A Hybrid Flow for Multiple Sequence Alignment with a BLASTn Based Pairwise Alignment Processor

2018 ◽  
Author(s):  
Mao-Jan Lin ◽  
Chih-Yu Chang ◽  
Yu-Cheng Li ◽  
Nae-Chyun Chen ◽  
Yi-Chang Lu
Keyword(s):  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Pairwise Alignment ◽  
Multiple Sequence

Multiple Sequence Alignment Optimization Using Meta-Heuristic Techniques

2020 ◽  
pp. 565-579 ◽  
Author(s):  
Mohamed Issa ◽  
Aboul Ella Hassanien
Keyword(s):  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Phylogenetic Trees ◽  
Pairwise Alignment ◽  
Accurate Method ◽  
Alignment Algorithm ◽  
Bacterial Foraging Optimization ◽  
Multiple Sequence ◽  
Speed Up ◽  
Dna Fragment Assembly

Sequence alignment is a vital process in many biological applications such as Phylogenetic trees construction, DNA fragment assembly and structure/function prediction. Two kinds of alignment are pairwise alignment which align two sequences and Multiple Sequence alignment (MSA) that align sequences more than two. The accurate method of alignment is based on Dynamic Programming (DP) approach which suffering from increasing time exponentially with increasing the length and the number of the aligned sequences. Stochastic or meta-heuristics techniques speed up alignment algorithm but with near optimal alignment accuracy not as that of DP. Hence, This chapter aims to review the recent development of MSA using meta-heuristics algorithms. In addition, two recent techniques are focused in more deep: the first is Fragmented protein sequence alignment using two-layer particle swarm optimization (FTLPSO). The second is Multiple sequence alignment using multi-objective based bacterial foraging optimization algorithm (MO-BFO).

scholarly journals Parallelization of Pairwise Alignment and Neighbor-Joining Algorithm in Progressive Multiple Sequence Alignment

2018 ◽  
Vol 9 (1) ◽  
pp. 234
Author(s):  
Agung Widyo Utomo
Keyword(s):  
Shared Memory ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Programming Model ◽  
Heuristic Method ◽  
Pairwise Alignment ◽  
Neighbor Joining ◽  
Progressive Alignment ◽  
Multiple Sequence ◽  
Progressive Multiple Sequence Alignment

Progressive multiple sequence alignment ClustalW is a widely used heuristic method for computing multiple sequence alignment (MSA). It has three stages: distance matrix computation using pairwise alignment, guide tree reconstruction using neighbor-joining and progressive alignment. To accelerate computing for large data, the progressive MSA algorithm needs to be parallelized. This research aims to identify, decompose and implement the pairwise alignment and neighbor-joining in progressive MSA using message passing, shared memory and hybrid programming model in the computer cluster. The experimental results obtained shared memory programming model as the best scenario implementation with speed up up to 12 times.

scholarly journals Efficient mapping of genomic sequences to optimize multiple pairwise alignment in hybrid cluster platforms

2014 ◽  
Vol 11 (3) ◽  
pp. 60-71
Author(s):  
Alberto Montañola ◽  
Concepció Roig ◽  
Porfidio Hernández
Keyword(s):  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Pairwise Alignment ◽  
Experimental Results ◽  
Genomic Sequences ◽  
Multiple Sequence ◽  
Optimal Amount ◽  
New Challenges ◽  
Best Parameters ◽  
Available Resources

Summary Multiple sequence alignment (MSA), used in biocomputing to study similarities between different genomic sequences, is known to require important memory and computation resources. Nowadays, researchers are aligning thousands of these sequences, creating new challenges in order to solve the problem using the available resources efficiently. Determining the efficient amount of resources to allocate is important to avoid waste of them, thus reducing the economical costs required in running for example a specific cloud instance. The pairwise alignment is the initial key step of the MSA problem, which will compute all pair alignments needed. We present a method to determine the optimal amount of memory and computation resources to allocate by the pairwise alignment, and we will validate it through a set of experimental results for different possible inputs. These allow us to determine the best parameters to configure the applications in order to use effectively the available resources of a given system.

scholarly journals GPU accelerated partial order multiple sequence alignment for long reads self-correction

2020 ◽  
Author(s):  
Francesco Peverelli ◽  
Lorenzo Di Tucci ◽  
Marco D. Santambrogio ◽  
Nan Ding ◽  
Steven Hofmeyr ◽  
...  
Keyword(s):  
Error Correction ◽  
Partial Order ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Consensus Sequence ◽  
Pairwise Alignment ◽  
Multiple Sequence ◽  
Graph Alignment ◽  
Correction Process ◽  
Long Reads

AbstractAs third generation sequencing technologies become more reliable and widely used to solve several genome-related problems, self-correction of long reads is becoming the preferred method to reduce the error rate of Pacific Biosciences and Oxford Nanopore long reads, that is now around 10-12%. Several of these self-correction methods rely on some form of Multiple Sequence Alignment (MSA) to obtain a consensus sequence for the original reads. In particular, error-correction tools such as RACON and CONSENT use Partial Order (PO) graph alignment to accomplish this task. PO graph alignment, which is computationally more expensive than optimal global pairwise alignment between two sequences, needs to be performed several times for each read during the error correction process. GPUs have proven very effective in accelerating several compute-intensive tasks in different scientific fields. We harnessed the power of these architectures to accelerate the error correction process of existing self-correction tools, to improve the efficiency of this step of genome analysis.In this paper, we introduce a GPU-accelerated version of the PO alignment presented in the POA v2 software library, implemented on an NVIDIA Tesla V100 GPU. We obtain up to 6.5x speedup compared to 64 CPU threads run on two 2.3 GHz 16-core Intel Xeon Processors E5-2698 v3. In our implementation we focused on the alignment of smaller sequences, as the CONSENT segmentation strategy based on k-mer chaining provides an optimal opportunity to exploit the parallel-processing power of GPUs. To demonstrate this, we have integrated our kernel in the CONSENT software. This accelerated version of CONSENT provides a speedup for the whole error correction step that ranges from 1.95x to 8.5x depending on the input reads.

Multiple Sequence Alignment Optimization Using Meta-Heuristic Techniques

2017 ◽  
pp. 409-423 ◽  
Author(s):  
Mohamed Issa ◽  
Aboul Ella Hassanien
Keyword(s):  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Phylogenetic Trees ◽  
Pairwise Alignment ◽  
Accurate Method ◽  
Alignment Algorithm ◽  
Bacterial Foraging Optimization ◽  
Multiple Sequence ◽  
Speed Up ◽  
Dna Fragment Assembly

Sequence alignment is a vital process in many biological applications such as Phylogenetic trees construction, DNA fragment assembly and structure/function prediction. Two kinds of alignment are pairwise alignment which align two sequences and Multiple Sequence alignment (MSA) that align sequences more than two. The accurate method of alignment is based on Dynamic Programming (DP) approach which suffering from increasing time exponentially with increasing the length and the number of the aligned sequences. Stochastic or meta-heuristics techniques speed up alignment algorithm but with near optimal alignment accuracy not as that of DP. Hence, This chapter aims to review the recent development of MSA using meta-heuristics algorithms. In addition, two recent techniques are focused in more deep: the first is Fragmented protein sequence alignment using two-layer particle swarm optimization (FTLPSO). The second is Multiple sequence alignment using multi-objective based bacterial foraging optimization algorithm (MO-BFO).

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
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