The Perfect Phylogeny Problem

Bold flights of a speculative mind

Biological sequence data mining is hot spot in bioinformatics. A biological sequence can be regarded as a set of characters. Time series is similar to biological sequences in terms of both representation and mechanism. Therefore, in the article, biological sequences are represented with time series to obtain biological time sequence (BTS). Hybrid ensemble learning framework (SaPt-CNN-LSTM-AR-EA) for BTS is proposed. Single-sequence and multi-sequence models are respectively constructed with self-adaption pre-training one-dimensional convolutional recurrent neural network and autoregressive fractional integrated moving average fused evolutionary algorithm. In DNA sequence experiments with six viruses, SaPt-CNN-LSTM-AR-EA realized the good overall prediction performance and the prediction accuracy and correlation respectively reached 1.7073 and 0.9186. SaPt-CNN-LSTM-AR-EA was compared with other five benchmark models so as to verify its effectiveness and stability. SaPt-CNN-LSTM-AR-EA increased the average accuracy by about 30%. The framework proposed in this article is significant in biology, biomedicine, and computer science, and can be widely applied in sequence splicing, computational biology, bioinformation, and other fields.

The relationships of adult butterflies were evaluated using the techniques of numerical taxonomy and various sets of characters. Using a total of 196 characters of the external and internal anatomy produced phenograms quite similar to the one arrived at by classical taxonomic techniques. There were, however, significant differences (e.g., relatively low of the Papilionidae and Pieridae). Relationships based on most subsets of characters showed a family resemblance to one another and to the classical relationships. Relationships based on 96 internal and 100 external characters were compared by a randomization test. When evaluated by correlation coefficients the internal and external relationships were significantly different. When evaluated by distances they were not. Our results in general seem to support a weak version of the nonspecificity hypothesis-analysis of different large samples of characters ordinarily will yield positively correlated but non-identical patterns of relationship. This study has led to an appraisal of ideas of general classification and the utility of numerical taxonomy. Theoretical considerations make it seem unlikely that the idea of similarity has any validity. In any case it is clear that overall cannot be dealt with operationally. All classifications are inherently special, and no one special classification is any more or less correct than any other. It is suggested that numerical taxonomy is a more sophisticated tool than is required for doing most routine taxonomic jobs. Numerical taxonomy, however, can be used in those situations where refined answers seem to be desirable, either where economic problems are involved or where light can be shed on general problems of population biology. In recent years the development of numerical taxonomy (Sokal and Sneath, 1963) has given population biologists new tools for studying the phenetic relationships among groups of organisms. These developments have been part of a general trend toward a more operational approach to population biology (Ehrlich and Holm, 1962), and have led to a searching evaluation of taxonomic principles and procedures (e.g., Michener, 1963; Ehrlich, 1964; Heywood and McNeill, 1964; Sokal and Michener, 1967). Some of the more interesting questions raised in connection with the numerical taxonomic revolution have concerned special versus general classifications and the relevance of taxonomies based on limited sets of characters. Although the matter has been widely discussed, only two substantial pieces of published work have been designed specifically to investigate this problem, that of Rohlf (1963) who compared adult and larval classifications in Aedes mosquitoes and that of Michener and Sokal (1966) who compared classifications of the Hoplitis complex of bees based on different sets of characters. The present paper summarizes a series of studies designed to evaluate special classifications of adult butterflies. Materials and methods.-Thirty-one species of butterflies (Papilionoidea) representing all five families and 17 of the 19 subfamilies (Ehrlich, 1958a) were chosen as OTU's for the study. Two additional OTU's representing two of the subfamilies of skippers (Hesperioidea) were included. These species and the numbers assigned to them were: PAPILIONIDAE: Papilioninae 1. Graphium philolaus Bdv. 2. Papilio glaucus L. 3. Papilio multicaudata Kirby 4. Battus philenor L. PAPILIONIDAE: Parnassiinae 5. Parnassius phoebus Fabr.

Outsourcing computation to cloud server has recently become popular in cloud computing. Cloud computing technologies enable clients with limited computational resources to outsource their massive computations to powerful cloud servers. Outsourcing computation has some new concerns, such as outsourced data and result privacy, verifiability and efficiency. Matrix multiplication is one of the most basic computational problems. In this paper, we are motivated to design a secure and efficient protocol for outsourcing the massive matrix multiplication computations to the cloud server. The existing works are mostly under amortized model that have an expensive phase as offline mode. Our proposed scheme does not have any expensive phase. So the client can outsource its matrices in online mode in which the efficiency of outsourcing will be increased. In our scheme, the client encrypts two matrices and sends them to a semi-honest cloud server. The cloud server computes matrix multiplication and a proof. After that, it sends the encrypted result to the client and finally the client checks the validity of the computation and decrypts the result. Our proposed scheme achieves privacy protection of outsourced data and multiplication result, unforgeability of proof, verification and high efficiency.

Suffix tree characterization of maximal motifs in biological sequences

Abstract. A study, based on examination of thirteen scarabaeoid families, was made of 134 adult and larval characters from the following character suites: 105 adult characters of the antennae, eye, epipharynx, mandible, maxillae, labium, tentorium, trochantin, procoxae, mesocoxae, mesothoracic spiracles, hind wing articulation, hind wing base, hind wing venation, hind wing folding, abdominal sternites, abdominal spiracles, male genitalia, ovarioles and karyotype; twenty larval characters of the antennae, fronto‐clypeal suture, stemmata, labial palpi, maxillae, mandibles, legs, stridulatory apparatus, spiracles and ecdysial process; and nine adult and larval biological characters. In order to assess the reliability of different characters in resolving scarabaeoid family relationships, six data sets were subjected to cladistic analysis: the total evidence character set (134 characters), restricted adult character set (thirty‐two characters, not including those of the wings), wing character set (seventy‐three characters), larval character set (twenty characters), biological character set (nine characters) and re‐coded character set (thirty‐nine characters). The complete character set and wing character set both produced phylograms with all nodes resolved; the restricted adult data set, larval data set, data set and biological data set produced phylograms with diminishing levels of node resolution. The reconstructed phylogeny, from the preferred phylogram of the total evidence character set, shows that the Scarabaeoidea comprises three major lineages; a glaresid, passalid and scarabaeid lineage. The glaresid lineage consists only of the Glaresidae. The passalid lineage comprises two major lines; a glaphyrid line (containing Glaphyridae, Passalidae, Lucanidae, Diphyllostomatidae, Trogidae, Bolboceratidae and Pleocomidae) and a geotrupid line (containing Geotrupidae, Ochodaeidae, Ceratocanthidae and Hybosoridae). The scarabaeid lineage contains those taxa traditionally included within the Scarabaeidae (Aphodiinae, Scarabaeinae, Orphninae, Melolonthinae, Acoma, Chasmatopterinae, Hopliinae, Oncerinae, Rutelinae, Dynastinae, Trichiinae, Cetoniinae and Valginae).

Motif discovery from bio sequences, a challenging task both experimentally and computationally, has been a topic of immense study in recent years. In this paper, we formulate the motif discovery problem as a multiple-instance problem and employ a multiple-instance learning method, the MILES method, to identify motif from biological sequences. Each sequence is mapped into a feature space defined by instances in training sequences with a novel instance-bag similarity measure. We employ I-norm SVM to select important features and construct classifiers simultaneously. These high-ranked features correspond to discovered motifs. We apply this method to discover transcriptional factor binding sites in promoters, a typical motif finding problem in biology, and show that the method is at least comparable to existing methods

One of the classical problem in computational biology is the character compatibility problem or perfect phylogeny problem. A standard formulation of this problem in terms of two closely related questions is the following. Given a data set consisting of a finite set X and a set $$ {\user1{\mathcal{R}}} $$ of partitions induced on X by a set of characters. Is $$ {\user1{\mathcal{R}}} $$ compatible, that is, does there exist an evolutionary tree that represents (in a well-defined sense) the data? If this is the case, is this tree unique? A fundamental result in phylogenetics states that the answer to the former of the two questions is yes precisely if the partition intersection graph $$ I_{{\user1{\mathcal{R}}}} $$ associated to $$ {\user1{\mathcal{R}}} $$ can be made chordal by obeying a certain rule. The main insight from this paper is that the relation graph $$ {\user1{\mathcal{G}}}_{{\user1{\mathcal{R}}}} $$ associated to a set $$ {\user1{\mathcal{R}}} $$ of partitions may provide a key for deciding whether such a chordalization of $$ I_{{\user1{\mathcal{R}}}} $$ exists. To prove our results, we introduce an extension of the concept of the partition intersection graph associated to $$ {\user1{\mathcal{R}}} $$ using $$ {\user1{\mathcal{G}}}_{{\user1{\mathcal{R}}}} $$ .

This work concerns the construction of techniques to facilitate numerical analysis to solve basic computational mathematics problems of Volterra and Fredholm integral equations. In an attempt to merge contemporary machine learning techniques with conventional methods like the trapezoidal rule, the proposed methods seek enhanced accuracy, stability, and computing efficiency in applications involving synthetic and realistic kernel and forcing function data. With Python and computational libraries such as SciPy and NumPy, the algorithms are applied to actual physics and engineering problems and show greater precision and performance in various types of kernels, namely smooth, oscillatory, and weakly singular kernels. The methods for Volterra-type problems are highly stable, with iterative systems solving them effectively, and matrix-based methods solving Fredholm-type equations. This book contributes to numerical analysis through the presentation of new algorithms that enhance the practical solution of integral equations and have ramifications in computer science, engineering, and physics. The creation of hybrid numerical algorithms blending machine learning with conventional techniques bridges gaps in the literature and opens the door to more effective solutions to intricate integral equation problems.

In biological problems such as protein sequence family identification and profile building the additive hypothesis of the probability measure is not well suited for modeling HMM based profiles because of a high degree of interdependency among homologous sequences of the same family . Fuzzy measure theory which is an extension of the classical additive theory is obtained by replacing the additive requirement of classical measures with weaker properties of monotonicity, continuity and semi-continuity. The strong correlations and the sequence preference involved in the protein structures make fuzzy measure architecture based models as suitable candidates for building profiles of a given family since fuzzy measures can handle uncertainties better than classical methods . In this paper we investigate the different measures(S-decomposable, λ and belief measures) of fuzzy measure theory for building profile models of protein sequence problems. The proposed fuzzy measure models have been tested on globin and kinase families . The results obtained from the fuzzy measure models establish the superiority of fuzzy measure theory compared to classical probability measures for biological sequence problems.

There are two basic theoretical and computational problems associated with batch distillation. The forward or direct problem consists on generating the distillation curve of a given mixture. This is an old problem, but new results are presented here to relate mathematical properties of the distillation curve with the physicochemical properties of the molecular species present in the mixture. The inverse problem consists of, given a distillation curve, finding a surrogate mixture that would accurately represent experimental data. There is more than one solution to the reverse problem because there are theoretically an infinite number of mixtures that will present very similar experimental distillation curves. The method developed in this paper requires the same number of molecular species in the surrogate mixture as the points of the distillation curve that will be matched precisely. The choice of exact points to match on the distillation curve allows to conform a square system of equations where the number of equations is equal to the number of unknowns. Other points of the distillation curve are satisfied within a prescribed small error tolerance. A surrogate mixture for gasoline was developed as an example.

An Easy-to-Use Research Tool for Algorithm Testing and Development Before the SeqAn project, there was clearly a lack of available implementations in sequence analysis, even for standard tasks. Implementations of needed algorithmic components were either unavailable or hard to access in third-party monolithic software products. Addressing these concerns, the developers of SeqAn created a comprehensive, easy-to-use, open source C++ library of efficient algorithms and data structures for the analysis of biological sequences. Written by the founders of this project, Biological Sequence Analysis Using the SeqAn C++ Library covers the SeqAn library, its documentation, and the supporting infrastructure. The first part of the book describes the general library design. It introduces biological sequence analysis problems, discusses the benefit of using software libraries, summarizes the design principles and goals of SeqAn, details the main programming techniques used in SeqAn, and demonstrates the application of these techniques in various examples. Focusing on the components provided by SeqAn, the second part explores basic functionality, sequence data structures, alignments, pattern and motif searching, string indices, and graphs. The last part illustrates applications of SeqAn to genome alignment, consensus sequence in assembly projects, suffix array construction, and more. This handy book describes a user-friendly library of efficient data types and algorithms for sequence analysis in computational biology. SeqAn enables not only the implementation of new algorithms, but also the sound analysis and comparison of existing algorithms. Visit SeqAn for more information.

