Maximal Words in Sequence Comparisons Based on Subword Composition

Motivation: Several methods in genetic information have recently been developed to estimate classification of protein sequences through their sequence similarity. These methods are essential for understanding the function of predicted open reading frames (ORFs) and their molecular evolutionary processes. However, since many protein sequences consist of a number of independently evolved structural units (we refer to these units as components), the combinatorial nature of the components makes it difficult to classify the sequences. Results: This paper presents a new method for classifying uncharacterized protein sequences. As the measure of sequence similarity, we use similarity score computed by a method based on the Smith-Waterman local alignment algorithm. Here we introduce how this method cope when sequences have multi-component structure. This method was applied to predicted ORFs on the Escherichia coli genome and we discuss the algorithm and experimental results.

Despite millions of SARS-CoV-2 genomes being sequenced and shared globally, manipulating such data sets is still challenging, especially selecting sequences for focused phylogenetic analysis. We present a novel method, uvaia, which is based on partial and exact sequence similarity for quickly extracting database sequences similar to query sequences of interest. Many SARS-CoV-2 phylogenetic analyses rely on very low numbers of ambiguous sites as a measure of quality since ambiguous sites do not contribute to single nucleotide polymorphism (SNP) differences. Uvaia overcomes this limitation by using measures of sequence similarity which consider partially ambiguous sites, allowing for more ambiguous sequences to be included in the analysis if needed. Such fine-grained definition of similarity allows not only for better phylogenetic analyses, but could also lead to improved classification and biogeographical inferences. Uvaia works natively with compressed files, can use multiple cores and efficiently utilises memory, being able to analyse large data sets on a standard desktop.

A key concept in comparing sequence collections is the issue of redundancy. The production of sequence collections free from redundancy is undoubtedly very useful, both in performing statistical analyses and accelerating extensive database searching on nucleotide sequences. Indeed, publicly available databases contain multiple entries of identical or almost identical sequences. Performing statistical analysis on such biased data makes the risk of assigning high significance to non-significant patterns very high. In order to carry out unbiased statistical analysis as well as more efficient database searching it is thus necessary to analyse sequence data that have been purged of redundancy. Given that a unambiguous definition of redundancy is impracticable for biological sequence data, in the present program a quantitative description of redundancy will be used, based on the measure of sequence similarity. A sequence is considered redundant if it shows a degree of similarity and overlapping with a longer sequence in the database greater than a threshold fixed by the user. In this paper we present a new algorithm based on an "approximate string matching' procedure, which is able to determine the overall degree of similarity between each pair of sequences contained in a nucleotide sequence database and to generate automatically nucleotide sequence collections free from redundancies.

Word matches are often used in sequence comparison methods, either as a measure of sequence similarity or in the first search steps of algorithms such as BLAST or BLAT. The D2 statistic is the number of matches of words of k letters between two sequences. Recent advances have been made in the characterization of this statistic and in the approximation of its distribution. Here, these results are extended to the case of approximate word matches. We compute the exact value of the variance of the D2 statistic for the case of a uniform letter distribution, and introduce a method to provide accurate approximations of the variance in the remaining cases. This enables the distribution of D2 to be approximated for typical situations arising in biological research. We apply these results to the identification of cis-regulatory modules, and show that this method detects such sequences with a high accuracy. The ability to approximate the distribution of D2 for both exact and approximate word matches will enable the use of this statistic in a more precise manner for sequence comparison, database searches, and identification of transcription factor binding sites.

We introduce here a novel index which precisely derives protein coding regions from cross-species genome alignments. The index is deeply related to frame recovery observed in coding sequence alignments, that is, if insertions or deletions of nucleotides causes frame shifts in coding regions, other in-dels which recover the reading frames will be often observed in the vicinity. In contrast, such frame recoveries are not observed in other conserved regions. We prepared two gene models: a model which finds gene by using sequence similarity and intrinsic gene measures (basic model), and the other model which finds gene by using frame recovery index in addition to sequence similarity and intrinsic gene measures (frame recovery model). We evaluated the prediction accuracies of the two models, and our benchmark test revealed that frame recovery model significantly improved the prediction accuracy in comparison with basic model.

