Sequence Analysis Algorithms Research Articles

A near-tight lower bound on the density of forward sampling schemes.

Sampling k -mers is a ubiquitous task in sequence analysis algorithms. Sampling schemes such as the often-used random minimizer scheme are particularly appealing as they guarantee at least one k -mer is selected out of every w consecutive k -mers. Sampling fewer k -mers often leads to an increase in efficiency of downstream methods. Thus, developing schemes that have low density, i.e., have a small proportion of sampled k -mers, is an active area of research. After over a decade of consistent efforts in both decreasing the density of practical schemes and increasing the lower bound on the best possible density, there is still a large gap between the two. We prove a near-tight lower bound on the density of forward sampling schemes, a class of schemes that generalizes minimizer schemes. For small w and k , we observe that our bound is tight when k ≡ 1 (mod w ). For large w and k , the bound can be approximated by . Importantly, our lower bound implies that existing schemes are much closer to achieving optimal density than previously known. For example, with the current default minimap2 HiFi settings w = 19 and k = 19, we show that the best known scheme for these parameters, the double decycling-set-based minimizer of Pellow et al., is at most 3% denser than optimal, compared to the previous gap of at most 50%. Furthermore, when k ≡ 1 (mod w ) and the alphabet size σ goes to ∞, we show that mod-minimizers introduced by Groot Koerkamp and Pibiri achieve optimal density matching our lower bound. Minimizer implementations: github.com/RagnarGrootKoerkamp/minimizers ILP and analysis: github.com/treangenlab/sampling-scheme-analysis.

How readers attentive and inattentive to task-related information scan: An eye-tracking study

PurposeAttentive to task-related information should be the highest priority for all readers engaged in task-reading. Investigating the scanning behaviors of attentive versus inattentive readers shed new insights into the sequential cognitive processes, but it has seldom been studied. This study investigates their global patterns on a global scale, pertaining to the whole length of scanpaths, and further compares local tactics, local strategies, and local strategy transitions on a local scale, related to the isolated regions of scanpath. Design/methodology/approachA regular style reading system with the question, navigating, and text areas on its interface, and two types of task, namely fact-finding (FF) and content understanding (CU), were designed in an eye-tracking experiment. 24 participants were placed into attentive (AR) or inattentive (IAR) readers groups according to their fixation duration on task-related paragraphs. A global sequence analysis algorithm, Needleman-Wunsch, was applied to uncover global patterns across the whole length of scanpaths (whole-scanpaths). A local sequence analysis method related to frequent sub-scanpaths was adopted to extract local tactics specific to the reader and task. Coding was performed to identify local strategies by classifying local tactics. A local strategy transition was further identified as a sequence of frequent local strategies at the beginning, middle, and ending phases. FindingsWhole-scanpaths of AR significantly differed from those of IAR, despite the absence of global patterns for each group. Five types of local strategy were identified, namely locating information(LI), evaluating and verifying text relevance (EVR), navigation heuristics (NH), synthesizing information(SI), and contextual clues (CC). AR applied all types in both tasks, whereas IAR applied only two types and stuck with EVR. Furthermore, two types of local strategy transition were identified: comprehensive exploration and iterative content evaluation. AR employed the former with the linear feature in FF and the spiral feature in CU, while IAR employed the latter in both tasks. OriginalityThis study advances the knowledge of dynamic cognitive processing from an attentive and inattentive to task-related information perspective. An objective analysis perspective for obtaining global patterns, local tactics, local strategies, and local strategy transitions is provided, then it can provide new insights into automatically classifying readers. The results also generate detailed and valuable guidance for improving reading system design and training readers.

