Biological Sequence Comparison Research Articles

Graph-based analysis of DNA sequence comparison in closed cotton species: A generalized method to unveil genetic connections.

Graph theory provides a systematic method for modeling and analysing complicated biological data as an effective bioinformatics tool. Based on current trends, the number of DNA sequences in the DNA database is growing quickly. To determine the origin of a species and identify homologous sequences, it is crucial to detect similarities in DNA sequences. Alignment-free techniques are required for accurate measures of sequence similarity, which has been one of the main issues facing computational biologists. The current study provides a mathematical technique for comparing DNA sequences that are constructed in graph theory. The sequences of each DNA were divided into pairs of nucleotides, from which weighted loop digraphs and corresponding weighted vectors were computed. To check the sequence similarity, distance measures like Cosine, Correlation, and Jaccard were employed. To verify the method, DNA segments from the genomes of ten species of cotton were tested. Furthermore, to evaluate the efficacy of the proposed methodology, a K-means clustering method was performed. This study proposes a proof-of-model that utilises a distance matrix approach that promises impressive outcomes with future optimisations to be made to the suggested solution to get the hundred percent accurate result. In the realm of bioinformatics, this paper highlights the use of graph theory as an effective tool for biological data study and sequence comparison. It's expected that further optimization in the proposed solution can bring remarkable results, as this paper presents a proof-of-concept implementation for a given set of data using the proposed distance matrix technique.

A Critical Review on Some Recent Developments in Comparison of Biological Sequences

The present review highlights some of the very important contributions to non-alignment ways of comparing biological sequences, which may be genome sequences of nucleotides, protein sequences of amino acids, or sequences of protein secondary structures. The discussion centers around specific methods applicable to the comparison of three types of sequences. The methods of comparison of genome sequences are based on three pairs of biological groups of nucleotides; the same for protein sequences are based on either physio-chemical property values of amino acids or on classified groups of amino acids of different cardinalities obtained from the physio-chemical properties; the same for sequences of secondary structures of proteins are based on their sequential expressions of structure elements of cardinality three and four. Comparison is made in the time domain and also in the frequency domain. Different taxa of known phylogeny are considered for comparison. It tries to find out the specific method of comparison, which can show the exact phylogeny of the taxa. If a new sequence appears in the database, it becomes essential to know its phylogeny. For this purpose, a phylogenetic tree is drawn on the sequences of the known taxa together with this new sequence using the best possible method. If the species having this new sequence belongs to the old taxa, there is nothing to worry about. Otherwise, the species with the new sequence has to be studied separately. This is the general reason for the construction of a phylogenetic tree in any form of biological sequence comparison.

PEPMatch: a tool to identify short peptide sequence matches in large sets of proteins

BackgroundNumerous tools exist for biological sequence comparisons and search. One case of particular interest for immunologists is finding matches for linear peptide T cell epitopes, typically between 8 and 15 residues in length, in a large set of protein sequences. Both to find exact matches or matches that account for residue substitutions. The utility of such tools is critical in applications ranging from identifying conservation across viral epitopes, identifying putative epitope targets for allergens, and finding matches for cancer-associated neoepitopes to examine the role of tolerance in tumor recognition.ResultsWe defined a set of benchmarks that reflect the different practical applications of short peptide sequence matching. We evaluated a suite of existing methods for speed and recall and developed a new tool, PEPMatch. The tool uses a deterministic k-mer mapping algorithm that preprocesses proteomes before searching, achieving a 50-fold increase in speed over methods such as the Basic Local Alignment Search Tool (BLAST) without compromising recall. PEPMatch’s code and benchmark datasets are publicly available.ConclusionsPEPMatch offers significant speed and recall advantages for peptide sequence matching. While it is of immediate utility for immunologists, the developed benchmarking framework also provides a standard against which future tools can be evaluated for improvements. The tool is available at https://nextgen-tools.iedb.org, and the source code can be found at https://github.com/IEDB/PEPMatch.

A biological sequence comparison algorithm using quantum computers

Genetic information is encoded as linear sequences of nucleotides, represented by letters ranging from thousands to billions. Differences between sequences are identified through comparative approaches like sequence analysis, where variations can occur at the individual nucleotide level or collectively due to various phenomena such as recombination or deletion. Detecting these sequence differences is vital for understanding biology and medicine, but the complexity and size of genomic data require substantial classical computing power. Inspired by human visual perception and pixel representation on quantum computers, we leverage these techniques to implement pairwise sequence analysis. Our method utilizes the Flexible Representation of Quantum Images (FRQI) framework, enabling comparisons at a fine granularity to single letters or amino acids within gene sequences. This novel approach enhances accuracy and resolution, surpassing traditional methods by capturing subtle genetic variations with precision. In summary, our approach offers algorithmic advantages, including reduced time complexity, improved space efficiency, and accurate sequence comparisons. The novelty lies in applying the FRQI algorithm to compare quantum images in genome sequencing, allowing for examination at the individual letter or amino acid level. This breakthrough holds promise for advancing biological data analysis and enables a more comprehensive understanding of genetic information.

