Accelerating Needleman-Wunsch global alignment algorithm with GPUs

2015 ◽  
Author(s):  
Maged Fakirah ◽  
Mohammed A. Shehab ◽  
Yaser Jararweh ◽  
Mahmoud Al-Ayyoub
Keyword(s):  
Global Alignment ◽  
Alignment Algorithm

CLASSIFICATION AND IDENTIFICATION OF FUNGAL SEQUENCES USING CHARACTERISTIC RESTRICTION ENDONUCLEASE CUT ORDER

2010 ◽  
Vol 08 (02) ◽  
pp. 181-198 ◽  
Author(s):  
RAJIB SENGUPTA ◽  
DHUNDY R. BASTOLA ◽  
HESHAM H. ALI
Keyword(s):  
Dna Sequences ◽  
Restriction Enzymes ◽  
Epidemiological Studies ◽  
Global Alignment ◽  
Target Sequence ◽  
Alignment Algorithm ◽  
Molecular Fingerprinting ◽  
Data Set ◽  
Alignment Free ◽  
Wet Lab

Restriction Fragment Length Polymorphism (RFLP) is a powerful molecular tool that is extensively used in the molecular fingerprinting and epidemiological studies of microorganisms. In a wet-lab setting, the DNA is cut with one or more restriction enzymes and subjected to gel electrophoresis to obtain signature fragment patterns, which is utilized in the classification and identification of organisms. This wet-lab approach may not be practical when the experimental data set includes a large number of genetic sequences and a wide pool of restriction enzymes to choose from. In this study, we introduce a novel concept of Enzyme Cut Order — a biological property-based characteristic of DNA sequences which can be defined and analyzed computationally without any alignment algorithm. In this alignment-free approach, a similarity matrix is developed based on the pairwise Longest Common Subsequences (LCS) of the Enzyme Cut Orders. The choice of an ideal set of restriction enzymes used for analysis is augmented by using genetic algorithms. The results obtained from this approach using internal transcribed spacer regions of rDNA from fungi as the target sequence show that the phylogenetically-related organisms form a single cluster and successful grouping of phylogenetically close or distant organisms is dependent on the choice of restriction enzymes used in the analysis. Additionally, comparison of trees obtained with this alignment-free and the legacy method revealed highly similar tree topologies. This novel alignment-free method, which utilizes the Enzyme Cut Order and restriction enzyme profile, is a reliable alternative to local or global alignment-based classification and identification of organisms.

scholarly journals Bit-parallel sequence-to-graph alignment

2019 ◽  
Vol 35 (19) ◽  
pp. 3599-3607 ◽  
Author(s):  
Mikko Rautiainen ◽  
Veli Mäkinen ◽  
Tobias Marschall
Keyword(s):  
Variant Calling ◽  
Supplementary Information ◽  
Global Alignment ◽  
Alignment Algorithm ◽  
Exact Matching ◽  
Worst Case ◽  
De Bruijn Graphs ◽  
Graph Alignment ◽  
Linear Sequence ◽  
String Graphs

Abstract Motivation Graphs are commonly used to represent sets of sequences. Either edges or nodes can be labeled by sequences, so that each path in the graph spells a concatenated sequence. Examples include graphs to represent genome assemblies, such as string graphs and de Bruijn graphs, and graphs to represent a pan-genome and hence the genetic variation present in a population. Being able to align sequencing reads to such graphs is a key step for many analyses and its applications include genome assembly, read error correction and variant calling with respect to a variation graph. Results We generalize two linear sequence-to-sequence algorithms to graphs: the Shift-And algorithm for exact matching and Myers’ bitvector algorithm for semi-global alignment. These linear algorithms are both based on processing w sequence characters with a constant number of operations, where w is the word size of the machine (commonly 64), and achieve a speedup of up to w over naive algorithms. For a graph with |V| nodes and |E| edges and a sequence of length m, our bitvector-based graph alignment algorithm reaches a worst case runtime of O(|V|+⌈mw⌉|E| log w) for acyclic graphs and O(|V|+m|E| log w) for arbitrary cyclic graphs. We apply it to five different types of graphs and observe a speedup between 3-fold and 20-fold compared with a previous (asymptotically optimal) alignment algorithm. Availability and implementation https://github.com/maickrau/GraphAligner Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Acceleration of Nucleotide Semi-Global Alignment with Adaptive Banded Dynamic Programming

