Multiple Sequence Alignment Tools Research Articles

SAliBASE: A Database of Simulated Protein Alignments.

Simulated alignments are alternatives to manually constructed multiple sequence alignments for evaluating performance of multiple sequence alignment tools. The importance of simulated sequences is recognized because their true evolutionary history is known, which is very helpful for reconstructing accurate phylogenetic trees and alignments. However, generating simulated alignments require expertise to use bioinformatics tools and consume several hours for reconstructing even a few hundreds of simulated sequences. It becomes a tedious job for an end user who needs a few datasets of variety of simulated sequences. Currently, there is no databank available which may help researchers to download simulated sequences/alignments for their study. Major focus of our study was to develop a database of simulated protein sequences (SAliBASE) based on different varying parameters such as insertion rate, deletion rate, sequence length, number of sequences, and indel size. Each dataset has corresponding alignment as well. This repository is very useful for evaluating multiple alignment methods.

Comparative Analysis of Multiple Sequence Alignment Tools

Comparative Analysis of Multiple Sequence Alignment Tools

Scalable Consistency in T-Coffee Through Apache Spark and Cassandra Database.

Next-generation sequencing, also known as high-throughput sequencing, has increased the volume of genetic data processed by sequencers. In the bioinformatic scientific area, highly rated multiple sequence alignment tools, such as MAFFT, ProbCons, and T-Coffee (TC), use the probabilistic consistency as a prior step to the progressive alignment stage to improve the final accuracy. However, such methods are severely limited by the memory required to store the consistency information. Big data processing and persistence techniques are used to manage and store the huge amount of information that is generated. Although these techniques have significant advantages, few biological applications have adopted them. In this article, a novel approach named big data tree-based consistency objective function for alignment evaluation (BDT-Coffee) is presented. BDT-Coffee is based on the integration of consistency information through Cassandra database in TC, previously generated by the MapReduce processing paradigm, to enable large data sets to be processed with the aim of improving the performance and scalability of the original algorithm.

Adaptive Local Realignment of Protein Sequences.

While mutation rates can vary markedly over the residues of a protein, multiple sequence alignment tools typically use the same values for their scoring-function parameters across a protein's entire length. We present a new approach, called adaptive local realignment, that in contrast automatically adapts to the diversity of mutation rates along protein sequences. This builds upon a recent technique known as parameter advising, which finds global parameter settings for an aligner, to now adaptively find local settings. Our approach in essence identifies local regions with low estimated accuracy, constructs a set of candidate realignments using a carefully-chosen collection of parameter settings, and replaces the region if a realignment has higher estimated accuracy. This new method of local parameter advising, when combined with prior methods for global advising, boosts alignment accuracy as much as 26% over the best default setting on hard-to-align protein benchmarks, and by 6.4% over global advising alone. Adaptive local realignment has been implemented within the Opal aligner using the Facet accuracy estimator.

Multiple Sequence Alignment Based on a Suffix Tree and Center-Star Strategy: A Linear Method for Multiple Nucleotide Sequence Alignment on Spark Parallel Framework.

Multiple sequence alignment (MSA) is an essential prerequisite and dominant method to deduce the biological facts from a set of molecular biological sequences. It refers to a series of algorithmic solutions for the alignment of evolutionarily related sequences while taking into account evolutionary events such as mutations, insertions, deletions, and rearrangements under certain conditions. These methods can be applied to DNA, RNA, or protein sequences. In this work, we take advantage of a center-star strategy to reduce the MSA problem to pairwise alignments, and we use a suffix tree to match identical substrings between two pairwise sequences. Multiple sequence alignment based on a suffix tree and center-star strategy (MASC) can accomplish MSA in O(mn), which is linear time complexity, where m is the number of sequences and n is the average length of sequences. Furthermore, we execute our method on the Spark-distributed parallel framework to deal with ever-increasing massive data sets. Our method is significantly faster than previous techniques, with no loss in accuracy for highly similar nucleotide sequences like homologous sequences, which we experimentally demonstrate. Comparing with mainstream MSA tools (e.g., MAFFT), MASC could finish the alignment of 67,200 sequences, longer than 10,000 bps, in 9 minutes, which takes MAFFT >3.5 days.

