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Journal articles on the topic 'Protein sequence alignment'

Author: Grafiati

Published: 14 March 2023

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1

Staritzbichler, René, Edoardo Sarti, Emily Yaklich, et al. "Refining pairwise sequence alignments of membrane proteins by the incorporation of anchors." PLOS ONE 16, no. 4 (2021): e0239881. http://dx.doi.org/10.1371/journal.pone.0239881.

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The alignment of primary sequences is a fundamental step in the analysis of protein structure, function, and evolution, and in the generation of homology-based models. Integral membrane proteins pose a significant challenge for such sequence alignment approaches, because their evolutionary relationships can be very remote, and because a high content of hydrophobic amino acids reduces their complexity. Frequently, biochemical or biophysical data is available that informs the optimum alignment, for example, indicating specific positions that share common functional or structural roles. Currently

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2

Pervez, Muhammad Tariq, Hayat Ali Shah, Masroor Ellahi Babar, Nasir Naveed, and Muhammad Shoaib. "SAliBASE: A Database of Simulated Protein Alignments." Evolutionary Bioinformatics 15 (January 2019): 117693431882108. http://dx.doi.org/10.1177/1176934318821080.

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Simulated alignments are alternatives to manually constructed multiple sequence alignments for evaluating performance of multiple sequence alignment tools. The importance of simulated sequences is recognized because their true evolutionary history is known, which is very helpful for reconstructing accurate phylogenetic trees and alignments. However, generating simulated alignments require expertise to use bioinformatics tools and consume several hours for reconstructing even a few hundreds of simulated sequences. It becomes a tedious job for an end user who needs a few datasets of variety of s

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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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Aadland, Kelsey, and Bryan Kolaczkowski. "Alignment-Integrated Reconstruction of Ancestral Sequences Improves Accuracy." Genome Biology and Evolution 12, no. 9 (2020): 1549–65. http://dx.doi.org/10.1093/gbe/evaa164.

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Abstract Ancestral sequence reconstruction (ASR) uses an alignment of extant protein sequences, a phylogeny describing the history of the protein family and a model of the molecular-evolutionary process to infer the sequences of ancient proteins, allowing researchers to directly investigate the impact of sequence evolution on protein structure and function. Like all statistical inferences, ASR can be sensitive to violations of its underlying assumptions. Previous studies have shown that, whereas phylogenetic uncertainty has only a very weak impact on ASR accuracy, uncertainty in the protein se

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6

Barton, Geoffrey J. "Protein Sequence Alignment Techniques." Acta Crystallographica Section D Biological Crystallography 54, no. 6 (1998): 1139–46. http://dx.doi.org/10.1107/s0907444998008324.

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The basic algorithms for alignment of two or more protein sequences are explained. Alternative methods for scoring substitutions and gaps (insertions and deletions) are described, as are global and local alignment methods. Multiple alignment techniques are explained, including methods for profile comparison. A summary is given of programs for the alignment and analysis of protein sequences, either from sequence alone, or from three-dimensional structure.

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Kanagarajadurai, Karuppiah, Singaravelu Kalaimathy, Paramasivam Nagarajan, and Ramanathan Sowdhamini. "PASS2." International Journal of Knowledge Discovery in Bioinformatics 2, no. 4 (2011): 53–66. http://dx.doi.org/10.4018/jkdb.2011100104.

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A detailed comparison of protein domains that belong to families and superfamilies shows that structure is better conserved than sequence during evolutionary divergence. Sequence alignments, guided by structural features, permit a better sampling of the protein sequence space and effective construction of libraries for fold recognition. Sequence alignments are useful evolutionary models in defining structure-function relationships for protein superfamilies. The PASS2 database, maintained by the authors, presents alignments of proteins related at the superfamily level and characterised by low s

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8

Pei, Jimin. "Multiple protein sequence alignment." Current Opinion in Structural Biology 18, no. 3 (2008): 382–86. http://dx.doi.org/10.1016/j.sbi.2008.03.007.

