Cognitively Economical Heuristic for Multiple Sequence Alignment under Uncertainties

Multi-objective two-level swarm intelligence approach for multiple RNA sequence-structure alignment

We present a new method for the calculation of multiple sequence alignments (MSAs). The input to our problem are n protein sequences. We assume that the sequences are related with each other and that there exists some unknown evolutionary tree that corresponds to the MSA. One advantage of our method is that the scoring can be done with reference to this phylogenetic tree, even though the tree structure itself may remain unknown. Instead of computing an evolutionary tree, we only need to compute a circular tour of the tree which is determined via a traveling salesman problem (TSP) algorithm. Our algorithm can calculate a near optimal MSA and has a performance guarantee of n-1/n.opt (where opt is the optimal score of the MSA). The algorithm runs in O(k/sup 2/n/sup 2/) time, where k is the length of the longest input sequence. From there, we improve the alignment further. Experimental results are shown at the end.

Aligning multiple DNA or protein sequences is a fundamental step in the analyses of phylogeny, homology and molecular structure. Heuristic algorithms are applied because optimal multiple sequence alignment is prohibitively expensive. Heuristic alignment algorithms represent a practical trade-off between speed and accuracy, but they can be improved. We present EVALYN (EVolved ALYNments), a novel approach to multiple sequence alignment in which sequences are progressively aligned based on a guide tree optimized by a genetic algorithm. We hypothesize that a genetic algorithm can find better guide trees than traditional, deterministic clustering algorithms. We compare our novel evolutionary approach to CLUSTAL W and find that EVALYN performs consistently and significantly better as measured by a common alignment scoring technique. Additionally, we hypothesize that evolutionary guide tree optimization is inherently efficient and has less time complexity than the commonly-used neighbor-joining algorithm. We present a compelling analysis in support of this scalability hypothesis.

Multiple Sequence Alignment (MSA) is one of the most fundamental problems in computational molecular biology. The running time of the best known scheme for finding an optimal alignment, based on dynamic programming, increases exponentially with the number of input sequences. Hence, many heuristics were suggested for the problem. We consider a version of the MSA problem where the goal is to find an optimal alignment in which matches are restricted to positions in predefined matching segments. We present several techniques for making the dynamic programming algorithm more efficient, while still finding an optimal solution under these restrictions. We prove that it suffices to find an optimal alignment of the predefined sequence segments, rather than single letters, thereby reducing the input size and thus improving the running time. We also identify "shortcuts" that expedite the dynamic programming scheme. Empirical study shows that, taken together, these observations lead to an improved running time over the basic dynamic programming algorithm by 4 to 12 orders of magnitude, while still obtaining an optimal solution. Under the additional assumption that matches between segments are transitive, we further improve the running time for finding the optimal solution by restricting the search space of the dynamic programming algorithm.

Electroless plating is a chemical process commonly used in semiconductor manufacturing that deposits a uniform metal coating onto a substrate through an auto-catalytic reduction reaction without using electricity. High quality deposition hinges on the precise control of bath composition and operating conditions, which in turn requires accurate and accessible monitoring of the bath chemical concentration. While chemical analyzers present an effective monitoring technique, they are generally limited by high costs, complexity, and maintenance. Time-delayed measurements also impose challenges on real-time operations, and any reduction to the monitoring lag would be highly beneficial. We introduce a data-driven machine learning (ML) approach able to achieve fast concentration predictions directly from operating conditions, and able to compute the prediction uncertainty that is valuable for subsequent robust control and optimization and for improving transparency and trustworthiness of ML tools in manufacturing settings. Notably, our ML procedure overcomes challenges of asynchronous time-series training data with missing values, and where available data are often noisy and sparse. Our ML approach begins with a data preprocessing step to handle asynchronous measurements and missing values by engineering features rooted in the underlying physical processes. Notably, a systematic feature selection is performed to down select features that are most correlated with the prediction target, thereby reduce model overfitting. We then compare a number of regression model architectures for capturing the sequential data relationships, including linear models, random forest, extreme gradient boosting, and fully connected, long short-term memory, and transformer neural networks. We quantify the model uncertainty by training them in a Bayesian manner using scalable Stein variational gradient descent, to compute the posterior probability distributions conditioned on the training data that reflect the uncertainty in the models induced by the quality and quantity of the available observations. We demonstrate the overall ML approach on a real-world dataset from a semiconductor manufacturing plant that exhibits the aforementioned data challenges. The best performing models exhibited 1, 5, 12% accuracy (defined as achieving within a 3% margin) improvements on predicting the metal ion, alkali, reductant concentrations, respectively, over a naive feature extraction technique. It also achieved 13, 10, 32% accuracy improvements over an autoregressive integrated moving average (ARIMA) baseline. These results show the approach’s effectiveness in providing reliable predictions with quantified uncertainty.

Multiple sequence alignment (MSA) is a vital problem in biology. Optimal alignment of multiple sequences becomes impractical even for a modest number of sequences [1] since the general version of the problem is NP-hard. Because of the high time complexity of traditional MSA algorithms, even today’s fast computers are not able to solve the problem for large number of sequences. In this paper we present a randomized algorithm to calculate distance matrices which is a major step in many multiple sequence alignment algorithms. The basic idea employed is sampling (along the lines of [2]).KeywordsMultiple Sequence AlignmentDistance MatrixInput FilePairwise AlignmentAlignment ScoreThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Gap costs for multiple sequence alignment

Multiple sequence alignment involves alignment of more than two sequences and is an NP-complete problem. Therefore, heuristic algorithms that use different criteria to find an approximation to the optimal solution are employed. At the heart of these approaches lie the scoring and objective functions that a given algorithm uses to compare competing solutions in constructing a multiple sequence alignment. These objective functions are often motivated by the biological paradigms that govern functional similarities and evolutionary relations. Most existing approaches utilize a progressive process where the final alignment is constructed sequentially by adding new sequences into an existing multiple sequence alignment matrix, which is dynamically updated. In doing this, the core scoring function to assess accuracies of pairwise alignments generally remains the same, while the objective functions used in intermediary steps differ. Nevertheless, the overall assessment of the final multiple sequence alignment is generally calculated by an extension of pairwise scorings. In this chapter, we explore different scoring and objective functions used in calculating the accuracy and optimization of a multiple sequence alignment and provide utilization of these criteria in popularly used multiple sequence alignment algorithms.