First detection and phylogenetic analysis of lumpy skin disease virus from Kinmen Island, Taiwan in 2020.

Lumpy skin disease is an arthropod-borne bovine disease caused by lumpy skin disease virus. A suspect lumpy skin disease case in a breeding cattle farm on Kinmen Island, Taiwan was reported on July 8, 2020 and later confirmed the first occurrence of lumpy skin disease in the country by molecular biological detections, electron microscopy, and sequence comparison. Implementation of control measures including blanket vaccination on the island effectively ceased the outbreaks. Phylogenetic analyses revealed that the virus discovered in the outbreaks was most similar to those identified in China in 2019. Identifying this virus in the coastal areas in East Asia indicated the rapid eastward spread of lumpy skin disease in Asia.

An alignment-free heuristic for fast sequence comparisons with applications to phylogeny reconstruction

BackgroundAlignment-free methods for sequence comparisons have become popular in many bioinformatics applications, specifically in the estimation of sequence similarity measures to construct phylogenetic trees. Recently, the average common substring measure, ACS, and its k-mismatch counterpart, ACSk, have been shown to produce results as effective as multiple-sequence alignment based methods for reconstruction of phylogeny trees. Since computing ACSk takes O(n logkn) time and hence impractical for large datasets, multiple heuristics that can approximate ACSk have been introduced.ResultsIn this paper, we present a novel linear-time heuristic to approximate ACSk, which is faster than computing the exact ACSk while being closer to the exact ACSk values compared to previously published linear-time greedy heuristics. Using four real datasets, containing both DNA and protein sequences, we evaluate our algorithm in terms of accuracy, runtime and demonstrate its applicability for phylogeny reconstruction. Our algorithm provides better accuracy than previously published heuristic methods, while being comparable in its applications to phylogeny reconstruction.ConclusionsOur method produces a better approximation for ACSk and is applicable for the alignment-free comparison of biological sequences at highly competitive speed. The algorithm is implemented in Rust programming language and the source code is available at https://github.com/srirampc/adyar-rs.

A Generalized Iterative Map for Analysis of Protein Sequences.

The similarities comparison of biological sequences is an important task in bioinformatics. The methods of the similarities comparison for biological sequences are divided into two classes: sequence alignment method and alignment-free method. The graphical representation of biological sequences is a kind of alignment-free method, which constitutes a tool for analyzing and visualizing the biological sequences. In this article, a generalized iterative map of protein sequences was suggested to analyze the similarities of biological sequences. Based on the normalized physicochemical indexes of 20 amino acids, each amino acid can be mapped into a point in 5D space. A generalized iterative function system was introduced to outline a generalized iterative map of protein sequences, which can not only reflect various physicochemical properties of amino acids but also incorporate with different compression ratios of the component of a generalized iterative map. Several properties were proved to illustrate the advantage of the generalized iterative map. The mathematical description of the generalized iterative map was suggested to compare the similarities and dissimilarities of protein sequences. Based on this method, similarities/dissimilarities were compared among ND5 protein sequences, as well as ND6 protein sequences of ten different species. By correlation analysis, the ClustalW results were compared with our similarity/dissimilarity results and other graphical representation results to show the utility of our approach. The comparison results show that our approach has better correlations with ClustalW for all species than other approaches and illustrate the effectiveness of our approach. Two examples show that our method not only has good performances and effects in the similarity/dissimilarity analysis of protein sequences but also does not require complex computation.

A CPU‐FPGA heterogeneous approach for biological sequence comparison using high‐level synthesis

SummaryThis article presents a high‐level synthesis implementation of the longest common subsequence (LCS) algorithm combined with a weighted‐based scheduler for comparing biological sequences prioritizing energy consumption or execution time. The LCS algorithm has been thoroughly tailored using Vivado High‐Level Synthesis tool, which is able to synthesize register transfer level (RTL) from high‐level language descriptions, such as C/C++. Performance and energy consumption results were obtained with a CPU Intel Core i7‐3770 CPU and an Alpha‐Data ADM‐PCIE‐KU3 board that has a Xilinx Kintex UltraScale XCKU060 FPGA chip. We executed a batch of 20 comparisons of sequences on 10k, 20k, and 50k sizes. Our experiments showed that the energy consumption on the combined approach was significantly lower when compared to the CPU, achieving 75% energy reduction on 50k comparisons. We also used the tool proposed in this article to do a case study on Covid‐19, with real SARS‐CoV‐2 sequences, comparing their LCS scores.

Using Multiple Fickett Bands to Accelerate Biological Sequence Comparisons

Most of the exact algorithms for biological sequence comparison obtain the optimal result by calculating dynamic programming (DP) matrices with quadratic time and space complexity. Fickett prunes the DP matrices by only computing values inside a band of size k, thus reducing time and space complexity to [Formula: see text]. Myers and Miller (MM) proposed a linear space algorithm that splits a sequence comparison into multiple comparisons of subsequences, using a divide-and-conquer approach. In this article, we propose a parallel strategy that combines the Fickett and MM algorithms, thus adding pruning capability to the MM algorithm. By using an appropriate Fickett band in each subsequence comparison, we can significantly reduce the number of cells computed in the DP matrices. Our strategy was integrated to stages 3 and 4 of CUDAlign, a state-of-the-art parallel tool for optimal biological sequence comparison, generating two implementations: Fickett-MM-4 and Fickett-MM-3-4. These implementations were used to compare real DNA sequences, reaching a speedup of 101.19 × in the 10 × 10 millions of base pairs comparison when compared with CUDAlign stages 3 and 4. In this case, the execution time was reduced from 71.42 to 0.7 seconds.

Formalization of Block Pruning: Reducing the Number of Cells Computed in Exact Biological Sequence Comparison Algorithms

This is a pre-copyedited, author-produced version of an article accepted for publication in The Computer Journal following peer review. The version of record Edans F O Sandes, George L M Teodoro, Maria Emilia M T Walter, Xavier Martorell, Eduard Ayguade, Alba C M A Melo; Formalization of Block Pruning: Reducing the Number of Cells Computed in Exact Biological Sequence Comparison Algorithms, The Computer Journal, Volume 61, Issue 5, 1 May 2018, Pages 687–713 is available online at: The Computer Journal https://academic.oup.com/comjnl/article-abstract/61/5/687/4539903 and https://doi.org/10.1093/comjnl/bxx090.

Characterization of Barley Yellow Mosaic Virus Pathotype by Biological and Molecular Methods in Iran = وصف سلالات فيروس تبرقش وإصفرار الشعير باستخدام الطرق البيولوجية والجزيئية في إيران

Barley yellow mosaic virus (BaYMV) is one of the economically important viruses infecting cereals. Different pathotypes of BaYMV divided in four pathotypes (I to IV) according to reaction on cultivars carrying different rym resistance genes. From March to April 2014,141 leaf samples of barley collected from four main growing areas of cereals in Iran. Samples were tested by double-antibody sandwich enzyme-linked immunosorbent assays (ELISA) using polyclonal antisera and also polymerase chain reaction (PCR). In order to determine BaYMV pathotype in Iran we investigated reaction of Iranian pathotypes of BaYMV on different barley cultivars. We also used specific primers in PCR reaction amplifying 690 bp of the VPg coding region, one of the important genes involved in viral pathogenicity. The amplified fragments from 5 isolates selected according to their geographical proprieties,were cloned and then sequenced. Biological properties and VPg sequence comparison of Iranian isolates with others retrieved from databases suggested that Iranian isolates are closely related to pathotype I of BaYMV. Nevertheless, some changes in the amino acid sequences of VPg of Iranian isolates might affect the biological behavior on cultivars carrying other rym resistant genes. According to our knowledge, this research is the first study of BaYMV pathotype in Iran.

Blast-i2b2: Blast for Biological Sequence Comparison in i2b2 Platform

Finding the similarities between biological sequences is an important process to analyze these sequences. The similarity between biological sequences can be determined by using sequence alignment methods. BLAST (Basic Local Alignment Search Tool) is a searching tool, which is designed to perform the sequence alignment process. The current BLAST platform compares a biological sequence with a biological sequences database, lists the similar results, and gives the user the ability to download these results to the local machine without storing those results in a form like data repository or data warehouse, thus lacking reusability and further updates of results. In this paper, we introduce BLAST-i2b2, which implements the BLAST on the i2b2 platform, stores BLAST results, and updates the stored result whenever there are changes in the target biological sequence database. A data warehouse is provided that can be used to efficiently store BLAST-i2b2 results, where it can be used for further analysis and faster updates. In addition, the paper presents the i2b2 (Informatics for Integrating Biology and the Bedside) platform and the update BLAST algorithm. The paper also shows the testing of BLAST-i2b2 performance against three different database sizes. The results show that BLAST-i2b2 update process reduces the BLAST run time to 84% for the small database size, 93% for the medium database size, and 97% for the big database size. The sequence analysis part is not within the scope of this paper and it will be taken into account for the future work.

Numerical Characterization of Protein Sequences Based on the Generalized Chou’s Pseudo Amino Acid Composition

The technique of comparison and analysis of biological sequences is playing an increasingly important role in the field of Computational Biology and Bioinformatics. One of the key steps in developing the technique is to identify an appropriate manner to represent a biological sequence. In this paper, on the basis of three physical–chemical properties of amino acids, a protein primary sequence is reduced into a six-letter sequence, and then a set of elements which reflect the global and local sequence-order information is extracted. Combining these elements with the frequencies of 20 native amino acids, a ( 21 + λ ) dimensional vector is constructed to characterize the protein sequence. The utility of the proposed approach is illustrated by phylogenetic analysis and identification of DNA-binding proteins.

An effective extension of the applicability of alignment-free biological sequence comparison algorithms with Hadoop

Alignment-free methods are one of the mainstays of biological sequence comparison, i.e., the assessment of how similar two biological sequences are to each other, a fundamental and routine task in computational biology and bioinformatics. They have gained popularity since, even on standard desktop machines, they are faster than methods based on alignments. However, with the advent of Next-Generation Sequencing Technologies, datasets whose size, i.e., number of sequences and their total length, is a challenge to the execution of alignment-free methods on those standard machines are quite common. Here, we propose the first paradigm for the computation of k-mer-based alignment-free methods for Apache Hadoop that extends the problem sizes that can be processed with respect to a standard sequential machine while also granting a good time performance. Technically, as opposed to a standard Hadoop implementation, its effectiveness is achieved thanks to the incremental management of a persistent hash table during the map phase, a task not contemplated by the basic Hadoop functions and that can be useful also in other contexts.

MACFP: Maximal Approximate Consecutive Frequent Pattern Mining under Edit Distance.

Consecutive pattern mining aiming at finding sequential patterns substrings, is a special case of frequent pattern mining and has been played a crucial role in many real world applications, especially in biological sequence analysis, time series analysis, and network log mining. Approximations, including insertions, deletions, and substitutions, between strings are widely used in biological sequence comparisons. However, most existing string pattern mining methods only consider hamming distance without insertions/deletions (indels). Little attention has been paid to the general approximate consecutive frequent pattern mining under edit distance, potentially due to the high computational complexity, particularly on DNA sequences with billions of base pairs. In this paper, we introduce an efficient solution to this problem. We first formulate the Maximal Approximate Consecutive Frequent Pattern Mining (MACFP) problem that identifies substring patterns under edit distance in a long query sequence. Then, we propose a novel algorithm with linear time complexity to check whether the support of a substring pattern is above a predefined threshold in the query sequence, thus greatly reducing the computational complexity of MACFP. With this fast decision algorithm, we can efficiently solve the original pattern discovery problem with several indexing and searching techniques. Comprehensive experiments on sequence pattern analysis and a study on cancer genomics application demonstrate the effectiveness and efficiency of our algorithm, compared to several existing methods.

Locating All Common Subsequences in Two DNA Sequences

Biological sequence comparison is one of the most important and basic problems in computational biology. Due to its high demands for computational power and memory, it is a very challenging task. The well-known algorithm proposed by Smith-Waterman obtains the best local alignments at the expense of very high computing power and huge memory requirements. This paper introduces a new efficient algorithm to locate the longest common subsequences (LCS) in two different DNA sequences. It is based on the convolution between the two DNA sequences: The major sequence is represented in the linked-list X while the minor one is represented in circular linked-list Y. An array of linked lists is established where each linked list is corresponding to an element of the linked-list X and a new node is added to it for each match between the two sequences. If two or more matches in different locations in string Y share the same location in string X, the corresponding nodes will construct a unique linked-list. Accordingly, by the end of processing, we obtain a group of linked-lists containing nodes that reflect all possible matches between the two sequences X and Y. The proposed algorithm has been implemented and tested using C# language. The benchmark test shows very good speedups and indicated that impressive improvements has been achieved.

Parallel Optimal Pairwise Biological Sequence Comparison

Many bioinformatics applications, such as the optimal pairwise biological sequence comparison, demand a great quantity of computing resource, thus are excellent candidates to run in high-performance computing (HPC) platforms. In the last two decades, a large number of HPC-based solutions were proposed for this problem that run in different platforms, targeting different types of comparisons with slightly different algorithms and making the comparative analysis of these approaches very difficult. This article proposes a classification of parallel optimal pairwise sequence comparison solutions, in order to highlight their main characteristics in a unified way. We then discuss several HPC-based solutions, including clusters of multicores and accelerators such as Cell Broadband Engines (CellBEs), Field-Programmable Gate Arrays (FPGAs), Graphics Processing Units (GPUs) and Intel Xeon Phi, as well as hybrid solutions, which combine two or more platforms, providing the actual landscape of the main proposals in this area. Finally, we present open questions and perspectives in this research field.

Circular Helix-Like Curve: An Effective Tool of Biological Sequence Analysis and Comparison.

This paper constructed a novel injection from a DNA sequence to a 3D graph, named circular helix-like curve (CHC). The presented graphical representation is available for visualizing characterizations of a single DNA sequence and identifying similarities and differences among several DNAs. A 12-dimensional vector extracted from CHC, as a numerical characterization of CHC, was applied to analyze phylogenetic relationships of 11 species, 74 ribosomal RNAs, 48 Hepatitis E viruses, and 18 eutherian mammals, respectively. Successful experiments illustrated that CHC is an effective tool of biological sequence analysis and comparison.

Brevundimonas denitrificans sp. nov., a denitrifying bacterium isolated from deep subseafloor sediment.

A novel Gram-stain-negative, aerobic, heterotrophic, stalked and capsulated bacterium with potential denitrification ability, designated strain TAR-002(T), was isolated from deep seafloor sediment in Japan. Colonies lacked lustre, and were viscous and translucent white. The ranges of temperature, pH and salt concentration for growth were 8-30 °C, pH 6.0-10.0 and 1-3% (w/v) NaCl. Phylogenetic analysis based on 16S rRNA gene sequences confirmed that strain TAR-002(T) belongs to the genus Brevundimonas of the class Alphaproteobacteria. Levels of similarity between the 16S rRNA gene sequence of strain TAR-002(T) and those of the type strains of species of the genus Brevundimonas were 93.5-98.9%; the most closely related species was Brevundimonas basaltis. In DNA-DNA hybridization assays between strain TAR-002(T) and its phylogenetic neighbours, Brevundimonas lenta DS-18(T), B. basaltis J22(T), Brevundimonas subvibrioides ATCC 15264(T) and Brevundimonas alba DSM 4736(T), mean hybridization levels were 6.4-27.7%. The G+C content of strain TAR-002(T) was 70.3 mol%. Q-10 was the major respiratory isoprenoid quinone. The major fatty acids were C(18:1)ω7c and C(16:0), and the presence of 1,2-di-O-acyl-3-O-[D-glucopyranosyl-(1 → 4)-α-D-glucopyranuronosyl]glycerol (DGL) indicates the affiliation of strain TAR-002(T) with the genus Brevundimonas. On the basis of biological characteristics and 16S rRNA gene sequence comparisons, strain TAR-002(T) is considered to represent a novel species of the genus Brevundimonas, for which the name Brevundimonas denitrificans sp. nov. is proposed; the type strain is TAR-002(T) ( =NBRC 110107(T) =CECT 8537(T)).

Reverse engineering of compact suffix trees and links: A novel algorithm

Invented in the 1970s, the Suffix Tree (ST) is a data structure that indexes all substrings of a text in linear space. Although more space demanding than other indexes, the ST remains likely an inspiring index because it represents substrings in a hierarchical tree structure. Along time, STs have acquired a central position in text algorithmics with myriad of algorithms and applications to for instance motif discovery, biological sequence comparison, or text compression. It is well known that different words can lead to the same suffix tree structure with different labels. Moreover, the properties of STs prevent all tree structures from being STs. Even the suffix links, which play a key role in efficient construction algorithms and many applications, are not sufficient to discriminate the suffix trees of distinct words. The question of recognising which trees can be STs has been raised and termed Reverse Engineering on STs. For the case where a tree is given with potential suffix links, a seminal work provides a linear time solution only for binary alphabets. Here, we also investigate the Reverse Engineering problem on ST with links and exhibit a novel approach and algorithm. Hopefully, this new suffix tree characterisation makes up a valuable step towards a better understanding of suffix tree combinatorics.