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9

PAI, TUN-WEN, RUEI-HSIANG CHANG, CHIEN-MING CHEN, et al. "MULTIPLE STRUCTURE ALIGNMENT BASED ON GEOMETRICAL CORRELATION OF SECONDARY STRUCTURE ELEMENTS." New Mathematics and Natural Computation 06, no. 01 (2010): 77–95. http://dx.doi.org/10.1142/s1793005710001621.

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Protein structure alignment facilitates the analysis of protein functionality. Through superimposed structures and the comparison of variant components, common or specific features of proteins can be identified. Several known protein families exhibit analogous tertiary structures but divergent primary sequences. These proteins in the same structural class are unable to be aligned by sequence-based methods. The main objective of the present study was to develop an efficient and effective algorithm for multiple structure alignment based on geometrical correlation of secondary structures, which a

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10

Henneke, Christina M., Michael J. Danson, David W. Hough, and David J. Osguthorpe. "Sequence alignment of citrate synthase proteins using a multiple sequence alignment algorithm and multiple scoring matrices." "Protein Engineering, Design and Selection" 2, no. 8 (1989): 597–604. http://dx.doi.org/10.1093/protein/2.8.597.

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11

Sierk, Michael L., Michael E. Smoot, Ellen J. Bass, and William R. Pearson. "Improving pairwise sequence alignment accuracy using near-optimal protein sequence alignments." BMC Bioinformatics 11, no. 1 (2010): 146. http://dx.doi.org/10.1186/1471-2105-11-146.

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12

Tu, Shin-Lin, Jeannette Staheli, Colum McClay, Kathleen McLeod, Timothy Rose, and Chris Upton. "Base-By-Base Version 3: New Comparative Tools for Large Virus Genomes." Viruses 10, no. 11 (2018): 637. http://dx.doi.org/10.3390/v10110637.

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Base-By-Base is a comprehensive tool for the creation and editing of multiple sequence alignments that is coded in Java and runs on multiple platforms. It can be used with gene and protein sequences as well as with large viral genomes, which themselves can contain gene annotations. This report describes new features added to Base-By-Base over the last 7 years. The two most significant additions are: (1) The recoding and inclusion of “consensus-degenerate hybrid oligonucleotide primers” (CODEHOP), a popular tool for the design of degenerate primers from a multiple sequence alignment of proteins

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13

Zhan, Qing, Yilei Fu, Qinghua Jiang, Bo Liu, Jiajie Peng, and Yadong Wang. "SpliVert: A Protein Multiple Sequence Alignment Refinement Method Based on Splitting-Splicing Vertically." Protein & Peptide Letters 27, no. 4 (2020): 295–302. http://dx.doi.org/10.2174/0929866526666190806143959.

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Background: Multiple Sequence Alignment (MSA) is a fundamental task in bioinformatics and is required for many biological analysis tasks. The more accurate the alignments are, the more credible the downstream analyses. Most protein MSA algorithms realign an alignment to refine it by dividing it into two groups horizontally and then realign the two groups. However, this strategy does not consider that different regions of the sequences have different conservation; this property may lead to incorrect residue-residue or residue-gap pairs, which cannot be corrected by this strategy. Objective: In

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Stamm, Marcus, René Staritzbichler, Kamil Khafizov, and Lucy R. Forrest. "AlignMe—a membrane protein sequence alignment web server." Nucleic Acids Research 42, W1 (2014): W246—W251. http://dx.doi.org/10.1093/nar/gku291.

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15

TAYLOR, WILLIAM R. "Motif-Biased Protein Sequence Alignment." Journal of Computational Biology 1, no. 4 (1994): 297–310. http://dx.doi.org/10.1089/cmb.1994.1.297.

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16

Fox, Gearóid, Fabian Sievers, and Desmond G. Higgins. "Using de novo protein structure predictions to measure the quality of very large multiple sequence alignments." Bioinformatics 32, no. 6 (2015): 814–20. http://dx.doi.org/10.1093/bioinformatics/btv592.