Pnina Abir-Am's recent paper challenges views which have attributed to the Rockefeller Foundation's policy a direct impact upon the rise of molecular biology, and suggests that the policy had inherent limitations for revolutionizing biology. She has performed a valuable service in this analysis of the role of policy in that she reveals much previously unpublished data concerning the decision-making surrounding Foundation support for 'experimental biology' in the 1930s. Much of her analysis concerning the 'colonization' of biology by physical techniques and physicists is of value, but her interpretation fails in claiming that such colonization was merely technical or social, that it did not involve genuine interest by physicists in biological problems, and in what appears to be its claim that the policy of technology transfer to biology was too narrow a conceptual basis for a true 'molecular biology'-promoting policy, which would have 'recoupled physical power with the knowledge of basic biological problems'. In contrast, I would claim that the 'colonization' of biology was also philosophical and conceptual, defining the problems which were addressed as much as providing new techniques for pre-existing problems; that the physical scientists Abir-Am chose as test cases were willing to think about biological problems (while re-defining them as physicochemical ones); and that the technology transfer policy was quite adequate as a basis for the molecular biology which did develop. Abir-Am's analysis of the policy decisions concerning Needham's and Waddington's research also fails in its claim that their programme was the genuine molecular biology, and therefore should have been supported by the Foundation. Such an analysis fails to recognize that a central feature of molecular biology, in all the 'schools' of true heuristic significance to its later development, was its reductionism , and that the Needham-Waddington programme would not have developed into molecular biology in its mature form, not only because of its non-reductionist philosophical foundations (which Abir-Am admits), but also because of its focus on problems of a different nature to those

Branched glycerol dialkyl glycerol tetraethers (brGDGTs) are bacterial membrane‐spanning lipids that change in response to temperature variations. Their adaptability to temperature and widespread presence in sedimentary archives make brGDGTs a valuable tool for reconstructing past temperatures. However, the models used to relate brGDGT distributions to temperature vary widely (e.g., global vs. regional vs. site‐specific, differing subsets of brGDGTs, and brGDGT‐based indices), leading to inconsistencies in their application and inaccurate temperature predictions in some locations. Using previously published lacustrine and soil brGDGT distributions, we determined whether the type of temperature data used for model calibration (i.e., in situ vs. modeled air temperatures) influences the degree to which temperature relates to brGDGT distributions and therefore the fidelity with which brGDGT‐based indices (i.e., MBT′5ME) may predict temperature. Accounting for differences in the number of samples among lake surface sediment and soil sample data sets, we find that the impact of temperature on brGDGT distribution significantly varies depending on both the temperature and sample data set used, with the most pronounced effects observed in lake surface sediment samples. Similarly, the MBT′5ME index shows varied correlations with temperature across different temperature and sample data sets. These findings suggest that other factors influence brGDGT distributions to a greater degree than temperature in some locations and that these effects are obscured when samples are combined into global data sets. This insight helps to explain why global brGDGT‐based calibration models may not accurately predict temperatures at specific locations and underscores the need for targeted (e.g., cluster‐based, regional, or site‐specific) temperature calibration models.

Advances in mass spectrometry-based proteomics have yielded a substantial mapping of the tyrosine phosphoproteome and thus provided an important step toward a systematic analysis of intracellular signaling networks in higher eukaryotes. In this study we decomposed an uncharacterized proteomics data set of 481 unique phosphotyrosine (Tyr(P)) peptides by sequence similarity to known ligands of the Src homology 2 (SH2) and the phosphotyrosine binding (PTB) domains. From 20 clusters we extracted 16 known and four new interaction motifs. Using quantitative mass spectrometry we pulled down Tyr(P)-specific binding partners for peptides corresponding to the extracted motifs. We confirmed numerous previously known interaction motifs and found 15 new interactions mediated by phosphosites not previously known to bind SH2 or PTB. Remarkably, a novel hydrophobic N-terminal motif ((L/V/I)(L/V/I)pY) was identified and validated as a binding motif for the SH2 domain-containing inositol phosphatase SHIP2. Our decomposition of the in vivo Tyr(P) proteome furthermore suggests that two-thirds of the Tyr(P) sites mediate interaction, whereas the remaining third govern processes such as enzyme activation and nucleic acid binding.