phylogenetic Inference (PI) is a statistical technique to trace the evolutionary legacy of a wide range of subjects; including biological taxa (species), biomolecules, languages, ancient texts and so on. The first part1 of this tutorial introduced a number of fundamental concepts including phenetics, cladistics, homology, homoplasy, synapomorphy, symplesiomorphy, orthology and paralogy. In this part, we will learn about models of molecular evolution, choosing the bestmodel, overview of various genetic loci used inPI,methods of PI (including distance matrix method, NJ, and discrete data methods ML, MP and BI), issue of lineage sorting and conclude with a worked-out example.

Genetic characterization of microsporidians infecting Indian non-mulberry silkworms (Antheraea assamensis and Samia cynthia ricini) by using PCR based ISSR and RAPD markers assay

This study established the genetic characterisation of 10 microsporidian isolates infecting non-mulberry silkworms (Antheraea assamensis and Samia cynthia ricini) collected from biogeographical forest locations in the State of Assam, India, using PCR-based markers assays: inter simple sequence repeat (ISSR) and random amplified polymorphic DNA (RAPD). A Nosema type species (NIK-1s_mys) was used as control for comparison. The shape of mature microsporidian spores were observed oval to elongated, measuring 3.80 to <TEX>$4.90{\mu}m$</TEX> in length and 2.60 to <TEX>$3.05{\mu}m$</TEX> in width. Fourteen ISSR primers generated reproducible profiles and yielded 178 fragments, of which 175 were polymorphic (98%), while 16 RAPD primers generated reproducible profiles with 198 amplified fragments displaying 95% of polymorphism. Estimation of genetic distance coefficients based on dice coefficients method and clustering with un-weighted pair group method using arithmetic average (UPGMA) analysis was done to unravel the genetic diversity of microsporidians infecting Indian muga and eri silkworm. The similarity coefficients varied from 0.385 to 0.941 in ISSR and 0.083 to 0.938 in RAPD data. UPGMA analysis generated dendrograms with two microsporidian groups, which appear to be different from each other. Based on Euclidean distance matrix method, 2-dimensional distribution also revealed considerable variability among different identified microsporidians. Clustering of these microsporidian isolates was in accordance with their host and biogeographic origin. Both techniques represent a useful and efficient tool for taxonomical grouping as well as for phylogenetic classification of different microsporidians in general and genotyping of these pathogens in particular.

Maximum likelihood inference of small trees in the presence of long branches.

The statistical basis of maximum likelihood (ML), its robustness, and the fact that it appears to suffer less from biases lead to it being one of the most popular methods for tree reconstruction. Despite its popularity, very few analytical solutions for ML exist, so biases suffered by ML are not well understood. One possible bias is long branch attraction (LBA), a regularly cited term generally used to describe a propensity for long branches to be joined together in estimated trees. Although initially mentioned in connection with inconsistency of parsimony, LBA has been claimed to affect all major phylogenetic reconstruction methods, including ML. Despite the widespread use of this term in the literature, exactly what LBA is and what may be causing it is poorly understood, even for simple evolutionary models and small model trees. Studies looking at LBA have focused on the effect of two long branches on tree reconstruction. However, to understand the effect of two long branches it is also important to understand the effect of just one long branch. If ML struggles to reconstruct one long branch, then this may have an impact on LBA. In this study, we look at the effect of one long branch on three-taxon tree reconstruction. We show that, counterintuitively, long branches are preferentially placed at the tips of the tree. This can be understood through the use of analytical solutions to the ML equation and distance matrix methods. We go on to look at the placement of two long branches on four-taxon trees, showing that there is no attraction between long branches, but that for extreme branch lengths long branches are joined together disproportionally often. These results illustrate that even small model trees are still interesting to help understand how ML phylogenetic reconstruction works, and that LBA is a complicated phenomenon that deserves further study. [analytic solutions; long branch attraction; maximum likelihood; simulation.]

DL-PRO: A Novel Deep Learning Method for Protein Model Quality Assessment.

