Multiple Sequence Alignment and Phylogenetic Trees

2004 ◽  
pp. 770-774
Author(s):  
Russell F. Doolittle
Keyword(s):  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Phylogenetic Trees ◽  
Multiple Sequence

scholarly journals SHOOT: phylogenetic gene search and ortholog inference

2021 ◽  
Author(s):  
David Emms ◽  
Steven Kelly
Keyword(s):  
Phylogenetic Analysis ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Phylogenetic Trees ◽  
Query Sequence ◽  
Gene Tree ◽  
Biological Research ◽  
Gene Sequences ◽  
Multiple Sequence ◽  
Gene Search

Determining the evolutionary relationships between gene sequences is fundamental to comparative biological research. However, conducting such analyses requires a high degree of technical proficiency in several computational tools including gene family construction, multiple sequence alignment, and phylogenetic inference. Here we present SHOOT, an easy to use phylogenetic search engine for fast and accurate phylogenetic analysis of biological sequences. SHOOT searches a user-provided query sequence against a database of phylogenetic trees of gene sequences (gene trees) and returns a gene tree with the given query sequence correctly grafted within it. We show that SHOOT can perform this search and placement with comparable speed to a conventional BLAST search. We demonstrate that SHOOT phylogenetic placements are as accurate as conventional multiple sequence alignment and maximum likelihood tree inference approaches. We further show that SHOOT can be used to identify orthologs with equivalent accuracy to conventional orthology inference methods. In summary, SHOOT is an accurate and fast tool for complete phylogenetic analysis of novel query sequences. An easy to use webserver is available online at www.shoot.bio.

Multiple Sequence Alignment Optimization Using Meta-Heuristic Techniques

2020 ◽  
pp. 565-579 ◽  
Author(s):  
Mohamed Issa ◽  
Aboul Ella Hassanien
Keyword(s):  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Phylogenetic Trees ◽  
Pairwise Alignment ◽  
Accurate Method ◽  
Alignment Algorithm ◽  
Bacterial Foraging Optimization ◽  
Multiple Sequence ◽  
Speed Up ◽  
Dna Fragment Assembly

Sequence alignment is a vital process in many biological applications such as Phylogenetic trees construction, DNA fragment assembly and structure/function prediction. Two kinds of alignment are pairwise alignment which align two sequences and Multiple Sequence alignment (MSA) that align sequences more than two. The accurate method of alignment is based on Dynamic Programming (DP) approach which suffering from increasing time exponentially with increasing the length and the number of the aligned sequences. Stochastic or meta-heuristics techniques speed up alignment algorithm but with near optimal alignment accuracy not as that of DP. Hence, This chapter aims to review the recent development of MSA using meta-heuristics algorithms. In addition, two recent techniques are focused in more deep: the first is Fragmented protein sequence alignment using two-layer particle swarm optimization (FTLPSO). The second is Multiple sequence alignment using multi-objective based bacterial foraging optimization algorithm (MO-BFO).

scholarly journals ProbPFP: a multiple sequence alignment algorithm combining hidden Markov model optimized by particle swarm optimization with partition function

2019 ◽  
Vol 20 (S18) ◽  
Author(s):  
Qing Zhan ◽  
Nan Wang ◽  
Shuilin Jin ◽  
Renjie Tan ◽  
Qinghua Jiang ◽  
...  
Keyword(s):  
Partition Function ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Phylogenetic Trees ◽  
Hidden Markov ◽  
Particle Swarm ◽  
Alignment Algorithm ◽  
Multiple Sequence

Abstract Background During procedures for conducting multiple sequence alignment, that is so essential to use the substitution score of pairwise alignment. To compute adaptive scores for alignment, researchers usually use Hidden Markov Model or probabilistic consistency methods such as partition function. Recent studies show that optimizing the parameters for hidden Markov model, as well as integrating hidden Markov model with partition function can raise the accuracy of alignment. The combination of partition function and optimized HMM, which could further improve the alignment’s accuracy, however, was ignored by these researches. Results A novel algorithm for MSA called ProbPFP is presented in this paper. It intergrate optimized HMM by particle swarm with partition function. The algorithm of PSO was applied to optimize HMM’s parameters. After that, the posterior probability obtained by the HMM was combined with the one obtained by partition function, and thus to calculate an integrated substitution score for alignment. In order to evaluate the effectiveness of ProbPFP, we compared it with 13 outstanding or classic MSA methods. The results demonstrate that the alignments obtained by ProbPFP got the maximum mean TC scores and mean SP scores on these two benchmark datasets: SABmark and OXBench, and it got the second highest mean TC scores and mean SP scores on the benchmark dataset BAliBASE. ProbPFP is also compared with 4 other outstanding methods, by reconstructing the phylogenetic trees for six protein families extracted from the database TreeFam, based on the alignments obtained by these 5 methods. The result indicates that the reference trees are closer to the phylogenetic trees reconstructed from the alignments obtained by ProbPFP than the other methods. Conclusions We propose a new multiple sequence alignment method combining optimized HMM and partition function in this paper. The performance validates this method could make a great improvement of the alignment’s accuracy.