—A variety of different metrics has been introduced to measure the similarity of two given sequences. These widely used metrics are ranging from spell correctors and categorizers to new sequence mining applications. Different metrics consider different aspects of sequences, but the essence of any sequence is extracted from the ordering of its elements. In this paper, we propose a novel sequence similarity measure that is based on all ordered pairs of one sequence and where a Hasse diagram is built in the other sequence. In contrast with existing approaches, the idea behind the proposed sequence similarity metric is to extract all ordering features to capture sequence properties. We designed a clustering problem to evaluate our sequence similarity metric. Experimental results showed the superiority of our proposed sequence similarity metric in maximizing the purity of clustering compared to metrics such as d2, Smith-Waterman, Levenshtein, and Needleman-Wunsch. The limitation of those methods originates from some neglected sequence features, which are considered in our proposed sequence similarity metric.

The recent metagenome analysis has been producing a large number of host-unassigned viruses. Although assigning viruses to their hosts is basically important not only for virology but also for prevention of epidemic, it has been a laborious and difficult task to date. The only effective method for this purpose has been to find them in a same microscopic view. Now, we tried a computational approach based on genome sequences of bacteria and phages, introducing a physicochemical parameter, SOSS (set of oligostickiness similarity score) derived from oligostickiness, a measure of binding affinity of oligonucleotides to template DNA. We could confirm host-parasite relationships of bacteria and their phages by SOSS analysis: all phages tested (25 species) had a remarkably higher SOSS value with its host than with unrelated bacteria. Interestingly, according to SOSS values, lysogenic phages such as lambda phage (host: Escherichia coli) or SPP1 (host: Bacillus subtilis) have distinctively higher similarity with its host than its non-lysogenic (excretive or virulent) ones such as fd and T4 (host: E.coli) or phages gamma and PZA (host: B.subtilis). This finding is very promising for assigning host-unknown viruses to its host. We also investigated the relationship in codon usage frequency or G+C content of genomes to interpret the phenomenon revealed by SOSS analysis, obtaining evidences which support the hypothesis that higher SOSS values resulted from the cohabitation in the same environment which may cause the common biased mutation. Thus, lysogenic phages which stay inside longer resemble the host.

Clustering of genes on the basis of expression profiles is generally the first step in understanding how a class of genes behaves in a biological process. A number of supervised and unsupervised algorithms are available in statistics and machine learning literature for clustering microarray data, but the algorithms are restricted in their ability to evaluate the results of a clustering algorithm in the light of biologically meaningful clusters. If two gene sequences are similar, then we would expect that their genetic expressions are similar and that they are similarly annotated in the Gene Ontology (GO) databases. Hence a comparison of the expression level similarity of two gene sequences against their corresponding similarity of annotation in the GO can establish this fact. Semantic similarity has now become a valuable tool for validating the results drawn from biomedical studies such as gene clustering and gene expression data analysis. This paper borrows from our previous work on meta-ensembles using cancer datasets where the output of several clustering algorithms are subsequently fed to a consensus building process to generate a stable set of cluster results. Next, these cluster results are further refined through a sequence of biological validation process for each gene pair of a given cluster using semantic similarity and sequence similarity. We have tested our approach on several benchmark cancer datasets in an attempt to provide a more accurate biological analysis of the clusters and the results have been found to be satisfactory.

In order to understand web-based application user behavior, web usage mining applies unsupervised learning techniques to discover hidden patterns from web data that captures user browsing on web sites. For this purpose, web session clustering has been among the most popular approaches to group users with similar browsing patterns that reflect their common interest. An adequate web session clustering implementation significantly depends on the measure that is used to evaluate the similarity of sessions. An efficient approach to evaluate session similarity is sequence alignment, which is known as the task of determining the similarity of elements between sequences. In this paper, we review and compare sequence alignment-based measures for web sessions, and also discuss sequence similarity measures that are not alignment-based. This review also provides a perspective of sequence similarity measures that manipulate web sessions in usage clustering process.

The use of programming online judges (POJs) has risen dramatically in recent years, owing to the fact that the auto-evaluation of codes during practice motivates students to learn programming. Since POJs have greater number of programming problems in their repository, learners experience information overload. Recommender systems are a common solution to information overload. Current recommender systems used in e-learning platforms are inadequate for POJ since recommendations should consider learners' current context, like learning goals and current skill level (topic knowledge and difficulty level). To overcome the issue, we propose a context-aware practice problem recommender system based on learners' skill level navigation patterns. Our system initially performs skill level navigation pattern mining to discover frequent skill level navigations in the POJ and to find learners' learning goals. Collaborative filtering (CF) and content-based filtering approaches are employed to recommend problems in the current and next skill levels based on frequent skill level navigation patterns. The sequence similarity measure is used to find the top k neighbors based on the sequence of problems solved by the learners. The experiment results based on the real-world POJ dataset show that our approach considering the learners' current skill level and learning goals outperforms the other approaches in practice problem recommender systems.