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Abstract Motivation: Multiple sequence alignments (MSAs) with large numbers of sequences are now commonplace. However, current multiple alignment benchmarks are ill-suited for testing these types of alignments, as test cases either contain a very small number of sequences or are based purely on simulation rather than empirical data. Results: We take advantage of recent developments in protein structure prediction methods to create a benchmark (ContTest) for protein MSAs containing many thousands of sequences in each test case and which is based on empirical biological data. We rank popular MSA

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MIAO, XIJIANG, PETER J. WADDELL, and HOMAYOUN VALAFAR. "TALI: LOCAL ALIGNMENT OF PROTEIN STRUCTURES USING BACKBONE TORSION ANGLES." Journal of Bioinformatics and Computational Biology 06, no. 01 (2008): 163–81. http://dx.doi.org/10.1142/s0219720008003370.

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Torsion angle alignment (TALI) is a novel approach to local structural motif alignment, based on backbone torsion angles (ϕ, ψ) rather than the more traditional atomic distance matrices. Representation of a protein structure in the form of a sequence of torsion angles enables easy integration of sequence and structural information, and adopts mature techniques in sequence alignment to improve performance and alignment quality. We show that TALI is able to match local structural motifs as well as identify global structural similarity. TALI is also compared to other structure alignment methods s

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18

Madhusudhan, M. S., Marc A. Marti-Renom, Roberto Sanchez, and Andrej Sali. "Variable gap penalty for protein sequence–structure alignment." Protein Engineering, Design and Selection 19, no. 3 (2006): 129–33. http://dx.doi.org/10.1093/protein/gzj005.

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19

SALEM, SAEED, MOHAMMED J. ZAKI, and CHRISTOPHER BYSTROFF. "ITERATIVE NON-SEQUENTIAL PROTEIN STRUCTURAL ALIGNMENT." Journal of Bioinformatics and Computational Biology 07, no. 03 (2009): 571–96. http://dx.doi.org/10.1142/s0219720009004205.

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Structural similarity between proteins gives us insights into their evolutionary relationships when there is low sequence similarity. In this paper, we present a novel approach called SNAP for non-sequential pair-wise structural alignment. Starting from an initial alignment, our approach iterates over a two-step process consisting of a superposition step and an alignment step, until convergence. We propose a novel greedy algorithm to construct both sequential and non-sequential alignments. The quality of SNAP alignments were assessed by comparing against the manually curated reference alignmen

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20

Kauffman, D. L., P. J. Keller, A. Bennick, and M. Blum. "Alignment of Amino Acid and DNA Sequences of Human Proline-rich Proteins." Critical Reviews in Oral Biology & Medicine 4, no. 3 (1993): 287–92. http://dx.doi.org/10.1177/10454411930040030501.

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Human proline-rich proteins (PRPs) constitute a complex family of salivary proteins that are encoded by a small number of genes. The primary gene product is cleaved by proteases, thereby giving rise to about 20 secreted proteins. To determine the genes for the secreted PRPs, therefore, it is necessary to obtain sequences of both the secreted proteins and the DNA encoding these proteins. We have sequenced most PRPs from one donor (D.K.) and aligned the protein sequences with available DNA sequences from unrelated individuals. Partial sequence data have now been obtained for an additional PRP fr

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21

Manavalan, Mani. "Fast Model-based Protein Homology Discovery without Alignment." Asia Pacific Journal of Energy and Environment 1, no. 2 (2014): 169–84. http://dx.doi.org/10.18034/apjee.v1i2.580.

