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Journal articles on the topic 'Sequence alignment (Bioinformatics) Genetic algorithms'

Author: Grafiati

Published: 4 June 2021

Last updated: 13 February 2022

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1

Rautiainen, Mikko, Veli Mäkinen, and Tobias Marschall. "Bit-parallel sequence-to-graph alignment." Bioinformatics 35, no. 19 (2019): 3599–607. http://dx.doi.org/10.1093/bioinformatics/btz162.

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Abstract Motivation Graphs are commonly used to represent sets of sequences. Either edges or nodes can be labeled by sequences, so that each path in the graph spells a concatenated sequence. Examples include graphs to represent genome assemblies, such as string graphs and de Bruijn graphs, and graphs to represent a pan-genome and hence the genetic variation present in a population. Being able to align sequencing reads to such graphs is a key step for many analyses and its applications include genome assembly, read error correction and variant calling with respect to a variation graph. Results

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Gupta, Ruchi, and Pankaj Agarwal. "SOGA: space oriented genetic algorithm for multiple sequence alignment." International Journal of Engineering & Technology 7, no. 4.5 (2018): 481. http://dx.doi.org/10.14419/ijet.v7i4.5.21138.

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Multiple sequence alignment is one of the recurrent assignments in Bioinformatics. This method allows organizing a set of molecular sequences in order to expose their similarities and their differences. Although several applicable techniques were observed in this re- search, from traditional method such as dynamic programming to the extent of widely used stochastic optimization method such as Simu- lated Annealing and motif finding for solving this problem, their use is limited by the computing demands which are necessary for ex- ploring such a large and complex search space. This paper presen

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Zhang, Ching, and Andrew K. C. Wong. "A genetic algorithm for multiple molecular sequence alignment." Bioinformatics 13, no. 6 (1997): 565–81. http://dx.doi.org/10.1093/bioinformatics/13.6.565.

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Silva, Fernando José Mateus da, Juan Manuel Sánchez Pérez, Juan Antonio Gómez Pulido, and Miguel A. Vega Rodríguez. "Parallel Niche Pareto AlineaGA – an Evolutionary Multiobjective approach on Multiple Sequence Alignment." Journal of Integrative Bioinformatics 8, no. 3 (2011): 57–72. http://dx.doi.org/10.1515/jib-2011-174.

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Summary Multiple sequence alignment is one of the most recurrent assignments in Bioinformatics. This method allows organizing a set of molecular sequences in order to expose their similarities and their differences. Although exact methods exist for solving this problem, their use is limited by the computing demands which are necessary for exploring such a large and complex search space. Genetic Algorithms are adaptive search methods which perform well in large and complex spaces. Parallel Genetic Algorithms, not only increase the speed up of the search, but also improve its efficiency, present

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Rehman, Hafiz Asadul, Kashif Zafar, Ayesha Khan, and Abdullah Imtiaz. "Multiple sequence alignment using enhanced bird swarm align algorithm." Journal of Intelligent & Fuzzy Systems 41, no. 1 (2021): 1097–114. http://dx.doi.org/10.3233/jifs-210055.

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Discovering structural, functional and evolutionary information in biological sequences have been considered as a core research area in Bioinformatics. Multiple Sequence Alignment (MSA) tries to align all sequences in a given query set to provide us ease in annotation of new sequences. Traditional methods to find the optimal alignment are computationally expensive in real time. This research presents an enhanced version of Bird Swarm Algorithm (BSA), based on bio inspired optimization. Enhanced Bird Swarm Align Algorithm (EBSAA) is proposed for multiple sequence alignment problem to determine

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Chang, Xian, Jordan Eizenga, Adam M. Novak, Jouni Sirén, and Benedict Paten. "Distance indexing and seed clustering in sequence graphs." Bioinformatics 36, Supplement_1 (2020): i146—i153. http://dx.doi.org/10.1093/bioinformatics/btaa446.

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Abstract Motivation Graph representations of genomes are capable of expressing more genetic variation and can therefore better represent a population than standard linear genomes. However, due to the greater complexity of genome graphs relative to linear genomes, some functions that are trivial on linear genomes become much more difficult in genome graphs. Calculating distance is one such function that is simple in a linear genome but complicated in a graph context. In read mapping algorithms such distance calculations are fundamental to determining if seed alignments could belong to the same

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Jayakumar, Jayapriya, and Michael Arock. "Cellular Automata-Based PSO Algorithm for Aligning Multiple Molecular Sequences." International Journal of Applied Evolutionary Computation 7, no. 1 (2016): 1–15. http://dx.doi.org/10.4018/ijaec.2016010101.