2017 ◽  
Author(s):  
Hajime Suzuki ◽  
Masahiro Kasahara
Keyword(s):  
Dynamic Programming ◽  
Single Molecule ◽  
Computation Time ◽  
Error Rates ◽  
Nucleotide Sequences ◽  
Sequencing Error ◽  
Local Alignment ◽  
Global Alignment ◽  
Alignment Algorithm ◽  
Short Read

AbstractMotivationPairwise alignment of nucleotide sequences has previously been carried out using the seed- and-extend strategy, where we enumerate seeds (shared patterns) between sequences and then extend the seeds by Smith-Waterman-like semi-global dynamic programming to obtain full pairwise alignments. With the advent of massively parallel short read sequencers, algorithms and data structures for efficiently finding seeds have been extensively explored. However, recent advances in single-molecule sequencing technologies have enabled us to obtain millions of reads, each of which is orders of magnitude longer than those output by the short-read sequencers, demanding a faster algorithm for the extension step that accounts for most of the computation time required for pairwise local alignment. Our goal is to design a faster extension algorithm suitable for single-molecule sequencers with high sequencing error rates (e.g., 10-15%) and with more frequent insertions and deletions than substitutions.ResultsWe propose an adaptive banded dynamic programming algorithm for calculating pairwise semi-global alignment of nucleotide sequences that allows a relatively high insertion or deletion rate while keeping band width relatively low (e.g., 32 or 64 cells) regardless of sequence lengths. Our new algorithm eliminated mutual dependences between elements in a vector, allowing an efficient Single-Instruction-Multiple-Data parallelization. We experimentally demonstrate that our algorithm runs approximately 5× faster than the extension alignment algorithm in NCBI BLAST+ while retaining similar sensitivity (recall).We also show that our extension algorithm is more sensitive than the extension alignment routine in DALIGNER, while the computation time is comparable.AvailabilityThe implementation of the algorithm and the benchmarking scripts are available at https://github.com/ocxtal/[email protected]

scholarly journals A survey and evaluations of histogram-based statistics in alignment-free sequence comparison

2017 ◽  
Vol 20 (4) ◽  
pp. 1222-1237 ◽  
Author(s):  
Brian B Luczak ◽  
Benjamin T James ◽  
Hani Z Girgis
Keyword(s):  
Query Sequence ◽  
Sequence Length ◽  
Local Alignment ◽  
Global Alignment ◽  
Alignment Algorithm ◽  
Earth Mover’S Distance ◽  
Earth Mover's Distance ◽  
Alignment Free ◽  
Length Difference ◽  
Alignment Algorithms

Abstract Motivation Since the dawn of the bioinformatics field, sequence alignment scores have been the main method for comparing sequences. However, alignment algorithms are quadratic, requiring long execution time. As alternatives, scientists have developed tens of alignment-free statistics for measuring the similarity between two sequences. Results We surveyed tens of alignment-free k-mer statistics. Additionally, we evaluated 33 statistics and multiplicative combinations between the statistics and/or their squares. These statistics are calculated on two k-mer histograms representing two sequences. Our evaluations using global alignment scores revealed that the majority of the statistics are sensitive and capable of finding similar sequences to a query sequence. Therefore, any of these statistics can filter out dissimilar sequences quickly. Further, we observed that multiplicative combinations of the statistics are highly correlated with the identity score. Furthermore, combinations involving sequence length difference or Earth Mover’s distance, which takes the length difference into account, are always among the highest correlated paired statistics with identity scores. Similarly, paired statistics including length difference or Earth Mover’s distance are among the best performers in finding the K-closest sequences. Interestingly, similar performance can be obtained using histograms of shorter words, resulting in reducing the memory requirement and increasing the speed remarkably. Moreover, we found that simple single statistics are sufficient for processing next-generation sequencing reads and for applications relying on local alignment. Finally, we measured the time requirement of each statistic. The survey and the evaluations will help scientists with identifying efficient alternatives to the costly alignment algorithm, saving thousands of computational hours. Availability The source code of the benchmarking tool is available as Supplementary Materials.

scholarly journals A generalized global alignment algorithm

2003 ◽  
Vol 19 (2) ◽  
pp. 228-233 ◽  
Author(s):  
X. Huang ◽  
K.-M. Chao
Keyword(s):  
Global Alignment ◽  
Alignment Algorithm

scholarly journals A novel algorithm for alignment of multiple PPI networks based on simulated annealing

