scholarly journals Viewing multiple sequence alignments with the JavaScript Sequence Alignment Viewer (JSAV)

F1000Research ◽  
2014 ◽  
Vol 3 ◽  
pp. 249 ◽  
Author(s):  
Andrew C. R. Martin
Keyword(s):  
Amino Acids ◽  
Sequence Alignment ◽  
Web Site ◽  
Web Pages ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments

The JavaScript Sequence Alignment Viewer (JSAV) is designed as a simple-to-use JavaScript component for displaying sequence alignments on web pages. The display of sequences is highly configurable with options to allow alternative coloring schemes, sorting of sequences and ’dotifying’ repeated amino acids. An option is also available to submit selected sequences to another web site, or to other JavaScript code. JSAV is implemented purely in JavaScript making use of the JQuery and JQuery-UI libraries. It does not use any HTML5-specific options to help with browser compatibility. The code is documented using JSDOC and is available from http://www.bioinf.org.uk/software/jsav/.

scholarly journals A minimum reporting standard for multiple sequence alignments

2020 ◽  
Author(s):  
Thomas KF Wong ◽  
Subha Kalyaanamoorthy ◽  
Karen Meusemann ◽  
David K Yeates ◽  
Bernhard Misof ◽  
...  
Keyword(s):  
Amino Acids ◽  
Sequence Data ◽  
Pivotal Role ◽  
Sequence Alignments ◽  
Reporting Standard ◽  
Multiple Sequence ◽  
Molecular Sequence Data ◽  
Molecular Sequence ◽  
Multiple Sequence Alignments

ABSTRACTMultiple sequence alignments (MSAs) play a pivotal role in studies of molecular sequence data, but nobody has developed a minimum reporting standard (MRS) to quantify the completeness of MSAs in terms of completely-specified nucleotides or amino acids. We present an MRS that relies on four simple completeness metrics. The metrics are implemented in AliStat, a program developed to support the MRS. A survey of published MSAs illustrates the benefits and unprecedented transparency offered by the MRS.

scholarly journals MSAC: Compression of multiple sequence alignment files

2017 ◽  
Author(s):  
Sebastian Deorowicz ◽  
Joanna Walczyszyn ◽  
Agnieszka Debudaj-Grabysz
Keyword(s):  
Sequence Alignment ◽  
Compression Ratio ◽  
Multiple Sequence Alignment ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments ◽  
Link Type ◽  
Bioinformatics Databases ◽  
Supplementary Material ◽  
Burrows Wheeler Transform

AbstractMotivationBioinformatics databases grow rapidly and achieve values hardly to imagine a decade ago. Among numerous bioinformatics processes generating hundreds of GB is multiple sequence alignments of protein families. Its largest database, i.e., Pfam, consumes 40–230 GB, depending of the variant. Storage and transfer of such massive data has become a challenge.ResultsWe propose a novel compression algorithm, MSAC (Multiple Sequence Alignment Compressor), designed especially for aligned data. It is based on a generalisation of the positional Burrows–Wheeler transform for non-binary alphabets. MSAC handles FASTA, as well as Stockholm files. It offers up to six times better compression ratio than other commonly used compressors, i.e., gzip. Performed experiments resulted in an analysis of the influence of a protein family size on the compression ratio.AvailabilityMSAC is available for free at https://github.com/refresh-bio/msac and http://sun.aei.polsl.pl/REFRESH/[email protected] materialSupplementary data are available at the publisher Web site.

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 A minimum reporting standard for multiple sequence alignments

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Thomas K F Wong ◽  
Subha Kalyaanamoorthy ◽  
Karen Meusemann ◽  
David K Yeates ◽  
Bernhard Misof ◽  
...  
Keyword(s):  
Amino Acids ◽  
Sequence Data ◽  
Pivotal Role ◽  
Sequence Alignments ◽  
Reporting Standard ◽  
Multiple Sequence ◽  
Molecular Sequence Data ◽  
Molecular Sequence ◽  
Multiple Sequence Alignments