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In Bioinformatics, sequence analysis is the basic important concept that provides information for structural and functional analysis. Multiple Sequence Alignment (MSA) is a keystone problem in the sequence analysis that is used for constructing phylogenetic tree, finding motif, gene expression, etc. Basically, all biological computation issues are NP-complete problems. In this paper, a novel approach using Cellular Automata (CA) and Particle Swarm Optimization (PSO) techniques are proposed for MSA problem. Both of these techniques handle NP-complete problems very skillfully. For experimental a

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Saha, Subrata, Jethro Johnson, Soumitra Pal, George M. Weinstock, and Sanguthevar Rajasekaran. "MSC: a metagenomic sequence classification algorithm." Bioinformatics 35, no. 17 (2019): 2932–40. http://dx.doi.org/10.1093/bioinformatics/bty1071.

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Abstract Motivation Metagenomics is the study of genetic materials directly sampled from natural habitats. It has the potential to reveal previously hidden diversity of microscopic life largely due to the existence of highly parallel and low-cost next-generation sequencing technology. Conventional approaches align metagenomic reads onto known reference genomes to identify microbes in the sample. Since such a collection of reference genomes is very large, the approach often needs high-end computing machines with large memory which is not often available to researchers. Alternative approaches fo

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Q. Abedulridha, Sara, and Eman S. Al-Shamery. "Optimal Pair DNA Sequence Alignment based on Matching Regions and Multi-Zone Genetic Algorithm." International Journal of Engineering & Technology 7, no. 4.19 (2018): 751. http://dx.doi.org/10.14419/ijet.v7i4.19.27993.

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DNA sequence alignment is an important and challenging task in Bioinformatics, which is used for finding the optimal arrangement between two sequences. In this paper, two methods are proposed in two stages to solve the pairwise sequence alignment problem. The first method is Matching Regions(MR) concerns on splitting the DNA into regions with adaptive interleaving windows to isolate the DNA tape into matched and non-matched regions. Additionally, a Multi-Zone Genetic Algorithm (MZGA) is proposed as an improved method in the second stage. It consists of segmenting a large non-matched region int

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Holmes, J. Bradley, Eric Moyer, Lon Phan, Donna Maglott, and Brandi Kattman. "SPDI: data model for variants and applications at NCBI." Bioinformatics 36, no. 6 (2019): 1902–7. http://dx.doi.org/10.1093/bioinformatics/btz856.

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Abstract Motivation Normalizing sequence variants on a reference, projecting them across congruent sequences and aggregating their diverse representations are critical to the elucidation of the genetic basis of disease and biological function. Inconsistent representation of variants among variant callers, local databases and tools result in discrepancies that complicate analysis. NCBI’s genetic variation resources, dbSNP and ClinVar, require a robust, scalable set of principles to manage asserted sequence variants. Results The SPDI data model defines variants as a sequence of four attributes:

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Biagini, Tommaso, Barbara Bartolini, Emanuela Giombini, et al. "Performances of Bioinformatics Pipelines for the Identification of Pathogens in Clinical Samples with the De Novo Assembly Approaches: Focus on 2009 Pandemic Influenza A (H1N1)." Open Bioinformatics Journal 8, no. 1 (2014): 1–5. http://dx.doi.org/10.2174/1875036201408010001.

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Diagnostic assays for pathogen detection are critical components of public-health monitoring efforts. In view of the limitations of methods that target specific agents, new approaches are required for the identification of novel, modified or ‘unsuspected’ pathogens in public-health monitoring schemes. Metagenomic approach is an attractive possibility for rapid identification of these pathogens. The analysis of metagenomic libraries requires fast computation and appropriate algorithms to characterize sequences. In this paper, we compared the computational efficiency of different bioinformatic p

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Thota, Lalitha Saroja, and Allam Appa Rao. "Identification of Biomarkers for Obesity associated with Diabetes using Sequence Mining Techniques." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 10, no. 4 (2013): 1510–21. http://dx.doi.org/10.24297/ijct.v10i4.3251.