Study on Response Strategy and Efficacy Improvement of China-ASEAN Emergency Security Symbiosis Mechanism Based on Data Mining Technology

Abstract The emergency decision support system proposed in this paper uses data mining technology to analyze and mine a large amount of dynamic operation data and historical operation data in the China-ASEAN natural disaster database, which can be used to analyze the dynamic safety risk and optimize the emergency rescue and disposal plan for China-ASEAN natural disasters. In order to optimize the emergency decision support system, the core algorithm of data mining in the system is improved, an improved sequence analysis algorithm is proposed, and improvement measures are proposed on the basis of the inadequacy of the original Apriori algorithm. Through examples, the data mining algorithm’s specific process of automatically generating response suggestions for security events is demonstrated. The analysis of the data shows that the test results meet the expected results, and the algorithm can effectively make response decisions. The average false detection rate is only 8.05%. In scenarios 2, 3, and 4, all of them have higher emergency response efficiency, respectively, of 0.4445, 0.4616, and 0.5299. The completion time of the emergency response increases as the response level increases.

Integration Development of Civic Education and Student Management in Colleges and Universities Based on Combining Data Fusion Model in the Context of Exquisite Parenting

Abstract This paper focuses on the design of an educational early warning mechanism based on the fusion of ideological education and multi-featured data so as to manage the educational situation of students in colleges and universities efficiently and accurately. In this paper, the wavelet transform, discrete Fourier transform, and lag sequence analysis algorithms are used to effectively extract temporal features of students’ behaviors. PageRank and Hit’s algorithms are employed to extract features related to student concept maps. The emotional tendencies recognition interface provided by Tencent Cloud was used to obtain the emotional features of students’ speeches. Following this, a multi-feature fusion was performed to depict the students’ learning. A Hive-based data warehouse is used to integrate heterogeneous data from multiple sources. Finally, the education early warning model based on multi-feature data fusion is introduced, and the operation mechanism of early warning mechanism for ideological and political education in colleges and universities is established. To verify the effect of this paper’s model against other algorithms, this paper’s model achieves the optimal performance in the F1 score in negative samples, which is 0.91, followed by the TPA-LSTM algorithm, which is 0.88. Before the optimization of the early warning mechanism, the average per capita absenteeism of the students was 1.32 sessions, and the rate of disciplinary actions was 0.0291. At the end of the academic year, the average per capita absence rate decreases to 1.24 sessions, and the disciplinary action rate decreases to 0.0245.

A Method for Bio-Sequence Analysis Algorithm Development Based on the PAR Platform

The problems of biological sequence analysis have great theoretical and practical value in modern bioinformatics. Numerous solving algorithms are used for these problems, and complex similarities and differences exist among these algorithms for the same problem, causing difficulty for researchers to select the appropriate one. To address this situation, combined with the formal partition-and-recur method, component technology, domain engineering, and generic programming, the paper presents a method for the development of a family of biological sequence analysis algorithms. It designs highly trustworthy reusable domain algorithm components and further assembles them to generate specifific biological sequence analysis algorithms. The experiment of the development of a dynamic programming based LCS algorithm family shows the proposed method enables the improvement of the reliability, understandability, and development efficiency of particular algorithms.

Computational Analysis of the Droplet-Stimulated Laryngeal Adductor Reflex in High-Speed Sequences.

The laryngeal adductor reflex (LAR) is an important protective mechanism of the airways. Its physiology is still not completely understood. The available methods for LAR evaluation offer limited reproducibility and/or rely on subjective interpretation. A new approach, termed Microdroplet Impulse Testing of the LAR (MIT-LAR), was recently introduced. Here, the LAR is elicited by a droplet and a laryngoscopic high-speed recording is acquired simultaneously. In the present work, image-processing algorithms for autonomous MIT-LAR sequence analysis were developed. This allowed the automated approximation of kinematic LAR parameters in humans. Development and testing of computational methods. Computational image processing enabled the autonomous estimation of the glottal area, the glottal angle, and the vocal fold edge distance in MIT-LAR sequences. A suitable analytical representation of these glottal parameters allowed the extraction of seven relevant LAR parameters. The obtained values were compared to the literature. A generalized logistic function showed the highest average goodness of fit among four different analytical approaches for each of the glottal parameters. Autonomous sequence analysis yielded bilateral LAR response latencies of (229 ± 116) ms and (182 ± 60) ms for cases of complete and incomplete glottal closure, respectively. The initial/average/maximum angular vocal fold adduction velocity was estimated at (157 ± 115) °s-1 /(891 ± 516) °s-1 /(929 ± 583) °s-1 and (88 ± 53) °s-1 /(421 ± 221) °s-1 /(520 ± 238) °s-1 for complete and incomplete glottal closure, respectively. The automated extraction of LAR parameters from laryngoscopic high-speed sequences can potentially increase the objectiveness of optical LAR characterization and reduce the associated workload. The proposed methods may thus be helpful for future research on this vital reflex. NA Laryngoscope, 132:2412-2419, 2022.

