Fast and Optimal Sequence-to-Graph Alignment Guided by Seeds

We present a novel A* seed heuristic enabling fast and optimal sequence-to-graph alignment, guaranteed to minimize the edit distance of the alignment assuming non-negative edit costs. We phrase optimal alignment as a shortest path problem and solve it by instantiating the A* algorithm with our novel seed heuristic. The key idea of the seed heuristic is to extract seeds from the read, locate them in the reference, mark preceding reference positions by crumbs, and use the crumbs to direct the A* search. We prove admissibility of the seed heuristic, thus guaranteeing alignment optimality. Our implementation extends the free and open source AStarix aligner and demonstrates that the seed heuristic outperforms all state-of-the-art optimal aligners including GraphAligner, Vargas, PaSGAL, and the prefix heuristic previously employed by AStarix. Specifically, we achieve a consistent speedup of >60x on both short Illumina reads and long HiFi reads (up to 25kbp), on both the E. coli linear reference genome (1Mbp) and the MHC variant graph (5Mbp). Our speedup is enabled by the seed heuristic consistently skipping >99.99% of the table cells that optimal aligners based on dynamic programming compute.

Stacking the Joints

This chapter presents foundational principles of optimal alignment based on biomechanics and body mapping. This approach to physical alignment moves away from a traditional static posture, toward a model that places the breath at the heart of balanced movement. Key components the music instructor can draw from to support retraining students’ physical alignment include heightening their kinesthetic awareness through body mapping movement explorations and sharing factual information about joint alignment and the breathing mechanism. Guidelines for the instructors’ approach to incorporating these elements are introduced. Such guidelines include use of imagery, prioritizing ease, and using appropriate language.

Multiple sequence alignment using enhanced bird swarm align algorithm

Discovering structural, functional and evolutionary information in biological sequences have been considered as a core research area in Bioinformatics. Multiple Sequence Alignment (MSA) tries to align all sequences in a given query set to provide us ease in annotation of new sequences. Traditional methods to find the optimal alignment are computationally expensive in real time. This research presents an enhanced version of Bird Swarm Algorithm (BSA), based on bio inspired optimization. Enhanced Bird Swarm Align Algorithm (EBSAA) is proposed for multiple sequence alignment problem to determine the optimal alignment among different sequences. Twenty-one different datasets have been used in order to compare performance of EBSAA with Genetic Algorithm (GA) and Particle Swarm Align Algorithm (PSAA). The proposed technique results in better alignment as compared to GA and PSAA in most of the cases.

Optimized Surgical Strategy for Adult Spinal Deformity: Quantitative Lordosis Correction versus Lordosis Morphology

Background: In surgical correction of adult spinal deformity (ASD), pelvic incidence (PI)-lumbar lordosis (LL) plays a key role to restore normal sagittal alignment. Recently, it has been found that postoperative lordosis morphology act as an important factor in preventing mechanical complications. However, there have been no studies on the effect of postoperative lordosis morphology on the restoration of sagittal alignment. The primary objective of this study was to evaluate the effect of postoperative lordosis morphology on achievement of optimal sagittal alignment. The secondary objective was to find out which radiographic or morphologic parameter affects sagittal alignment in surgical correction of ASD. Methods: 228 consecutive patients with lumbar degenerative kyphosis who underwent deformity correction and long-segment fixation from T10 to S1 with sacropelvic fixation and follow-up over 2 years were enrolled. Patients were divided according to whether optimal alignment was achieved (balanced group) or not (non-balanced group) at last follow-up. We analyzed the differences of postoperative radiographic parameters and morphologic parameters between two groups. Correlation analysis and stepwise multiple linear regression analysis was performed to predict the effect of PI-LL and morphologic parameters on the sagittal vertical axis (SVA). Results: Of 228 patients, 195 (85.5%) achieved optimal alignment at last follow-up. Two groups significantly differed in postoperative and last follow-up LL (p < 0.001 and p = 0.028, respectively) and postoperative and last follow-up PI-LL (p < 0.001 and p = 0.001, respectively). Morphologic parameters did not significantly differ between the two groups except lower lordosis arc angle (=postoperative sacral slope). In correlation analysis and stepwise multiple linear regression analysis, postoperative PI-LL was the only parameter which had significant association with last follow-up SVA (R2 = 0.134, p < 0.001). Morphologic parameters did not have any association with last follow-up SVA. Conclusions: When planning spine reconstruction surgery, although considering postoperative lordosis morphology is necessary, it is still very important considering proportional lordosis correction based on individual spinopelvic alignment (PI-LL) to achieve optimal sagittal alignment.

The Dynamic Time Warping as a means to assess modeled solar wind time series

&lt;p&gt;In this work, the Dynamic Time Warping (DTW) technique is presented as an alternative method to assess the performance of modeled solar wind time series at Earth (or at any other point in the heliosphere). This method can quantify how similar two time series are by providing a temporal alignment between them, in an optimal way and under certain restrictions. It eventually estimates the optimal alignment between an observed and a modeled series, which we call the warping path, by providing a single number, the so-called DTW cost. A description on the reasons why DTW should be applied as a metric for the assessment of solar wind time series, is presented. Furthermore, examples on how exactly the technique is applied to our modeled solar wind datasets with EUHFORIA, are shown and discussed.&lt;/p&gt;&lt;p&gt;&lt;span&gt;&lt;span&gt;&lt;em&gt;This project has received funding from the European Union&amp;#8217;s Horizon 2020 research and innovation programme under grant agreement No 870437 (SafeSpace).&lt;/em&gt;&lt;/span&gt;&lt;/span&gt;&lt;/p&gt;

Identity: rapid alignment-free prediction of sequence alignment identity scores using self-supervised general linear models

Pairwise global alignment is a fundamental step in sequence analysis. Optimal alignment algorithms are quadratic—slow especially on long sequences. In many applications that involve large sequence datasets, all what is needed is calculating the identity scores (percentage of identical nucleotides in an optimal alignment—including gaps—of two sequences); there is no need for visualizing how every two sequences are aligned. For these applications, we propose Identity, which produces global identity scores for a large number of pairs of DNA sequences using alignment-free methods and self-supervised general linear models. For the first time, the new tool can predict pairwise identity scores in linear time and space. On two large-scale sequence databases, Identity provided the best compromise between sensitivity and precision while being faster than BLAST, Mash, MUMmer4 and USEARCH by 2–80 times. Identity was the best performing tool when searching for low-identity matches. While constructing phylogenetic trees from about 6000 transcripts, the tree due to the scores reported by Identity was the closest to the reference tree (in contrast to andi, FSWM and Mash). Identity is capable of producing pairwise identity scores of millions-of-nucleotides-long bacterial genomes; this task cannot be accomplished by any global-alignment-based tool. Availability: https://github.com/BioinformaticsToolsmith/Identity.

Texture Recognition Using Gabor Filter for Extracting Feature Vectors With the Regression Mining Algorithm

This article considered only natural types of texture and then applying the Gabor filter for better classifications. The concept used is to discard the stochastic features to avoid any mixing of feature vector while it is extracting from the image dataset. The proposed approach has considered the Gabor filter for texture recognition primarily but with the combined method of spatial width and orientation to get the optimal alignment, this optical alignment mine the maximum feature vector by applying the REP algorithm over the data mined from the texture. This will result in better accuracy in the results. Initially, the frequency response over the surface due to applying Gabor filter has been calculated and then the work proceeded in a manner that first natural images are loaded into the MATLAB tool then it is preprocessed, and then final classifications are performed for final results. The primarily concentrated over texture information of image datasets rather than the multispectral information along with REP regression algorithm to do actual mining of feature vectors. Unlike the conventional approach of the Gabor filter, this article focuses on the variance and spatial relationship between two or more than two pixels. The deviation calculated is used for normalizing the feature vectors, and the accuracy can be hence increase using the proposed commuted technique.

ComPotts: Optimal alignment of coevolutionary models for protein sequences

To assign structural and functional annotations to the ever increasing amount of sequenced proteins, the main approach relies on sequence-based homology search methods, e.g. BLAST or the current state-of-the-art methods based on profile Hidden Markov Models (pHMMs), which rely on significant alignments of query sequences to annotated proteins or protein families. While powerful, these approaches do not take coevolution between residues into account. Taking advantage of recent advances in the field of contact prediction, we propose here to represent proteins by Potts models, which model direct couplings between positions in addition to positional composition. Due to the presence of non-local dependencies, aligning two Potts models is computationally hard. To tackle this task, we introduce an Integer Linear Programming formulation of the problem and present ComPotts, an implementation able to compute the optimal alignment of two Potts models representing proteins in tractable time. A first experimentation on 59 low sequence identity pairwise alignments, extracted from 3 reference alignments from sisyphus and BaliBase3 databases, shows that ComPotts finds better alignments than the other tested methods in the majority of these cases.