Seaview Version 5: A Multiplatform Software for Multiple Sequence Alignment, Molecular Phylogenetic Analyses, and Tree Reconciliation

2020 ◽  
pp. 241-260
Author(s):  
Manolo Gouy ◽  
Eric Tannier ◽  
Nicolas Comte ◽  
David P. Parsons
Keyword(s):  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Phylogenetic Analyses ◽  
Multiple Sequence ◽  
Tree Reconciliation ◽  
Molecular Phylogenetic

scholarly journals SequenceBouncer: A method to remove outlier entries from a multiple sequence alignment

2020 ◽  
Author(s):  
Cory D. Dunn
Keyword(s):  
Nucleic Acid ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Phylogenetic Analyses ◽  
Protein Sequences ◽  
Mitochondrial Genomes ◽  
Dna Barcodes ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments

AbstractPhylogenetic analyses can take advantage of multiple sequence alignments as input. These alignments typically consist of homologous nucleic acid or protein sequences, and the inclusion of outlier or aberrant sequences can compromise downstream analyses. Here, I describe a program, SequenceBouncer, that uses the Shannon entropy values of alignment columns to identify outlier alignment sequences in a manner responsive to overall alignment context. I demonstrate the utility of this software using alignments of available mammalian mitochondrial genomes, bird cytochrome c oxidase-derived DNA barcodes, and COVID-19 sequences.

scholarly journals Gotree/Goalign : Toolkit and Go API to facilitate the development of phylogenetic workflows

2021 ◽  
Author(s):  
Frederic Lemoine ◽  
Olivier Gascuel
Keyword(s):  
Phylogenetic Analysis ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Phylogenetic Analyses ◽  
Bootstrap Support ◽  
Complex Task ◽  
Important Data ◽  
Multiple Sequence ◽  
Tree Comparison ◽  
User Friendly

Besides computer intensive steps, phylogenetic analysis workflows are usually composed of many small, reccuring, but important data manipulations steps. Among these, we can find file reformatting, sequence renaming, tree re-rooting, tree comparison, bootstrap support computation, etc. These are often performed by custom scripts or by several heterogeneous tools, which may be error prone, uneasy to maintain and produce results that are challenging to reproduce. For all these reasons, the development and reuse of phylogenetic workflows is often a complex task. We identified many operations that are part of most phylogenetic analyses, and implemented them in a toolkit called Gotree/Goalign. The Gotree/Goalign toolkit implements more than 120 user-friendly commands and an API dedicated to multiple sequence alignment and phylogenetic tree manipulations. It is developed in Go, which makes executables efficient, easily installable, integrable in workflow environments, and parallelizable when possible. This toolkit is freely available on most platforms (Linux, MacOS and Windows) and most architectures (amd64, i386). Sources and binaries are available on GitHub at https://github.com/evolbioinfo/{gotree|goalign} , Bioconda, and DockerHub.

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

Amino Acids Sequence-based Analysis of Arginine Deiminase from Different Prokaryotic Organisms: An In Silico Approach

2020 ◽  
Vol 14 (3) ◽  
pp. 235-246
Author(s):  
Sara Abdollahi ◽  
Mohammad H. Morowvat ◽  
Amir Savardashtaki ◽  
Cambyz Irajie ◽  
Sohrab Najafipour ◽  
...  
Keyword(s):  
Amino Acid ◽  
Physicochemical Properties ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
In Silico ◽  
Bacterial Species ◽  
Arginine Deiminase ◽  
Antitumor Effects ◽  
Multiple Sequence ◽  
In Silico Studies

Background: Arginine deiminase is a bacterial enzyme, which degrades L-arginine. Some human cancers such as hepatocellular carcinoma (HCC) and melanoma are auxotrophic for arginine. Therefore, PEGylated arginine deiminase (ADI-PEG20) is a good anticancer candidate with antitumor effects. It causes local depletion of L-arginine and growth inhibition in arginineauxotrophic tumor cells. The FDA and EMA have granted orphan status to this drug. Some recently published patents have dealt with this enzyme or its PEGylated form. Objective: Due to increasing attention to it, we aimed to evaluate and compare 30 arginine deiminase proteins from different bacterial species through in silico analysis. Methods: The exploited analyses included the investigation of physicochemical properties, multiple sequence alignment (MSA), motif, superfamily, phylogenetic and 3D comparative analyses of arginine deiminase proteins thorough various bioinformatics tools. Results: The most abundant amino acid in the arginine deiminase proteins is leucine (10.13%) while the least amino acid ratio is cysteine (0.98%). Multiple sequence alignment showed 47 conserved patterns between 30 arginine deiminase amino acid sequences. The results of sequence homology among 30 different groups of arginine deiminase enzymes revealed that all the studied sequences located in amidinotransferase superfamily. Based on the phylogenetic analysis, two major clusters were identified. Considering the results of various in silico studies; we selected the five best candidates for further investigations. The 3D structures of the best five arginine deiminase proteins were generated by the I-TASSER server and PyMOL. The RAMPAGE analysis revealed that 81.4%-91.4%, of the selected sequences, were located in the favored region of arginine deiminase proteins. Conclusion: The results of this study shed light on the basic physicochemical properties of thirty major arginine deiminase sequences. The obtained data could be employed for further in vivo and clinical studies and also for developing the related therapeutic enzymes.

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