Abstract Multiple sequence alignments (MSAs) play a pivotal role in studies of molecular sequence data, but nobody has developed a minimum reporting standard (MRS) to quantify the completeness of MSAs in terms of completely specified nucleotides or amino acids. We present an MRS that relies on four simple completeness metrics. The metrics are implemented in AliStat, a program developed to support the MRS. A survey of published MSAs illustrates the benefits and unprecedented transparency offered by the MRS.

scholarly journals Molecular Characterization of a Novel Fusarivirus Infecting the Plant-pathogenic Fungus Alternaria Solani

2021 ◽  
Author(s):  
Jie Wei Gong ◽  
Hong Liu ◽  
Fei Xiao Zhu ◽  
Yun Shi Zhao ◽  
Le Jia Cheng ◽  
...  
Keyword(s):  
Amino Acids ◽  
Pathogenic Fungus ◽  
Alternaria Solani ◽  
Open Reading Frames ◽  
Phytopathogenic Fungus ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments ◽  
Putative Protein

Abstract A novel mycovirus belonging to the proposed family "Fusariviridae" was discovered in Alternaria Solani by sequencing a double-stranded RNA extracted from this phytopathogenic fungus. The virus was tentatively named “Alternaria solani fusarivirus 1” (AsFV1). AsFV1 has a single-stranded positive-sense (+ssRNA) genome of 6,845 nucleotides containing three open reading frames (ORFs) and a poly(A) tail. The largest ORF, ORF1 encodes a large polypeptide of 1,556 amino acids (aa) with conserved RNA-dependent RNA polymerase and helicase domains. The ORF2 and ORF3 have overlapping regions, encoding a putative protein of 522 amino acids (aa) and a putative protein of 105 amino acids (aa) respectively, for which function is unknown now. Multiple sequence alignments and phylogenetic analysis revealed AsFV1 belonging to Fusariviridae. This is the first report of the full-length nucleotide sequence of a fusarivirus infected with Alternaria solani.

scholarly journals ClipKIT: a multiple sequence alignment-trimming algorithm for accurate phylogenomic inference

2020 ◽  
Author(s):  
Jacob L. Steenwyk ◽  
Thomas J. Buida ◽  
Yuanning Li ◽  
Xing-Xing Shen ◽  
Antonis Rokas
Keyword(s):  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Phylogenetic Inference ◽  
Recent Analysis ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Time Saving ◽  
Multiple Sequence Alignments

AbstractHighly divergent sites in multiple sequence alignments, which stem from erroneous inference of homology and saturation of substitutions, are thought to negatively impact phylogenetic inference. Trimming methods aim to remove these sites before phylogenetic inference, but recent analysis suggests that doing so can worsen inference. We introduce ClipKIT, a trimming method that instead aims to retain phylogenetically-informative sites; phylogenetic inference using ClipKIT-trimmed alignments is accurate, robust, and time-saving.

scholarly journals Numerical Encodings of Amino Acids in Multivariate Gaussian Modeling of Protein Multiple Sequence Alignments

Molecules ◽  
2018 ◽  
Vol 24 (1) ◽  
pp. 104
Author(s):  
Patrice Koehl ◽  
Henri Orland ◽  
Marc Delarue
Keyword(s):  
Amino Acids ◽  
Amino Acid ◽  
Principal Components ◽  
Gaussian Model ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments ◽  
Substitution Matrices ◽  
Multivariate Gaussian ◽  
Multivariate Gaussian Model

Residues in proteins that are in close spatial proximity are more prone to covariate as their interactions are likely to be preserved due to structural and evolutionary constraints. If we can detect and quantify such covariation, physical contacts may then be predicted in the structure of a protein solely from the sequences that decorate it. To carry out such predictions, and following the work of others, we have implemented a multivariate Gaussian model to analyze correlation in multiple sequence alignments. We have explored and tested several numerical encodings of amino acids within this model. We have shown that 1D encodings based on amino acid biochemical and biophysical properties, as well as higher dimensional encodings computed from the principal components of experimentally derived mutation/substitution matrices, do not perform as well as a simple twenty dimensional encoding with each amino acid represented with a vector of one along its own dimension and zero elsewhere. The optimum obtained from representations based on substitution matrices is reached by using 10 to 12 principal components; the corresponding performance is less than the performance obtained with the 20-dimensional binary encoding. We highlight also the importance of the prior when constructing the multivariate Gaussian model of a multiple sequence alignment.

