Composition Vector Research Articles

Using Composite Classifier Systems to Predict Protein Locations

Predicting protein location is both an important and challenging topic in molecular and cellular biology. As we all know that the location of proteins sheds light upon the function of a protein whose location was uncertain. But the success of human genome project led to a protein sequence explosion. It is in a great need to develop a computational method for fast and reliably predicting the locations of proteins according to their primary sequences. In this paper, we use composite classifier system that was formed by a set of k-nearest neighbor (K-NN) classifiers, each of which is defined in a different pseudo amino composition vector. The location of a queried protein is determined by the outcome of voting among these constituent individual classifiers. It is show through the outcome that the classifier outperformed single classifier widely used in biological literature.

Analyzing multi-locus plant barcoding datasets with a composition vector method based on adjustable weighted distance.

BackgroundThe composition vector (CV) method has been proved to be a reliable and fast alignment-free method to analyze large COI barcoding data. In this study, we modify this method for analyzing multi-gene datasets for plant DNA barcoding. The modified method includes an adjustable-weighted algorithm for the vector distance according to the ratio in sequence length of the candidate genes for each pair of taxa.Methodology/Principal FindingsThree datasets, matK+rbcL dataset with 2,083 sequences, matK+rbcL dataset with 397 sequences and matK+rbcL+trnH-psbA dataset with 397 sequences, were tested. We showed that the success rates of grouping sequences at the genus/species level based on this modified CV approach are always higher than those based on the traditional K2P/NJ method. For the matK+rbcL datasets, the modified CV approach outperformed the K2P-NJ approach by 7.9% in both the 2,083-sequence and 397-sequence datasets, and for the matK+rbcL+trnH-psbA dataset, the CV approach outperformed the traditional approach by 16.7%.ConclusionsWe conclude that the modified CV approach is an efficient method for analyzing large multi-gene datasets for plant DNA barcoding. Source code, implemented in C++ and supported on MS Windows, is freely available for download at http://math.xtu.edu.cn/myphp/math/research/source/Barcode_source_codes.zip.

Effect of glass composition on activation energy of viscosity in glass-melting-temperature range

In the high-temperature range, where the viscosity (η) of molten glass is <103Pas, the activation energy (B) is virtually independent of temperature (T). Moreover, the coefficient A in the Arrhenius relationship, ln(η)=A+B/T, is nearly independent of melt composition. Hence, the viscosity–composition relationship for η<103Pas is defined by B as a function of composition. Using a database encompassing over 1300 compositions of high-level waste glasses with nearly 7000 viscosity data, we developed mathematical models for B(x), where x is the composition vector in terms of mass fractions of components. In this paper, we present 13 versions of B(x) as first- and second-order polynomials with coefficients for 15 to 39 components, including Others, a component that sums constituents having little effect on viscosity.

Constructing Constant Composition Codes via Distance-Increasing Mappings

A distance-preserving mapping is a one-to-one function $f$ from $p$-ary vectors of length $m$ to $q$-ary vectors of length $n$ such that any two distinct $p$-ary vectors are mapped to two different $q$-ary vectors with an equal or greater Hamming distance. A distance-increasing mapping is a special distance-preserving mapping which strictly increases the distance by at least one if the distance of two distinct input vectors is less than the length of the output vectors. A constant composition code over a $k$-ary alphabet has the property that the numbers of occurrences of the $k$ symbols within a codeword are fixed for each codeword. One of the most important applications of distance-preserving mappings and distance-increasing mappings is to construct constant composition codes, of which the permutation codes are a special subclass. There are two results in this paper. First, we propose a swap-based distance-increasing mapping from binary vectors to quaternary constant composition vectors. Second, we prove that it is impossible to construct any swap-based distance-preserving mappings from binary vectors to ternary constant composition vectors under the swap model that we defined.

A novel ensemble and composite approach for classifying proteins based on Chou’s pseudo amino acid composition

For the fact that the location of proteins gave some details about the function of a protein whose location was uncertain, protein classification was regarded as a very important task in the field of biological data mining. However, the success of a human genome project led to a protein sequence explosion. There is a great need to develop a computational method for fast and reliable prediction of the locations of proteins according to their primary sequences. In this paper, we used the composite classifier system that was formed by a set of k-nearest neighbor (K-NN) classifiers, each of which was defined in a different pseudo amino composition vector. In the pseudo amino composition vector space, protein can be presented by Pseudo amino acid composition. The location of a queried protein is determined by the outcome of choice made among these constituent individual classifiers. It is shown through the outcome that the classifier outperformed the single classifier widely used in biological literature. So the composite classifier can be employed as a robust method to predict protein location in the field of biological data mining. Key words: Composite classifier system, biological data mining, atomic classifiers, pseudo amino acid composition.

