Alignment Algorithm Research Articles

Protein structure alignment by reseek improves sensitivity to remote homologs.

Recent breakthroughs in protein fold prediction from amino acid sequences have unleashed a deluge of new structures, presenting new opportunities and challenges to bioinformatics. Reseek is a novel protein structure alignment algorithm based on sequence alignment where each residue in the protein backbone is represented by a letter in a "mega-alphabet" of 85,899,345,920 (∼1011) distinct states. Reseek achieves substantially improved sensitivity to remote homologs compared to state-of-the-art methods including DALI, TMalign and Foldseek, with comparable speed to Foldseek, the fastest previous method. Scaling to large databases of AI-predicted folds is analyzed. Foldseek E-values are shown to be under-estimated by several orders of magnitude, while Reseek E-values are in good agreement with measured error rates. https://github.com/rcedgar/reseek. Supplementary data are available at Bioinformatics online.

Assessing 3D volumetric asymmetry in facial palsy patients via advanced multi-view landmarks and radial curves

The research on facial palsy, a unilateral palsy of the facial nerve, is a complex field with many different causes and symptoms. Even modern approaches to evaluate the facial palsy state rely mainly on stills and 2D videos of the face and rarely on dynamic 3D information. Many of these analysis and visualization methods require manual intervention, which is time-consuming and error-prone. Moreover, they often depend on alignment algorithms or Euclidean measurements and consider only static facial expressions. Volumetric changes by muscle movement are essential for facial palsy analysis but require manual extraction. We propose to extract an estimated unilateral volumetric description for dynamic expressions from 3D scans. Accurate landmark positioning is required for processing the unstructured facial scans. In our case, it is attained via a multi-view method compatible with any existing 2D predictors. We analyze prediction stability and robustness against head rotation during video sequences. Further, we investigate volume changes in static and dynamic facial expressions for 34 patients with unilateral facial palsy and visualize volumetric disparities on the face surface. In a case study, we observe a decrease in the volumetric difference between the face sides during happy expressions at the beginning (13.8 ± 10.0 mm3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {mm}^{3}$$\\end{document}) and end (12.8 ± 10.3 mm3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {mm}^{3}$$\\end{document}) of a ten-day biofeedback therapy. The neutral face kept a consistent volume range of 11.8-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$-$$\\end{document}12.1 mm3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {mm}^3$$\\end{document}. The reduced volumetric difference after therapy indicates less facial asymmetry during movement, which can be used to monitor and guide treatment decisions. Our approach minimizes human intervention, simplifying the clinical routine and interaction with 3D scans to provide a more comprehensive analysis of facial palsy.

Large DNA deletions occur during DNA repair at 20-fold lower frequency for base editors and prime editors than for Cas9 nucleases.

When used to edit genomes, Cas9 nucleases produce targeted double-strand breaks in DNA. Subsequent DNA-repair pathways can induce large genomic deletions (larger than 100 bp), which constrains the applicability of genome editing. Here we show that Cas9-mediated double-strand breaks induce large deletions at varying frequencies in cancer cell lines, human embryonic stem cells and human primary T cells, and that most deletions are produced by two repair pathways: end resection and DNA-polymerase theta-mediated end joining. These findings required the optimization of long-range amplicon sequencing, the development of a k-mer alignment algorithm for the simultaneous analysis of large DNA deletions and small DNA alterations, and the use of CRISPR-interference screening. Despite leveraging mutated Cas9 nickases that produce single-strand breaks, base editors and prime editors also generated large deletions, yet at approximately 20-fold lower frequency than Cas9. We provide strategies for the mitigation of such deletions.

A general Bayesian algorithm for the autonomous alignment of beamlines.

Autonomous methods to align beamlines can decrease the amount of time spent on diagnostics, and also uncover better global optima leading to better beam quality. The alignment of these beamlines is a high-dimensional expensive-to-sample optimization problem involving the simultaneous treatment of many optical elements with correlated and nonlinear dynamics. Bayesian optimization is a strategy of efficient global optimization that has proved successful in similar regimes in a wide variety of beamline alignment applications, though it has typically been implemented for particular beamlines and optimization tasks. In this paper, we present a basic formulation of Bayesian inference and Gaussian process models as they relate to multi-objective Bayesian optimization, as well as the practical challenges presented by beamline alignment. We show that the same general implementation of Bayesian optimization with special consideration for beamline alignment can quickly learn the dynamics of particular beamlines in an online fashion through hyperparameter fitting with no prior information. We present the implementation of a concise software framework for beamline alignment and test it on four different optimization problems for experiments on X-ray beamlines at the National Synchrotron Light Source II and the Advanced Light Source, and an electron beam at the Accelerator Test Facility, along with benchmarking on a simulated digital twin. We discuss new applications of the framework, and the potential for a unified approach to beamline alignment at synchrotron facilities.