Diagnosis and Prognosis of Oral Cancer using classification algorithm with Data Mining Techniques

Data mining is the process of researching data from different view points and condensing it into useful information. There are several types of algorithms in data mining such as Classification algorithms, Regression, Segmentation algorithms, association algorithms, sequence analysis algorithms, etc.,. The classification algorithm can be used to bifurcate the data set from the given data set and foretell one or more discrete variables, based on the other attributes in the dataset. Our method of creating new algorithm GA+ID3 easily identifies oral cancer data set from the given data set. The genetic based ID3 classification algorithm diagnosis and prognosis of oral cancer data set is identified by this paper.

MapReduce paradigm: DNA sequence clustering based on repeats as features

AbstractClustering is one of the major operations to analyse genome sequence data. Sophisticated sequencing technologies generate huge DNA sequence data; consequently, the complexity of analysing sequences is also increased. So, there is an enormous need for faster sequence analysis algorithms. Most of the existing tools focused on alignment‐based approaches, which are slow‐paced for sequence comparison. Alignment‐free approaches are more successful for fast clustering. The state‐of‐the‐art methods have been applied to cluster small genome sequences of various species; however, they are sensitive to large size sequences. To subdue this limitation, we propose a novel alignment‐free method called DNA sequence clustering with map‐reduce (DCMR). Initially, MapReduce paradigm is used to speed up the process of extracting eight different types of repeats. Then, the frequency of each type of repeat in a sequence is considered as a feature for clustering. Finally, K‐means (DCMR‐Kmeans) and K‐median (DCMR‐Kmedian) algorithms are used to cluster large DNA sequences by using extracted features. The two variants of proposed method are evaluated to cluster large genome sequences of 21 different species and the results show that sequences are very well clustered. Our method is tested for different benchmark data sets like viral genome, influenza A virus, mtDNA, and COXI data sets. Proposed method is compared with MeshClust, UCLUST, STARS, and ClustalW. DCMR‐Kmeans outperforms MeshClust, UCLUST, and DCMR‐Kmedian with respect to purity and NMI on virus data sets. The computational time of DCMR‐Kmeans is less than STARS, DCMR‐Kmedian, and much less than UCLUST on COXI data set.

Restricted genetic heterogeneity of the Plasmodium vivax transmission-blocking vaccine (TBV) candidate Pvs48/45 in a low transmission setting: Implications for the Plasmodium vivax malaria vaccine development.

Plasmodium vivax is the most widespread malaria species parasitizing humans outside Africa, with approximately 100 million cases reported per year. Most human cases of P. vivax are asymptomatic with low parasitemia, making active case detection-based elimination programme challenging and less effective. Despite the widespread distribution of P. vivax, no effective vaccines are currently available. Transmission blocking vaccines have recently emerged as potential vaccine candidates to reduce transmission rates to below the essential levels required for the maintenance of the parasite life cycle. Here, we demonstrated that P. vivax was the predominant species found in a malaria-endemic area, although P. vivax/P. falciparum co-infections were also common. Through genomic sequence analysis and neighbor-joining algorithms, we demonstrated limited genetic heterogeneity in the P. vivax transmission-blocking vaccine candidate Pvs48/45 among clinical isolates of P. vivax. Restricted genetic polymorphism occurred at both nucleotide and amino acid levels. The most frequent mutation was A→G at nucleotide position 77 (46.7%), whereas the least frequent was C→T at nucleotide position 1230 (3.3%). The occurrence of single nucleotide polymorphisms (SNPs) distribution at 6/8 positions (75%) led to changes in amino acid sequences in the Pvs48/45 loci, whereas 2/8 (25%) of SNPs resulted in no amino acid sequence variations. Consistently, the nucleotide diversity in the Pvs48/45 locus among the P. vivax population studied was extremely low (π=0.000525). Changes in amino acid sequences in the Pvs48/45 protein did not result in substantial conformational modifications in the tertiary structures of these proteins. Unveiling the population genetic structure and genetic heterogeneity of vaccine target antigens are necessary for rational design of transmission-blocking antibody vaccines and to monitor the vaccine efficacy in clinical trials in endemic areas for malaria.

