Biological Sequence Similarity Research Articles

BioSeq-Diabolo: Biological sequence similarity analysis using Diabolo.

As the key for biological sequence structure and function prediction, disease diagnosis and treatment, biological sequence similarity analysis has attracted more and more attentions. However, the exiting computational methods failed to accurately analyse the biological sequence similarities because of the various data types (DNA, RNA, protein, disease, etc) and their low sequence similarities (remote homology). Therefore, new concepts and techniques are desired to solve this challenging problem. Biological sequences (DNA, RNA and protein sequences) can be considered as the sentences of "the book of life", and their similarities can be considered as the biological language semantics (BLS). In this study, we are seeking the semantics analysis techniques derived from the natural language processing (NLP) to comprehensively and accurately analyse the biological sequence similarities. 27 semantics analysis methods derived from NLP were introduced to analyse biological sequence similarities, bringing new concepts and techniques to biological sequence similarity analysis. Experimental results show that these semantics analysis methods are able to facilitate the development of protein remote homology detection, circRNA-disease associations identification and protein function annotation, achieving better performance than the other state-of-the-art predictors in the related fields. Based on these semantics analysis methods, a platform called BioSeq-Diabolo has been constructed, which is named after a popular traditional sport in China. The users only need to input the embeddings of the biological sequence data. BioSeq-Diabolo will intelligently identify the task, and then accurately analyse the biological sequence similarities based on biological language semantics. BioSeq-Diabolo will integrate different biological sequence similarities in a supervised manner by using Learning to Rank (LTR), and the performance of the constructed methods will be evaluated and analysed so as to recommend the best methods for the users. The web server and stand-alone package of BioSeq-Diabolo can be accessed at http://bliulab.net/BioSeq-Diabolo/server/.

Community development, implementation, and assessment of a NIBLSE bioinformatics sequence similarity learning resource.

As powerful computational tools and ‘big data’ transform the biological sciences, bioinformatics training is becoming necessary to prepare the next generation of life scientists. Furthermore, because the tools and resources employed in bioinformatics are constantly evolving, bioinformatics learning materials must be continuously improved. In addition, these learning materials need to move beyond today’s typical step-by-step guides to promote deeper conceptual understanding by students. One of the goals of the Network for Integrating Bioinformatics into Life Sciences Education (NIBSLE) is to create, curate, disseminate, and assess appropriate open-access bioinformatics learning resources. Here we describe the evolution, integration, and assessment of a learning resource that explores essential concepts of biological sequence similarity. Pre/post student assessment data from diverse life science courses show significant learning gains. These results indicate that the learning resource is a beneficial educational product for the integration of bioinformatics across curricula.

Genetic Similarity Analysis Based on Positive and Negative Sequence Patterns of DNA

Similarity analysis of DNA sequences can clarify the homology between sequences and predict the structure of, and relationship between, them. At the same time, the frequent patterns of biological sequences explain not only the genetic characteristics of the organism, but they also serve as relevant markers for certain events of biological sequences. However, most of the aforementioned biological sequence similarity analysis methods are targeted at the entire sequential pattern, which ignores the missing gene fragment that may induce potential disease. The similarity analysis of such sequences containing a missing gene item is a blank. Consequently, some sequences with missing bases are ignored or not effectively analyzed. Thus, this paper presents a new method for DNA sequence similarity analysis. Using this method, we first mined not only positive sequential patterns, but also sequential patterns that were missing some of the base terms (collectively referred to as negative sequential patterns). Subsequently, we used these frequent patterns for similarity analysis on a two-dimensional plane. Several experiments were conducted in order to verify the effectiveness of this algorithm. The experimental results demonstrated that the algorithm can obtain various results through the selection of frequent sequential patterns and that accuracy and time efficiency was improved.

A computational method to analyze the similarity of biological sequences under uncertainty

In this paper, we propose a new method to analyze the difference and similarity of biological sequences, based on the fuzzy sets theory. Considering the sequence order and some chemical and structural properties, we present a computational method to cluster the biological sequences. By some examples, we show that the new method is relatively easy and we are able to compare the sequences of arbitrary lengths.

Plant Molecular Biology Databases

Many of plant biological database comprises data from study fields including metabolomics, proteomics, genomics, microarray gene expression, and phylogenetic. Information in biological databases involves the role of genes, composition, chromosomal location, clinical impacts of mutations, and similarity of biological sequences and characteristics. We are here targeting to review and rank some of the plant databases according to their use and feature. Hoping that scientists could use this study to pick, combine and correlate distinct databases, based on their study needs and objective specifications.