Multiple Function Merging for Code Size Reduction

Resource-constrained environments, such as embedded devices, have limited amounts of memory and storage. Practical programming languages such as C++ and Rust tend to output multiple similar functions by monomorphizing polymorphic functions. An optimization technique called Function Merging, which merges similar functions into a single function, has been studied. However, in the state-of-the-art approach, the number of functions that can be merged at once is limited to two; thus, efficiently merging three or more functions, which are often generated from polymorphic functions, has been impossible. In this study, we propose Multiple Function Merging optimization, which targets merging three or more similar functions into a single function using a multiple sequence alignment algorithm. With multiple aligned information, Multiple Function Merging can increase merge opportunities and reduce extra branching overheads at the code generation stage. We evaluated it using the SPEC CPU benchmark suite and some large-scale C/C++ programs, and the results show that it reduces code size by as much as 7.61% compared with the state-of-the-art approach.

Broadband 3-bit coding metasurface antenna with integrated radiation and scattering performance

This paper presents the design of an integrated metasurface antenna, which combines a central radiating patch with a quadru-arc (QAS) structure. The metasurface antenna simultaneously achieves high-gain radiation and complex scattering functionality. The modulation of the radiation function is primarily achieved through phase manipulation of the power division feed network, while modulation of the X-polarization scattering function is mainly accomplished by adjusting the arc of the QAS. The effectiveness of this design is verified by designing two metasurface antennas with distinct functionalities. The feed network phases are arranged in a checkerboard pattern in the first approach, resulting in four-beam radiation with a gain of 16 dBi per beam. Additionally, the scattering component utilizes eight scattering structures with a phase difference of 45 degrees to form a 3-bit coding, enabling vortex beam scattering. The second configuration arranges the feed network in phase with the deflected beam, resulting in a deflected beam radiation pattern characterized by a gain of 22.3 dBi. The scattering function is optimized using a simulated annealing-genetic algorithm for phase alignment, resulting in the achievement of RCS reduction across a wide bandwidth range of 8-24 GHz. The proposed metasurface antenna is ultimately fabricated and subjected to rigorous measurements.

BIMSA: Accelerating Long Sequence Alignment Using Processing-In-Memory.

Recent advances in sequencing technologies have stressed the critical role of sequence analysis algorithms and tools in genomics and healthcare research. In particular, sequence alignment is a fundamental building block in many sequence analysis pipelines and is frequently a performance bottleneck both in terms of execution time and memory usage. Classical sequence alignment algorithms are based on dynamic programming and often require quadratic time and memory with respect to the sequence length. As a result, classical sequence alignment algorithms fail to scale with increasing sequence lengths and quickly become memory-bound due to data-movement penalties. Processing-In-Memory (PIM) is an emerging architectural paradigm that seeks to accelerate memory-bound algorithms by bringing computation closer to the data to mitigate data-movement penalties. This work presents BIMSA (Bidirectional In-Memory Sequence Alignment), a PIM design and implementation for the state-of-the-art sequence alignment algorithm BiWFA (Bidirectional Wavefront Alignment), incorporating new hardware-aware optimizations for a production-ready PIM architecture (UPMEM). BIMSA supports aligning sequences up to 100K bases, exceeding the limitations of state-of-the-art PIM implementations. First, BIMSA achieves speedups up to 22.24 × (11.95× on average) compared to state-of-the-art PIM-enabled implementations of sequence alignment algorithms. Second, achieves speedups up to 5.84 × (2.83× on average) compared to the highest-performance multicore CPU implementation of BiWFA. Third, BIMSA exhibits linear scalability with the number of compute units in memory, enabling further performance improvements with upcoming PIM architectures equipped with more compute units and achieving speedups up to 9.56 × (4.7× on average). Code and documentation are publicly available at https://github.com/AlejandroAMarin/BIMSA.