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The need for quick gene categorization tools is growing as more genomes are sequenced. To evaluate a newly sequenced genome, the genes must first be identified and translated into amino acid sequences, which are then categorized into structural or functional classes. Protein homology detection using sequence alignment algorithms is the most effective way for protein categorization. Discriminative approaches such as support vector machines (SVMs) and position-specific scoring matrices (PSSM) derived from PSI-BLAST have recently been used to improve alignment algorithms. However, if a fresh sequ

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22

Th.Mevissen, Heina, and Martin Vingron. "Quantifying the local reliability of a sequence alignment." "Protein Engineering, Design and Selection" 9, no. 2 (1996): 127–32. http://dx.doi.org/10.1093/protein/9.2.127.

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23

Jeon, Yoon-Seong, Kihyun Lee, Sang-Cheol Park, et al. "EzEditor: a versatile sequence alignment editor for both rRNA- and protein-coding genes." International Journal of Systematic and Evolutionary Microbiology 64, Pt_2 (2014): 689–91. http://dx.doi.org/10.1099/ijs.0.059360-0.

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EzEditor is a Java-based molecular sequence editor allowing manipulation of both DNA and protein sequence alignments for phylogenetic analysis. It has multiple features optimized to connect initial computer-generated multiple alignment and subsequent phylogenetic analysis by providing manual editing with reference to biological information specific to the genes under consideration. It provides various functionalities for editing rRNA alignments using secondary structure information. In addition, it supports simultaneous editing of both DNA sequences and their translated protein sequences for p

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24

Lee, Sung Jong, Keehyoung Joo, Sangjin Sim, Juyong Lee, In-Ho Lee, and Jooyoung Lee. "CRFalign: A Sequence-Structure Alignment of Proteins Based on a Combination of HMM-HMM Comparison and Conditional Random Fields." Molecules 27, no. 12 (2022): 3711. http://dx.doi.org/10.3390/molecules27123711.

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Sequence–structure alignment for protein sequences is an important task for the template-based modeling of 3D structures of proteins. Building a reliable sequence–structure alignment is a challenging problem, especially for remote homologue target proteins. We built a method of sequence–structure alignment called CRFalign, which improves upon a base alignment model based on HMM-HMM comparison by employing pairwise conditional random fields in combination with nonlinear scoring functions of structural and sequence features. Nonlinear scoring part is implemented by a set of gradient boosted regr

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Kuchaiev, Oleksii, Tijana Milenković, Vesna Memišević, Wayne Hayes, and Nataša Pržulj. "Topological network alignment uncovers biological function and phylogeny." Journal of The Royal Society Interface 7, no. 50 (2010): 1341–54. http://dx.doi.org/10.1098/rsif.2010.0063.

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Sequence comparison and alignment has had an enormous impact on our understanding of evolution, biology and disease. Comparison and alignment of biological networks will probably have a similar impact. Existing network alignments use information external to the networks, such as sequence, because no good algorithm for purely topological alignment has yet been devised. In this paper, we present a novel algorithm based solely on network topology, that can be used to align any two networks. We apply it to biological networks to produce by far the most complete topological alignments of biological

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Daniels, Noah M., Shilpa Nadimpalli, and Lenore J. Cowen. "Formatt: Correcting protein multiple structural alignments by incorporating sequence alignment." BMC Bioinformatics 13, no. 1 (2012): 259. http://dx.doi.org/10.1186/1471-2105-13-259.

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27

Carpentier, Mathilde, and Jacques Chomilier. "Protein multiple alignments: sequence-based versus structure-based programs." Bioinformatics 35, no. 20 (2019): 3970–80. http://dx.doi.org/10.1093/bioinformatics/btz236.

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Abstract Motivation Multiple sequence alignment programs have proved to be very useful and have already been evaluated in the literature yet not alignment programs based on structure or both sequence and structure. In the present article we wish to evaluate the added value provided through considering structures. Results We compared the multiple alignments resulting from 25 programs either based on sequence, structure or both, to reference alignments deposited in five databases (BALIBASE 2 and 3, HOMSTRAD, OXBENCH and SISYPHUS). On the whole, the structure-based methods compute more reliable a

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28

Piña, Johan S., Simon Orozco-Arias, Nicolas Tobón-Orozco, Leonardo Camargo-Forero, Reinel Tabares-Soto, and Romain Guyot. "G-SAIP: Graphical Sequence Alignment Through Parallel Programming in the Post-Genomic Era." Evolutionary Bioinformatics 19 (January 2023): 117693432211505. http://dx.doi.org/10.1177/11769343221150585.