A bacterial riboswitch class senses xanthine and uric acid to regulate genes associated with purine oxidation.

Dozens of candidate orphan riboswitch classes have been discovered previously by using comparative sequence analysis algorithms to search bacterial genomic sequence databases. Each orphan is classified by the presence of distinct conserved nucleotide sequences and secondary structure features, and by its association with particular types of genes. One previously reported orphan riboswitch candidate is the “NMT1 motif,” which forms a hairpin structure with an internal bulge that includes numerous highly conserved nucleotides. This motif associates with genes annotated to encode various dioxygenase enzymes, transporters, or proteins that have roles associated with thiamin or histidine metabolism. Biochemical evaluation of numerous ligand candidates revealed that NMT1 motif RNA constructs most tightly bind 8-azaxanthine, xanthine, and uric acid, whereas most other closely related compounds are strongly rejected. Genetic assays revealed that NMT1 motif RNAs function to turn off gene expression upon ligand binding, likely by regulating translation initiation. These results suggest that NMT1 motif RNAs function as aptamer domains for a riboswitch class that specifically responds to high concentrations of oxidized purines. Members of this “xanthine riboswitch” class appear to regulate genes predominantly related to purine transport and oxidation, thus avoiding the effects of overproduction of these common purine derivatives.

Component-Based Design and Assembly of Heuristic Multiple Sequence Alignment Algorithms.

In recent years, there has been an explosive increase in the amount of bioinformatics data produced, but data are not information. The purpose of bioinformatics research is to obtain information with biological significance from large amounts of data. Multiple sequence alignment is widely used in sequence homology detection, protein secondary and tertiary structure prediction, phylogenetic tree analysis, and other fields. Existing research mainly focuses on the specific steps of the algorithm or on specific problems, and there is a lack of high-level abstract domain algorithm frameworks. As a result, multiple sequence alignment algorithms are complex, redundant, and difficult to understand, and it is not easy for users to select the appropriate algorithm, which may lead to computing errors. Here, through in-depth study and analysis of the heuristic multiple sequence alignment algorithm (HMSAA) domain, a domain-feature model and an interactive model of HMSAA components have been established according to the generative programming method. With the support of the PAR (partition and recur) platform, the HMSAA algorithm component library is formalized and a specific alignment algorithm is assembled, thus improving the reliability of algorithm assembly. This work provides a valuable theoretical reference for the applications of other biological sequence analysis algorithms.

Kidney Failure Due to Diabetics – Detection using Classification Algorithm in Data Mining

In order to analyse the chosen data from various points of view, data mining is used as the effective process. This process is also used to sum up all those views into useful information. There are several types of algorithms in data mining such as Classification algorithms, Regression, Segmentation algorithms, association algorithms, sequence analysis algorithms, etc.,. The classification algorithm can be used to bifurcate the data set from the given data set and foretell one or more discrete variables, based on the other attributes in the dataset. The ID3 (Iterative Dichotomiser 3) algorithm is an original data set S as the root node. An unutilised attribute of the data set S calculates the entropy H(S) (or Information gain IG (A)) of the attribute. Upon its selection, the attribute should have the smallest entropy (or largest information gain) value. The prime objective of this paper is to analyze the data from a Kidney disorder due to diabetics by using classification technique to predict class accurately.

