Phylogenetic Trees Construction with Compressed DNA Sequences Using GENBIT COMPRESS Tool

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.

The normalized information distance (NID) is an universal metric distance based on Kolmogorov complexity. However, NID is not computable in a Turing sense. The normalized compression distance (NCD) is a computable distance that approximates NID by using normal compressors. NCD is a parameter-free distance that compares two signals by their lengths after separate compression relative to the length of the signal resulting from their concatenation after compression. The use of NCD for image retrieval over large image databases is difficult due to the computational cost of compressing the query image concatenated with every image in the database. The use of dictionaries extracted by dictionary-based compressors, such as the LZW compression algorithm, has been proposed to overcome this problem. Here we propose a Content-Based Image Retrieval system based on such dictionaries for the mining of hyperspectral databases. We compare results using the Normalized Dictionary Distance (NDD) and the Fast Dictionary Distance (FDD) against the NCD over different datasets of hyperspectral images. Results validate the applicability of dictionaries for hyperspectral image

Image distortion analysis is a fundamental issue in many image processing problems, including compression, restoration, recognition, classification, and retrieval. Traditional image distortion evaluation approaches tend to be heuristic and are often limited to specific application environment. In this work, we investigate the problem of image distortion measurement based on the theory of Kolmogorov complexity, which has rarely been studied in the context of image processing. This work is motivated by the normalized information distance (NID) measure that has been shown to be a valid and universal distance metric applicable to similarity measurement of any two objects (Li et al. in IEEE Trans Inf Theory 50:3250–3264, 2004). Similar to Kolmogorov complexity, NID is non-computable. A useful practical solution is to approximate it using normalized compression distance (NCD) (Li et al. in IEEE Trans Inf Theory 50:3250–3264, 2004), which has led to impressive results in many applications such as construction of phylogeny trees using DNA sequences (Li et al. in IEEE Trans Inf Theory 50:3250–3264, 2004). In our earlier work, we showed that direct use of NCD on image processing problems is difficult and proposed a normalized conditional compression distance (NCCD) measure (Nikvand and Wang, 2010), which has significantly wider applicability than existing image similarity/distortion measures. To assess the distortions between two images, we first transform them into the wavelet transform domain. Assuming stationarity and good decorrelation of wavelet coefficients beyond local regions and across wavelet subbands, the Kolmogorov complexity may be approximated using Shannon entropy (Cover et al. in Elements of information theory. Wiley-Interscience, New York, 1991). Inspired by Sheikh and Bovik (IEEE Trans Image Process 15(2):430–444, 2006), we adopt a Gaussian scale mixture model for clusters of neighboring wavelet coefficients and a Gaussian channel model for the noise distortions in the human visual system. Combining these assumptions with the NID framework, we derive a novel normalized perceptual information distance measure, where maximal likelihood estimation and least square regression are employed for parameter fitting. We validate the proposed distortion measure using three large-scale, publicly available, and subject-rated image databases, which include a wide range of practical image distortion types and levels. Our results demonstrate the good prediction power of the proposed method for perceptual image distortions.

For efficiently handling thousands of malware specimens, we aim to quickly and automatically categorize those into malware families. A solution for this could be the neighbor-joining method using NCD (Normalized Compression Distance) as similarity of malware. It creates a phylogenetic tree of malware based on the NCDs between malware binaries for clustering. However, it is frustratingly slow because it requires \((N^2+N)/2\) compression attempts for the NCDs, where N is the number of given specimens. For fast clustering, this paper presents an algorithm for efficiently constructing a phylogenetic tree by greatly reducing compression attempts. The key idea to do so is not to construct a tree of N specimens all at once. Instead, it divides N specimens into temporal clusters in advance, constructs a small tree for each temporal cluster, and joins the trees as a united tree. Intuitively, separately constructing small trees requires a much smaller number of compression attempts than \((N^2+N)/2\). With experiments using 4,109 in-the-wild malware specimens, we confirm that our algorithm achieved clustering 22 times faster than the neighbor-joining method with a good accuracy of 97%.

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.

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.

In studying the mitochondrial genome (mitogenome) composition and gene sequences of deep-sea organisms, it is of great importance to understand and explore the molecular mechanisms underlying their adaptation to the deep-sea environment. Glyphocrangon regalis belongs to Caridea, Crangonoidea, and is a typical deep-sea shrimp. In this study, the complete mitogenome of G. regalis was obtained using next-generation sequencing technology. The mitochondrial genes were annotated, and their sequence structures were analysed. The complete mitogenome sequence of G. regalis was 15 918 bp in length, with a base composition of A (36.85%), T (36.68%), C (15.53%) and G (9.94%), exhibiting a significant AT bias with an A + T content of 75.53%. G. regalis encoded a total of 37 genes, including 13 protein-coding genes (PCGs), 22 tRNAs and 2 rRNAs; among them, 14 genes were located on the negative strand, and 23 genes were located on the positive strand, similar to other caridean shrimp mitogenomes. Additionally, a phylogenetic tree was constructed based on the 13 PCGs of the mitogenomes of 95 species from 12 families within Caridea, supporting the monophyly of various families within Caridea and revealing that G. regalis and species of Pandalidae were the most closely related. This study provides insights into the complete mitogenome of G. regalis, elucidating its genomic characteristics and structural functions. It clarifies the evolutionary status of G. regalis and explores the evolutionary patterns of various families within Caridea through phylogenetic tree construction, thereby providing more references for the study of Caridea systematics.

