Exact Multiple Sequence Alignment by Synchronized Decision Diagrams

2020 ◽  
Author(s):  
Amin Hosseininasab ◽  
Willem-Jan van Hoeve
Keyword(s):  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
State Of The Art ◽  
Mixed Integer ◽  
Mixed Integer Program ◽  
Second Phase ◽  
Polynomial Space ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
First Time

This paper develops an exact solution algorithm for the multiple sequence alignment (MSA) problem. In the first step, we design a dynamic programming model and use it to construct a novel multivalued decision diagram (MDD) representation of all pairwise sequence alignments (PSA). PSA MDDs are then synchronized using side constraints to model the MSA problem as a mixed-integer program (MIP), for the first time, in polynomial space complexity. Two bound-based filtering procedures are developed to reduce the size of the MDDs, and the resulting MIP is solved using logic-based Benders decomposition. For a more effective algorithm, we develop a two-phase solution approach. In the first phase, we use optimistic filtering to quickly obtain a near-optimal bound, which we then use for exact filtering in the second phase to prove or obtain an optimal solution. Numerical results on benchmark instances show that our algorithm solves several instances to optimality for the first time, and, in case optimality cannot be proven, considerably improves upon a state-of-the-art heuristic MSA solver. Comparison with an existing state-of-the-art exact MSA algorithm shows that our approach is more time efficient and yields significantly smaller optimality gaps.

scholarly journals Molecular homology and multiple-sequence alignment: an analysis of concepts and practice

2015 ◽  
Vol 28 (1) ◽  
pp. 46 ◽  
Author(s):  
David A. Morrison ◽  
Matthew J. Morgan ◽  
Scot A. Kelchner
Keyword(s):  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Molecular Data ◽  
Simple Relationship ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Molecular Change ◽  
Nucleotide Homology ◽  
Tree Building ◽  
Molecular Homology

Sequence alignment is just as much a part of phylogenetics as is tree building, although it is often viewed solely as a necessary tool to construct trees. However, alignment for the purpose of phylogenetic inference is primarily about homology, as it is the procedure that expresses homology relationships among the characters, rather than the historical relationships of the taxa. Molecular homology is rather vaguely defined and understood, despite its importance in the molecular age. Indeed, homology has rarely been evaluated with respect to nucleotide sequence alignments, in spite of the fact that nucleotides are the only data that directly represent genotype. All other molecular data represent phenotype, just as do morphology and anatomy. Thus, efforts to improve sequence alignment for phylogenetic purposes should involve a more refined use of the homology concept at a molecular level. To this end, we present examples of molecular-data levels at which homology might be considered, and arrange them in a hierarchy. The concept that we propose has many levels, which link directly to the developmental and morphological components of homology. Of note, there is no simple relationship between gene homology and nucleotide homology. We also propose terminology with which to better describe and discuss molecular homology at these levels. Our over-arching conceptual framework is then used to shed light on the multitude of automated procedures that have been created for multiple-sequence alignment. Sequence alignment needs to be based on aligning homologous nucleotides, without necessary reference to homology at any other level of the hierarchy. In particular, inference of nucleotide homology involves deriving a plausible scenario for molecular change among the set of sequences. Our clarifications should allow the development of a procedure that specifically addresses homology, which is required when performing alignment for phylogenetic purposes, but which does not yet exist.

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 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.

ProgSIO-MSA: Progressive-based single iterative optimization framework for multiple sequence alignment using an effective scoring system

2020 ◽  
Vol 18 (02) ◽  
pp. 2050005
Author(s):  
Sanjay Bankapur ◽  
Nagamma Patil
Keyword(s):  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Scoring System ◽  
State Of The Art ◽  
Biological Sequences ◽  
Alignment Quality ◽  
Multiple Sequence ◽  
Iterative Optimization ◽  
Optimization Framework ◽  
Proposed Model

