Sequence Alignment Problem Research Articles

Exact Multiple Sequence Alignment by Synchronized Decision Diagrams

This paper develops an exact solution algorithm for the multiple sequence alignment (MSA) problem. In the first step, we design a dynamic programming model and use it to construct a novel multivalued decision diagram (MDD) representation of all pairwise sequence alignments (PSA). PSA MDDs are then synchronized using side constraints to model the MSA problem as a mixed-integer program (MIP), for the first time, in polynomial space complexity. Two bound-based filtering procedures are developed to reduce the size of the MDDs, and the resulting MIP is solved using logic-based Benders decomposition. For a more effective algorithm, we develop a two-phase solution approach. In the first phase, we use optimistic filtering to quickly obtain a near-optimal bound, which we then use for exact filtering in the second phase to prove or obtain an optimal solution. Numerical results on benchmark instances show that our algorithm solves several instances to optimality for the first time, and, in case optimality cannot be proven, considerably improves upon a state-of-the-art heuristic MSA solver. Comparison with an existing state-of-the-art exact MSA algorithm shows that our approach is more time efficient and yields significantly smaller optimality gaps.

Cohort analytics: efficiency and applicability

The abundant availability of health-care data calls for effective analysis methods to help medical experts gain a better understanding of their patients and their health. The focus of existing work has been largely on prediction. In this paper, we introduce Core, a framework for cohort “representation” and “exploration.” Our contributions are twofold: First, we formalize cohort representation as the problem of aggregating the trajectories of its patients. This problem is challenging because cohorts often consist of hundreds of patients who underwent medical actions of various types at different points in time. We prove that producing a representative cohort trajectory is NP-complete with a reduction in the multiple sequence alignment problem. We propose a heuristic that extends the Needleman–Wunsch algorithm for sequence matching to handle temporal sequences. To further improve cohort representation efficiency, we introduce “trajectory families” and “stratified sampling.” Our second contribution is formalizing the problem of cohort exploration as finding a set of cohorts that are similar to a cohort of interest and that maximize entropy. This problem is challenging because the potential number of similar cohorts is huge. We prove NP-completeness with a reduction in the maximum edge subgraph problem. To address complexity, we develop a multi-staged approach based on limiting the search space to “contrast cohorts.” To speed up the computation of cohort similarity, we use “event sets” that are inspired from the double dictionary encoding proposed for keyword search. Moreover, we explore the usefulness and efficiency of Core using an extensive set of qualitative and quantitative experiments on two real health-care datasets. In a user study with medical experts, we show that Core reduces time-to-insight from hours to seconds and helps them find better insights than baseline approaches. Moreover, we show that the obtained cohort representations offer the right trade-off between quality and performance. We study the benefits of trajectory families and stratified sampling for cohort representation and show their applicability on large and heterogeneous cohorts. We also show the benefit of event sets for cohort exploration in providing interactive performance.

Calibrating Seed-Based Heuristics to Map Short Reads With Sesame.

The increasing throughput of DNA sequencing technologies creates a need for faster algorithms. The fate of most reads is to be mapped to a reference sequence, typically a genome. Modern mappers rely on heuristics to gain speed at a reasonable cost for accuracy. In the seeding heuristic, short matches between the reads and the genome are used to narrow the search to a set of candidate locations. Several seeding variants used in modern mappers show good empirical performance but they are difficult to calibrate or to optimize for lack of theoretical results. Here we develop a theory to estimate the probability that the correct location of a read is filtered out during seeding, resulting in mapping errors. We describe the properties of simple exact seeds, skip seeds and MEM seeds (Maximal Exact Match seeds). The main innovation of this work is to use concepts from analytic combinatorics to represent reads as abstract sequences, and to specify their generative function to estimate the probabilities of interest. We provide several algorithms, which together give a workable solution for the problem of calibrating seeding heuristics for short reads. We also provide a C implementation of these algorithms in a library called Sesame. These results can improve current mapping algorithms and lay the foundation of a general strategy to tackle sequence alignment problems. The Sesame library is open source and available for download at https://github.com/gui11aume/sesame.