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The advancements in the field of information technology are moving ahead in the discipline of medicine empowering the researchers with superior tools. By taking the advantage of Information Technology, today's researcher successfully navigate the flood of data and many diabetic complications can be overcome. Biomarker plays very major role in disease detection at early stages of its stages and also helpful in knowing the state of treatment and how body is acting or responding to the medication. The dramatic rise in obesity-associated diabetes resulted in an alarming increase in the incidence a

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Zhu, J., J. S. Liu, and C. E. Lawrence. "Bayesian adaptive sequence alignment algorithms." Bioinformatics 14, no. 1 (1998): 25–39. http://dx.doi.org/10.1093/bioinformatics/14.1.25.

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Rangwala, H., and G. Karypis. "Incremental window-based protein sequence alignment algorithms." Bioinformatics 23, no. 2 (2007): e17-e23. http://dx.doi.org/10.1093/bioinformatics/btl297.

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Woodwark, K. Cara, Simon J. Hubbard, and Stephen G. Oliver. "Sequence Search Algorithms for Single Pass Sequence Identification: Does One Size Fit All?" Comparative and Functional Genomics 2, no. 1 (2001): 4–9. http://dx.doi.org/10.1002/cfg.61.

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Bioinformatic tools have become essential to biologists in their quest to understand the vast quantities of sequence data, and now whole genomes, which are being produced at an ever increasing rate. Much of these sequence data are single-pass sequences, such as sample sequences from organisms closely related to other organisms of interest which have already been sequenced, or cDNAs or expressed sequence tags (ESTs). These single-pass sequences often contain errors, including frameshifts, which complicate the identification of homologues, especially at the protein level. Therefore, sequence sea

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Abbasi, M., L. Paquete, A. Liefooghe, M. Pinheiro, and P. Matias. "Improvements on bicriteria pairwise sequence alignment: algorithms and applications." Bioinformatics 29, no. 8 (2013): 996–1003. http://dx.doi.org/10.1093/bioinformatics/btt098.

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Hirosawa, Makoto, Yasushi Totoki, Masaki Hoshida, and Masato Ishikawa. "Comprehensive study on iterative algorithms of multiple sequence alignment." Bioinformatics 11, no. 1 (1995): 13–18. http://dx.doi.org/10.1093/bioinformatics/11.1.13.

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Alser, Mohammed, Hasan Hassan, Akash Kumar, Onur Mutlu, and Can Alkan. "Shouji: a fast and efficient pre-alignment filter for sequence alignment." Bioinformatics 35, no. 21 (2019): 4255–63. http://dx.doi.org/10.1093/bioinformatics/btz234.

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AbstractMotivationThe ability to generate massive amounts of sequencing data continues to overwhelm the processing capability of existing algorithms and compute infrastructures. In this work, we explore the use of hardware/software co-design and hardware acceleration to significantly reduce the execution time of short sequence alignment, a crucial step in analyzing sequenced genomes. We introduce Shouji, a highly parallel and accurate pre-alignment filter that remarkably reduces the need for computationally-costly dynamic programming algorithms. The first key idea of our proposed pre-alignment

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Ge, Xinzhou, Haowen Zhang, Lingjue Xie, Wei Vivian Li, Soo Bin Kwon, and Jingyi Jessica Li. "EpiAlign: an alignment-based bioinformatic tool for comparing chromatin state sequences." Nucleic Acids Research 47, no. 13 (2019): e77-e77. http://dx.doi.org/10.1093/nar/gkz287.

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AbstractThe availability of genome-wide epigenomic datasets enables in-depth studies of epigenetic modifications and their relationships with chromatin structures and gene expression. Various alignment tools have been developed to align nucleotide or protein sequences in order to identify structurally similar regions. However, there are currently no alignment methods specifically designed for comparing multi-track epigenomic signals and detecting common patterns that may explain functional or evolutionary similarities. We propose a new local alignment algorithm, EpiAlign, designed to compare c

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Chen, Swaine L. "Implementation of a Stirling number estimator enables direct calculation of population genetics tests for large sequence datasets." Bioinformatics 35, no. 15 (2018): 2668–70. http://dx.doi.org/10.1093/bioinformatics/bty1012.