Changes in vector species composition and current vector biology and behaviour will favour malaria elimination in Santa Isabel Province, Solomon Islands.

BackgroundIn 2009, Santa Isabel Province in the Solomon Islands embarked on a malaria elimination programme. However, very little is known in the Province about the anopheline fauna, which species are vectors, their bionomics and how they may respond to intensified intervention measures. The purpose of this study was to provide baseline data on the malaria vectors and to ascertain the possibility of successfully eliminating malaria using the existing conventional vector control measures, such as indoor residual spraying (IRS) and long-lasting insecticidal nets (LLIN).MethodsEntomological surveys were undertaken during October 2009. To determine species composition and distribution larval surveys were conducted across on the whole island. For malaria transmission studies, adult anophelines were sampled using human landing catches from two villages - one coastal and one inland.ResultsFive Anopheles species were found on Santa Isabel: Anopheles farauti, Anopheles hinesorum, Anopheles lungae, Anopheles solomonis, and Anopheles nataliae. Anopheles hinesorum was the most widespread species. Anopheles farauti was abundant, but found only on the coast. Anopheles punctulatus and Anopheles koliensis were not found. Anopheles farauti was the only species found biting in the coastal village, it was incriminated as a vector in this study; it fed early in the night but equally so indoors and outdoors, and had a low survival rate. Anopheles solomonis was the main species biting humans in the inland village, it was extremely exophagic, with low survival rates, and readily fed on pigs.ConclusionThe disappearance of the two major vectors, An. punctulatus and An. koliensis, from Santa Isabel and the predominance of An. hinesorum, a non-vector species may facilitate malaria elimination measures. Anopheles farauti was identified as the main coastal vector with An. solomonis as a possible inland vector. The behaviour of An. solomonis is novel as it has not been previously found biting humans in any numbers. Both species appear to be short-lived, a characteristic that will limit their transmission potential. The early night feeding behaviour and a degree of outdoor biting seen in An. farauti and particularly in An. solomonis will require that their response to IRS and LLIN be closely monitored. In coastal villages, where large, favourable breeding sites allow for high numbers of An. farauti may require the addition of larval control to achieve elimination.

Nephele: genotyping via complete composition vectors and MapReduce

BackgroundCurrent sequencing technology makes it practical to sequence many samples of a given organism, raising new challenges for the processing and interpretation of large genomics data sets with associated metadata. Traditional computational phylogenetic methods are ideal for studying the evolution of gene/protein families and using those to infer the evolution of an organism, but are less than ideal for the study of the whole organism mainly due to the presence of insertions/deletions/rearrangements. These methods provide the researcher with the ability to group a set of samples into distinct genotypic groups based on sequence similarity, which can then be associated with metadata, such as host information, pathogenicity, and time or location of occurrence. Genotyping is critical to understanding, at a genomic level, the origin and spread of infectious diseases. Increasingly, genotyping is coming into use for disease surveillance activities, as well as for microbial forensics. The classic genotyping approach has been based on phylogenetic analysis, starting with a multiple sequence alignment. Genotypes are then established by expert examination of phylogenetic trees. However, these traditional single-processor methods are suboptimal for rapidly growing sequence datasets being generated by next-generation DNA sequencing machines, because they increase in computational complexity quickly with the number of sequences.ResultsNephele is a suite of tools that uses the complete composition vector algorithm to represent each sequence in the dataset as a vector derived from its constituent k-mers by passing the need for multiple sequence alignment, and affinity propagation clustering to group the sequences into genotypes based on a distance measure over the vectors. Our methods produce results that correlate well with expert-defined clades or genotypes, at a fraction of the computational cost of traditional phylogenetic methods run on traditional hardware. Nephele can use the open-source Hadoop implementation of MapReduce to parallelize execution using multiple compute nodes. We were able to generate a neighbour-joined tree of over 10,000 16S samples in less than 2 hours.ConclusionsWe conclude that using Nephele can substantially decrease the processing time required for generating genotype trees of tens to hundreds of organisms at genome scale sequence coverage.