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.

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.

Evolutionary and systems biology increasingly rely on the construction of large phylogenetic trees which represent the relationships between species of interest. As the number and size of such trees increases, so does the need for efficient data storage and query capabilities. Although much attention has been focused on XML as a tree data model, phylogenetic trees differ from document-oriented applications in their size and depth, and their need for structure-based queries rather than path-based queries.This paper focuses on Crimson, a tree storage system for phylogenetic trees used to evaluate phylogenetic tree reconstruction algorithms within the context of the NSF CIPRes project. A goal of the modeling component of the CIPRes project is to construct a huge simulation tree representing a gold standard of evolutionary history against which phylogenetic tree reconstruction algorithms can be tested.In this demonstration, we highlight our storage and indexing strategies and show how Crimson is used for benchmarking phylogenetic tree reconstruction algorithms. We also show how our design can be used to support more general queries over phylogenetic trees.

At the present, coding sequence (CDS) has been discovered and larger CDS is being revealed frequently. Approaches and related tools have also been developed and upgraded concurrently, especially for phylogenetic tree analysis. This paper proposes an integrated automatic Taverna workflow for the phylogenetic tree inferring analysis using public access web services at European Bioinformatics Institute (EMBL-EBI) and Swiss Institute of Bioinformatics (SIB), and our own deployed local web services. The workflow input is a set of CDS in the Fasta format. The workflow supports 1,000 to 20,000 numbers in bootstrapping replication. The workflow performs the tree inferring such as Parsimony (PARS), Distance Matrix - Neighbor Joining (DIST-NJ), and Maximum Likelihood (ML) algorithms of EMBOSS PHYLIPNEW package based on our proposed Multiple Sequence Alignment (MSA) similarity score. The local web services are implemented and deployed into two types using the Soaplab2 and Apache Axis2 deployment. There are SOAP and Java Web Service (JWS) providing WSDL endpoints to Taverna Workbench, a workflow manager. The workflow has been validated, the performance has been measured, and its results have been verified. Our workflow's execution time is less than ten minutes for inferring a tree with 10,000 replicates of the bootstrapping numbers. This paper proposes a new integrated automatic workflow which will be beneficial to the bioinformaticians with an intermediate level of knowledge and experiences. All local services have been deployed at our portal http://bioservices.sci.psu.ac.th.

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.

Unlike classical systems based on the use of morphological data, modern phylogenetic analyses use genetic information to construct phylogenetic trees. Ongoing research in the field of phylogenetics is evaluating the accuracy of phylogenetic estimation results and the reliability of phylogenetic trees to explain evolutionary relationships. Recently, the probability of stochastic errors in large-scale phylogenetic datasets has decreased, while the probability of systematic errors has increased. Therefore, before constructing a phylogenetic tree, it is necessary to assess the causes of systematic bias to improve the accuracy of phylogenetic estimates. We performed analyses of three datasets (Terebelliformia, Daphniid, and Glires clades) using bioinformatics software to assess systematic error and improve phylogenetic tree accuracy. Then, we proposed a combination of systematic biases capable of discerning the most suitable gene markers within a series of taxa and generating conflicting phylogenetic topologies. Our findings will help improve the reliability of phylogenetic software to estimate phylogenies more accurately by exploiting systematic bias.

A molecular technique based on quasispecies analysis for tracing postexposure HIV transmission was applied in an investigation of a possible case of HIV transmission after blood transfusion. Sixteen plasma specimens were collected from 3 HIV infections (T1-T3) involved in a possible HIV transmission chain and 13 HIV/AIDS (C1-C13) controls. The RNAs were extracted and then amplified by RT-PCR, the PCR products were cloned and sequenced.BioEdit 6.0.7 and MEGA 4.0 software were used to analyze gene sequences, calculate gene dispersion ratio and construct phylogenetic tree. The sequences of 13 specimens were successfully obtained.The HIV strains from T1, T2 and T3 were CRF07_BC recombinants, those from 5 out of the 6 controls lived in the same city with T2 and T3 were CRF07_BC recombinants as well, while those from 4 controls living in the same city with T1 were CRF01_AE recombinants. Compared with the clone sequences from T1, the mean gene dispersion ratio of T2 was the least (2.0%), followed by C12 (2.8%) , T3 (2.9%) and others. The phylogenetic tree showed that all clones from T1, T2, T3 and C12 might cluster together,and implied that the direction of HIV transmission was from T3 to T2, and then to T1. The results support the possible epidemiological clue that HIV was transmitted from T3 to T2, and then to T1, indicating that molecular epidemiological investigation could provide more direct evidence for tracing postexposure HIV transmission.

Information distance is a parameter-free similarity measure based on compression, used in pattern recognition, data mining, phylogeny, clustering and classification. The notion of information distance is extended from pairs to multiples (finite lists). We study maximal overlap, metricity, universality, minimal overlap, additivity and normalized information distance in multiples. We use the theoretical notion of Kolmogorov complexity which for practical purposes is approximated by the length of the compressed version of the file involved, using a real-world compression program.