FLASHQuant: A Fast Algorithm for Proteoform Quantification in Top-Down Proteomics.

Accurate quantification of individual proteoforms is a crucial step in identifying proteome-wide alterations in different biological conditions. Intact proteoforms have been analyzed predominantly by liquid chromatography-mass spectrometry (LC-MS)-based top-down proteomics (TDP) and quantified primarily by the label-free quantification (LFQ) method, as it requires no additional costly labeling. In TDP, due to frequent coelution and complex signal structures, overlapping signals deriving from multiple proteoforms complicate accurate quantification. Here, we introduce FLASHQuant for MS1-level LFQ analysis in TDP, which is capable of automatically resolving and quantifying coeluting proteoforms. In benchmark tests performed with both spike-in proteins and proteome-level mixture data sets, FLASHQuant was shown to perform highly accurate and reproducible quantification in short runtimes of just a few minutes per LC-MS run. In particular, it was demonstrated that resolving overlapping proteoforms boosts the quantification accuracy. FLASHQuant is publicly available as platform-independent open-source software at https://openms.org/flashquant/, accompanied by the simple alignment algorithm ConsensusFeatureGroupDetector for multiple LC-MS runs.

AUTOMATIC MACHINE TOOL ALIGNMENT ON ITS FOUNDATION

This article deals with the issue of the automatic setting of machines on their base. The focus is on machines with a self-supporting frame, freestanding, on more than three supports. The alignment uses the previously presented method of precise and repeatable machine placement on the foundation. As a result, the machine has the necessary parameters in terms of its geometry, and at the same time a defined weight distribution on the individual building elements is guaranteed. The paper presents a new method for automatic machine alignment using so-called “smart feet”. Due to this, the process of machine alignment is clearly defined, repeatable and does not depend on the experience of the personnel performing the alignment. The presented algorithm makes it possible to automate the entire machine-setting process. The article includes the presentation of a machine placement on its base, which is used for testing and verification of the functionality of the alignment algorithm.

An Operations Chain Model for Automatic Assessment of Operation Procedure for Equipment Operators

Automatic assessment of the operation ability of operators based on computers is an essential approach for improving the effectiveness of equipment operation training and enhancing equipment safety. Present methods primarily focus on the operation results but pay less attention to the operation procedure. One reason is that there is a lack of a model that has the ability to describe all probable paths to accomplish the same task. Therefore, an operations chain model is put forward for the first time to describe the standard operation procedure and relationships among operations based on the decomposition of operational tasks and the relationships among the various operations required to fulfill the task. A specific operation task corresponds to an operations chain, which will form one or multiple standard operation sequences that will allow trainees to complete the same task through different paths. The Needleman–Wunsch sequence alignment algorithm is introduced to match the trainees’ operation sequence with all standard sequences. The maximum alignment result is the score of the trainees’ operations. An example shows that the operations chain model can accurately describe the complex structure of the standard operating procedures. The Needleman–Wunsch sequence alignment algorithm can objectively evaluate the trainee’s operation capabilities. The combination of the operations chain model and sequence alignment algorithm can form a complete operation procedure assessment method that is friendlier to trainees and has more objective evaluation results. The method will help to improve the effectiveness of the competency assessment of equipment operators.

Timed alignments with mixed moves

We study conformance checking for timed models, that is, process models that consider both the sequence of events that occur, as well as the timestamps at which each event is recorded. Time-aware process mining is a growing subfield of research, and as tools that seek to discover timing-related properties in processes develop, so does the need for conformance-checking techniques that can tackle time constraints and provide insightful quality measures for time-aware process models. One of the most useful conformance artefacts is the alignment, that is, finding the minimal changes necessary to correct a new observation to conform to a process model. In this paper, we extend the notion of timed distance from a previous work where an edit on an event’s timestamp came in two types, depending on whether or not it would propagate to its successors. Here, these different types of edits have a weighted cost each, and the ratio of their costs is denoted by α. We then solve the purely timed alignment problem in this setting for a large class of these weighted distances (corresponding to α∈{1}∪[2,∞)). For these distances, we provide linear time algorithms for both distance computation and alignment on models with sequential causal processes.

Hyperdimensional computing: A fast, robust, and interpretable paradigm for biological data.