Methodology and Application of Pattern Mining in Multiple Alarm Flood Sequences

An evolutionary methodology using multiple sequence alignment technique with optimal search algorithms particle swarm optimization and differential evaluation is proposed in this paper. Proposed methodology algorithms are termed as Multiple Sequence Alignment and Particle Swarm Optimization (MSAPSO) and Multiple Sequence Alignment and Differential Evaluation (MSADE). These techniques are developed to categorize gene sequences based on optimal result produced for each generation. These evolutionary techniques encompasses of two phases in designing. In first phase MSA is pragmatic on pair wise sequences to generate aligned sequence as output. The sequence generated as output in the first level will be given as input to second phase. In the second segment optimal search algorithms PSO or DE are applied on sequences which generate optimal value and generation best value for each generation. These values are considered for further categorization. This paper presents analysis of MSAPSO and MSADE on gene sequences.

Multiple Sequence Alignment (MSA) is an important operation in Bioinformatics, used to simultaneously compare 3 or more sequences. The MSA problem was proven NP-Hard, so strategies have been proposed to reduce the search space and solve it in parallel. Recently, asymmetric multicore processors (AMPs) have become popular, with performance and energy-efficient cores, like the P-Cores and E-cores from Intel. However, parallel MSA applications have complex access patterns and adapting them for AMPs can be challenging. In this paper, we propose PA-Star2<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>, an asymmetric-aware strategy based on A-Star, which computes optimal MSAs taking asymmetry into account when distributing the search space among threads. Our experimental results show that the proposed optimizations can reduce considerably the average execution time of PA-Star2 achieving a speedup of up to 7.70×. We also show that the asymmetric-aware strategy can reduce the average execution time for one of the hardest sequences set from the BAliBASE benchmark, when compared to the symmetric counterpart. Finally, we show that our approach is energy-efficient.<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>1PA-Star2 is open source and the code is publicly available at1PA-Star2 is open source and the code is publicly available at https://github.com/danielsundfeld/astar_msa

The conventional multiple sequence alignment algorithms are classi ed into two categories: iterative improvement strategies (e.g., [1]) and simulated annealing methods (e.g., [6]). Recently, a genetic algorithm has been used in computational molecular biology as a powerful combinatorial optimizer. A genetic algorithm has been applied to the problem of multiple sequence alignment on a parallel computer (e.g., [7]). In a simple genetic algorithm [3], a solution of a given problem is represented as \chromosomes which consists of bit strings of 0's and 1's. The genetic operations, such as reproduction, crossover and mutation, are applied to a population of chromosomes to create a new population of chromosomes. This process is repeated many times so that we can obtain a nearly optimal alignment. Here, we propose a improved method to apply a genetic algorithm to the problem of multiple sequence aligment. The processing was performed on a Fujitsu SPARCstation 20 and NEC UP4800.

Resource elasticity and server consolidation have long been among two of cloud computing’s most relevant management tools. Yet, exemplified with a scientific application use case, this paper highlights how judicious scheduling of tasks can help maximize resource utilization and improve performance and costs for both users and cloud providers. Developing an efficient cloud service for DNA sequence comparisons is adopted as a motivating use case. Using the bioinformatics tool MASA that finds an optimal pair-wise sequence alignment, we propose a model for co-scheduling multiple alignments on a single cloud instance. The resulting, practically optimal, non-preemptive schedule can effectively double the throughput of MASA-based sequence alignment workflows.

Sign language is a crucial communication tool for hearing and deaf communities, with each language containing unique hand positions, motion profiles, and shapes. To develop sign language recognition systems, common input data formats include RGB and depth, considering perspective, articulation distributions, joint visibility, and hand shape. However, difficulties endure in intricate and crowded settings, resulting in increased average errors. An improved deep learning method that makes use of Google's MediaPipe framework and combines Convolutional and Recurrent Neural Networks (RNNs) is suggested as a solution to these problems. While RNNs record temporal correlations in sign language gesture data, CNNs extract spatial characteristics from static images. MediaPipe is essential for preprocessing and feature extraction, as well as for supplying precise hand landmarks and enabling real-time processing. The proposed system shows a significant improvement in recognition accuracy and processing efficiency, with up to 5% improvement in recognition accuracy compared to traditional methods. This work contributes to the advancement of sign language recognition technologies, offering a more reliable and efficient solution for real-time sign language interpretation and text conversion.

The object of this work was to improve multiple sequence alignments using public-domain software and methods as far as possible. A method is described where the secondary structure of proteins is predicted and this information, coupled with a simplified description of the amino acids, is used to produce multiple sequence alignments. This method improved the accuracy of the resulting alignments by between 5 and 14% when compared with full sequence profile alignments (as scored against structural alignments). These improved alignments were used to predict the secondary structure of the sequences they contain. The resultant predictions were more accurate than those produced from less optimal alignments. An improvement of 6% for a three-state (helix, sheet and coil) prediction was observed when using the best alignment from the method presented here and the alignment obtained using sequence only. The method makes use of public domain software and all the associated files required to repeat the work are available from the primary author.