A High Efficient Biological Language Model for Predicting Protein⁻Protein Interactions.

Many life activities and key functions in organisms are maintained by different types of protein–protein interactions (PPIs). In order to accelerate the discovery of PPIs for different species, many computational methods have been developed. Unfortunately, even though computational methods are constantly evolving, efficient methods for predicting PPIs from protein sequence information have not been found for many years due to limiting factors including both methodology and technology. Inspired by the similarity of biological sequences and languages, developing a biological language processing technology may provide a brand new theoretical perspective and feasible method for the study of biological sequences. In this paper, a pure biological language processing model is proposed for predicting protein–protein interactions only using a protein sequence. The model was constructed based on a feature representation method for biological sequences called bio-to-vector (Bio2Vec) and a convolution neural network (CNN). The Bio2Vec obtains protein sequence features by using a “bio-word” segmentation system and a word representation model used for learning the distributed representation for each “bio-word”. The Bio2Vec supplies a frame that allows researchers to consider the context information and implicit semantic information of a bio sequence. A remarkable improvement in PPIs prediction performance has been observed by using the proposed model compared with state-of-the-art methods. The presentation of this approach marks the start of “bio language processing technology,” which could cause a technological revolution and could be applied to improve the quality of predictions in other problems.

Use of image texture analysis to find DNA sequence similarities

Sequence similarity analysis is a basic method in computational biological studies. Determining the similarity of biological sequences is a vital step in much research, such as exploring the evolutionary relationship among species, gene function analysis, protein structure prediction, and sequence retrieving. This paper introduces a method that uses the theory of the gray-level co-occurrence matrix, which is important in image texture analysis, to define and calculate the features of a DNA sequence. The proposed method can make a quantitative analysis and compute the defined texture features of a DNA sequence. Using these quantified sequence features, a similarity distance matrix can be computed and phylogenetic relationships also can be inferred. From the quantified features, we found that the DNA sequence of humans has the highest entropy and lowest energy. From human to chimpanzee, orangutan, gorilla, and other species, the entropy decreases and energy increases. The advantage of the proposed method is that it can compute multiple features inherent in each sequence. Furthermore, the defined features can be the key values or tags for each sequence for sequence retrieval and similarity analysis.

Chemical reaction optimization for solving longest common subsequence problem for multiple string

Longest common subsequence (LCS) is a well-known NP-hard optimization problem that finds out the longest subsequence of each member of a given set of strings. In computational biology, sequence alignment is a fundamental technique to measure the similarity of biological sequences, such as DNA and genome sequences. A high sequence similarity often applied to molecular structural as well as functional similarities and can be used to determine whether (and how) sequences are related. Finding the longest common subsequence (LCS) is one way to measure the similarity of sequences. It has also applications in data compression, FPGA circuit minimization, and bioinformatics, etc. Exact algorithms are impractical since they fail to solve this problem for multiple instances of long lengths in polynomial time. There are some approximations, heuristic, and metaheuristic methods proposed to solve the problem. Chemical reaction optimization (CRO) is a new metaheuristic method that mimics the nature of chemical reaction into optimization problems. In this paper, we have proposed chemical reaction optimization technique to solve the longest common subsequence problem for multiple instances. Here, we have redesigned four elementary operators of CRO for LCS problem. Operators of CRO algorithm are used to explore the search space both locally and globally. A novel correction method has been designed to correct the solution. Correction method works after each search operator to ensure the validity of the changes made by operators. Both solution quality and execution time are considered while designing the operators and the correction method. Thus proposed system brings robustness, efficiency, and effectiveness while solving MLCS problem. Our approach is compared with hyper-heuristic, ant colony optimization, beam ant colony optimization, and memory-bound anytime algorithms. The experimental results in lengths of the returned common sequences show that our proposed algorithm gives either same or better results than all other algorithms in less execution time.

Melodic Similarity and Applications Using Biologically-Inspired Techniques

Music similarity is a complex concept that manifests itself in areas such as Music Information Retrieval (MIR), musicological analysis and music cognition. Modelling the similarity of two music items is key for a number of music-related applications, such as cover song detection and query-by-humming. Typically, similarity models are based on intuition, heuristics or small-scale cognitive experiments; thus, applicability to broader contexts cannot be guaranteed. We argue that data-driven tools and analysis methods, applied to songs known to be related, can potentially provide us with information regarding the fine-grained nature of music similarity. Interestingly, music and biological sequences share a number of parallel concepts; from the natural sequence-representation, to their mechanisms of generating variations, i.e., oral transmission and evolution respectively. As such, there is a great potential for applying scientific methods and tools from bioinformatics to music. Stripped-down from biological heuristics, certain bioinformatics approaches can be generalized to any type of sequence. Consequently, reliable and unbiased data-driven solutions to problems such as biological sequence similarity and conservation analysis can be applied to music similarity and stability analysis. Our paper relies on such an approach to tackle a number of tasks and more notably to model global melodic similarity.