Advances in bioinformatics are primarily due to new algorithms for processing diverse biological data sources. While sophisticated alignment algorithms have been pivotal in analyzing biological sequences, deep learning has substantially transformed bioinformatics, addressing sequence, structure, and functional analyses. However, these methods are incredibly data-hungry, compute-intensive, and hard to interpret. Hyperdimensional computing (HDC) has recently emerged as an exciting alternative. The key idea is that random vectors of high dimensionality can represent concepts such as sequence identity or phylogeny. These vectors can then be combined using simple operators for learning, reasoning, or querying by exploiting the peculiar properties of high-dimensional spaces. Our work reviews and explores HDC's potential for bioinformatics, emphasizing its efficiency, interpretability, and adeptness in handling multimodal and structured data. HDC holds great potential for various omics data searching, biosignal analysis, and health applications.

Approximate IsoRank for Scalable and Functionally Meaningful Cross-Species Alignments of Protein Interaction Networks.

The IsoRank algorithm of Singh, Xu, and Berger was a pioneering algorithmic advance that applied spectral methods to the problem of cross-species global alignment of biological networks. We develop a new IsoRank approximation that exploits the mathematical properties of IsoRank's linear system to solve the problem in quadratic time with respect to the maximum size of the two protein-protein interaction (PPI) networks. We further propose a refinement to this initial approximation so that the updated result is even closer to the original IsoRank formulation while remaining computationally inexpensive. In experiments on synthetic and real PPI networks with various proposed metrics to measure alignment quality, we find the results of our approximate IsoRank are nearly as accurate as the original IsoRank. In fact, for functional enrichment-based measures of global network alignment quality, our approximation performs better than the exact IsoRank, which is doubtless because it is more robust to the noise of missing or incorrect edges. It also performs competitively against two more recent global network alignment algorithms. We also present an analogous approximation to IsoRankN, which extends the network alignment to more than two species.

Multi-Position Inertial Alignment Method for Underground Pipelines Using Data Backtracking Based on Single-Axis FOG/MIMU.

The inertial measurement method of pipelines utilizes a Micro-Electro-Mechanical Systems Inertial Measurement Unit (MIMU) to get the three-dimensional trajectory of underground pipelines. The initial attitude is significant for the inertial measurement method of pipelines. The traditional method to obtain the initial attitude uses three-axis magnetometers to measure the Earth's magnetic field. However, the magnetic field in urban underground pipelines is intricate, which leads to the initial attitude being inaccurate. To overcome this challenge, a novel multi-position initial alignment method based on data backtracking for a single-axis FOG and a three-axis Micro-Electro-Mechanical Inertial Measurement Unit (MIMU) is proposed. Firstly, the configuration of the sensors is determined. Secondly, according to the three-point support structure of the pipeline measuring instrument, a three-position alignment scheme is designed. Additionally, an initial alignment algorithm using the data backtracking method is developed. In this algorithm, a rough initial alignment is conducted by the data from single-axis FOG, and a fine initial alignment is conducted by the data from FOG/MIMU. Finally, an experiment was conducted to validate this method. The experiment results indicate that the pitch and roll angle errors are less than 0.05°, and the azimuth angle errors are less than 0.2°. This improved the precision of the 3-D trajectory of underground pipelines.

Research on Multi-Modal Music Score Alignment Model for Online Music Education

As music data storage becomes increasingly diverse in the era of big data, ensuring alignment of music works with the same semantics for online music education is crucial. To achieve this, a multi-modal music score alignment algorithm model based on deep learning was developed and optimized. Experimental results demonstrated that Note + DCO feature combination yielded the best MIDI input characteristics (mean value: 13.27 ms), whereas CQT feature comparison produced the best results for audio input (average: 12.85 ms). The ResNet-34 network was noted to have the most effective music score alignment effect with alignment errors averaging less than one frame. Compared with other algorithms, the proposed algorithm had the lowest average value of 9.28 ms, median value of 5.85 ms, and standard deviation of 20.17 ms. Actual music retrieval showed a Top-1 retrieval accuracy of 10.93% that was close to 11%. Overall, the proposed algorithm is significant for score alignment and music retrieval recognition in online music education.