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Abstract Motivation Stirling numbers enter into the calculation of several population genetics statistics, including Fu’s Fs. However, as alignments become large (≥50 sequences), the Stirling numbers required rapidly exceed the standard floating point range. Another recursive method for calculating Fu’s Fs suffers from floating point underflow issues. Results I implemented an estimator for Stirling numbers that has the advantage of being uniformly applicable to the full parameter range for Stirling numbers. I used this to create a hybrid Fu’s Fs calculator that accounts for floating point unde

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NARIMANI, ZAHRA, HAMID BEIGY, and HASSAN ABOLHASSANI. "A NEW GENETIC ALGORITHM FOR MULTIPLE SEQUENCE ALIGNMENT." International Journal of Computational Intelligence and Applications 11, no. 04 (2012): 1250023. http://dx.doi.org/10.1142/s146902681250023x.

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Multiple sequence alignment (MSA) is one of the basic and important problems in molecular biology. MSA can be used for different purposes including finding the conserved motifs and structurally important regions in protein sequences and determine evolutionary distance between sequences. Aligning several sequences cannot be done in polynomial time and therefore heuristic methods such as genetic algorithms can be used to find approximate solutions of MSA problems. Several algorithms based on genetic algorithms have been developed for this problem in recent years. Most of these algorithms use ver

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Gunawardana, D., V. A. Likic, and K. R. Gayler. "A Comprehensive Bioinformatics Analysis of the Nudix Superfamily inArabidopsis thaliana." Comparative and Functional Genomics 2009 (2009): 1–13. http://dx.doi.org/10.1155/2009/820381.

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Nudix enzymes are a superfamily with a conserved common reaction mechanism that provides the capacity for the hydrolysis of a broad spectrum of metabolites. We used hidden Markov models based on Nudix sequences from the PFAM and PROSITE databases to identify Nudix hydrolases encoded by theArabidopsisgenome. 25 Nudix hydrolases were identified and classified into 11 individual families by pairwise sequence alignments. Intron phases were strikingly conserved in each family. Phylogenetic analysis showed that all multimember families formed monophyletic clusters. Conserved familial sequence motifs

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Benítez-Hidalgo, Antonio, Antonio J. Nebro, and José F. Aldana-Montes. "Sequoya: multiobjective multiple sequence alignment in Python." Bioinformatics 36, no. 12 (2020): 3892–93. http://dx.doi.org/10.1093/bioinformatics/btaa257.

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Abstract Motivation Multiple sequence alignment (MSA) consists of finding the optimal alignment of three or more biological sequences to identify highly conserved regions that may be the result of similarities and relationships between the sequences. MSA is an optimization problem with NP-hard complexity (non-deterministic polynomial-time hardness), because the time needed to find optimal alignments raises exponentially along with the number of sequences and their length. Furthermore, the problem becomes multiobjective when more than one score is considered to assess the quality of an alignmen

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Othman, Mohamed, and Gamil Abdel-Azim. "Genetic Algorithms with Permutation Coding for Multiple Sequence Alignment." Recent Patents on DNA & Gene Sequences 7, no. 2 (2013): 105–14. http://dx.doi.org/10.2174/1872215611307020004.

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Orobitg, Miquel, Fernando Cores, Fernando Guirado, Concepció Roig, and Cedric Notredame. "Improving multiple sequence alignment biological accuracy through genetic algorithms." Journal of Supercomputing 65, no. 3 (2013): 1076–88. http://dx.doi.org/10.1007/s11227-012-0856-9.

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Kalinin, M., and V. Krundyshev. "Sequence Alignment Algorithms for Intrusion Detection in the Internet of Things." Nonlinear Phenomena in Complex Systems 23, no. 4 (2020): 397–404. http://dx.doi.org/10.33581/1561-4085-2020-23-4-397-404.

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The paper reviews the intrusion detection approach based on bioinformatics algorithms for alignment and comparing of the nucleotide sequences. Sequence alignment is a natureclose computational procedure for matching the coded strings by searching for the regions of individual characteristics that are located in the same order. A calculated rank of similarity is used instead of equity checking to estimate the distance between a sequence of the monitored operational acts and a generalized intrusion pattern. Multiple alignment schema is more effective and accurate than the Smith–Waterman local al

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Daugelaite, Jurate, Aisling O' Driscoll, and Roy D. Sleator. "An Overview of Multiple Sequence Alignments and Cloud Computing in Bioinformatics." ISRN Biomathematics 2013 (August 14, 2013): 1–14. http://dx.doi.org/10.1155/2013/615630.