An average-case sublinear forward algorithm for the haploid Li and Stephens model

BackgroundHidden Markov models of haplotype inheritance such as the Li and Stephens model allow for computationally tractable probability calculations using the forward algorithm as long as the representative reference panel used in the model is sufficiently small. Specifically, the monoploid Li and Stephens model and its variants are linear in reference panel size unless heuristic approximations are used. However, sequencing projects numbering in the thousands to hundreds of thousands of individuals are underway, and others numbering in the millions are anticipated.ResultsTo make the forward algorithm for the haploid Li and Stephens model computationally tractable for these datasets, we have created a numerically exact version of the algorithm with observed average case sublinear runtime with respect to reference panel size k when tested against the 1000 Genomes dataset.ConclusionsWe show a forward algorithm which avoids any tradeoff between runtime and model complexity. Our algorithm makes use of two general strategies which might be applicable to improving the time complexity of other future sequence analysis algorithms: sparse dynamic programming matrices and lazy evaluation.

Alignment-free method for DNA sequence clustering using Fuzzy integral similarity

A larger amount of sequence data in private and public databases produced by next-generation sequencing put new challenges due to limitation associated with the alignment-based method for sequence comparison. So, there is a high need for faster sequence analysis algorithms. In this study, we developed an alignment-free algorithm for faster sequence analysis. The novelty of our approach is the inclusion of fuzzy integral with Markov chain for sequence analysis in the alignment-free model. The method estimate the parameters of a Markov chain by considering the frequencies of occurrence of all possible nucleotide pairs from each DNA sequence. These estimated Markov chain parameters were used to calculate similarity among all pairwise combinations of DNA sequences based on a fuzzy integral algorithm. This matrix is used as an input for the neighbor program in the PHYLIP package for phylogenetic tree construction. Our method was tested on eight benchmark datasets and on in-house generated datasets (18 s rDNA sequences from 11 arbuscular mycorrhizal fungi (AMF) and 16 s rDNA sequences of 40 bacterial isolates from plant interior). The results indicate that the fuzzy integral algorithm is an efficient and feasible alignment-free method for sequence analysis on the genomic scale.

Research on the Anomaly Detection Method in Intelligent Patrol Based on Big Data Analysis

The network anomaly detection in intelligent patrol is based on the trigger of a single threshold of network element performance parameters in patrol task, which has a high false alarm rate and low efficiency. In order to effectively and accurately integrate network performance, this paper proposes to mine network element performance data and network element log information in the integrated automatic patrol to detect network anomalies. Because log files have a large amount of data and a variety of types, and log data has a complex structure and contains large implied information. The relationship between network anomalies and time can actively discover through the analysis of the log files. Therefore, big data mining and classification can greatly improve the efficiency of data processing. However, the accuracy of finding network anomalies is insufficient only for log analysis. Therefore, this paper puts forward the performance indexes collected in the log analysis and patrol inspection system and adopts the sequence analysis algorithm to detect network anomalies, so as to improve the accuracy and efficiency of detection.

Multilevel Classification Algorithm using Diagnosis and Prognosis of Breast Cancer

In order to analyse the chosen data from various points of view, data mining is used as the effective process. This process is also used to sum up all those views into useful information. There are several types of algorithms in data mining such as Classification algorithms, Regression, Segmentation algorithms, association algorithms, sequence analysis algorithms, etc.,. The classification algorithm can be used to bifurcate the affected image from the given affected image and foretell one or more discrete variables, based on the other attributes in the dataset. The ID3 (Iterative Dichotomiser 3) algorithm is an original affected image S as the root node. An unutilised attribute of the affected image S calculates the entropy H(S) (or Information gain IG (A)) of the attribute. Upon its selection, the attribute should have the smallest entropy (or largest information gain) value. A genetic algorithm (GA) is a heuristic quest that imitates the process of natural selection. Genetic algorithm can easily select cancer affected image using GA operators, such as mutation, selection, and crossover. A method existed earlier (KNN+GA) was not successful for breast cancer and primary tumor. Our method of creating new algorithm GA and decision tree algorithm easily identifies breast cancer affected image. The genetic based classification algorithm diagnosis and prognosis of breast cancer affected is identified by this paper.