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A common task in bioinformatics is to compare DNA sequences to identify similarities between organisms at the sequence level. An approach to such comparison is the dot-plots, a 2-dimensional graphical representation to analyze DNA or protein alignments. Dot-plots alignment software existed before the sequencing revolution, and now there is an ongoing limitation when dealing with large-size sequences, resulting in very long execution times. High-Performance Computing (HPC) techniques have been successfully used in many applications to reduce computing times, but so far, very few applications fo

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CHUANG, LI-YEH, CHENG-HONG YANG, CHAO-CHING CHANG, WEN-SHYONG TZOU, and LI-CHENG JIN. "VSA-TOOL: A TOOL FOR DATA VISUALIZATION IN SEQUENCE ALIGNMENT." Biomedical Engineering: Applications, Basis and Communications 16, no. 02 (2004): 68–72. http://dx.doi.org/10.4015/s1016237204000116.

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Sequence alignment is a fundamental and important tool for sequence data analysis in molecular biology. Many applications in molecular biology require the detection of a similarity pattern displayed by a number of DNA and protein sequences. Visual front-ends are useful for an intuitive viewing of alignment and help to analyze the structure, functions, and evolution of the DNA and protein. In this paper, we designed and implemented an interactive system for data visualization in DNA and proteins, which can be used in determining a sequence alignment, similarity search of sequence data, and func

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Fallaize, Christopher J., Peter J. Green, Kanti V. Mardia, and Stuart Barber. "Bayesian protein sequence and structure alignment." Journal of the Royal Statistical Society: Series C (Applied Statistics) 69, no. 2 (2020): 301–25. http://dx.doi.org/10.1111/rssc.12394.

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31

Gotoh, Osamu. "Direct mapping and alignment of protein sequences onto genomic sequence." Bioinformatics 24, no. 21 (2008): 2438–44. http://dx.doi.org/10.1093/bioinformatics/btn460.

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32

Ahola, Virpi, Tero Aittokallio, Esa Uusipaikka, and Mauno Vihinen. "Statistical Methods for Identifying Conserved Residues in Multiple Sequence Alignment." Statistical Applications in Genetics and Molecular Biology 3, no. 1 (2004): 1–28. http://dx.doi.org/10.2202/1544-6115.1074.

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The assessment of residue conservation in a multiple sequence alignment is a central issue in bioinformatics. Conserved residues and regions are used to determine structural and functional motifs or evolutionary relationships between the sequences of a multiple sequence alignment. For this reason, residue conservation is a valuable measure for database and motif search or for estimating the quality of alignments. In this paper, we present statistical methods for identifying conserved residues in multiple sequence alignments. While most earlier studies examine the positional conservation of the

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Roca, Alberto I., Aaron C. Abajian, and David J. Vigerust. "ProfileGrids solve the large alignment visualization problem: influenza hemagglutinin example." F1000Research 2 (January 4, 2013): 2. http://dx.doi.org/10.12688/f1000research.2-2.v1.

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Large multiple sequence alignments are a challenge for current visualization programs. ProfileGrids are a solution that reduces alignments to a matrix, color-shaded according to the residue frequency at each column position. ProfileGrids are not limited by the number of sequences and so solves this visualization problem. We demonstrate the new metadata searching and grep filtering features of the JProfileGrid version 2.0 software on an alignment of 11,900 hemagglutinin protein sequences. JProfileGrid is free and available from http://www.ProfileGrid.org.