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Multiple sequence alignment (MSA) of DNA, RNA, and protein sequences is one of the most essential techniques in the fields of molecular biology, computational biology, and bioinformatics. Next-generation sequencing technologies are changing the biology landscape, flooding the databases with massive amounts of raw sequence data. MSA of ever-increasing sequence data sets is becoming a significant bottleneck. In order to realise the promise of MSA for large-scale sequence data sets, it is necessary for existing MSA algorithms to be run in a parallelised fashion with the sequence data distributed

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Linard, Benjamin, Krister Swenson, and Fabio Pardi. "Rapid alignment-free phylogenetic identification of metagenomic sequences." Bioinformatics 35, no. 18 (2019): 3303–12. http://dx.doi.org/10.1093/bioinformatics/btz068.

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Abstract Motivation Taxonomic classification is at the core of environmental DNA analysis. When a phylogenetic tree can be built as a prior hypothesis to such classification, phylogenetic placement (PP) provides the most informative type of classification because each query sequence is assigned to its putative origin in the tree. This is useful whenever precision is sought (e.g. in diagnostics). However, likelihood-based PP algorithms struggle to scale with the ever-increasing throughput of DNA sequencing. Results We have developed RAPPAS (Rapid Alignment-free Phylogenetic Placement via Ancest

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Zhang, Jikai, Haidong Lan, Yuandong Chan, Yuan Shang, Bertil Schmidt, and Weiguo Liu. "BGSA: a bit-parallel global sequence alignment toolkit for multi-core and many-core architectures." Bioinformatics 35, no. 13 (2018): 2306–8. http://dx.doi.org/10.1093/bioinformatics/bty930.

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Abstract Motivation Modern bioinformatics tools for analyzing large-scale NGS datasets often need to include fast implementations of core sequence alignment algorithms in order to achieve reasonable execution times. We address this need by presenting the BGSA toolkit for optimized implementations of popular bit-parallel global pairwise alignment algorithms on modern microprocessors. Results BGSA outperforms Edlib, SeqAn and BitPAl for pairwise edit distance computations and Parasail, SeqAn and BitPAl when using more general scoring schemes for pairwise alignments of a batch of sequence reads o

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Chen, Chun-Chi, Hyundoo Jeong, Xiaoning Qian, and Byung-Jun Yoon. "TOPAS: network-based structural alignment of RNA sequences." Bioinformatics 35, no. 17 (2019): 2941–48. http://dx.doi.org/10.1093/bioinformatics/btz001.

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Abstract Motivation For many RNA families, the secondary structure is known to be better conserved among the member RNAs compared to the primary sequence. For this reason, it is important to consider the underlying folding structures when aligning RNA sequences, especially for those with relatively low sequence identity. Given a set of RNAs with unknown structures, simultaneous RNA alignment and folding algorithms aim to accurately align the RNAs by jointly predicting their consensus secondary structure and the optimal sequence alignment. Despite the improved accuracy of the resulting alignmen

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Orozco-Arias, Simon, Mariana S. Candamil-Cortés, Paula A. Jaimes, et al. "K-mer-based machine learning method to classify LTR-retrotransposons in plant genomes." PeerJ 9 (May 19, 2021): e11456. http://dx.doi.org/10.7717/peerj.11456.

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Every day more plant genomes are available in public databases and additional massive sequencing projects (i.e., that aim to sequence thousands of individuals) are formulated and released. Nevertheless, there are not enough automatic tools to analyze this large amount of genomic information. LTR retrotransposons are the most frequent repetitive sequences in plant genomes; however, their detection and classification are commonly performed using semi-automatic and time-consuming programs. Despite the availability of several bioinformatic tools that follow different approaches to detect and class

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Eizenga, Jordan M., Adam M. Novak, Jonas A. Sibbesen, et al. "Pangenome Graphs." Annual Review of Genomics and Human Genetics 21, no. 1 (2020): 139–62. http://dx.doi.org/10.1146/annurev-genom-120219-080406.

