DNA Sequence Alignment Algorithm Research Articles

Accelerating DNA pairwise sequence alignment using FPGA and a customized convolutional neural network

An optimized software and hardware digital implementation of two widely used DNA sequence alignment algorithms based on lookup table(LUT) is illustrated in this study. These algorithms are the best means for identifying similar regions between sequences. The proposed implementation relies on the complete parallelization of these foundational algorithms under certain limitations to overcome most of the problems of dynamic programming and hardware implementation. The proposed method takes O(N/4) calculation steps, where N is the length of each sequence with a minimum value of four (i.e., N = 4,8,12,…). A performance comparison between the state of art and our proposed algorithm is conducted for software and hardware implementation. Combinational circuits are used for FPGA-based hardware implementation of DNA sequence alignment algorithms. Performance and device resource usage are evaluated for different hardware designs. A customized convolution neural network model is used to implement global alignment and achieve 98.3% accuracy.

Sequence Alignment Using Machine Learning-Based Needleman–Wunsch Algorithm

Biological pairwise sequence alignment can be used as a method for arranging two biological sequence characters to identify regions of similarity. This operation has elicited considerable interest due to its significant influence on various critical aspects of life (e.g., identifying mutations in coronaviruses). Sequence alignment over large databases cannot yield results within a reasonable time, power, and cost. heuristic methods, such as FASTA, the BLAST family have been demonstrated to perform 40 times faster than DP-based (e.g., Needleman-Wunsch) techniques they cannot guarantee an optimum alignment result An optimized software platform of a widely used DNA sequence alignment algorithm called the Needleman-Wunsch (NW) algorithm based on a lookup table, is described in this study. This global alignment algorithm is the best approach for identifying similar regions between sequences. This study presents a new application of classical machine learning (ML) to global sequence alignment. Customized ML models are used to implement NW global alignment. An accuracy of 99.7% is achieved when using a multilayer perceptron with the ADAM optimizer, and up to 2912 Giga cell updates per second are realized on two real DNA sequences with a length of 4.1 M nucleotides. Our implementation is valid for RNA/DNA sequences. This study aims to parallelize the computation steps involved in the algorithm to accelerate its performance by using ML algorithms. All datasets used in this study are available from https://ieee-dataport.org/documents/dna-sequence-alignment-datasets-based-nw-algorithm.

OptCAM: An ultra‐fast all‐optical architecture for DNA variant discovery

Nowadays, the accelerated expansion of genetic data challenges speed of current DNA sequence alignment algorithms due to their electrical implementations. Essential needs of an efficient and accurate method for DNA variant discovery demand new approaches for parallel processing in real time. Fortunately, photonics, as an emerging technology in data computing, proposes optical correlation as a fast similarity measurement algorithm; while complexity of existing local alignment algorithms severely limits their applicability. Hence, in this paper, employing optical correlation for global alignment, we present an optical processing approach for local DNA sequence alignment to benefit both high-speed processing and operational parallelism, inherently exist in optics. The proposed method, named as OptCAM, utilizes amplitude and wavelength of the optical signals, to accurately locate mutations through three main procedures. Furthermore, an all-optical implementation of the OptCAM method is proposed consisting of three units, corresponding to the three OptCAM procedures. Performing considerably fast processes by passing optical signals through high-throughput photonic devices, OptCAM avoids various limitations of electrical implementations. Accuracy and efficiency of the OptCAM method and its optical implementation are validated through numerical simulation by a gold standard simulation benchmark. The results indicate the proposed method is significantly faster than its electrical counterparts, in both single node and grid computation.

Real-Time Low-Power FPGA Architecture for Stereo Vision

Stereo vision is a well-known technique used to extract depth information from two or more images. In the last years, many efforts have been made to achieve high quality and efficient results. Although different techniques have been proposed to develop a working system. In this brief, we implemented a previous proposed algorithm for DNA sequence alignment, in order to align images on a field programmable gate array architecture. The design is embedded in a full processing pipeline for the use on low budget boards. The main goal of the project is to develop a real-time device with low power consumption. Such requirements should allow the use in critical and battery dependent applications. The written code is well parametrized and the synthesis is supported for different image resolution and disparity levels. Thanks to an easy refactoring, it is possible to migrate the architecture to every FPGA present on the market. The design reaches the processing ability of $1024\boldsymbol {\times }768$ pixels, 64 disparity levels, 30 FPS with a power usage on chip of only 0.17 W.

DNA Sequence Alignment Algorithm Based on k-tuple Statistics

DNA Sequence Alignment is one of the most basic and most important operations in bioinformatics. In this paper, we put forward to SDkS algorithm based on k-tuple statistic, which is a kind of probability method. The positive transition probability matrix, the negative transition probability matrix, and the logarithmic ration are computed based on Markov model. After obtaining converting sequence, we get the results of k-tuple statistic. Subsequently, the similarity measurement can be gained according to above parameters. When the minimum support is given, we can identify two DNA sequences is similarity or not. The experimental results show that SDkS algorithm has better effective performance in saving computing time.

Simple scalable nucleotic FPGA based short read aligner for exhaustive search of substitution errors

Abstract With the advent of the new and continuously improving technologies, in a couple of years DNA sequencing can be as commonplace as a simple blood test. The growth of sequencing efficiency has a larger exponent than the Moore’s law of standard processors, hence alignment and further processing of sequenced data is the bottleneck. The usage of FPGA (Field Programmable Gate Arrays) technology may provide an efficient alternative. We propose a simple algorithm for DNA sequence alignment, which can be realized efficiently by nucleotic principal agents of Non.Neumann nature. The prototype FPGA implementation runs on a small Terasic DE1-SoC demo board with a Cyclone V chip. We present test results and furthermore analyse the theoretical scalability of this system, showing that the execution time is independent of the length of reference genome sequences. A special advantage of this parallel algorithm is that it performs exhaustive search producing all match variants up to a predetermined number of point (mutation) errors.

A Novel Approach to Detect Malware Based on API Call Sequence Analysis

In the era of ubiquitous sensors and smart devices, detecting malware is becoming an endless battle between ever-evolving malware and antivirus programs that need to process ever-increasing security related data. For malware detection, various approaches have been proposed. Among them, dynamic analysis is known to be effective in terms of providing behavioral information. As malware authors increasingly use obfuscation techniques, it becomes more important to monitor how malware behaves for its detection. In this paper, we propose a novel approach for dynamic analysis of malware. We adopt DNA sequence alignment algorithms and extract common API call sequence patterns of malicious function from malware in different categories. We find that certain malicious functions are commonly included in malware even in different categories. From checking the existence of certain functions or API call sequence patterns matched, we can even detect new unknown malware. The result of our experiment shows high enough F-measure and accuracy. API call sequence can be extracted from most of the modern devices; therefore, we believe that our method can detect the malware for all types of the ubiquitous devices.

Fast DNA Sequence Alignment Algorithm Based on Quality Score Using Improved Dynamic Programming and Fuzzy Gap Cost Control

A sequence alignment algorithm is a basic building block for protein analysis and nucleic acid analysis in bioinformatics. Such alignment represents the similarities and differences of two or more compared sequences. Thus, there have been many algorithms and tools studied and developed. In this paper, we focus on the PHRED based sequence alignment algorithm using Needleman-Wunsch dynamic programming. Although it is well known and proven to be reliable to some extent, it suffers from the heavy computation of producing scoring matrix based on dynamic programming whose time complexity is O(mn). We propose a method applying quadrant method in that process to reduce the computational loads. Also, PHRED based algorithms suffer from the environment when low quality bases are frequently in tips of DNA fragments. Thus, we designed a fuzzy logic system to control the gap cost dynamically to improve the quality of the alignment. In the experiment using real genome data from NCBI (National Center for Biotechnology Information), we verify that the proposed method reduces the computational loads by half in producing scoring matrix and thus the alignment quality is also improved by our fuzzy inference system. Keywords: DNA sequence alignment, dynamic programming, fuzzy inference system, gap cost, quadrant, quality score.

Channels Reallocation In Cognitive Radio Networks Based On DNA Sequence Alignment

Nowadays, It has been shown that spectrum scarcity increased due to tremendous growth of new players in wireless base system by the evolution of the radio communication. Resent survey found that there are many areas of the radio spectrum that are occupied by authorized user/primary user (PU), which are not fully utilized. Cognitive radios (CR) prove to next generation wireless communication system that proposed as a way to reuse this under-utilised spectrum in an opportunistic and non-interfering basis. A CR is a self-directed entity in a wireless communications environment that senses its environment, tracks changes, and reacts upon its findings and frequently exchanges information with the networks for secondary user (SU). However, CR facing collision problem with tracks changes i.e. reallocating of other empty channels for SU while PU arrives. In this paper, channels reallocation technique based on DNA sequence alignment algorithm for CR networks has been proposed.

A DNA sequence alignment algorithm using quality information and a fuzzy inference method

DNA sequence alignment algorithms in computational molecular biology have been improved by diverse methods. In this paper, we propose a DNA sequence alignment that uses quality information and a fuzzy inference method developed based on the characteristics of DNA fragments and a fuzzy logic system in order to improve conventional DNA sequence alignment methods that uses DNA sequence quality information. In conventional algorithms, DNA sequence alignment scores are calculated by the global sequence alignment algorithm proposed by Needleman–Wunsch, which is established by using quality information of each DNA fragment. However, there may be errors in the process of calculating DNA sequence alignment scores when the quality of DNA fragment tips is low, because only the overall DNA sequence quality information are used. In our proposed method, an exact DNA sequence alignment can be achieved in spite of the low quality of DNA fragment tips by improvement of conventional algorithms using quality information. Mapping score parameters used to calculate DNA sequence alignment scores are dynamically adjusted by the fuzzy logic system utilizing lengths of DNA fragments and frequencies of low quality DNA bases in the fragments. From the experiments by applying real genome data of National Center for Biotechnology Information, we could see that the proposed method is more efficient than conventional algorithms.

