scholarly journals Logomaker: beautiful sequence logos in Python

2019 ◽  
Vol 36 (7) ◽  
pp. 2272-2274 ◽  
Author(s):  
Ammar Tareen ◽  
Justin B Kinney
Keyword(s):  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Source Code ◽  
Protein Sequences ◽  
Biological Properties ◽  
Programming Environment ◽  
Multiple Sequence ◽  
Sequence Logos ◽  
Python Programming ◽  
Publication Quality

Abstract Summary Sequence logos are visually compelling ways of illustrating the biological properties of DNA, RNA and protein sequences, yet it is currently difficult to generate and customize such logos within the Python programming environment. Here we introduce Logomaker, a Python API for creating publication-quality sequence logos. Logomaker can produce both standard and highly customized logos from either a matrix-like array of numbers or a multiple-sequence alignment. Logos are rendered as native matplotlib objects that are easy to stylize and incorporate into multi-panel figures. Availability and implementation Logomaker can be installed using the pip package manager and is compatible with both Python 2.7 and Python 3.6. Documentation is provided at http://logomaker.readthedocs.io; source code is available at http://github.com/jbkinney/logomaker.

scholarly journals Logomaker: Beautiful sequence logos in python

2019 ◽  
Author(s):  
Ammar Tareen ◽  
Justin B. Kinney
Keyword(s):  
Source Code ◽  
Biological Properties ◽  
Programming Environment ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments ◽  
Link Type ◽  
Sequence Logos ◽  
Python Programming ◽  
Publication Quality

AbstractSequence logos are visually compelling ways of illustrating the biological properties of DNA, RNA, and protein sequences, yet it is currently difficult to generate such logos within the Python programming environment. Here we introduce Logomaker, a Python API for creating publication-quality sequence logos. Logomaker can produce both standard and highly customized logos from any matrix-like array of numbers. Logos are rendered as vector graphics that are easy to stylize using standard matplotlib functions. Methods for creating logos from multiple-sequence alignments are also included.Availability and ImplementationLogomaker can be installed using the pip package manager and is compatible with both Python 2.7 and Python 3.6. Source code is available athttp://github.com/jbkinney/logomaker.Supplemental InformationDocumentation is provided athttp://[email protected].

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.

scholarly journals Benchmarking Statistical Multiple Sequence Alignment

2018 ◽  
Author(s):  
Michael Nute ◽  
Ehsan Saleh ◽  
Tandy Warnow
Keyword(s):  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Structural Alignment ◽  
Estimation Method ◽  
Simulated Data ◽  
Protein Sequences ◽  
Data Sets ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Simulated Data Sets

AbstractThe estimation of multiple sequence alignments of protein sequences is a basic step in many bioinformatics pipelines, including protein structure prediction, protein family identification, and phylogeny estimation. Statistical co-estimation of alignments and trees under stochastic models of sequence evolution has long been considered the most rigorous technique for estimating alignments and trees, but little is known about the accuracy of such methods on biological benchmarks. We report the results of an extensive study evaluating the most popular protein alignment methods as well as the statistical co-estimation method BAli-Phy on 1192 protein data sets from established benchmarks as well as on 120 simulated data sets. Our study (which used more than 230 CPU years for the BAli-Phy analyses alone) shows that BAli-Phy is dramatically more accurate than the other alignment methods on the simulated data sets, but is among the least accurate on the biological benchmarks. There are several potential causes for this discordance, including model misspecification, errors in the reference alignments, and conflicts between structural alignment and evolutionary alignments; future research is needed to understand the most likely explanation for our observations. multiple sequence alignment, BAli-Phy, protein sequences, structural alignment, homology

scholarly journals A benchmark for evaluation of phylogeny reconstruction programs

2016 ◽  
Author(s):  
Sergei Spirin
Keyword(s):  
Maximum Likelihood ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Protein Sequences ◽  
Relative Accuracy ◽  
Phylogeny Reconstruction ◽  
Multiple Sequence ◽  
Natural Protein ◽  
Large Sets ◽  
The Moment

There are a lot of algorithms and programs for reconstruction of phylogeny of a set of proteins basing on multiple sequence alignment. Many programs allow users to choose a number of parameters, for example, a model for maximum likelihood programs. Different programs and different parameters often produce different results. However at the moment all published benchmarks for evaluation of relative accuracy of programs or different choices of parameters are based on simulated sequences. The aim of the present work is to create a benchmark that allows a comparison of phylogenetic programs on large sets of alignments of natural protein sequences.