scholarly journals MSABrowser: dynamic and fast visualization of sequence alignments, variations, and annotations

2021 ◽  
Author(s):  
Furkan M. Torun ◽  
Halil I. Bilgin ◽  
Oktay I. Kaplan
Keyword(s):  
Sequence Alignment ◽  
Scientific Community ◽  
Protein Sequences ◽  
Genetic Variations ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Web Browser ◽  
Post Translational Modifications ◽  
Multiple Sequence Alignments ◽  
Similarities And Differences

Sequence alignment is an excellent way to visualize the similarities and differences between DNA, RNA, or protein sequences, yet it is currently difficult to jointly view sequence alignment data with genetic variations, modifications such as post-translational modifications, and annotations (i.e. protein domains). Here, we develop the MSABrowser tool that makes it easy to co-visualize genetic variations, modifications, and annotations on the respective positions of amino acids or nucleotides in pairwise or multiple sequence alignments. MSABrowser is developed entirely in JavaScript and works on any modern web browser at any platform including Linux, Mac OS X, and Windows systems without any installation. MSABrowser is also freely available for the benefit of the scientific community.

scholarly journals fastMSA: Accelerating Multiple Sequence Alignment with Dense Retrieval on Protein Language

2021 ◽  
Author(s):  
Liang Hong ◽  
Siqi Sun ◽  
Liangzhen Zheng ◽  
Qingxiong Tan ◽  
Yu Li
Keyword(s):  
Protein Structure ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Structure Prediction ◽  
Structure And Function ◽  
Sequence Alignments ◽  
Protein Structure And Function ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments ◽  
And Function

Evolutionarily related sequences provide information for the protein structure and function. Multiple sequence alignment, which includes homolog searching from large databases and sequence alignment, is efficient to dig out the information and assist protein structure and function prediction, whose efficiency has been proved by AlphaFold. Despite the existing tools for multiple sequence alignment, searching homologs from the entire UniProt is still time-consuming. Considering the success of AlphaFold, foreseeably, large- scale multiple sequence alignments against massive databases will be a trend in the field. It is very desirable to accelerate this step. Here, we propose a novel method, fastMSA, to improve the speed significantly. Our idea is orthogonal to all the previous accelerating methods. Taking advantage of the protein language model based on BERT, we propose a novel dual encoder architecture that can embed the protein sequences into a low-dimension space and filter the unrelated sequences efficiently before running BLAST. Extensive experimental results suggest that we can recall most of the homologs with a 34-fold speed-up. Moreover, our method is compatible with the downstream tasks, such as structure prediction using AlphaFold. Using multiple sequence alignments generated from our method, we have little performance compromise on the protein structure prediction with much less running time. fastMSA will effectively assist protein sequence, structure, and function analysis based on homologs and multiple sequence alignment.

scholarly journals Some remarks on evaluating the quality of the multiple sequence alignment based on the BAliBASE benchmark

2009 ◽  
Vol 19 (4) ◽  
pp. 675-678 ◽  
Author(s):  
Jacek Błażewicz ◽  
Piotr Formanowicz ◽  
Paweł Wojciechowski
Keyword(s):  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments ◽  
Formal Definitions ◽  
Accuracy Measures ◽  
Total Column ◽  
Better Than

Some remarks on evaluating the quality of the multiple sequence alignment based on the BAliBASE benchmarkBAliBASE is one of the most widely used benchmarks for multiple sequence alignment programs. The accuracy of alignment methods is measured bybali_score—an application provided together with the database. The standard accuracy measures are the Sum of Pairs (SP) and the Total Column (TC). We have found that, for non-core block columns, results calculated bybali_scoreare different from those obtained on the basis of the formal definitions of the measures. We do not claim that one of these measures is better than the other, but they are definitely different. Such a situation can be the source of confusion when alignments obtained using various methods are compared. Therefore, we propose a new nomenclature for the measures of the quality of multiple sequence alignments to distinguish which one was actually calculated. Moreover, we have found that the occurrence of a gap in some column in the first sequence of the reference alignment causes column discarding.

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