A mathematical consideration of the word-composition vector method in comparison of biological sequences

Abstract To measure the similarity or dissimilarity between two given biological sequences, several papers proposed metrics based on the “word-composition vector”. The essence of these metrics is as follows. First, we count the appearance frequencies of all the K- tuple words throughout each of two given sequences. Then, the two given sequences are transformed into their respective word-composition vectors. Next, the distance metrics, for example the angle between the two vectors, are calculated. A significant issue is to determine the optimal word size K. With a mathematical model of mutational events (including substitutions, insertions, deletions and duplications) that occur in sequences, we analyzed how the angle between the composition vectors depends on the mutational events. We also considered the optimal word size (=resolution) from our original approach. Our results were verified by computational experiments using artificially generated sequences, amino acid sequences of hemoglobin and nucleotide sequences of 16S ribosomal RNA.

Prediction of GABA A receptor proteins using the concept of Chou's pseudo-amino acid composition and support vector machine

The amino acid gamma-aminobutyric-acid receptors (GABA ARs) belong to the ligand-gated ion channels (LGICs) superfamily. GABA ARs are highly diverse in the central nervous system. These channels play a key role in regulating behavior. As a result, the prediction of GABA ARs from the amino acid sequence would be helpful for research on these receptors. We have developed a method to predict these proteins using the features obtained from Chou's pseudo-amino acid composition concept and support vector machine as a powerful machine learning approach. The predictor efficiency was assessed by five-fold cross-validation. This method achieved an overall accuracy and Matthew's correlation coefficient (MCC) of 94.12% and 0.88, respectively. Furthermore, to evaluate the effect and power of each feature, the minimum Redundancy and Maximum Relevance (mRMR) feature selection method was implemented. An interesting finding in this study is the presence of all six characters (hydrophobicity, hydrophilicity, side chain mass, pK1, pK2 and pI) or combination of the characters among the 5 higher ranked features (pk2 and pI, hydrophobicity and mass, pk1, hydrophilicity and mass) obtained from the mRMR feature selection method. The results show a biologically justifiable ranked attributes of pk2 and pI; hydrophobicity, hydrophilicity and mass; mass and pk1; pk2 and mass. Based on our results, using the concept of Chou's pseudo-amino acid composition and support vector machine is an effective approach for the prediction of GABA ARs.

Using Markov model to improve word normalization algorithm for biological sequence comparison

There are two crucial problems with statistical measures for sequence comparison: overlapping structures and background information of words in biological sequences. Word normalization in improved composition vector method took into account these problems and achieved better performance in evolutionary analysis. The word normalization is desirable, but not sufficient, because it assumes that the four bases A, C, T, and G occur randomly with equal chance. This paper proposed an improved word normalization which uses Markov model to estimate exact k-word distribution according to observed biological sequence and thus has the ability to adjust the background information of the k-word frequencies in biological sequences. The improved word normalization was tested with three experiments and compared with the existing word normalization. The experiment results confirm that the improved word normalization using Markov model to estimate the exact k-word distribution in biological sequences is more efficient.

Multi-vector composition and its application in landscape patch shape deformation and dynamic analysis

The quantitative expression and monitoring of patch shape deformation is an important content of landscape ecology. In this paper, a new multi-vector method is put forward based on curve representation in differential geometry, which is the composition of Centroid Displacement Vector (CDV), Local Deformation Vector (LDV) and the unit normal vector on the patch boundary. These composition vectors (CVs) not only add a new direction dimension to the landscape pattern metrics already available, but also provide multi-scale information, i.e. CV at every point and the boundary segment CV (BSCV). This new information can provide a quantification basis for a deeper understanding of the mechanisms of shape deformation and dynamic analysis. An application of CVs to patch dynamics in the Yancheng National Nature Reserve (YNNR) in China is given to validate the methodology. The susceptibility to invasion of the Common Seepweed in the core area of YNNR is evaluated quantitatively by CVs method. Our results indicate that the rate of invasion of Smooth Cord-grass into Common Seepweed patch has increased by 1.5 times from 1995 to 2002. This is considered to be the dominant factor causing Common Seepweed patches to shrink and change dynamically. These results help us better understand the landscape patch shape deformation and analyze the driver forces of landscape change, and they also provide the quantitative basis for the effective protection of the nature reserve area.