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.

Sequoya: multiobjective multiple sequence alignment in Python

2020 ◽  
Vol 36 (12) ◽  
pp. 3892-3893
Author(s):  
Antonio Benítez-Hidalgo ◽  
Antonio J Nebro ◽  
José F Aldana-Montes
Keyword(s):  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Software Tool ◽  
Computing System ◽  
Supplementary Information ◽  
Optimization Approach ◽  
Multiple Sequence ◽  
Graphical Tool ◽  
Optimal Alignments ◽  
Python Programming

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 alignment, such as maximizing the percentage of totally conserved columns and minimizing the number of gaps. Our motivation is to provide a Python tool for solving MSA problems using evolutionary algorithms, a nonexact stochastic optimization approach that has proven to be effective to solve multiobjective problems. Results The software tool we have developed, called Sequoya, is written in the Python programming language, which offers a broad set of libraries for data analysis, visualization and parallelism. Thus, Sequoya offers a graphical tool to visualize the progress of the optimization in real time, the ability to guide the search toward a preferred region in run-time, parallel support to distribute the computation among nodes in a distributed computing system, and a graphical component to assist in the analysis of the solutions found at the end of the optimization. Availability and implementation Sequoya can be freely obtained from the Python Package Index (pip) or, alternatively, it can be downloaded from Github at https://github.com/benhid/Sequoya. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals In Silico Characterization of Histidine Acid Phytase Sequences

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Vinod Kumar ◽  
Gopal Singh ◽  
A. K. Verma ◽  
Sanjeev Agrawal
Keyword(s):  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
In Silico ◽  
Animal Feed ◽  
Protein Sequences ◽  
Homology Search ◽  
Protein Database ◽  
Multiple Sequence ◽  
Conserved Sequence ◽  
Signature Sequence

Histidine acid phytases (HAPhy) are widely distributed enzymes among bacteria, fungi, plants, and some animal tissues. They have a significant role as an animal feed enzyme and in the solubilization of insoluble phosphates and minerals present in the form of phytic acid complex. A set of 50 reference protein sequences representing HAPhy were retrieved from NCBI protein database and characterized for various biochemical properties, multiple sequence alignment (MSA), homology search, phylogenetic analysis, motifs, and superfamily search. MSA using MEGA5 revealed the presence of conserved sequences at N-terminal “RHGXRXP” and C-terminal “HD.” Phylogenetic tree analysis indicates the presence of three clusters representing different HAPhy, that is, PhyA, PhyB, and AppA. Analysis of 10 commonly distributed motifs in the sequences indicates the presence of signature sequence for each class. Motif 1 “SPFCDLFTHEEWIQYDYLQSLGKYYGYGAGNPLGPAQGIGF” was present in 38 protein sequences representing clusters 1 (PhyA) and 2 (PhyB). Cluster 3 (AppA) contains motif 9 “KKGCPQSGQVAIIADVDERTRKTGEAFAAGLAPDCAITVHTQADTSSPDP” as a signature sequence. All sequences belong to histidine acid phosphatase family as resulted from superfamily search. No conserved sequence representing 3- or 6-phytase could be identified using multiple sequence alignment. This in silico analysis might contribute in the classification and future genetic engineering of this most diverse class of phytase.

scholarly journals A benchmark for evaluation of phylogeny reconstruction programs

2016 ◽  
Author(s):  
Sergei Spirin
Keyword(s):  
Maximum Likelihood ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Protein Sequences ◽  
Relative Accuracy ◽  
Phylogeny Reconstruction ◽  
Multiple Sequence ◽  
Natural Protein ◽  
Large Sets ◽  
The Moment

There are a lot of algorithms and programs for reconstruction of phylogeny of a set of proteins basing on multiple sequence alignment. Many programs allow users to choose a number of parameters, for example, a model for maximum likelihood programs. Different programs and different parameters often produce different results. However at the moment all published benchmarks for evaluation of relative accuracy of programs or different choices of parameters are based on simulated sequences. The aim of the present work is to create a benchmark that allows a comparison of phylogenetic programs on large sets of alignments of natural protein sequences.

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