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Kaur, Navjot, Rajbir Singh Cheema, and Harmandeep Singh Harmandeep Singh. "Multiple Sequence Alignment and Profile Analysis of Protein Family Utsing Hidden Markov Model." International Journal of Scientific Research 2, no. 6 (2012): 208–11. http://dx.doi.org/10.15373/22778179/june2013/66.

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Lebsir, Rabah, Abdesslem Layeb, and Tahi Fariza. "A Greedy Clustering Algorithm for Multiple Sequence Alignment." International Journal of Cognitive Informatics and Natural Intelligence 15, no. 4 (2021): 1–17. http://dx.doi.org/10.4018/ijcini.20211001.oa41.

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This paper presents a strategy to tackle the Multiple Sequence Alignment (MSA) problem, which is one of the most important tasks in the biological sequence analysis. Its role is to align the sequences in their entirety to derive relationships and common characteristics between a set of protein or nucleotide sequences. The MSA problem was proved to be an NP-Hard problem. The proposed strategy incorporates a new idea based on the well-known divide and conquer paradigm. This paper presents a novel method of clustering sequences as a preliminary step to improve the final alignment; this decomposit

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Bond, Charles Simon, and Alexander Wolfgang Schüttelkopf. "ALINE: a WYSIWYG protein-sequence alignment editor for publication-quality alignments." Acta Crystallographica Section D Biological Crystallography 65, no. 5 (2009): 510–12. http://dx.doi.org/10.1107/s0907444909007835.

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Sauder, J. Michael, Jonathan W. Arthur, and Roland L. Dunbrack Jr. "Large-scale comparison of protein sequence alignment algorithms with structure alignments." Proteins: Structure, Function, and Genetics 40, no. 1 (2000): 6–22. http://dx.doi.org/10.1002/(sici)1097-0134(20000701)40:1<6::aid-prot30>3.0.co;2-7.

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38

Chen, Jing, and Jia Huang. "A novel network aligner for the analysis of multiple protein-protein interaction networks." Computer Science and Information Systems, no. 00 (2021): 30. http://dx.doi.org/10.2298/csis200909030c.

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The analysis of protein-protein interaction networks can transfer the knowledge of well-studied biological functions to functions that are not yet adequately investigated by constructing networks and extracting similar network structures in different species. Multiple network alignment can be used to find similar regions among multiple networks. In this paper, we introduce Accurate Combined Clustering Multiple Network Alignment (ACCMNA), which is a new and accurate multiple network alignment algorithm. It uses both topology and sequence similarity information. First, the importance of all the

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Pazos, Florencio. "Prediction of Protein Sites and Physicochemical Properties Related to Functional Specificity." Bioengineering 8, no. 12 (2021): 201. http://dx.doi.org/10.3390/bioengineering8120201.

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Specificity Determining Positions (SDPs) are protein sites responsible for functional specificity within a family of homologous proteins. These positions are extracted from a family’s multiple sequence alignment and complement the fully conserved positions as predictors of functional sites. SDP analysis is now routinely used for locating these specificity-related sites in families of proteins of biomedical or biotechnological interest with the aim of mutating them to switch specificities or design new ones. There are many different approaches for detecting these positions in multiple sequence

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Md Isa, Mohd Nazrin, Sohiful Anuar Zainol Murad, Mohamad Imran Ahmad, Muhammad M. Ramli, and Rizalafande Che Ismail. "An Efficient Scheduling Technique for Biological Sequence Alignment." Applied Mechanics and Materials 754-755 (April 2015): 1087–92. http://dx.doi.org/10.4028/www.scientific.net/amm.754-755.1087.

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Computing alignment matrix score to search for regions of homology between biological sequences is time consuming task. This is due to the recursive nature of the dynamic programming-based algorithms such as the Smith-Waterman and the Needleman-Wunsch algorithmns. Typical FPGA-based protein sequencer comprises of two main logic blocks. One for computing alignment scores i.e. the processing element (PE), while another logic block for configuring the PE with coefficients. During alignment matrix computation, the logic block for configuring the PE are left unused until the time consuming alignmen

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ESKIN, ELEAZAR, and SAGI SNIR. "INCORPORATING HOMOLOGUES INTO SEQUENCE EMBEDDINGS FOR PROTEIN ANALYSIS." Journal of Bioinformatics and Computational Biology 05, no. 03 (2007): 717–38. http://dx.doi.org/10.1142/s0219720007002734.