BMC Genomics ◽  
2019 ◽  
Vol 20 (S13) ◽  
Author(s):  
Jialu Hu ◽  
Junhao He ◽  
Jing Li ◽  
Yiqun Gao ◽  
Yan Zheng ◽  
...  
Keyword(s):  
Protein Function ◽  
Global Alignment ◽  
Alignment Algorithm ◽  
Algorithm Performance ◽  
Ppi Networks ◽  
Annotation Data ◽  
Increasing Demand ◽  
Almost All ◽  
Functional Analyses

AbstractProteins play essential roles in almost all life processes. The prediction of protein function is of significance for the understanding of molecular function and evolution. Network alignment provides a fast and effective framework to automatically identify functionally conserved proteins in a systematic way. However, due to the fast growing genomic data, interactions and annotation data, there is an increasing demand for more accurate and efficient tools to deal with multiple PPI networks. Here, we present a novel global alignment algorithm NetCoffee2 based on graph feature vectors to discover functionally conserved proteins and predict function for unknown proteins. To test the algorithm performance, NetCoffee2 and three other notable algorithms were applied on eight real biological datasets. Functional analyses were performed to evaluate the biological quality of these alignments. Results show that NetCoffee2 is superior to existing algorithms IsoRankN, NetCoffee and multiMAGNA++ in terms of both coverage and consistency. The binary and source code are freely available under the GNU GPL v3 license at https://github.com/screamer/NetCoffee2.

scholarly journals LePrimAlign: local entropy-based alignment of PPI networks to predict conserved modules

BMC Genomics ◽  
2019 ◽  
Vol 20 (S9) ◽  
Author(s):  
Sawal Maskey ◽  
Young-Rae Cho
Keyword(s):  
Computational Cost ◽  
Network Alignment ◽  
System Level ◽  
Local Network ◽  
Local Alignment ◽  
Global Alignment ◽  
Alignment Algorithm ◽  
Interaction Patterns ◽  
Ppi Networks ◽  
Alignment Algorithms

Abstract Background Cross-species analysis of protein-protein interaction (PPI) networks provides an effective means of detecting conserved interaction patterns. Identifying such conserved substructures between PPI networks of different species increases our understanding of the principles deriving evolution of cellular organizations and their functions in a system level. In recent years, network alignment techniques have been applied to genome-scale PPI networks to predict evolutionary conserved modules. Although a wide variety of network alignment algorithms have been introduced, developing a scalable local network alignment algorithm with high accuracy is still challenging. Results We present a novel pairwise local network alignment algorithm, called LePrimAlign, to predict conserved modules between PPI networks of three different species. The proposed algorithm exploits the results of a pairwise global alignment algorithm with many-to-many node mapping. It also applies the concept of graph entropy to detect initial cluster pairs from two networks. Finally, the initial clusters are expanded to increase the local alignment score that is formulated by a combination of intra-network and inter-network scores. The performance comparison with state-of-the-art approaches demonstrates that the proposed algorithm outperforms in terms of accuracy of identified protein complexes and quality of alignments. Conclusion The proposed method produces local network alignment of higher accuracy in predicting conserved modules even with large biological networks at a reduced computational cost.

scholarly journals Bit-parallel sequence-to-graph alignment

2018 ◽  
Author(s):  
Mikko Rautiainen ◽  
Veli Mäkinen ◽  
Tobias Marschall
Keyword(s):  
Variant Calling ◽  
Global Alignment ◽  
Alignment Algorithm ◽  
Exact Matching ◽  
Worst Case ◽  
De Bruijn Graphs ◽  
Graph Alignment ◽  
Linear Sequence ◽  
String Graphs ◽  
Linear Algorithms

Graphs are commonly used to represent sets of sequences. Either edges or nodes can be labeled by sequences, so that each path in the graph spells a concatenated sequence. Examples include graphs to represent genome assemblies, such as string graphs and de Bruijn graphs, and graphs to represent a pan-genome and hence the genetic variation present in a population. Being able to align sequencing reads to such graphs is a key step for many analyses and its applications include genome assembly, read error correction, and variant calling with respect to a variation graph. Here, we generalize two linear sequence-to-sequence algorithms to graphs: the Shift-And algorithm for exact matching and Myers’ bitvector algorithm for semi-global alignment. These linear algorithms are both based on processing w sequence characters with a constant number of operations, where w is the word size of the machine (commonly 64), and achieve a speedup of w over naive algorithms. Our bitvector-based graph alignment algorithm reaches a worst case runtime of for acyclic graphs and O(V + mE log w) for arbitrary cyclic graphs. We apply it to four different types of graphs and observe a speedup between 3.1-fold and 10.1-fold compared to previous algorithms.

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