Functional coherence metrics in protein families.

BackgroundBiological sequences, such as proteins, have been provided with annotations that assign functional information. These functional annotations are associations of proteins (or other biological sequences) with descriptors characterizing their biological roles. However, not all proteins are fully (or even at all) annotated. This annotation incompleteness limits our ability to make sound assertions about the functional coherence within sets of proteins. Annotation incompleteness is a problematic issue when measuring semantic functional similarity of biological sequences since they can only capture a limited amount of all the semantic aspects the sequences may encompass.MethodsInstead of relying uniquely on single (reductive) metrics, this work proposes a comprehensive approach for assessing functional coherence within protein sets. The approach entails using visualization and term enrichment techniques anchored in specific domain knowledge, such as a protein family. For that purpose we evaluate two novel functional coherence metrics, mUI and mGIC that combine aspects of semantic similarity measures and term enrichment.ResultsThese metrics were used to effectively capture and measure the local similarity cores within protein sets. Hence, these metrics coupled with visualization tools allow an improved grasp on three important functional annotation aspects: completeness, agreement and coherence.ConclusionsMeasuring the functional similarity between proteins based on their annotations is a non trivial task. Several metrics exist but due both to characteristics intrinsic to the nature of graphs and extrinsic natures related to the process of annotation each measure can only capture certain functional annotation aspects of proteins. Hence, when trying to measure the functional coherence of a set of proteins a single metric is too reductive. Therefore, it is valuable to be aware of how each employed similarity metric works and what similarity aspects it can best capture. Here we test the behaviour and resilience of some similarity metrics.Electronic supplementary materialThe online version of this article (doi:10.1186/s13326-016-0076-y) contains supplementary material, which is available to authorized users.

LAF: Logic Alignment Free and its application to bacterial genomes classification.

Alignment-free algorithms can be used to estimate the similarity of biological sequences and hence are often applied to the phylogenetic reconstruction of genomes. Most of these algorithms rely on comparing the frequency of all the distinct substrings of fixed length (k-mers) that occur in the analyzed sequences.In this paper, we present Logic Alignment Free (LAF), a method that combines alignment-free techniques and rule-based classification algorithms in order to assign biological samples to their taxa. This method searches for a minimal subset of k-mers whose relative frequencies are used to build classification models as disjunctive-normal-form logic formulas (if-then rules).We apply LAF successfully to the classification of bacterial genomes to their corresponding taxonomy. In particular, we succeed in obtaining reliable classification at different taxonomic levels by extracting a handful of rules, each one based on the frequency of just few k-mers.State of the art methods to adjust the frequency of k-mers to the character distribution of the underlying genomes have negligible impact on classification performance, suggesting that the signal of each class is strong and that LAF is effective in identifying it.

Alignment-free Sequence Comparison for Biologically Realistic Sequences of Moderate Length

The D(2) statistic, defined as the number of matches of words of some pre-specified length k, is a computationally fast alignment-free measure of biological sequence similarity. However there is some debate about its suitability for this purpose as the variability in D(2) may be dominated by the terms that reflect the noise in each of the single sequences only. We examine the extent of the problem and the effectiveness of overcoming it by using two mean-centred variants of this statistic, D(2)* and D(2c). We conclude that all three statistics are potentially useful measures of sequence similarity, for which reasonably accurate p-values can be estimated under a null hypothesis of sequences composed of identically and independently distributed letters. We show that D(2) and D(2)c, and to a somewhat lesser extent D(2)*, perform well in tests to classify moderate length query sequences as putative cis-regulatory modules.

Measuring the Similarity of Protein Structures Using Image Compression Algorithms

For measuring the similarity of biological sequences and structures such as DNA sequences, protein sequences, and tertiary structures, several compression-based methods have been developed. However, they are based on compression algorithms only for sequential data. For instance, protein structures can be represented by two-dimensional distance matrices. Therefore, it is expected that image compression is useful for measuring the similarity of protein structures because image compression algorithms compress data horizontally and vertically. This paper proposes series of methods for measuring the similarity of protein structures. In the methods, an original protein structure is transformed into a distance matrix, which is regarded as a two-dimensional image. Then, the similarity of two protein structures is measured by a kind of compression ratio of the concatenated image. We employed several image compression algorithms, JPEG, GIF, PNG, IFS, and SPC. Since SPC often gave better results among the other image compression methods, and it is simple and easy to be modified, we modified SPC and obtained MSPC. We applied the proposed methods to clustering of protein structures, and performed Receiver Operating Characteristic (ROC) analysis. The results of computational experiments suggest that MSPC has the best performance among existing compression-based methods. We also present some theoretical results on the time complexity and Kolmogorov complexity of image compression-based protein structure comparison.