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Low-cost whole-genome assembly has enabled the collection of haplotype-resolved pangenomes for numerous organisms. In turn, this technological change is encouraging the development of methods that can precisely address the sequence and variation described in large collections of related genomes. These approaches often use graphical models of the pangenome to support algorithms for sequence alignment, visualization, functional genomics, and association studies. The additional information provided to these methods by the pangenome allows them to achieve superior performance on a variety of bioin

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Urgese, Gianvito, Emanuele Parisi, Orazio Scicolone, Santa Di Cataldo, and Elisa Ficarra. "BioSeqZip: a collapser of NGS redundant reads for the optimization of sequence analysis." Bioinformatics 36, no. 9 (2020): 2705–11. http://dx.doi.org/10.1093/bioinformatics/btaa051.

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Abstract Motivation High-throughput next-generation sequencing can generate huge sequence files, whose analysis requires alignment algorithms that are typically very demanding in terms of memory and computational resources. This is a significant issue, especially for machines with limited hardware capabilities. As the redundancy of the sequences typically increases with coverage, collapsing such files into compact sets of non-redundant reads has the 2-fold advantage of reducing file size and speeding-up the alignment, avoiding to map the same sequence multiple times. Method BioSeqZip generates

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Rechid, Renas, Martin Vingron, and Patrick Argos. "A new interactive protein sequence alignment program and comparison of its results with widely used algorithms." Bioinformatics 5, no. 2 (1989): 107–13. http://dx.doi.org/10.1093/bioinformatics/5.2.107.

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Cavanaugh, David, and Krishnan Chittur. "A hydrophobic proclivity index for protein alignments." F1000Research 4 (October 21, 2015): 1097. http://dx.doi.org/10.12688/f1000research.6348.1.

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Sequence alignment algorithms are fundamental to modern bioinformatics. Sequence alignments are widely used in diverse applications such as phylogenetic analysis, database searches for related sequences to aid identification of unknown protein domain structures and classification of proteins and protein domains. Additionally, alignment algorithms are integral to the location of related proteins to secure understanding of unknown protein functions, to suggest the folded structure of proteins of unknown structure from location of homologous proteins and/or by locating homologous domains of known

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Cavanaugh, David, and Krishnan Chittur. "A hydrophobic proclivity index for protein alignments." F1000Research 4 (October 15, 2020): 1097. http://dx.doi.org/10.12688/f1000research.6348.2.

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Sequence alignment algorithms are fundamental to modern bioinformatics. Sequence alignments are widely used in diverse applications such as phylogenetic analysis, database searches for related sequences to aid identification of unknown protein domain structures and classification of proteins and protein domains. Additionally, alignment algorithms are integral to the location of related proteins to secure understanding of unknown protein functions, to suggest the folded structure of proteins of unknown structure from location of homologous proteins and/or by locating homologous domains of known

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Ortuño, Francisco M., Olga Valenzuela, Fernando Rojas, et al. "Optimizing multiple sequence alignments using a genetic algorithm based on three objectives: structural information, non-gaps percentage and totally conserved columns." Bioinformatics 29, no. 17 (2013): 2112–21. http://dx.doi.org/10.1093/bioinformatics/btt360.

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Ben Othman, Mohamed Tahar. "Survey of the use of genetic algorithm for multiple sequence alignment." Journal of Advanced Computer Science & Technology 5, no. 2 (2016): 28. http://dx.doi.org/10.14419/jacst.v5i2.6079.

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Multiple Sequence Alignment (MSA) is used in genomic analysis, such as the identification of conserved sequence motifs, the estimation of evolutionary divergence between sequences, and the genes’ historical relationships inference. Several researches were conducted to determine the level of similarity of a set of sequences. Due to the problem of the NP-complete class property, a number of researches use genetic algorithms (GA) to find a solution to the multiple sequence alignment. However, the nature of genetic algorithms makes the complexity extremely high due to the redundancy provided by th

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DEOROWICZ, SEBASTIAN, and AGNIESZKA DANEK. "BIT-PARALLEL ALGORITHMS FOR THE MERGED LONGEST COMMON SUBSEQUENCE PROBLEM." International Journal of Foundations of Computer Science 24, no. 08 (2013): 1281–98. http://dx.doi.org/10.1142/s0129054113500342.

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It is often a necessity to compare some sequences to find out how similar they are. There are many similarity measures that can be used, e.g., longest common subsequence, edit distance, sequence alignment. Recently a merged longest common subsequence (MergedLCS) problem was formulated with applications in bioinformatics. We propose the bit-parallel algorithms for the MergedLCS problem and evaluate them in practice showing that they are usually tens times faster than the already published methods.

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Morrison, David A. "Multiple sequence alignment for phylogenetic purposes." Australian Systematic Botany 19, no. 6 (2006): 479. http://dx.doi.org/10.1071/sb06020.

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I have addressed the biological rather than bioinformatics aspects of molecular sequence alignment by covering a series of topics that have been under-valued, particularly within the context of phylogenetic analysis. First, phylogenetic analysis is only one of the many objectives of sequence alignment, and the most appropriate multiple alignment may not be the same for all of these purposes. Phylogenetic alignment thus occupies a specific place within a broader context. Second, homology assessment plays an intricate role in phylogenetic analysis, with sequence alignment consisting of primary h

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Hooper, P. M., H. Zhang, and D. S. Wishart. "Prediction of genetic structure in eukaryotic DNA using reference point logistic regression and sequence alignment." Bioinformatics 16, no. 5 (2000): 425–38. http://dx.doi.org/10.1093/bioinformatics/16.5.425.

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Arenas-Díaz, Edgar D., Helga Ochoterena, and Katya Rodríguez-Vázquez. "Multiple Sequence Alignment Using a Genetic Algorithm and GLOCSA." Journal of Artificial Evolution and Applications 2009 (August 27, 2009): 1–10. http://dx.doi.org/10.1155/2009/963150.

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Algorithms that minimize putative synapomorphy in an alignment cannot be directly implemented since trivial cases with concatenated sequences would be selected because they would imply a minimum number of events to be explained (e.g., a single insertion/deletion would be required to explain divergence among two sequences). Therefore, indirect measures to approach parsimony need to be implemented. In this paper, we thoroughly present a Global Criterion for Sequence Alignment (GLOCSA) that uses a scoring function to globally rate multiple alignments aiming to produce matrices that minimize the n

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HUO, HONG-WEI, VOJISLAV STOJKOVIC, and QIAO-LUAN XIE. "A QUANTUM-INSPIRED GENETIC ALGORITHM BASED ON PROBABILISTIC CODING FOR MULTIPLE SEQUENCE ALIGNMENT." Journal of Bioinformatics and Computational Biology 08, no. 01 (2010): 59–75. http://dx.doi.org/10.1142/s0219720010004549.

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Quantum parallelism arises from the ability of a quantum memory register to exist in a superposition of base states. Since the number of possible base states is 2n, where n is the number of qubits in the quantum memory register, one operation on a quantum computer performs what an exponential number of operations on a classical computer performs. The power of quantum algorithms comes from taking advantages of quantum parallelism. Quantum algorithms are exponentially faster than classical algorithms. Genetic optimization algorithms are stochastic search algorithms which are used to search large

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Solano-Roman, A., C. Cruz-Castillo, D. Offenhuber, and A. Colubri. "NX4: a web-based visualization of large multiple sequence alignments." Bioinformatics 35, no. 22 (2019): 4800–4802. http://dx.doi.org/10.1093/bioinformatics/btz457.

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Abstract Summary Multiple Sequence Alignments (MSAs) are a fundamental operation in genome analysis. However, MSA visualizations such as sequence logos and matrix representations have changed little since the nineties and are not well suited for displaying large-scale alignments. We propose a novel, web-based MSA visualization tool called NX4, which can handle genome alignments comprising thousands of sequences. NX4 calculates the frequency of each nucleotide along the alignment and visually summarizes the results using a color-blind friendly palette that helps identifying regions of high gene

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Santamaría, Rodrigo, Roberto Therón, Laura Durán, et al. "Genome-wide search of nucleosome patterns using visual analytics." Bioinformatics 35, no. 13 (2018): 2185–92. http://dx.doi.org/10.1093/bioinformatics/bty971.

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Abstract Motivation The Burrows-Wheeler transform (BWT) is widely used for the fast alignment of high-throughput sequence data. This method also has potential applications in other areas of bioinformatics, and it can be specially useful for the fast searching of patterns on coverage data from different sources. Results We present a nucleosome pattern search method that converts levels of nucleosomal occupancy to a sequence-like format to which BWT searches can be applied. The method is embedded in a nucleosome map browser, ‘Nucleosee‘, an interactive visual tool specifically designed to enhanc

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Long, Hai Xia, Li Hua Wu, and Yu Zhang. "Multiple Sequence Alignment Based on Profile Hidden Markov Model and Quantum-Behaved Particle Swarm Optimization with Selection Method." Advanced Materials Research 282-283 (July 2011): 7–12. http://dx.doi.org/10.4028/www.scientific.net/amr.282-283.7.

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Multiple sequence alignment (MSA) is an NP-complete and important problem in bioinformatics. Currently, profile hidden Markov model (HMM) is widely used for multiple sequence alignment. In this paper, Quantum-behaved Particle Swarm Optimization with selection operation (SQPSO) is presented, which is used to train profile HMM. Furthermore, an integration algorithm based on the profile HMM and SQPSO for the MSA is constructed. The approach is examined by using multiple nucleotides and protein sequences and compared with other algorithms. The results of the comparisons show that the HMM trained w

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Crysup, Benjamin, August E. Woerner, Jonathan L. King, and Bruce Budowle. "Graph Algorithms for Mixture Interpretation." Genes 12, no. 2 (2021): 185. http://dx.doi.org/10.3390/genes12020185.

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The scale of genetic methods are presently being expanded: forensic genetic assays previously were limited to tens of loci, but now technologies allow for a transition to forensic genomic approaches that assess thousands to millions of loci. However, there are subtle distinctions between genetic assays and their genomic counterparts (especially in the context of forensics). For instance, forensic genetic approaches tend to describe a locus as a haplotype, be it a microhaplotype or a short tandem repeat with its accompanying flanking information. In contrast, genomic assays tend to provide not

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Quadrini, Michela, Luca Tesei, and Emanuela Merelli. "ASPRAlign: a tool for the alignment of RNA secondary structures with arbitrary pseudoknots." Bioinformatics 36, no. 11 (2020): 3578–79. http://dx.doi.org/10.1093/bioinformatics/btaa147.

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Abstract Summary Current methods for comparing RNA secondary structures are based on tree representations and exploit edit distance or alignment algorithms. Most of them can only process structures without pseudoknots. To overcome this limitation, we introduce ASPRAlign, a Java tool that aligns particular algebraic tree representations of RNA. These trees neglect the primary sequence and can handle structures with arbitrary pseudoknots. A measure of comparison, called ASPRA distance, is computed with a worst-case time complexity of O(n2) where n is the number of nucleotides of the longer struc

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Neelakanta, Perambur S., and Deepti Pappusetty. "Bioinformatics-Inspired Algorithms for 2D-Image Analysis-Application to Synthetic and Medical Images Part I." International Journal of Biomedical and Clinical Engineering 1, no. 1 (2012): 14–38. http://dx.doi.org/10.4018/ijbce.2012010102.

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To ascertain specific features in bio-/medical-images, a new avenue of using the so-called Needleman-Wunsch (NW) and Smith-Waterman (SW) algorithms (of bioinformatics) is indicated. In general, NW/SW algorithms are adopted in genomic science to obtain optimal (global and local) alignment of two linear sequences (like DNA nucleotide bases) to determine the similarity features between them and such 1D-sequence algorithms are presently extended to compare 2D-images via binary correlation. The efficacy of the proposed method is tested with synthetic images and a brain scan image. Thus, the way of

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Li, Huixing, Yan Xue, and Xiancai Zeng. "Investigation of data mining technique and artificial intelligence algorithm in microflora bioinformatics." E3S Web of Conferences 267 (2021): 01040. http://dx.doi.org/10.1051/e3sconf/202126701040.

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Bioinformatics has gradually received widespread attention and has shown the characteristics of a large amount of calculation and high complexity. Therefore, it is required to adopt computer algorithms in bioinformatics to improve the efficiency of bioinformatics processing problems. Big data and artificial intelligence technologies have the characteristics of supporting bioinformatics and have achieved certain results in the field of bioinformatics. Introduced the application basis of big data and artificial intelligence in bioinformatics, analyzed data collection, preprocessing, data storage

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