Computational protein structure prediction is very important for many applications in bioinformatics. In the process of predicting protein structures, it is essential to accurately assess the quality of generated models. Although many single-model quality assessment (QA) methods have been developed, their accuracy is not high enough for most real applications. In this paper, a new approach based on C-α atoms distance matrix and machine learning methods is proposed for single-model QA and the identification of native-like models. Different from existing energy/scoring functions and consensus approaches, this new approach is purely geometry based. Furthermore, a novel algorithm based on deep learning techniques, called DL-Pro, is proposed. For a protein model, DL-Pro uses its distance matrix that contains pairwise distances between two residues' C-α atoms in the model, which sometimes is also called contact map, as an orientation-independent representation. From training examples of distance matrices corresponding to good and bad models, DL-Pro learns a stacked autoencoder network as a classifier. In experiments on selected targets from the Critical Assessment of Structure Prediction (CASP) competition, DL-Pro obtained promising results, outperforming state-of-the-art energy/scoring functions, including OPUS-CA, DOPE, DFIRE, and RW.

English

The purpose of the present study was to add new 18S and 28S DNA gene sequences data to Sphaerirostris picae (Rudolphi, 1819) Golvan, 1960 and analyze the generated sequences to define the taxonomic placement of genus Sphaerirostris and providing a better resolution inside the Palaeacanthocephala. Two regions: 18S and 28S of nuclear ribosomal DNA of S. picae were amplified using polymerase chain reaction and sequenced following the instructions of GATC German company facility. Mealign module in the DNAStar Lasergene V7 was used to design a forward and reverse primer of 28S DNA gene. 18S and 28S DNA gene sequences of S. picae were aligned with sequences for both genes of Palacanthocephalans retrieved from GenBank. Results were analyzed using distance matrix methods UPGMA. The resulting phylogenetic trees suggest a paraphyletic arrangement of the two Palaeacanthocephala orders; Echi- norhynchida and Polymorphida depending on the placement of the three echinorhynchids, Transvena, Rhadinorhynchus and Gorgorhyn- choides in the polymorphid clade. The present study is the first to generate gene sequences of genus Sphaerirostris and discuss its rela- tionships within Palaeacanthocephala. Further comprehensive studies should be done for other species of genus Sphaerirostris and fami- ly Centrorhynchidae as all based on molecular phylogenetic analysis to solve their taxonomic overlapping.

Determining Protein Structures from NOESY Distance Constraints by Semidefinite Programming

Contemporary practical methods for protein nuclear magnetic resonance (NMR) structure determination use molecular dynamics coupled with a simulated annealing schedule. The objective of these methods is to minimize the error of deviating from the nuclear overhauser effect (NOE) distance constraints. However, the corresponding objective function is highly nonconvex and, consequently, difficult to optimize. Euclidean distance matrix (EDM) methods based on semidefinite programming (SDP) provide a natural framework for these problems. However, the high complexity of SDP solvers and the often noisy distance constraints provide major challenges to this approach. The main contribution of this article is a new SDP formulation for the EDM approach that overcomes these two difficulties. We model the protein as a set of intersecting two- and three-dimensional cliques. Then, we adapt and extend a technique called semidefinite facial reduction to reduce the SDP problem size to approximately one quarter of the size of the original problem. The reduced SDP problem can be solved approximately 100 times faster, and it is also more resistant to numerical problems from erroneous and inexact distance bounds.

Improvements to a Class of Distance Matrix Methods for Inferring Species Trees from Gene Trees

Among the methods currently available for inferring species trees from gene trees, the GLASS method of Mossel and Roch (2010), the Shallowest Divergence (SD) method of Maddison and Knowles (2006), the STEAC method of Liu et al. (2009), and a related method that we call Minimum Average Coalescence (MAC) are computationally efficient and provide branch length estimates. Further, GLASS and STEAC have been shown to be consistent estimators of tree topology under a multispecies coalescent model. However, divergence time estimates obtained with these methods are all systematically biased under the model because the pairwise interspecific gene divergence times on which they rely must be more ancient than the species divergence time. Jewett and Rosenberg (2012) derived an expression for the bias of GLASS and used it to propose an improved method that they termed iGLASS. Here, we derive the biases of SD, STEAC, and MAC, and we propose improved analogues of these methods that we call iSD, iSTEAC, and iMAC. We conduct simulations to compare the performance of these methods with their original counterparts and with GLASS and iGLASS, finding that each of them decreases the bias and mean squared error of pairwise divergence time estimates. The new methods can therefore contribute to improvements in the estimation of species trees from information on gene trees.