Metric-space search in bioinformatics

Of the many problems in biological data retrieval, the problem of biological sequence retrieval has the highest profile. The standard solution to this problem, BLAST, has ascended, like google , to become synonymous with search. Also like Google, BLAST leverages statistical properties as heuristics to create a good user experience. Ironically, many early biological sequence similarity efforts explicitly sought to model evolutionary distance as a metric-distance. Recent interest in metric-index methods has rekindled these early directions. A review of these efforts provides an opportunity to characterize the challenges and opportunities in similarity search of biological data.

A simple method to analyze the similarity of biological sequences based on the fuzzy theory

In this paper, we propose a simple method to analyze the similarity of biological sequences. By taking the average contents of biological sequences and their information entropies as the variables, the fuzzy method is used to cluster them. From the results of application, it finds that the method is relatively easy and rapid. Unlike other methods such as the graphical representation methods, which is usually very complex to compute some invariants of matric derived from graphical representation, our method pays more attention to the information of biological sequences themselves. Especially with the help of the software (SPSS), it seems to be very convenient. Therefore, it may be used to study the new biological sequences such as their evolution relationship and structures.

The Burrows–Wheeler similarity distribution between biological sequences based on Burrows–Wheeler transform

This work aims at the similarity of biological sequences. Based on the Burrows–Wheeler transform, a definition of Burrows–Wheeler similarity distribution of two sequences is proposed to compare two sequences. Some distance measures are naturally followed by the distribution. The expectation and entropy of the similarity distribution are used to construct phylogenetic trees on two independent data sets. The result demonstrates that the method is efficient and powerful.

An S-Curve-Based Approach of Identifying Biological Sequences

The main idea of S-curve diagram is to assign different angle values (from 0 degrees to 180 degrees ) to different nucleotide acid residues or to different protein amino acids, and then according to cos alpha(j) and sin alpha(j), the values are accumulated to construct an S-curve diagram, which is in strict one-to-one correspondence with the biological sequence. In addition, the S-curve diagram proves to be without the degeneracy phenomenon, so that both the degeneracy problem represented by diagrams and the problem of visualization for biological sequence data are solved. Meanwhile, a new approach to differentiate the similarity of biological sequences--the degree of similarity--is put forward on the basis of the S-curve diagram. To put it in detail, the least square approach is first adopted to obtain a straight line equation according to the S-curve diagram, then according to the distance formula of the point to the straight line, the average ratio of square sum for the distance between the S-curve and the straight line is calculated, and finally, the similarity of the biological sequences is presented by the new standard--the degree of similarity. As is shown by the experimental results, the S-curve diagram can better represent biological sequences (such as protein's) within Cartesian coordinate system, and the mutation point of biological sequence. Thus, it turns out that the new standard-the degree of similarity is of obviously great advantage.

A Novel Method to Analyze the Similarity of Biological Sequences

In this paper, we propose a new method based on the 2-D graphical representation to analyze the similarity of biological sequences and classify the protein secondary structure sequences. Instead of computing some characteristics from the distance matrix, the average area surrounded by the curve and X axis is computed as a new invariant. The new method is tested on two sets: the coding sequences of 30 mitochondrial genes from NCBI and 12 protein secondary structure sequences. The similarity/disimilarity and phylogenetic tree (dendrogram) of these sequences verify the validity of our method.

Novel visualization method for biological sequence similarity reports

Previously, we presented a system called Alignment Viewer that uses information visualization techniques to visualize similarities between a single DNA sequence and a large database of other sequences [Chi et al., IEEE Visualization ’95, pp. 44–51, IEEE CS (1995); Chi et al., IEEE Visualization ’96, pp. 133–140 and 477, IEEE CS (1996)]. In this paper, we extend, summarize, and describe the system using several interesting case studies. We present our comb glyph technique for visualizing alignments between sequences. In this paper, we also extend the original system by incorporating computational steering, and the visualization of differences between data sets. The case studies and the new extended system present our approach of extracting significant relationships in the biological data set.