Composition Vector Method Based on Maximum Entropy Principle for Sequence Comparison

The composition vector (CV) method is an alignment-free method for sequence comparison. Because of its simplicity when compared with multiple sequence alignment methods, the method has been widely discussed lately; and some formulas based on probabilistic models, like Hao’s and Yu’s formulas, have been proposed. In this paper, we improve these formulas by using the entropy principle which can quantify the nonrandomness occurrence of patterns in the sequences. More precisely, existing formulas are used to generate a set of possible formulas from which we choose the one that maximizes the entropy. We give the closed-form solution to the resulting optimization problem. Hence, from any given CV formula, we can find the corresponding one that maximizes the entropy. In particular, we show that Hao’s formula is itself maximizing the entropy and we derive a new entropy-maximizing formula from Yu’s formula. We illustrate the accuracy of our new formula by using both simulated and experimental data sets. For the simulated data sets, our new formula gives the best consensus and significant values for three different kinds of evolution models. For the data set of tetrapod 18S rRNA sequences, our new formula groups the clades of bird and reptile together correctly, where Hao’s and Yu’s formulas failed. Using real data sets with different sizes, we show that our formula is more accurate than Hao’s and Yu’s formulas even for small data sets.

Attainable regions of reactive distillation—Part III. Complex reaction scheme: Van de Vusse reaction

The main underlying principle behind the application of reactive distillation (RD) for selectivity enhancement is to facilitate the separation of selected components and favorably manipulate the composition profiles in the reactive zone to expedite the desired reaction. In this paper, a geometric approach of attainable region (AR), which is already developed for conventional reactor network, is extended further to include few representative RD configurations. We define new RD building blocks and the corresponding composition vectors that indicate a need of networking of RD units among themselves and with conventional reactors to enlarge the set of attainable compositions. A model reaction scheme of van de Vusse type ( A ⟹ k 1 B ⟹ k 2 C , 2 A ⟹ k 3 D ) is studied as an illustrative example and an algorithm is developed to obtain the attainable region for the given kinetics, feed composition and relative volatilities. In almost all the different cases, which are studied by varying the kinetics and volatility pattern, network consisting of RD units performs better than the conventional reactor network.

Limit distributions for large Pólya urns

We consider a two-color Pólya urn in the case when a fixed number S of balls is added at each step. Assume it is a large urn that is, the second eigenvalue m of the replacement matrix satisfies 1∕2<m∕S≤1. After n drawings, the composition vector has asymptotically a first deterministic term of order n and a second random term of order nm∕S. The object of interest is the limit distribution of this random term. The method consists in embedding the discrete-time urn in continuous time, getting a two-type branching process. The dislocation equations associated with this process lead to a system of two differential equations satisfied by the Fourier transforms of the limit distributions. The resolution is carried out and it turns out that the Fourier transforms are explicitly related to Abelian integrals over the Fermat curve of degree m. The limit laws appear to constitute a new family of probability densities supported by the whole real line.

A protein fold classifier formed by fusing different modes of pseudo amino acid composition via PSSM

Protein function is related to its chemical reaction to the surrounding environment including other proteins. On the other hand, this depends on the spatial shape and tertiary structure of protein and folding of its constituent components in space. The correct identification of protein domain fold solely using extracted information from protein sequence is a complicated and controversial task in the current computational biology. In this article a combined classifier based on the information content of extracted features from the primary structure of protein has been introduced to face this challenging problem. In the first stage of our proposed two-tier architecture, there are several classifiers each of which is trained with a different sequence based feature vector. Apart from the application of the predicted secondary structure, hydrophobicity, van der Waals volume, polarity, polarizability, and different dimensions of pseudo-amino acid composition vectors in similar studies, the position specific scoring matrix (PSSM) has also been used to improve the correct classification rate (CCR) in this study. Using K-fold cross validation on training dataset related to 27 famous folds of SCOP, the 28 dimensional probability output vector from each evidence theoretic K-NN classifier is used to determine the information content or expertness of corresponding feature for discrimination in each fold class. In the second stage, the outputs of classifiers for test dataset are fused using Sugeno fuzzy integral operator to make better decision for target fold class. The expertness factor of each classifier in each fold class has been used to calculate the fuzzy integral operator weights. Results make it possible to provide deeper interpretation about the effectiveness of each feature for discrimination in target classes for query proteins.

Jackknife and Bootstrap Tests of the Composition Vector Trees

Composition vector trees (CVTrees) are inferred from whole-genome data by an alignment-free and parameter-free method. The agreement of these trees with the corresponding taxonomy provides an objective justification of the inferred phylogeny. In this work, we show the stability and self-consistency of CVTrees by performing bootstrap and jackknife re-sampling tests adapted to this alignment-free approach. Our ultimate goal is to advocate the viewpoint that time-consuming statistical re-sampling tests can be avoided at all in using this alignment-free approach. Agreement with taxonomy should be taken as a major criterion to estimate prokaryotic phylogenetic trees.