In recent years, a strong interest has been given to web usage prediction and recommendation methods to improve e-commerce, search engines and other online applications. There have been various efforts carried out in this field, particularly focused on using recordings of web user interactions with websites. In this context, our research focuses on developing a novel approach for web prediction and recommendation. The proposed method relies on hierarchical session clustering by sequence similarity measure and takes advantage of access activity time and access position in prediction session to make a recommendation. The performed experiments reveal that hierarchical parameter and prediction accuracy are relevant. In addition, the paper introduces cost estimation to adapt web visitor behavior to web business purposes using prediction ansd recommendation results.

Time sequences, which are ordered sets of observations, have been studied in various database applications. In this paper, we introduce a new class of time sequences of which each observation is represented by an interval rather than a number. Such sequences may arise in many situations. For instance, we may not be able to determine the exact value at a time point due to uncertainty or aggregation. In such a case, the observation may be represented better by a range of possible values. Similarity search for interval time sequences has not been studied to the best of our knowledge and poses a new challenge for research. We first address the issue of (dis)similarity measures for interval time sequences. We choose a \(\mathcal{L}_1\) norm-based measure because it is semantically better than other alternatives. We next propose an efficient indexing technique for fast retrieval of similar interval time sequences from large databases. More specifically, we propose: (1) to extract a segment-based feature vector for each sequence, and (2) to map each feature vector to either a point or a hyper-rectangle in a multi-dimensional feature space. We then show how we can use existing multi-dimensional index structures such as the R-tree for efficient query processing. Our proposed method guarantees that no false dismissals would occur.

In many data mining applications, both classification and clustering algorithms require a distance/similarity measure. The central problem in similarity based clustering/classification comprising sequential data is deciding an appropriate similarity metric. The existing metrics like Euclidean, Jaccard, Cosine, and so forth do not exploit the sequential nature of data explicitly. In this paper, the authors propose a similarity preserving function called Sequence and Set Similarity Measure (S3M) that captures both the order of occurrence of items in sequences and the constituent items of sequences. The authors demonstrate the usefulness of the proposed measure for classification and clustering tasks. Experiments were conducted on benchmark datasets, that is, DARPA’98 and msnbc, for classification task in intrusion detection and clustering task in web mining domains. Results show the usefulness of the proposed measure.

Recommender Systems is a special type of information filtering system which has become important in the information overloaded and strategic decision making environment. Recommender System is used to produce meaningful suggestions about new items for particular consumers. These recommendations may be based on the user profile or item ratings, facilitate the users to make decisions in multiple contexts, such as what items to buy, what online news to read or what music to listen. Recommender Systems helps their founders to increase profits by recommending items and attracting new consumers. Collaborative filtering technique recommends items basis of conclusion of opinions about various products by users of similar profile to the active user. This technique requires user-items-ratings matrix. Although this is the most mature and commonly implemented technique, it faces major problem of Data Sparsity problem. Sparsity Problem occurs as a result of lack of enough information when only a few of the total number of items are rated by the users. This produces a sparse user item matrix leads to weak recommendations. This paper presents a recommender system using collaborative filtering implemented with RapidMiner tool. The proposed recommendation system is designed with users’ similarity calculated by Sequence and Set Similarity Measure (S3M) with utilizing similarity upper approximation and a Singular Value Decomposition (SVD) model based technique used for recommending ratings for removing sparsity.

In this review we present eMap 2.0, a web-based application for predicting electron/hole transfer pathways in proteins and protein families based on their structures. The underlying model can be viewed as a coarse-grained version of the Pathways approach by Beratan and Onuchic [Beratan etal. J. Chem. Phys. 1987, 86, 4488]. Similar to the original framework, eMap employs graph-theory algorithms to search for the most efficient electron transfer pathways as shortest paths on a graph representation of the protein. In eMap, the nodes represent electron transfer active sites and only through-space tunneling is considered for each individual electron/hole hop. eMap 2.0 takes this model one step further by aiming at identifying shared electron transfer pathways in protein sets. From a graph theory standpoint, this is achieved using frequent subgraph mining (FSM) algorithms. Lastly, eMap 2.0 utilizes sequence and structural similarity measures to analyze and cluster the results. Here, we show how this robust method can be utilized to rapidly provide insights regarding conserved electron transfer pathways within protein families and to identify outliers, in which the conserved electron transfer pathway is blocked either by a mutation or conformational changes. This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics.