A Complete Suffix Array-Based String Match Search Algorithm of Sliding Windows

2012 ◽  
Author(s):  
Lu Wang ◽  
Kun Huang ◽  
Jian Zhang ◽  
Jin Yao
Keyword(s):  
Search Algorithm ◽  
Suffix Array ◽  
String Match ◽  
Sliding Windows

scholarly journals GHOSTX: An Improved Sequence Homology Search Algorithm Using a Query Suffix Array and a Database Suffix Array

PLoS ONE ◽  
2014 ◽  
Vol 9 (8) ◽  
pp. e103833 ◽  
Author(s):  
Shuji Suzuki ◽  
Masanori Kakuta ◽  
Takashi Ishida ◽  
Yutaka Akiyama
Keyword(s):  
Sequence Homology ◽  
Search Algorithm ◽  
Suffix Array ◽  
Homology Search

Efficient RNA structure comparison algorithms

2017 ◽  
Vol 15 (06) ◽  
pp. 1740009 ◽  
Author(s):  
Abdullah N. Arslan ◽  
Jithendar Anandan ◽  
Eric Fry ◽  
Keith Monschke ◽  
Nitin Ganneboina ◽  
...  
Keyword(s):  
Rna Structure ◽  
Search Algorithm ◽  
Suffix Array ◽  
Rna Structures ◽  
Structure Comparison ◽  
Structure Representation ◽  
Substructure Search ◽  
Common Substructure ◽  
Search Tool ◽  
Comparison Algorithms

Recently proposed relative addressing-based ([Formula: see text]) RNA secondary structure representation has important features by which an RNA structure database can be stored into a suffix array. A fast substructure search algorithm has been proposed based on binary search on this suffix array. Using this substructure search algorithm, we present a fast algorithm that finds the largest common substructure of given multiple RNA structures in [Formula: see text] format. The multiple RNA structure comparison problem is NP-hard in its general formulation. We introduced a new problem for comparing multiple RNA structures. This problem has more strict similarity definition and objective, and we propose an algorithm that solves this problem efficiently. We also develop another comparison algorithm that iteratively calls this algorithm to locate nonoverlapping large common substructures in compared RNAs. With the new resulting tools, we improved the RNASSAC website (linked from http://faculty.tamuc.edu/aarslan ). This website now also includes two drawing tools: one specialized for preparing RNA substructures that can be used as input by the search tool, and another one for automatically drawing the entire RNA structure from a given structure sequence.

scholarly journals Sapling: Accelerating Suffix Array Queries with Learned Data Models

2020 ◽  
Author(s):  
Melanie Kirsche ◽  
Arun Das ◽  
Michael C. Schatz
Keyword(s):  
Open Source ◽  
Sequence Alignment ◽  
Search Algorithm ◽  
Piecewise Linear ◽  
Network Models ◽  
Suffix Array ◽  
Binary Search ◽  
Data Models ◽  
Supplementary Information ◽  
Neural Network Models

AbstractMotivationAs genomic data becomes more abundant, efficient algorithms and data structures for sequence alignment become increasingly important. The suffix array is a widely used data structure to accelerate alignment, but the binary search algorithm used to query it requires widespread memory accesses, causing a large number of cache misses on large datasets.ResultsHere we present Sapling, an algorithm for sequence alignment which uses a learned data model to augment the suffix array and enable faster queries. We investigate different types of data models, providing an analysis of different neural network models as well as providing an open-source aligner with a compact, practical piecewise linear model. We show that Sapling outperforms both an optimized binary search approach and multiple existing read aligners on a wide collection of genomes, including human, bacteria, and plants, speeding up the algorithm by more than a factor of two while adding less than 1% to the suffix array’s memory footprint.Availability and implementationThe source code and tutorial are available open-source at https://github.com/mkirsche/sapling.Supplementary InformationSupplementary notes and figures are available online.

scholarly journals Sapling: accelerating suffix array queries with learned data models

2020 ◽  
Author(s):  
Melanie Kirsche ◽  
Arun Das ◽  
Michael C Schatz
Keyword(s):  
Open Source ◽  
Sequence Alignment ◽  
Search Algorithm ◽  
Piecewise Linear ◽  
Network Models ◽  
Suffix Array ◽  
Binary Search ◽  
Data Models ◽  
Supplementary Information ◽  
Neural Network Models

Abstract Motivation As genomic data becomes more abundant, efficient algorithms and data structures for sequence alignment become increasingly important. The suffix array is a widely used data structure to accelerate alignment, but the binary search algorithm used to query, it requires widespread memory accesses, causing a large number of cache misses on large datasets. Results Here, we present Sapling, an algorithm for sequence alignment, which uses a learned data model to augment the suffix array and enable faster queries. We investigate different types of data models, providing an analysis of different neural network models as well as providing an open-source aligner with a compact, practical piecewise linear model. We show that Sapling outperforms both an optimized binary search approach and multiple widely used read aligners on a diverse collection of genomes, including human, bacteria and plants, speeding up the algorithm by more than a factor of two while adding <1% to the suffix array’s memory footprint. Availability and implementation The source code and tutorial are available open-source at https://github.com/mkirsche/sapling. Supplementary information Supplementary data are available at Bioinformatics online.

Optimal broadcast scheduling method for VANETs: An adaptive discrete firefly approach

2020 ◽  
Vol 39 (6) ◽  
pp. 8125-8137
Author(s):  
Jackson J Christy ◽  
D Rekha ◽  
V Vijayakumar ◽  
Glaucio H.S. Carvalho
Keyword(s):  
Intelligent Transportation System ◽  
Search Algorithm ◽  
Mac Protocol ◽  
High Mobility ◽  
Optimal Solution ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Traffic Density ◽  
Scheduling Problem ◽  
Broadcast Scheduling

Vehicular Adhoc Networks (VANET) are thought-about as a mainstay in Intelligent Transportation System (ITS). For an efficient vehicular Adhoc network, broadcasting i.e. sharing a safety related message across all vehicles and infrastructure throughout the network is pivotal. Hence an efficient TDMA based MAC protocol for VANETs would serve the purpose of broadcast scheduling. At the same time, high mobility, influential traffic density, and an altering network topology makes it strenuous to form an efficient broadcast schedule. In this paper an evolutionary approach has been chosen to solve the broadcast scheduling problem in VANETs. The paper focusses on identifying an optimal solution with minimal TDMA frames and increased transmissions. These two parameters are the converging factor for the evolutionary algorithms employed. The proposed approach uses an Adaptive Discrete Firefly Algorithm (ADFA) for solving the Broadcast Scheduling Problem (BSP). The results are compared with traditional evolutionary approaches such as Genetic Algorithm and Cuckoo search algorithm. A mathematical analysis to find the probability of achieving a time slot is done using Markov Chain analysis.

Comparison of Meta-heuristic Algorithms on Benchmark Functions

2019 ◽  
Vol 2 (3) ◽  
pp. 508-517
Author(s):  
FerdaNur Arıcı ◽  
Ersin Kaya
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Time Interval ◽  
Bee Colony ◽  
Different Characteristics

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC’17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

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