Whole-genome based Archaea phylogeny and taxonomy: A composition vector approach.

The newly proposed alignment-free and parameter-free composition vector (CVtree) method has been successfully applied to infer phylogenetic relationship of viruses, chloroplasts, bacteria, and fungi from their whole-genome data. In this study we pay special attention to the phylogenetic positions of 56 Archaea genomes among which 7 species have not been listed either in Bergey’s Manual of Systematic Bacteriology or in Taxonomic Outline of Bacteria and Archaea (TOBA). By inspecting the stable monophyletic branchings in CVTrees reconstructed from a total of 861 genomes (56 Archaea plus 797 Bacteria, using 8 Eukarya as outgroups) definite taxonomic assignments were proposed for these not-fully-classified species. Further development of Archaea taxonomy may verify the predicted phylogenetic results of the CVTree approach.

Whole-proteome phylogeny of large dsDNA viruses and parvoviruses through a composition vector method related to dynamical language model.

BackgroundThe vast sequence divergence among different virus groups has presented a great challenge to alignment-based analysis of virus phylogeny. Due to the problems caused by the uncertainty in alignment, existing tools for phylogenetic analysis based on multiple alignment could not be directly applied to the whole-genome comparison and phylogenomic studies of viruses. There has been a growing interest in alignment-free methods for phylogenetic analysis using complete genome data. Among the alignment-free methods, a dynamical language (DL) method proposed by our group has successfully been applied to the phylogenetic analysis of bacteria and chloroplast genomes.ResultsIn this paper, the DL method is used to analyze the whole-proteome phylogeny of 124 large dsDNA viruses and 30 parvoviruses, two data sets with large difference in genome size. The trees from our analyses are in good agreement to the latest classification of large dsDNA viruses and parvoviruses by the International Committee on Taxonomy of Viruses (ICTV).ConclusionsThe present method provides a new way for recovering the phylogeny of large dsDNA viruses and parvoviruses, and also some insights on the affiliation of a number of unclassified viruses. In comparison, some alignment-free methods such as the CV Tree method can be used for recovering the phylogeny of large dsDNA viruses, but they are not suitable for resolving the phylogeny of parvoviruses with a much smaller genome size.

Proper distance metrics for phylogenetic analysis using complete genomes without sequence alignment.

A shortcoming of most correlation distance methods based on the composition vectors without alignment developed for phylogenetic analysis using complete genomes is that the “distances” are not proper distance metrics in the strict mathematical sense. In this paper we propose two new correlation-related distance metrics to replace the old one in our dynamical language approach. Four genome datasets are employed to evaluate the effects of this replacement from a biological point of view. We find that the two proper distance metrics yield trees with the same or similar topologies as/to those using the old “distance” and agree with the tree of life based on 16S rRNA in a majority of the basic branches. Hence the two proper correlation-related distance metrics proposed here improve our dynamical language approach for phylogenetic analysis.

When phylogenetic assumptions are violated: base compositional heterogeneity and among-site rate variation in beetle mitochondrial phylogenomics

The ability to generate large molecular datasets for phylogenetic studies benefits biologists, but such data expansion introduces numerous analytical problems. A typical molecular phylogenetic study implicitly assumes that sequences evolve under stationary, reversible and homogeneous conditions, but this assumption is often violated in real datasets. When an analysis of large molecular datasets results in unexpected relationships, it often reflects violation of phylogenetic assumptions, rather than a correct phylogeny. Molecular evolutionary phenomena such as base compositional heterogeneity and among-site rate variation are known to affect phylogenetic inference, resulting in incorrect phylogenetic relationships. The ability of methods to overcome such bias has not been measured on real and complex datasets. We investigated how base compositional heterogeneity and among-site rate variation affect phylogenetic inference in the context of a mitochondrial genome phylogeny of the insect order Coleoptera. We show statistically that our dataset is affected by base compositional heterogeneity regardless of how the data are partitioned or recoded. Among-site rate variation is shown by comparing topologies generated using models of evolution with and without a rate variation parameter in a Bayesian framework. When compared for their effectiveness in dealing with systematic bias, standard phylogenetic methods tend to perform poorly, and parsimony without any data transformation performs worst. Two methods designed specifically to overcome systematic bias, LogDet and a Bayesian method implementing variable composition vectors, can overcome some level of base compositional heterogeneity, but are still affected by among-site rate variation. A large degree of variation in both noise and phylogenetic signal among all three codon positions is observed. We caution and argue that more data exploration is imperative, especially when many genes are included in an analysis.