TM-Aligner: Multiple sequence alignment tool for transmembrane proteins with reduced time and improved accuracy

Membrane proteins plays significant role in living cells. Transmembrane proteins are estimated to constitute approximately 30% of proteins at genomic scale. It has been a difficult task to develop specific alignment tools for transmembrane proteins due to limited number of experimentally validated protein structures. Alignment tools based on homology modeling provide fairly good result by recapitulating 70–80% residues in reference alignment provided all input sequences should have known template structures. However, homology modeling tools took substantial amount of time, thus aligning large numbers of sequences becomes computationally demanding. Here we present TM-Aligner, a new tool for transmembrane protein sequence alignment. TM-Aligner is based on Wu-Manber and dynamic string matching algorithm which has significantly improved its accuracy and speed of multiple sequence alignment. We compared TM-Aligner with prevailing other popular tools and performed benchmarking using three separate reference sets, BaliBASE3.0 reference set7 of alpha-helical transmembrane proteins, structure based alignment of transmembrane proteins from Pfam database and structure alignment from GPCRDB. Benchmarking against reference datasets indicated that TM-Aligner is more advanced method having least turnaround time with significant improvements over the most accurate methods such as PROMALS, MAFFT, TM-Coffee, Kalign, ClustalW, Muscle and PRALINE. TM-Aligner is freely available through http://lms.snu.edu.in/TM-Aligner/.

ProtDec-LTR2.0: an improved method for protein remote homology detection by combining pseudo protein and supervised Learning to Rank.

As one of the most important tasks in protein sequence analysis, protein remote homology detection is critical for both basic research and practical applications. Here, we present an effective web server for protein remote homology detection called ProtDec-LTR2.0 by combining ProtDec-Learning to Rank (LTR) and pseudo protein representation. Experimental results showed that the detection performance is obviously improved. The web server provides a user-friendly interface to explore the sequence and structure information of candidate proteins and find their conserved domains by launching a multiple sequence alignment tool. The web server is free and open to all users with no login requirement at http://bioinformatics.hitsz.edu.cn/ProtDec-LTR2.0/. bliu@hit.edu.cn.

PA-Star: A disk-assisted parallel A-Star strategy with locality-sensitive hash for multiple sequence alignment

Multiple Sequence Alignment (MSA) is a basic operation in Bioinformatics, and is used to highlight the similarities among a set of sequences. The MSA problem was proven NP-Hard, thus requiring a high amount of memory and computing power. This problem can be modeled as a search for the path with minimum cost in a graph, and the A-Star algorithm has been adapted to solve it sequentially and in parallel. The design of a parallel version for MSA with A-Star is subject to challenges such as irregular dependency pattern and substantial memory requirements. In this paper, we propose PA-Star, a locality-sensitive multithreaded strategy based on A-Star, which computes optimal MSAs using both RAM and disk to store nodes. The experimental results obtained in 3 different machines show that the optimizations used in PA-Star can achieve an acceleration of 1.88× in the serial execution, and the parallel execution can attain an acceleration of 5.52× with 8 cores. We also show that PA-Star outperforms a state-of-the-art MSA tool based on A-Star, executing up to 4.77× faster. Finally, we show that our disk-assisted strategy is able to retrieve the optimal alignment when other tools fail.

Foldit Standalone: a video game-derived protein structure manipulation interface using Rosetta.

SummaryFoldit Standalone is an interactive graphical interface to the Rosetta molecular modeling package. In contrast to most command-line or batch interactions with Rosetta, Foldit Standalone is designed to allow easy, real-time, direct manipulation of protein structures, while also giving access to the extensive power of Rosetta computations. Derived from the user interface of the scientific discovery game Foldit (itself based on Rosetta), Foldit Standalone has added more advanced features and removed the competitive game elements. Foldit Standalone was built from the ground up with a custom rendering and event engine, configurable visualizations and interactions driven by Rosetta. Foldit Standalone contains, among other features: electron density and contact map visualizations, multiple sequence alignment tools for template-based modeling, rigid body transformation controls, RosettaScripts support and an embedded Lua interpreter.Availability and ImplementationFoldit Standalone is available for download at https://fold.it/standalone, under the Rosetta license, which is free for academic and non-profit users. It is implemented in cross-platform C ++ and binary executables are available for Windows, macOS and Linux.