Using the PGAS Programming Paradigm for Biological Sequence Alignment on a Chip Multi-Threading Architecture

The Partitioned Global Address Space (PGAS) programming paradigm offers ease-of-use in expressing parallelism through a global shared address space while emphasizing performance by providing locality awareness through the partitioning of this address space. Therefore, the interest in PGAS programming languages is growing and many new languages have emerged and are becoming ubiquitously available on nearly all modern parallel architectures. Recently, new parallel machines with multiple cores are designed for targeting high performance applications. Most of the efforts have gone into benchmarking but there are a few examples of real high performance applications running on multicore machines. In this paper, we present and evaluate a parallelization technique for implementing a local DNA sequence alignment algorithm using a PGAS based language, UPC (Unified Parallel C) on a chip multithreading architecture, the UltraSPARC T1. Keywords—Partitioned Global Address Space, Unified Parallel C, Multicore machines, Multi-threading Architecture, Sequence alignment.

Uncertainty in homology inferences: Assessing and improving genomic sequence alignment

Sequence alignment underpins all of comparative genomics, yet it remains an incompletely solved problem. In particular, the statistical uncertainty within inferred alignments is often disregarded, while parametric or phylogenetic inferences are considered meaningless without confidence estimates. Here, we report on a theoretical and simulation study of pairwise alignments of genomic DNA at human-mouse divergence. We find that >15% of aligned bases are incorrect in existing whole-genome alignments, and we identify three types of alignment error, each leading to systematic biases in all algorithms considered. Careful modeling of the evolutionary process improves alignment quality; however, these improvements are modest compared with the remaining alignment errors, even with exact knowledge of the evolutionary model, emphasizing the need for statistical approaches to account for uncertainty. We develop a new algorithm, Marginalized Posterior Decoding (MPD), which explicitly accounts for uncertainties, is less biased and more accurate than other algorithms we consider, and reduces the proportion of misaligned bases by a third compared with the best existing algorithm. To our knowledge, this is the first nonheuristic algorithm for DNA sequence alignment to show robust improvements over the classic Needleman-Wunsch algorithm. Despite this, considerable uncertainty remains even in the improved alignments. We conclude that a probabilistic treatment is essential, both to improve alignment quality and to quantify the remaining uncertainty. This is becoming increasingly relevant with the growing appreciation of the importance of noncoding DNA, whose study relies heavily on alignments. Alignment errors are inevitable, and should be considered when drawing conclusions from alignments. Software and alignments to assist researchers in doing this are provided at http://genserv.anat.ox.ac.uk/grape/.

FPGA ACCELERATION FOR DNA SEQUENCE ALIGNMENT

In this paper, we present two new hardware architectures that implement the Smith–Waterman algorithm for DNA sequence alignment. Previous low-cost approaches based on Field Programmable Gate Array (FPGA) technology are reviewed in detail and then improved with the goal of increased performance at the same cost (i.e., area). This goal is achieved through low level optimizations aimed to adapt the systolic structure implementing the algorithm to the regular structure of FPGAs, essentially finding the optimum granularity of the systolic cells. The proposed architectures achieve processing rates close to 1 Gbps, clearly outperforming previous approaches. Comparing to the reported FPGA results of the computation of the edit-distance between two DNA sequences, throughput is doubled for the same clock frequency with a minimum area penalty. The design has been implemented on an FPGA-based prototyping board integrated into a bioinformatics system. This has allowed validating the approach in a real system (i.e., including I/O and database access), and comparing the proposed hardware solution to purely software approaches. As shown in the paper, the results are outstanding even for slow-rate buses.

Improvement of Performance of MegaBlast Algorithm for DNA Sequence Alignment

MegaBlast is one of the most important programs in NCBI BLAST (Basic Local Alignment Search Tool) toolkits. However, MegaBlast is computation and I/O intensive. It consumes a great deal of memory w...

A local algorithm for DNA sequence alignment with inversions.

A dynamic programming algorithm to find all optimal alignments of DNA subsequences is described. The alignments use not only substitutions, insertions and deletions of nucleotides but also inversions (reversed complements) of substrings of the sequences. The inversion alignments themselves contain substitutions, insertions and deletions of nucleotides. We study the problem of alignment with non-intersecting inversions. To provide a computationally efficient algorithm we restrict candidate inversions to the K highest scoring inversions. An algorithm to find the J best non-intersecting alignments with inversions is also described. The new algorithm is applied to the regions of mitochondrial DNA of Drosophila yakuba and mouse coding for URF6 and cytochrome b and the inversion of the URF6 gene is found. The open problem of intersecting inversions is discussed.

A local algorithm for dna sequence alignment with inversions

A local algorithm for dna sequence alignment with inversions