Crimson: a data management system to support evaluating phylogenetic tree reconstruction algorithms

Since 2020, the SARS-CoV-2 virus has infected billions of people and spread to 185 countries. The virus spreads by making new copies of its genome inside human cells and exploits the cells’ machinery to synthesise viral proteins it needs to infect further cells. Each time the virus copies its genetic material there’s a chance that the replication process introduces an error to the genetic sequence. Over time, these mutations accumulate which can give rise to new variants with different properties. These new variants, originating from a common ancestor, may spread faster or be able to evade immune systems that have learnt to recognise previous variants. To understand where new variants of SARS-CoV-2 come from and how related they are to each other, scientists build family trees called ‘phylogenetic trees’ based on similarities in the genetic sequences of different variants of the virus. Looking at these trees researchers can track how a variant spreads geographically, and also attempt to identify new worrying variants that might lead to a new wave of infections. The scale of the COVID-19 pandemic together with the global effort by clinicians and researchers to sequence SARS-CoV-2 genetic material means a library of over 13 million SARS-CoV-2 genomes now exists, making it the largest such collection for any organism. Although phylogenetic trees of viruses have been studied for a long time, exploring the SARS-CoV-2 library presents technical and practical challenges due to its sheer size. Sanderson has developed an open-source web tool called Taxonium that allows users to explore phylogenetic trees with millions of sequences. With help from collaborators at the University of California, Santa Cruz, Sanderson built a website called Cov2Tree, that uses the Taxonium platform to allow immediate access to an expansive tree of all publicly available SARS-CoV-2 sequences. Cov2Tree enables users to visualise all SARS-CoV-2 genomes in a birds-eye view akin to a ‘Google Earth for virus sequences’ where anyone can zoom in on a related family of viruses down to the level of individual sequences. This can be used to compare variants and follow geographic spread. Using Taxonium, scientists can explore how virus sequences are related to each other. They can also see the individual mutations that have occurred at each branch of the tree, and can search for sequences based on mutation, geographical location, or other factors. Interestingly, a trend appearing in the SARS-CoV-2 phylogenetic tree is the emergence of identical mutations at different branches of the tree without a common origin. These mutations may be a result of convergent evolution, a phenomenon that occurs when a mutation appears independently in different variants as it confers an advantage to the virus making such mutations more likely to persist. This means that scientists may be able to expect certain mutations to appear in more distantly related variants if they have appeared independently in several different variants already. Overall, Taxonium is an important tool for monitoring SARS-CoV-2 genomes, but it also has broader applications. The tool can be used to browse phylogenetic trees of other viruses and organisms. Furthermore, the Taxonium website offers a way to browse a tree of life, with images and links to Wikipedia. The SARS-CoV-2 library might be the largest now, but in the future even bigger datasets will likely be available, highlighting the importance of tools like Taxonium.

It has been proposed that phylogenetic trees, intended to show divergence of eukaryotic protein and nucleic acid sequences, be extended to include those from bacteria. However, we have compared the amino acid sequences of 18 of the most divergent mitochondrial cytochromes c with those of 18 bacterial cytochromes c2 and have found that the average percentage difference between these mitochondrial cytochromes c and cytochromes c2 was not significantly greater than that among the cytochromes c2 alone. The large discontinuities in physical-chemical properties recognized between the prokaryote and eukaryote cytochromes render it highly improbable that members of the two classes should be no more different from one another than members of either class alone, assuming that sequence differences can accurately reveal evolutionary divergence. Instead, we propose that divergent amino acid sequences approach a limit of change considerably less than for comparison of random sequences. This limit of change presumably is determined by the structure/function relationship. When two homologous protein sequences have reached such a limit, convergence or back-mutations and parallel mutations become as frequent as divergent mutations. As two diverging proteins approach this steady-state condition, sequence differences no longer reflect the numbers of mutations resulting in amino acid substitution and therefore species cannot be positioned on a phylogenetic tree. Insertions and deletions are less reversible than are amino acid substitutions and, provided they are well-documented, might be more reliable indicators of bacterial relationships. Nevertheless, we suggest that data available on bacterial protein sequences do not permit construction of all-inclusive phylogenetic trees. Comparisons of protein and rRNA trees suggest that similar restrictions apply to use of rRNA sequence data.

It has been our endower to present and explore different steps involved in the construction of a phylogenetic tree using online services. The application of different bioinformatic tools is necessary for the realization of the desirable results. A stepwise construction strategy using on line ClustalW is proposed with suitable application. The method can hope fully will aid the reader to construct the phylogenetic tree. Construction of phylogenetic tree is useful for comparison of homology of functional proteins in one species and between species.

We consider the problem of reconstructing near-perfect phylogenetic trees using binary character states (referred to as BNPP). A perfect phylogeny assumes that every character mutates at most once in the evolutionary tree, yielding an algorithm for binary character states that is computationally efficient but not robust to imperfections in real data. A near-perfect phylogeny relaxes the perfect phylogeny assumption by allowing at most a constant number of additional mutations. We develop two algorithms for constructing optimal near-perfect phylogenies and provide empirical evidence of their performance. The first simple algorithm is fixed parameter tractable when the number of additional mutations and the number of characters that share four gametes with some other character are constants. The second, more involved algorithm for the problem is fixed parameter tractable when only the number of additional mutations is fixed. We have implemented both algorithms and shown them to be extremely efficient in practice on biologically significant data sets. This work proves the BNPP problem fixed parameter tractable and provides the first practical phylogenetic tree reconstruction algorithms that find guaranteed optimal solutions while being easily implemented and computationally feasible for data sets of biologically meaningful size and complexity.

With the development of sequencing technologies, more and more amounts of sequence data are available. This poses additional challenges, such as processing them is usually a complex and time-consuming computational task. During the construction of phylogenetic trees, the relationship between the sequences is examined, and an attempt is made to represent the evolutionary relationship. There are several algorithms for this problem, but with the development of computer science, the question arises as to whether new technologies can be exploited in these areas of computational biology. In the following publication, we investigate whether the reinforced learning model of machine learning can generate accurate phylogenetic trees based on the distance matrix.

Systems are becoming increasingly complex in the software industry, which also makes them more prone to failure. Ensuring software quality through rigorous testing is crucial, but this task still needs to be improved and more manageable because it still consumes significant parts of the project budget. Automation is a critical factor in improving testing efficiency, with new methodologies and tools being developed continuously. Machine Learning has attracted considerable interest in several areas over the last ten years, driven by advances in computing power and the ability to manage large volumes of data. Tricorder is a testing methodology designed to detect potential software faults by analyzing changes in the resource consumption behavior of the computing system under test. Unsupervised machine learning provided by DAMICORE characterizes resource consumption behavior. The methodology used by DAMICORE is based on a pipeline of three main steps. The pipeline uses Normalized Compression Distance (NCD) to generate a distance matrix, the Neighbor-Joining algorithm to build a phylogenetic tree, and the Fast Newman method for community detection, which is essential for data clustering. DAMICORE monitors and identifies anomalies in resource usage patterns, such as CPU, memory, and I/O, to indicate the presence of software faults. This project studies the impact generated by DAMICORE in the detection of software faults provided by Tricorder in the context of agglomerative clustering and phylogenetic trees, testing several techniques adapted to DAMICORE. The literature review on Machine Learning applied to Software Testing highlighted that the most widely studied techniques for agglomerative clustering are based on Single, Complete, and Average (UPGMA) Linkages. Neighbor-joining and UPGMA are prominent in the construction of phylogenetic trees. To achieve the project objectives, we consolidated the theoretical foundations, followed by an in-depth review of the state of the art and the specific methodologies of Tricorder and DAMICORE. We then systematically designed and executed a series of experiments, analyzing the generated results. The project demonstrated the potential of using Levenshtein distance, leveraging the network topology for community detection, and incorporating all system metrics into a single analysis. These approaches yielded better results in our context than other techniques found in the literature. NCD and Neighbor-Joining have significant limitations, especially due to their high computational demands, which hinder their practical application in more extensive and complex projects. The improvements introduced in this project should improve DAMICOREs accuracy in detecting software faults. The results of this project contribute to the state-of-the-art application of Machine Learning to Software Testing and reinforce Tricorders position in the Software Testing community.