Aligning more than two biological sequences is termed multiple sequence alignment (MSA). To analyze biological sequences, MSA is one of the primary activities with potential applications in phylogenetics, homology markers, protein structure prediction, gene regulation, and drug discovery. MSA problem is considered as NP-complete. Moreover, with the advancement of Next-Generation Sequencing techniques, all the gene and protein databases are consistently loaded with a vast amount of raw sequence data which are neither analyzed nor annotated. To analyze these growing volumes of raw sequences, the need of computationally-efficient (polynomial time) models with accurate alignment is high. In this study, a progressive-based alignment model is proposed, named ProgSIO-MSA, which consists of an effective scoring system and an optimization framework. The proposed scoring system aligns sequences effectively using the combination of two scoring strategies, i.e. Look Back Ahead, that scores a residue pair dynamically based on the status information of the previous position to improve the sum-of-pair score, and Position-Residue-Specific Dynamic Gap Penalty, that dynamically penalizes a gap using mutation matrix on the basis of residue and its position information. The proposed single iterative optimization (SIO) framework identifies and optimizes the local optima trap to improve the alignment quality. The proposed model is evaluated against progressive-based state-of-the-art models on two benchmark datasets, i.e. BAliBASE and SABmark. The alignment quality (biological accuracy) of the proposed model is increased by a factor of 17.7% on BAliBASE dataset. The proposed model’s efficiency is compared with state-of-the-art models using time complexity as well as runtime analysis. Wilcoxon signed-rank statistical test results concluded that the quality of the proposed model significantly outperformed progressive-based state-of-the-art models.

scholarly journals An Evaluation of Phylogenetic Workflows in Viral Molecular Epidemiology

2020 ◽  
Author(s):  
Colin Young ◽  
Sarah Meng ◽  
Niema Moshiri
Keyword(s):  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Sequence Data ◽  
Phylogenetic Inference ◽  
The Other ◽  
Computational Techniques ◽  
Viral Sequence ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Branch Lengths

AbstractThe use of computational techniques to analyze viral sequence data and ultimately inform public health intervention has become increasingly common in the realm of epidemiology. These methods typically attempt to make epidemiological inferences based on multiple sequence alignments and phylogenies estimated from the raw sequence data. Like all estimation techniques, multiple sequence alignment and phylogenetic inference tools are error-prone, and the impacts of such imperfections on downstream epidemiological inferences are poorly understood. To address this, we executed multiple commonly-used workflows for conducting viral phylogenetic analyses on simulated viral sequence data modeling HIV, HCV, and Ebola, and we computed multiple methods of accuracy motivated by transmission clustering techniques. For multiple sequence alignment, MAFFT consistently outperformed MUSCLE and Clustal Omega in both accuracy and runtime. For phylogenetic inference, FastTree 2, IQ-TREE, RAxML-NG, and PhyML had similar topological accuracies, but branch lengths and pairwise distances were consistently most accurate in phylogenies inferred by RAxML-NG. However, FastTree 2 was orders of magnitude faster than the other tools, and when the other tools were used to optimize branch lengths along a fixed topology provided by FastTree 2 (i.e., no tree search), the resulting phylogenies had accuracies that were indistinguishable from their original counterparts, but with a fraction of the runtime. Our results indicate that an ideal workflow for viral phylogenetic inference is to (1) use MAFFT to perform MSA, (2) use FastTree 2 under the GTR model with discrete gamma-distributed site-rate heterogeneity to quickly obtain a reasonable tree topology, and (3) use RAxML-NG to optimize branch lengths along the fixed FastTree 2 topology.

scholarly journals Performance Evaluation of Leading Protein Multiple Sequence Alignment Methods

2019 ◽  
Vol 9 (1) ◽  
pp. 771-776
Keyword(s):  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Evolutionary Relationship ◽  
Biological Data ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Sequencing Technologies ◽  
Benchmark Database ◽  
Execution Speed ◽  
Protein Multiple Sequence Alignment

Protein Multiple sequence alignment (MSA) is a process, that helps in alignment of more than two protein sequences to establish an evolutionary relationship between the sequences. As part of Protein MSA, the biological sequences are aligned in a way to identify maximum similarities. Over time the sequencing technologies are becoming more sophisticated and hence the volume of biological data generated is increasing at an enormous rate. This increase in volume of data poses a challenge to the existing methods used to perform effective MSA as with the increase in data volume the computational complexities also increases and the speed to process decreases. The accuracy of MSA is another factor critically important as many bioinformatics inferences are dependent on the output of MSA. This paper elaborates on the existing state of the art methods of protein MSA and performs a comparison of four leading methods namely MAFFT, Clustal Omega, MUSCLE and ProbCons based on the speed and accuracy of these methods. BAliBASE version 3.0 (BAliBASE is a repository of manually refined multiple sequence alignments) has been used as a benchmark database and accuracy of alignment methods is computed through the two widely used criteria named Sum of pair score (SPscore) and total column score (TCscore). We also recorded the execution time for each method in order to compute the execution speed.

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 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.

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