Non-parametric and semi-parametric support estimation using SEquential RESampling random walks on biomolecular sequences

Non-parametric and semi-parametric resampling procedures are widely used to perform support estimation in computational biology and bioinformatics. Among the most widely used methods in this class is the standard bootstrap method, which consists of random sampling with replacement. While not requiring assumptions about any particular parametric model for resampling purposes, the bootstrap and related techniques assume that sites are independent and identically distributed (i.i.d.). The i.i.d. assumption can be an over-simplification for many problems in computational biology and bioinformatics. In particular, sequential dependence within biomolecular sequences is often an essential biological feature due to biochemical function, evolutionary processes such as recombination, and other factors. To relax the simplifying i.i.d. assumption, we propose a new non-parametric/semi-parametric sequential resampling technique that generalizes “Heads-or-Tails” mirrored inputs, a simple but clever technique due to Landan and Graur. The generalized procedure takes the form of random walks along either aligned or unaligned biomolecular sequences. We refer to our new method as the SERES (or “SEquential RESampling”) method. To demonstrate the performance of the new technique, we apply SERES to estimate support for the multiple sequence alignment problem. Using simulated and empirical data, we show that SERES-based support estimation yields comparable or typically better performance compared to state-of-the-art methods.

Practical regular expression constrained sequence alignment

The regular expression constrained sequence alignment problem is that of producing an optimal score alignment of two sequences which, at the same time, has aligned portions that are both described by a regular expression.In this article we introduce a series of improvements over existing algorithms to solve the problem when the regular expression has no Kleene closures. This is the case of biological patterns, such as those described by PROSITE and other databases. Such improvements include the construction of more compact ϵ-free NFAs for patterns and a significant reduction on automata transitions tracking during the alignment. The time complexity of our solution is O(nmk2), improving on the best known solution O(nmk3|Σ|3/log⁡(k|Σ|)), where n and m are sequence lengths, k is the number of single elements in a PROSITE pattern and Σ is the alphabet.Our experiments have shown that practical running time is significantly smaller than the theoretical worst case mentioned above, and that the solution may be used efficiently in practice for solving the regular expression constrained alignment problem for biological sequences.

Aligning molecular sequences using hybrid bioinspired algorithm in GPU

To explicate the functionality of the basic cell, there is a need for the study of bioinformatics. To better understand the structural and functional information of molecules, sequence analysis is considered as the root domain. In this, aligning the sequence is the first step, an NP-complete problem like all biological problems. Owing to the increased molecular data in biology, there is a demand for the development of efficient approaches to this sequence alignment problem. From the study it is concluded that there is trade-off between accuracy and computational time. Focusing on the latter in this paper, a new parallel hybridised bio-inspired approach (PGWOGO) is proposed without sacrificing the accuracy. A grey wolf optimiser technique is hybridised with the genetic operators and the parallel phases are implemented in Quadro 4,000 graphics processing unit. A new crossover and mutation operator's namely horizontal crossover and local gaps shuffle mutation operator between aligned blocks are employed. The performance of proposed algorithm is evaluated using the cells update per second (CUPS) and compared with the state-of-the-art techniques. The results show that the proposed algorithm yields better alignment than other techniques.

A Categorization of Relevant Sequence Alignment Algorithms with Respect to Data Structures

Sequence Alignment is an active research subfield of bioinformatics. Today, sequence databases are rapidly and steadily increasing. Thus, to overcome this issue, many efficient algorithms have been developed depending on various data structures. The latter have demonstrated considerable efficacy in terms of run-time and memory consumption. In this paper, we briefly outline existing methods applied to the sequence alignment problem. Then we present a qualitative categorization of some remarkable algorithms based on their data structures. Specifically, we focus on research works published in the last two decades (i.e. the period from 2000 to 2020). We describe the employed data structures and expose some important algorithms using each. Then we show potential strengths and weaknesses among all these structures. This will guide biologists to decide which program is best suited for a given purpose, and it also intends to highlight weak points that deserve attention of bioinformaticians in future research.