Phytopathogenic mollicutes, which include spiroplasmas and phytoplasmas, are cell wall-less bacteria that parasitize plant hosts and insect vectors. Knowledge of the evolution of these agents is important in understanding their biology. The availability of the first complete phytoplasma and several partial spiroplasma and phytoplasma genome sequences made possible an investigation of evolutionary relationships between phytopathogenic mollicutes and other micro-organisms, especially Gram-positive bacteria, using a comparative genomics approach. Genome data from a total of 41 bacterial species were used in the analysis. Sixty-one conserved proteins were selected from each species for the construction of a hypothetical phylogenetic tree. The genes encoding these selected proteins are among a core of genetic elements that constitute a hypothetical minimal genome. The proteins were concatenated into five superproteins according to their functional categories, and phylogenetic trees were reconstructed using distance, parsimony and likelihood methods. Phylogenetic trees based on the five sets of concatenated proteins were congruent in both clade topology and relative branching length. Spiroplasma kunkelii and phytoplasmas clustered together with other mollicutes, forming a monophyletic group. Phytoplasmas diverged from spiroplasmas and mycoplasmas at early stages in the evolution of mollicutes. Branch lengths on the phylogenetic trees were noticeably longer in the Mollicutes clade, suggesting that the genes encoding the five sets of proteins evolved at a greater rate in this clade than in other clades. This observation reinforces the concept that mollicutes have rapidly evolving genomes.

Distance measure is an important issue in phylogenetic analysis. Traditional measure approaches are time-consuming due to the fact that they require multiple sequence alignment, while the k-tuple distance is easy to compute and has been used in phylogenetic tree reconstruction. However, k-tuple distance is not effective for analysing identical sequences. Considering the occurrences of k-tuples and the sequence structure which may contain k-tuple locations as well as the order relation among them, we propose a new distance measure method to construct phylogenetic tree in this paper. The experimental results show that the new approach is capable of efficiently building phylogenetic trees.

Phylogenetic tree reconstruction via graph cut presented using a quantum-inspired computer

Thousands of putative open reading frames (ORFs) are identified via annotation of sequenced plant genomes. Classification of these ORFs into gene families has a crucial importance to understand the evolution, function, and structure of the encoded proteins such as transcription factors, and the non-coding RNAs such as microRNAs (miRNAs). Thus, molecular mechanisms underlying the metabolic processes in plants are uncovered as well. Some members of the gene families are species-specific being more dynamic during evolution whereas others are more conserved, phylogenetically sharing common features. The latter are especially important for the annotation of putative ORFs by revealing known counterparts with high sequence identity via sequence alignment to discover conserved motifs. Various bioinformatic tools are available to find out gene families in plants. The BLAST tool (http://blast.ncbi.nlm.nih.gov/Blast.cgi) is widely used for identification of homologous sequences. Phytozome (http://www.phytozome.net) or GreenPhyl (http://www.greenphyl.org) are the web resources utilized for the functional and comparative genomics in plants to analyze gene families. TRAPID (http://bioinformatics.psb.ugent.be/webtools/trapid) offers a free of charge web source for functional and comparative analyses of transcriptome data sets for identification of gene families, alignment of multiple sequences and phylogenetic tree construction. Some of the databases store specific type of gene families such as plant transcription factor databases PlantTFDB (http://planttfdb.cbi.pku.edu.cn) and PlnTFDB (http://plntfdb.bio.uni-potsdam.de/v3.0), or miRBase (http://www.mirbase.org) for miRNAs. Molecular Evolutionary Genetics Analysis (MEGA) software is an integrated tool for the analyses such as alignment of sequence, construction of phylogenetic trees, and access to online databases. In this chapter, the bioinformatic tools for analyses of genomics and/or transcriptomics data sets to discover gene families as well as sample researches are discussed.