MSAProbs-MPI: parallel multiple sequence aligner for distributed-memory systems.

Source code in C ++ and MPI running on Linux systems as well as a reference manual are available at http://msaprobs.sourceforge.net CONTACT: jgonzalezd@udc.esSupplementary information: Supplementary data are available at Bioinformatics online.

PnpProbs: a better multiple sequence alignment tool by better handling of guide trees.

BackgroundThis paper describes a new MSA tool called PnpProbs, which constructs better multiple sequence alignments by better handling of guide trees. It classifies sequences into two types: normally related and distantly related. For normally related sequences, it uses an adaptive approach to construct the guide tree needed for progressive alignment; it first estimates the input’s discrepancy by computing the standard deviation of their percent identities, and based on this estimate, it chooses the better method to construct the guide tree. For distantly related sequences, PnpProbs abandons the guide tree and uses instead some non-progressive alignment method to generate the alignment.ResultsTo evaluate PnpProbs, we have compared it with thirteen other popular MSA tools, and PnpProbs has the best alignment scores in all but one test. We have also used it for phylogenetic analysis, and found that the phylogenetic trees constructed from PnpProbs’ alignments are closest to the model trees.ConclusionsBy combining the strength of the progressive and non-progressive alignment methods, we have developed an MSA tool called PnpProbs. We have compared PnpProbs with thirteen other popular MSA tools and our results showed that our tool usually constructed the best alignments.

UniProt Tools.

The Universal Protein Resource (UniProt) is a comprehensive resource for protein sequence and annotation data (UniProt Consortium, 2015). The UniProt Web site receives ∼400,000 unique visitors per month and is the primary means to access UniProt. Along with various datasets that you can search, UniProt provides three main tools. These are the 'BLAST' tool for sequence similarity searching, the 'Align' tool for multiple sequence alignment, and the 'Retrieve/ID Mapping' tool for using a list of identifiers to retrieve UniProtKB proteins and to convert database identifiers from UniProt to external databases or vice versa. This unit provides three basic protocols, three alternate protocols, and two support protocols for using UniProt tools.

HAMSA: Highly Accelerated Multiple Sequence Aligner

For biologists, the existence of an efficient tool for multiple sequence alignment is essential. This work presents a new parallel aligner called HAMSA. HAMSA is a bioinformatics application designed for highly accelerated alignment of multiple sequences of proteins and DNA/RNA on a multi-core cluster system. The design of HAMSA is based on a combination of our new optimized algorithms proposed recently of vectorization, partitioning, and scheduling. It mainly operates on a distance vector instead of a distance matrix. It accomplishes similarity computations and generates the guide tree in a highly accelerated and accurate manner. HAMSA outperforms MSAProbs with 21.9- fold speedup, and ClustalW-MPI of 11-fold speedup. It can be considered as an essential tool for structure prediction, protein classification, motive finding and drug design studies

Microbial Carboxylesterases: An Insight into Thermal Adaptation Using In Silico Approach

Carboxylesterases (CE’s), are a group of esterases that catalyze the hydrolysis of carboxylic ester molecule to form alcohol and carboxylic acid in the presence of water. In silico analysis of ten carboxylesterases (E.C. 3.1.1.1) sequences from mesophilic and thermophilic organisms for various physiochemical parameters and amino acid comparison has been done. Ten sequences, five from each group were retrieved from NCBI and were aligned using multiple sequence alignment tool MUSCLE. The phylogenetic relationship between the two groups has been found using maximum parsimony method of MEGA-6. These sequences were further analyzed using online ProtParam ExPASy tool for some important physicochemical properties. Multiple sequence alignment (MSA) showed the presence of conserved catalytic triad S157, D254, H284. Maximum parsimony method using MEGA-6 distinguished the mesophilic and thermophilic esterases into their respected subgroups. The two groups showed significant variation in their physical and chemical properties. Theoretical pI and negatively charged residues were found to be significant in the present study. Amino acid Gln (Q), Val (V) showed significant statistical variation with 1.7 and 1.5 fold. Fold value statistics showed dominance of Asn (1.4), Gln (1.7), Cys (1.4), Thr (1.3), Trp (1.4) and Lys (1.2) in mesophiles whereas, Ala (1.1), Arg (1.5), Ileu (1.3), Glu (1.2), Tyr (1.2), Val (1.5) in thermophiles. Polar amino acid residues Gln (Q), Asn (N), Cys (C) Lys (K), Thr (T) and Glu (D) beside to their dominance in one and in other group help to distinguish lipases from esterases for their affinity for water soluble short chain fatty esters. Because of affinity of these amino acids to water molecules, they have tendency to be on the exterior of a protein thus can interact with polar region of surfaces.