Oscillations During Flow Boiling in Single Microchannels

Flow boiling of degassed double-distilled water in a single 50 × 50 μm and 100 × 50 μm microchannel was investigated on the basis of experimental measurements and high-speed visualization. The visualized events during boiling were analyzed in terms of the bubble frequencies and boiling front oscillations in microchannels. A digital image sequence analysis algorithm was composed to determine the time dependence of bubble and meniscus locations. The results show (i) the dynamic characteristics of boiling in microchannels, (ii) the increase of fundamental oscillation frequencies with increasing heat flux and temperature of the microchannel bottom, (iii) the amplitudes of the flow boiling oscillations are inversely proportional to the fundamental frequencies. The outcomes of the study are important as the oscillations during boiling in single microchannels are experimentally confirmed to be predictable in terms of oscillation frequencies and amplitudes trends and dependencies. This knowledge is especially significant at constructing efficient two-phase micro heat exchangers, micro mixers or micro reactors, as the cross section and the length of the channel become exceedingly important design parameters in micro devices with boiling.

Novel feline viruses: Emerging significance of gammaherpesvirus and morbillivirus infections.

New technologies capable of sequencing the genetic material in any given biological sample, combined with computer-based algorithms for sequence assembly and analysis, have revolutionised infectious disease research. The rate at which novel viruses are being discovered now exceeds our understanding of their clinical relevance. Novel viruses may contribute to diseases that are major causes of feline morbidity and mortality, including cancer and chronic kidney disease. The identification of new viral pathogens raises the prospect of not only improved patient outcomes through specific treatment but even disease prevention through viral control measures. It can be difficult to determine the role of a novel virus in disease development. Disease may be an occasional outcome, often years after infection. A high prevalence of infection in the general population can make disease associations harder to identify and almost impossible to rule out. Host cofactors such as immune dysfunction, genetic background or coinfections may be required for manifestation of disease, and one virus species may be linked to a range of pathological sequelae. Establishing causality relies on evaluating accumulating evidence from multiple investigations, which is often hard to access by practitioners. The worldwide distribution of gammaherpesvirus and morbillivirus infections in domestic cats underlines the potential of these viruses to negatively impact feline health and welfare globally. This review relies on grade la-III evidence.

Diagnosis and Prognosis of Breast Cancer Using Multi Classification Algorithm

Data mining is the process of analysing data from different views points and condensing it into useful information. There are several types of algorithms in data mining such as Classification algorithms, Regression,Segmentation algorithms, Association algorithms, Sequence analysis algorithms, etc.,. The classification algorithm can be usedto bifurcate the data set from the given data set and foretell one or more discrete variables, based on the other attributes in the dataset. The ID3 (Iterative Dichotomiser 3) algorithm is an original data set S as the root node. An unutilised attribute of the data set S calculates the entropy H(S) (or Information gain IG (A)) of the attribute. Upon its selection, the attribute should have the smallest entropy (or largest information gain) value. A genetic algorithm (GA) is aheuristic quest that imitates the process of natural selection. Genetic algorithm can easily select cancer data set, from the given data set using GA operators, such as mutation, selection, and crossover. A method existed earlier (KNN+GA) was not successful for breast cancer and primary tumor. Our method of creating new algorithm GA+ID3 easily identifies breast cancer data set from the given data set. The multi classification algorithm diagnosis and prognosis of breast cancer data set is identified by this paper.

Parallel algorithms for the automated discovery of declarative process models

The aim of process discovery is to build a process model from an event log without prior information about the process. The discovery of declarative process models is useful when a process works in an unpredictable and unstable environment since several allowed paths can be represented as a compact set of rules. One of the tools available in the literature for discovering declarative models from logs is the Declare Miner, a plug-in of the process mining tool ProM. Using this plug-in, the discovered models are represented using Declare, a declarative process modeling language based on ltl for finite traces. However, the high execution times of the Declare Miner when processing large sets of data hampers the applicability of the tool to real-life settings. Therefore, in this paper, we propose a new approach for the discovery of Declare models based on the combination of an Apriori algorithm and a group of algorithms for Sequence Analysis to enhance the time performance of the plug-in. The approach has been developed in a way that it is easy to be parallelized using two different partitioning methods: the search space partitioning, in which different groups of candidate constraints are processed in parallel, and the database partitioning, in which different chunks of the log are processed at the same time. The approach has been implemented in ProM in its sequential version and in two multi-threading implementations leveraging these two partitioning methods. All the new variants of the plug-in have been evaluated using a large set of synthetic and real-life event logs.