Aligning molecular sequences using hybrid bioinspired algorithm in GPU

To explicate the functionality of the basic cell, there is a need for the study of bioinformatics. To better understand the structural and functional information of molecules, sequence analysis is considered as the root domain. In this, aligning the sequence is the first step, an NP-complete problem like all biological problems. Owing to the increased molecular data in biology, there is a demand for the development of efficient approaches to this sequence alignment problem. From the study it is concluded that there is trade-off between accuracy and computational time. Focusing on the latter in this paper, a new parallel hybridised bio-inspired approach (PGWOGO) is proposed without sacrificing the accuracy. A grey wolf optimiser technique is hybridised with the genetic operators and the parallel phases are implemented in Quadro 4,000 graphics processing unit. A new crossover and mutation operator's namely horizontal crossover and local gaps shuffle mutation operator between aligned blocks are employed. The performance of proposed algorithm is evaluated using the cells update per second (CUPS) and compared with the state-of-the-art techniques. The results show that the proposed algorithm yields better alignment than other techniques.

Using deep reinforcement learning approach for solving the multiple sequence alignment problem

In the present paper, we use a deep reinforcement learning (DRL) approach for solving the multiple sequence alignment problem which is an NP-complete problem. Multiple Sequence Alignment problem simply refers to the process of arranging initial sequences of DNA, RNA or proteins in order to maximize their regions of similarity. Multiple Sequence Alignment is the first step in solving many bioinformatics problems such as constructing phylogenetic trees. In this study, our proposed approach models the Multiple Sequence Alignment problem as a DRL problem and utilizes long short-term memory networks for estimation phase in the reinforcement learning algorithm. Furthermore, the actor-critic algorithm with experience-replay method is used for much quicker convergence process. Using deep Q-learning (an RL approach) and Q-network overcomes the complexity of other approaches. The experimental evaluation is performed on 8 different real-life datasets and in every used dataset our approach outperforms other well-known approaches and tools such as MAFFT, ClustalW, and other heuristic approaches in case of scoring in solving the MSA problem.

Exploring efficient data parallelism for genome read mapping on multicore and manycore architectures

Nowadays heterogeneous architectures formed by multicore and manycore systems have become attractive solutions to cope with the data booming in genomic-based studies. Our work explores the efficient usage of heterogeneous architectures in such area. In particular, we have studied the use of manycore components like the Xeon Phi accelerator, which has proved to be a convenient choice because it allows an easy migration of applications developed for multicore servers based on the × 86 architecture. Our study also focuses on the problem of sequence alignment, which is one of the fundamental and most costly computational stages in most genome variant studies. We concentrate our attention on BWA, one of the most popular sequence aligners, and we have focused our attention on three types of heterogeneous systems, one containing Intel multi-core CPUs and accelerators, one that are made up of several multi-core servers, and one large-scale system. Each with different characteristics in terms of number of CPUs, number of cores and system organization memory. Although the problem of alignment of sequences fits in the embarrassingly parallel pattern, achieving good performance and good scalability in heterogeneous environments can be complex. We have analyzed different strategies based on the distribution of data and the replication of certain data structures and we found that MDPR (Multi-level Data Parallelization and Replication) strategy has shown the best results in all the heterogeneous platforms tested. Its results have surpassed other strategies proposed in the literature and have shown its malleability to be used in different heterogeneous environments without the need to apply specific adjustments according to the underlying architecture. In the design of MDPR, different static and dynamic data distribution strategies have also been evaluated. The best results were obtained by the static strategy, which has a significant preprocessing cost. However, the dynamic strategy of data distribution using a round-robin mechanism obtained similar times without the need for the preprocessing stage. Although our proposal was applied to BWA using human genome data samples, this strategy can be easily applied to other sequence datasets and alignment tools that have similar operating principles with those of BWA aligner.

Semiglobal Sequence Alignment with Gaps Using GPU.

In this paper, we consider the pair-wise semiglobal sequence alignment problem with gaps, which is motivated by the re-sequencing problem that requires to assemble short reads sequences into a genome sequence by referring to a reference sequence. The problem has been studied before for single gap and bounded number of gaps. For single gap, there is a GPU-based algorithm proposed (Barton et al., 2015). In our work, we propose a GPU-based algorithm for the bounded number of gaps case, called GPUGapsMis. We implement the algorithm and compare the performance with the CPU-based algorithm, called CPUGapsMis. The algorithm has two distinct stages: the alignment phase, and the backtrack phase. We investigate several different approaches, in order to determine the most favorable for this problem, by means of a Hybrid model or a wholly-GPU based model, as well as the alignment of single text sequences or multiple text sequences on the GPU at a time. We show that the alignment phase of the algorithm is a good candidate for parallelization, with peak speedup of 11 times. We show that although the backtracking phase is sequential, it is more beneficial to perform it on the GPU, as opposed to returning to the CPU and performing there. When performing both phases on the GPU, GPUGapsMis achieves a peak speedup of 10.4 times against CPUGapsMis. Our data parallel GPU algorithm achieves results which are an improvement on those of an existing GPU data parallel implementation (Ojiaku, 2014).