The EMBL-EBI bioinformatics web and programmatic tools framework.

Since 2009 the EMBL-EBI Job Dispatcher framework has provided free access to a range of mainstream sequence analysis applications. These include sequence similarity search services (https://www.ebi.ac.uk/Tools/sss/) such as BLAST, FASTA and PSI-Search, multiple sequence alignment tools (https://www.ebi.ac.uk/Tools/msa/) such as Clustal Omega, MAFFT and T-Coffee, and other sequence analysis tools (https://www.ebi.ac.uk/Tools/pfa/) such as InterProScan. Through these services users can search mainstream sequence databases such as ENA, UniProt and Ensembl Genomes, utilising a uniform web interface or systematically through Web Services interfaces (https://www.ebi.ac.uk/Tools/webservices/) using common programming languages, and obtain enriched results with novel visualisations. Integration with EBI Search (https://www.ebi.ac.uk/ebisearch/) and the dbfetch retrieval service (https://www.ebi.ac.uk/Tools/dbfetch/) further expands the usefulness of the framework. New tools and updates such as NCBI BLAST+, InterProScan 5 and PfamScan, new categories such as RNA analysis tools (https://www.ebi.ac.uk/Tools/rna/), new databases such as ENA non-coding, WormBase ParaSite, Pfam and Rfam, and new workflow methods, together with the retirement of depreciated services, ensure that the framework remains relevant to today's biological community.

Recovering accuracy methods for scalable consistency library

Multiple sequence alignment (MSA) is crucial for high-throughput next generation sequencing applications. Large-scale alignments with thousands of sequences are necessary for these applications. However, the quality of the alignment of current MSA tools decreases sharply when the number of sequences grows to several thousand. This accuracy degradation can be mitigated using global consistency information as in the T-Coffee MSA-Tool, which implements a consistency library. However, consistency-based methods do not scale well because of the computational resources required to calculate and store the consistency information, which grows quadratically. In this paper, we propose an alternative method for building the consistency-library. To allow unlimited scalability, consistency information must be discarded to avoid exceeding the environment memory. Our first approach deals with the memory limitation by identifying the most important entries, which provide better consistency. This method is able to achieve scalability, although there is a negative impact on accuracy. The second proposal, aims to reduce this degradation of accuracy, with three different methods presented to attain a better alignment.

High Performance computing improvements on bioinformatics consistency-based multiple sequence alignment tools

BGT method improves the execution time of progressive alignment by 62%.OLM decreases the memory requirements by 75%, aligning up twice more sequences than previous method.MTA improves the alignments accuracy, letting it to regain the quality lost due to the performance improvements. Multiple Sequence Alignment (MSA) is essential for a wide range of applications in Bioinformatics. Traditionally, the alignment accuracy was the main metric used to evaluate the goodness of MSA tools. However, with the growth of sequencing data, other features, such as performance and the capacity to align larger datasets, are gaining strength. To achieve these new requirements, without affecting accuracy, the use of high-performance computing (HPC) resources and techniques is crucial. In this paper, we apply HPC techniques in T-Coffee, one of the more accurate but less scalable MSA tools. We integrate three innovative solutions into T-Coffee: the Balanced Guide Tree to increase the parallelism/performance, the Optimized Library Method with the aim of enhancing the scalability and the Multiple Tree Alignment, which explores different alignments in parallel to improve the accuracy. The results obtained show that the resulting tool, MTA-TCoffee, is able to improve the scalability in both the execution time and also the number of sequences to be aligned. Furthermore, not only is the alignment accuracy not affected by these improvements, as would be expected, but it improves significantly. Finally, we emphasize that the presented methods are not just restricted to T-Coffee, but may be implemented in any other alignment tools that use similar algorithms (progressive alignment, consistency or guide trees).

FPoxDB: fungal peroxidase database for comparative genomics.