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Statistical and learning techniques are becoming increasingly popular for different tasks in bioinformatics. Many of the most powerful statistical and learning techniques are applicable to points in a Euclidean space but not directly applicable to discrete sequences such as protein sequences. One way to apply these techniques to protein sequences is to embed the sequences into a Euclidean space and then apply these techniques to the embedded points. In this work we introduce a biologically motivated sequence embedding, the homology kernel, which takes into account intuitions from local alignme

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BIANCHETTI, LAURENT, JULIE DAWN THOMPSON, ODILE LECOMPTE, FREDERIC PLEWNIAK, and OLIVIER POCH. "vALId: VALIDATION OF PROTEIN SEQUENCE QUALITY BASED ON MULTIPLE ALIGNMENT DATA." Journal of Bioinformatics and Computational Biology 03, no. 04 (2005): 929–47. http://dx.doi.org/10.1142/s0219720005001326.

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The validation of sequences is essential to perform accurate phylogeny and structure/function analysis. However among the thousands of protein sequences available in the public databases, most have been predicted in silico and have not systematically undergone a quality verification. It has recently become evident that they often contain sequence errors. To address the problem of automatic protein quality control, we have developed vALId, an interactive web interfaced software. Taking advantage of high quality multiple alignments of complete protein sequences (MACS), vALId first warns about th

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Lee, Justin, and Shawn X. Wang. "A software tool for protein sequence alignment." International Journal of Bioinformatics Research and Applications 16, no. 4 (2020): 319. http://dx.doi.org/10.1504/ijbra.2020.113018.

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Lee, Justin, and Shawn X. Wang. "A software tool for protein sequence alignment." International Journal of Bioinformatics Research and Applications 16, no. 4 (2020): 319. http://dx.doi.org/10.1504/ijbra.2020.10035352.

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Somasundar, K., and S. Radhakrish. "Nimble Protein Sequence Alignment in Grid (NPSAG)." Journal of Computer Science 4, no. 1 (2008): 36–41. http://dx.doi.org/10.3844/jcssp.2008.36.41.

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Md. Isa, Mohd Nazrin, Ku Noor Dhaniah Ku Muhsen, Dayana Saiful Nurdin, et al. "FPGA-based protein sequence alignment : A review." EPJ Web of Conferences 162 (2017): 01075. http://dx.doi.org/10.1051/epjconf/201716201075.

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47

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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48

Elofsson, Arne. "A study on protein sequence alignment quality." Proteins: Structure, Function, and Genetics 46, no. 3 (2002): 330–39. http://dx.doi.org/10.1002/prot.10043.

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Sherman, Westley Arthur, Durga Bhavani Kuchibhatla, Vachiranee Limviphuvadh, Sebastian Maurer-Stroh, Birgit Eisenhaber, and Frank Eisenhaber. "HPMV: Human protein mutation viewer — relating sequence mutations to protein sequence architecture and function changes." Journal of Bioinformatics and Computational Biology 13, no. 05 (2015): 1550028. http://dx.doi.org/10.1142/s0219720015500286.

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Abstract:

Next-generation sequencing advances are rapidly expanding the number of human mutations to be analyzed for causative roles in genetic disorders. Our Human Protein Mutation Viewer (HPMV) is intended to explore the biomolecular mechanistic significance of non-synonymous human mutations in protein-coding genomic regions. The tool helps to assess whether protein mutations affect the occurrence of sequence-architectural features (globular domains, targeting signals, post-translational modification sites, etc.). As input, HPMV accepts protein mutations — as UniProt accessions with mutations (e.g. HG

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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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Abstract:

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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