Analysis of Resource Utilization on GPU

The problems arising due to massive data storage and data analysis can be handled by recent technologies, like cloud computing and parallel computing. MapReduce, MPI, CUDA, OpenMP, OpenCL are some of the widely available tools and techniques that use multithreading approach. However, it is a challenging task to use these technologies effectively to handle the compute intensive problems in the fields like life science, environment, fluid dynamics, image processing, etc. In this paper, we have used many core platforms with graphics processing units (GPU) to implement one of very important and fundamental problem of sequence alignment in the field of bioinformatics. Dynamic and concurrent kernel features offered by graphics card are used to speed up the performance. With these features, we achieved a speed up of around 120X and 55X. We have coupled well-known tiling technique with these features and observed a performance improvement up to 4X and 2X, as compared to non-tiling execution. The paper also analyses resource parameters, GPU occupancy and proposes their relationship with the design parameters for the chosen algorithm. These observations have been quantified and the relationship between the parameters is presented. The results of study can be extended further to study similar algorithms in this area.

Multiple Sequence Alignment Problem Solution Using Genetic Algorithm

Multiple Sequence Alignment Problem Solution Using Genetic Algorithm

Optimal Pair DNA Sequence Alignment based on Matching Regions and Multi-Zone Genetic Algorithm

DNA sequence alignment is an important and challenging task in Bioinformatics, which is used for finding the optimal arrangement between two sequences. In this paper, two methods are proposed in two stages to solve the pairwise sequence alignment problem. The first method is Matching Regions(MR) concerns on splitting the DNA into regions with adaptive interleaving windows to isolate the DNA tape into matched and non-matched regions. Additionally, a Multi-Zone Genetic Algorithm (MZGA) is proposed as an improved method in the second stage. It consists of segmenting a large non-matched region into smaller search space. Then, the MZGA is implemented in parallel to save time. Genetic Algorithm can be applied as an optimization toolto produce multiple solutions. Furthermore, the improvement focuses on the enhancement of Simple GA operators. In the selection, the population is divided into three Zones according to the fitness score. A new crossover approach is proposed depending on cut-points and location of gaps. The proposed method guarantees that the value of fitness tends to improvement or convergence in each successive generation. Thus, the offspring of populations will have better fitness value. The system has been applied to the real-world dataset of DNA with variable lengths which are ranged from 66 bases up to 26037 bases. As a result, the proposed technique satisfied the best alignment score of the DNA sequences. Finally, it is worth mentioning that the proposed system proved to be generalizable.

A hybrid solver for protein multiple sequence alignment problem.

In this work, a novel hybrid model called PSOSA for solving multiple sequence alignment (MSA) problem is proposed. The developed approach is a combination between particle swarm optimization (PSO) algorithm and simulated annealing (SA) technique. In our PSOSA approach, PSO is exploited in global search, but it is easily trapped into local optimum and may lead to premature convergence. SA is incorporated as local improvement approach to overcome local optimum problem and intensify the search in local regions to improve solution quality. Numerical results on BAliBASE benchmark have shown the effectiveness of the proposed method and its ability to achieve good quality solutions when compared with those given by other existing methods.

Optimality Regions and Fluctuations for Bernoulli Last Passage Models

We study the sequence alignment problem and its independent version, the discrete Hammersley process with an exploration penalty. We obtain rigorous upper bounds for the number of optimality regions in both models near the soft edge. At zero penalty the independent model becomes an exactly solvable model and we identify cases for which the law of the last passage time converges to a Tracy-Widom law.