BackgroundPeroxidases are a group of oxidoreductases which mediate electron transfer from hydrogen peroxide (H2O2) and organic peroxide to various electron acceptors. They possess a broad spectrum of impact on industry and fungal biology. There are numerous industrial applications using peroxidases, such as to catalyse highly reactive pollutants and to breakdown lignin for recycling of carbon sources. Moreover, genes encoding peroxidases play important roles in fungal pathogenicity in both humans and plants. For better understanding of fungal peroxidases at the genome-level, a novel genomics platform is required. To this end, Fungal Peroxidase Database (fPoxDB; http://peroxidase.riceblast.snu.ac.kr/) has been developed to provide such a genomics platform for this important gene family.DescriptionIn order to identify and classify fungal peroxidases, 24 sequence profiles were built and applied on 331 genomes including 216 from fungi and Oomycetes. In addition, NoxR, which is known to regulate NADPH oxidases (NoxA and NoxB) in fungi, was also added to the pipeline. Collectively, 6,113 genes were predicted to encode 25 gene families, presenting well-separated distribution along the taxonomy. For instance, the genes encoding lignin peroxidase, manganese peroxidase, and versatile peroxidase were concentrated in the rot-causing basidiomycetes, reflecting their ligninolytic capability. As a genomics platform, fPoxDB provides diverse analysis resources, such as gene family predictions based on fungal sequence profiles, pre-computed results of eight bioinformatics programs, similarity search tools, a multiple sequence alignment tool, domain analysis functions, and taxonomic distribution summary, some of which are not available in the previously developed peroxidase resource. In addition, fPoxDB is interconnected with other family web systems, providing extended analysis opportunities.ConclusionsfPoxDB is a fungi-oriented genomics platform for peroxidases. The sequence-based prediction and diverse analysis toolkits with easy-to-follow web interface offer a useful workbench to study comparative and evolutionary genomics of peroxidases in fungi.

Simultaneous Alignment and Folding of Protein Sequences

Accurate comparative analysis tools for low-homology proteins remains a difficult challenge in computational biology, especially sequence alignment and consensus folding problems. We present partiFold-Align, the first algorithm for simultaneous alignment and consensus folding of unaligned protein sequences; the algorithm's complexity is polynomial in time and space. Algorithmically, partiFold-Align exploits sparsity in the set of super-secondary structure pairings and alignment candidates to achieve an effectively cubic running time for simultaneous pairwise alignment and folding. We demonstrate the efficacy of these techniques on transmembrane β-barrel proteins, an important yet difficult class of proteins with few known three-dimensional structures. Testing against structurally derived sequence alignments, partiFold-Align significantly outperforms state-of-the-art pairwise and multiple sequence alignment tools in the most difficult low-sequence homology case. It also improves secondary structure prediction where current approaches fail. Importantly, partiFold-Align requires no prior training. These general techniques are widely applicable to many more protein families (partiFold-Align is available at http://partifold.csail.mit.edu/ ).

Multiple Sequence Alignment with Hidden Markov Models Learned by Random Drift Particle Swarm Optimization.

Hidden Markov Models (HMMs) are powerful tools for multiple sequence alignment (MSA), which is known to be an NP-complete and important problem in bioinformatics. Learning HMMs is a difficult task, and many meta-heuristic methods, including particle swarm optimization (PSO), have been used for that. In this paper, a new variant of PSO, called the random drift particle swarm optimization (RDPSO) algorithm, is proposed to be used for HMM learning tasks in MSA problems. The proposed RDPSO algorithm, inspired by the free electron model in metal conductors in an external electric field, employs a novel set of evolution equations that can enhance the global search ability of the algorithm. Moreover, in order to further enhance the algorithmic performance of the RDPSO, we incorporate a diversity control method into the algorithm and, thus, propose an RDPSO with diversity-guided search (RDPSO-DGS). The performances of the RDPSO, RDPSO-DGS and other algorithms are tested and compared by learning HMMs for MSA on two well-known benchmark data sets. The experimental results show that the HMMs learned by the RDPSO and RDPSO-DGS are able to generate better alignments for the benchmark data sets than other most commonly used HMM learning methods, such as the Baum-Welch and other PSO algorithms. The performance comparison with well-known MSA programs, such as ClustalW and MAFFT, also shows that the proposed methods have advantages in multiple sequence alignment.