Multiple Sequence Alignment Optimization Using Meta-Heuristic Techniques

2017 ◽  
pp. 409-423 ◽  
Author(s):  
Mohamed Issa ◽  
Aboul Ella Hassanien
Keyword(s):  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Phylogenetic Trees ◽  
Pairwise Alignment ◽  
Accurate Method ◽  
Alignment Algorithm ◽  
Bacterial Foraging Optimization ◽  
Multiple Sequence ◽  
Speed Up ◽  
Dna Fragment Assembly

Sequence alignment is a vital process in many biological applications such as Phylogenetic trees construction, DNA fragment assembly and structure/function prediction. Two kinds of alignment are pairwise alignment which align two sequences and Multiple Sequence alignment (MSA) that align sequences more than two. The accurate method of alignment is based on Dynamic Programming (DP) approach which suffering from increasing time exponentially with increasing the length and the number of the aligned sequences. Stochastic or meta-heuristics techniques speed up alignment algorithm but with near optimal alignment accuracy not as that of DP. Hence, This chapter aims to review the recent development of MSA using meta-heuristics algorithms. In addition, two recent techniques are focused in more deep: the first is Fragmented protein sequence alignment using two-layer particle swarm optimization (FTLPSO). The second is Multiple sequence alignment using multi-objective based bacterial foraging optimization algorithm (MO-BFO).

Computational Analysis of Therapeutic Enzyme Uricase from Different Source Organisms

2020 ◽  
Vol 17 (1) ◽  
pp. 59-77
Author(s):  
Anand Kumar Nelapati ◽  
JagadeeshBabu PonnanEttiyappan
Keyword(s):  
Uric Acid ◽  
Amino Acid ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Protein Sequences ◽  
Amino Acid Sequences ◽  
Amino Acid Residues ◽  
Multiple Sequence ◽  
Physiochemical Properties ◽  
Pharmaceutical Industries

Background:Hyperuricemia and gout are the conditions, which is a response of accumulation of uric acid in the blood and urine. Uric acid is the product of purine metabolic pathway in humans. Uricase is a therapeutic enzyme that can enzymatically reduces the concentration of uric acid in serum and urine into more a soluble allantoin. Uricases are widely available in several sources like bacteria, fungi, yeast, plants and animals.Objective:The present study is aimed at elucidating the structure and physiochemical properties of uricase by insilico analysis.Methods:A total number of sixty amino acid sequences of uricase belongs to different sources were obtained from NCBI and different analysis like Multiple Sequence Alignment (MSA), homology search, phylogenetic relation, motif search, domain architecture and physiochemical properties including pI, EC, Ai, Ii, and were performed.Results:Multiple sequence alignment of all the selected protein sequences has exhibited distinct difference between bacterial, fungal, plant and animal sources based on the position-specific existence of conserved amino acid residues. The maximum homology of all the selected protein sequences is between 51-388. In singular category, homology is between 16-337 for bacterial uricase, 14-339 for fungal uricase, 12-317 for plants uricase, and 37-361 for animals uricase. The phylogenetic tree constructed based on the amino acid sequences disclosed clusters indicating that uricase is from different source. The physiochemical features revealed that the uricase amino acid residues are in between 300- 338 with a molecular weight as 33-39kDa and theoretical pI ranging from 4.95-8.88. The amino acid composition results showed that valine amino acid has a high average frequency of 8.79 percentage compared to different amino acids in all analyzed species.Conclusion:In the area of bioinformatics field, this work might be informative and a stepping-stone to other researchers to get an idea about the physicochemical features, evolutionary history and structural motifs of uricase that can be widely used in biotechnological and pharmaceutical industries. Therefore, the proposed in silico analysis can be considered for protein engineering work, as well as for gout therapy.

LegumeDB: Development of Legume Medicinal Plant Database and Comparative Molecular Evolutionary Analysis of matK Proteins of Legumes and Mangroves

2019 ◽  
Vol 15 (4) ◽  
pp. 353-362
Author(s):  
Sambhaji B. Thakar ◽  
Maruti J. Dhanavade ◽  
Kailas D. Sonawane
Keyword(s):  
Phylogenetic Analysis ◽  
Medicinal Plants ◽  
Homology Modeling ◽  
Sequence Alignment ◽  
Vigna Unguiculata ◽  
Multiple Sequence Alignment ◽  
Legume Species ◽  
Mangrove Species ◽  
Multiple Sequence ◽  
Thespesia Populnea

Background: Legume plants are known for their rich medicinal and nutritional values. Large amount of medicinal information of various legume plants have been dispersed in the form of text. Objective: It is essential to design and construct a legume medicinal plants database, which integrate respective classes of legumes and include knowledge regarding medicinal applications along with their protein/enzyme sequences. Methods: The design and development of Legume Medicinal Plants Database (LegumeDB) has been done by using Microsoft Structure Query Language Server 2017. DBMS was used as back end and ASP.Net was used to lay out front end operations. VB.Net was used as arranged program for coding. Multiple sequence alignment, phylogenetic analysis and homology modeling techniques were also used. Results: This database includes information of 50 Legume medicinal species, which might be helpful to explore the information for researchers. Further, maturase K (matK) protein sequences of legumes and mangroves were retrieved from NCBI for multiple sequence alignment and phylogenetic analysis to understand evolutionary lineage between legumes and mangroves. Homology modeling technique was used to determine three-dimensional structure of matK from Legume species i.e. Vigna unguiculata using matK of mangrove species, Thespesia populnea as a template. The matK sequence analysis results indicate the conserved residues among legume and mangrove species. Conclusion: Phylogenetic analysis revealed closeness between legume species Vigna unguiculata and mangrove species Thespesia populnea to each other, indicating their similarity and origin from common ancestor. Thus, these studies might be helpful to understand evolutionary relationship between legumes and mangroves. : LegumeDB availability: http://legumedatabase.co.in

A Hierarchical Classification for the Selection of the Most Suitable Multiple Sequence Alignment Methodology

2015 ◽  
Vol 10 (2) ◽  
pp. 199-207
Author(s):  
Francisco Ortuño ◽  
Hector Pomares ◽  
Olga Valenzuela ◽  
Carolina Torres ◽  
Ignacio Rojas
Keyword(s):  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Hierarchical Classification ◽  
Multiple Sequence ◽  
Selection Of
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