Three different GB virus C/hepatitis G virus genotypes: Phylogenetic analysis and a genotyping assay based on restriction fragment length polymorphism

The field of structural biology has received a major thrust, thanks to the important advancements in cryo-electron microscopy coupled with the development of highly reliable computer-predicted structural models. Throughout evolution, the tertiary structure of proteins is more conserved than their primary structure. The superposition of protein tertiary structures has been a powerful tool in the identification of proteins with remote homology, whose evolutionary footprints are no longer detectable through their primary sequence alignment, and more recently in the reconstruction of evolutionary trees. These structure-based phylogenetic trees have been demonstrated to be quite useful in the study of deep evolutionary events and in the analysis of biological entities with extremely high mutation rates, such as RNA viruses. The construction of phylogenetic trees from tertiary structures starts by performing pairwise comparisons between a set of proteins, from which certain parameters, such as the number of spatially aligned residues and the average distance between their ɑ-carbons, are collected. These measurements are then used to calculate a structural distance metric, which can be used as input for the construction of an evolutionary tree.

Introduction BASIC A phylogenetic tree is a construction which describes the ancestor–descendant relations of a set of entities. When the entities are, for example, languages, the tree describes how languages have evolved. Usually the leaves of the tree represent the present-day entities (no descendants yet) and the internal nodes represent common ancestors which existed in the past. Most phylogenetic trees have an implicit notion of evolution from ancestors to current-day entities. Phylogenetic tree construction is an important area of research in biology with applications also in linguistics, archeology, literature, mythology, history, software genealogy, etc. Applications Two of the most prominent examples are the tree of life and historical linguistics. The tree of life BACKGROUND Ancient origins A first classification scheme for animals was proposed by A ristotle , in his De Partibus Animalium around 350 BC. There he already distinguished and classified more than 500 different species in classes – with remarkable accuracy. Some of his insights were forgotten during the next 2000 years, for example that dolphins and whales are not fish since they have lungs and are viviparous. A first classification of plants was given by Aristotle's disciple T heophrastus of E resus in his work De Historia Plantarum about 320 BC. He classified plants as trees, bushes, shrubs etc., basing his classification on the form of growth. This method is not correct from a modern viewpoint.

The data contained in the DNA atom for even basic unicellular life forms is huge and requires proficient capacity. Proficient capacity implies, expulsion of all excess from the information being put away. The Proposed Compression calculation “GENBIT Compress” is solely intended to dispense with all repetition from the DNA groupings of extensive genomes. We characterize a pressure separation, taking into account an ordinary compressor to show it is a permissible separation. Just as of late have researchers started to value the way that pressure proportions imply a lot of essential measurable data. In applying the methodology, we have utilized another DNA succession compressor “GENBIT Compress”. The NCD is universal in that it is not restricted to a specific application area, and works across application area boundaries. A theoretical precursor, the normalized information distance, is provably optimal in the sense that it minimises every computable normalized metric that satisfies a certain density requirement. However, the optimality comes at the price of using the non-computable notion of Kolmogorov complexity. We propose precise notions of similarity metric, normal compressor, and show that the NCD based on a normal compressor is a similarity metric that approximates optimality The normalized compression distance, an efficiently computable, and thus practically applicable form of the normalized information distance is used to calculate Distance Matrix The normalized compression distance, an effectively processable, and along these lines for all intents and purposes relevant type of the standardized data separation is utilized to figure Distance Matrix. In this paper this new separation framework is proposed to recreate Phylogenetic tree. Phylogeny are the fundamental device for speaking to the relationship among organic elements. Phylogenetic remaking techniques endeavor to locate the developmental history of given arrangement of species. This history is generally depicted by an edge weighted tree, where edges relate to various branches of advancement, and the heaviness of an edge compares to the measure of developmental change on that specific branch. We developed a phylogenetic tree with BChE DNA arrangements of warm blooded creatures giving new proposed separation grid by GENBIT compressor to NJ (Neighbor-Joining calculation) tree. The results in the present research confirm the existence of low compression ratios for natural DNA sequences with high repetitive DNA bases(A, C, G, T), the more repetitive bases, the less is their compression ratios. The ultimate goal is, of course, to learn the “genome organization” principles, and explain this organization using our knowledge about evolution.

Genetic analysis of bovine viral diarrhoea viruses from Australia