Solving Large Problems with Heuristic Search: General-Purpose Parallel External-Memory Search


 
 
 Classic best-first heuristic search algorithms, like A*, record every unique state they encounter in RAM, making them infeasible for solving large problems. In this paper, we demonstrate how best-first search can be scaled to solve much larger problems by exploiting disk storage and parallel processing and, in some cases, slightly relaxing the strict best-first node expansion order. Some previous disk-based search algorithms abandon best-first search order in an attempt to increase efficiency. We present two case studies showing that A*, when augmented with Delayed Duplicate Detection, can actually be more efficient than these non-best-first search orders. First, we present a straightforward external variant of A*, called PEDAL, that slightly relaxes best-first order in order to be I/O efficient in both theory and practice, even on problems featuring real-valued node costs. Because it is easy to parallelize, PEDAL can be faster than in-memory IDA* even on domains with few duplicate states, such as the sliding-tile puzzle. Second, we present a variant of PEDAL, called PE2A*, that uses partial expansion to handle problems that have large branching factors. When tested on the problem of Multiple Sequence Alignment, PE2A* is the first algorithm capable of solving the entire Reference Set 1 of the standard BAliBASE benchmark using a biologically accurate cost function. This work shows that classic best-first algorithms like A* can be applied to large real-world problems. We also provide a detailed implementation guide with source code both for generic parallel disk-based best-first search and for Multiple Sequence Alignment with a biologically accurate cost function. Given its effectiveness as a general-purpose problem-solving method, we hope that this makes parallel and disk-based search accessible to a wider audience.
 
 

Swarm intelligence for optimizing the parameters of multiple sequence aligners

Abstract Different aligner heuristics can be found in the literature to solve the Multiple Sequence Alignment problem. These aligners rely on the parameter configuration proposed by their authors (also known as default parameter configuration), that tried to obtain good results (alignments with high accuracy and conservation) for any input set of unaligned sequences. However, the default parameter configuration is not always the best parameter configuration for every input set; namely, depending on the biological characteristics of the input set, one may be able to find a better parameter configuration that outputs a more accurate and conservative alignment. This work's main contributions include: to study the input set's biological characteristics and to then apply the best parameter configuration found depending on those characteristics. The framework uses a pre-computed file to take the best parameter configuration found for a dataset with similar biological characteristics. In order to create this file, we use a Particle Swarm Optimization (PSO) algorithm, that is, an algorithm based on swarm intelligence. To test the effectiveness of the characteristic-based framework, we employ five well-known aligners: Clustal W, DIALIGN-TX, Kalign2, MAFFT, and MUSCLE. The results of these aligners see clear improvements when using the proposed characteristic-based framework.

A Simple Linear Space Algorithm for Computing Nonoverlapping Inversion and Transposition Distance in Quadratic Average Time.

In the sequence alignment problem, it is important to compare DNA sequences to retrieve relevant information and align these sequences. An inversion and a translocation are important operations in comparing DNA sequences in biosequence analysis. The alignment problem with nonoverlapping inversions and translocations is to find an alignment with nonoverlapping inversions and translocations for the given two strings X and Y. This problem has interesting application for finding a common sequence from two mutated sequences. A linear space and quadratic average time algorithm to compute the mutation distance between two strings of the same length under nonoverlapping inversions and transpositions is presented in this article. The recursive formula for this purpose is novel to the best of our knowledge. The space costs of the algorithms to solve the same problem are typically quadratic, and thus, our original algorithm is the first linear space algorithm to solve the mutation distance problem.

EGSA: a new enhanced gravitational search algorithm to resolve multiple sequence alignment problem

Multiple sequence alignment is a very important and useful tool for genomic analysis in many tasks in bioinformatics. However, finding an accurate alignment of DNA or protein sequences is very difficult since the computational effort required grows exponentially with the sequences number. In this paper, we propose a new sequence alignment algorithm based on gravitational search algorithm (GSA). The gravitational search algorithm (GSA) is a recent metaheuristic inspired from Newton's laws of universal gravitation and motion. Moreover, to avoid the convergence toward local optima, we enhance GSA behaviour by introducing a new mechanism based on simulated annealing concept. Such concept offers a good balance between the exploration and exploitation in GSA and can lead to good alignment quality for the MSA problem. The accuracy and efficiency of the proposed algorithm are compared with recent and well-known alignment methods using BAliBASE benchmark database. The analysis of the experimental results shows that the algorithm can achieve competitive solutions quality.