GANs-generated synthetic datasets for face alignment algorithms in complex environments

Face alignment has matured over the past several decades, but privacy violations or data abuse have also triggered global controversy. Moreover, existing face algorithms are still challenging in complex environments. For the question: ”Can synthetic datasets introduce novel variations in real-world data?”. We proposed a new research direction concerning key point detection tasks utilizing synthetic datasets, aiming to reduce the model’s reliance on real-world datasets. Considering the differences between synthetic and real-world data, our work proposed two different transfer ways based on GANs: (1) S→R model converts the synthetic face images generated by the Face middleware 3D model (FaceGen) into more realistic face images for training face alignment. (2) R→S model converts the real-world face images into a synthetic style image for testing face alignment. Extensive experiments explored the synthetic data complementarity and availability.

In-motion fine alignment algorithm for AUV based on improved extended state observer and Kalman filter

Abstract Achieving fast initial alignment has always been a major challenge for autonomous underwater vehicle navigation, especially in motion. During the fine alignment process, traditional algorithms often face the slow convergence of the azimuth misalignment angle. To solve this problem, this paper proposes a fast alignment algorithm based on the improved extended state observer (ESO) and Kalman filter. Specifically, since the application potential of ESO in fine alignment has not been fully explored by previous studies, this paper introduces the improved ESO through the in-depth research of traditional ESO. The obtained improved ESO is then combined with Kalman filter to constitute the proposed fast alignment algorithm. The algorithm first uses Kalman filter to estimate the two horizontal misalignment angles. Once the horizontal angles estimation of Kalman filter tends to be stable, the improved ESO is used to estimate the azimuth misalignment angle. Simulation and lake test show the proposed algorithm has excellent performance of in-motion alignment. The comparative analysis shows that the algorithm significantly improves the convergence speed of azimuth alignment angle.

Enhancing representation in radiography-reports foundation model: a granular alignment algorithm using masked contrastive learning

Recently, multi-modal vision-language foundation models have gained significant attention in the medical field. While these models offer great opportunities, they still face crucial challenges, such as the requirement for fine-grained knowledge understanding in computer-aided diagnosis and the capability of utilizing very limited or even no task-specific labeled data in real-world clinical applications. In this study, we present MaCo, a masked contrastive chest X-ray foundation model that tackles these challenges. MaCo explores masked contrastive learning to simultaneously achieve fine-grained image understanding and zero-shot learning for a variety of medical imaging tasks. It designs a correlation weighting mechanism to adjust the correlation between masked chest X-ray image patches and their corresponding reports, thereby enhancing the model’s representation learning capabilities. To evaluate the performance of MaCo, we conducted extensive experiments using 6 well-known open-source X-ray datasets. The experimental results demonstrate the superiority of MaCo over 10 state-of-the-art approaches across tasks such as classification, segmentation, detection, and phrase grounding. These findings highlight the significant potential of MaCo in advancing a wide range of medical image analysis tasks.

Molecular similarities between the genes for Trypanosoma cruzi microtubule-associated proteins, mammalian interferons, and TRIMs

Initial studies using bioinformatics analysis revealed DNA sequence similarities between Trypanosoma cruzi GenBank® M21331, coding for Antigen 36 (Ag 36), and tripartite motif (TRIM) genes. TRIM40 showed 9.7% identity to GenBank M21331, and four additional TRIM genes had identities greater than 5.0%. TRIM37 showed a continuous stretch of identity of 12 nucleotides, that is, at least 25% longer than any of the other TRIMs. When we extended our analysis on the relationships of GenBank M21331 to further innate immune genes, using the Needleman-Wunsch (NW) algorithm for alignment, identities to human IFN-α, IFN-β, and IFN-γ genes of 13.6%, 12.6%, and 17.9%, respectively, were found. To determine the minimum number of genes coding for proteins closely related to Ag 36, a BLAST-p search was conducted with it versus the T. cruzi genome. The BLAST-p search revealed that T. cruzi GenBank M21331 had 14 gene sequences homologous to microtubule-associated protein (MAP) genes with 100% amino acid sequence identity. To verify the similarities in non-human genes, a study comparing TRIM21 region sequences among mammalian species to the comparable human TRIM21 region showed that related sequences were also present in 11 mammalian species. The MAP genes homologous to Ag 36 form a family of at least 14 genes which mimic human immune genes in the IFN and TRIM families. This mimicry is of gene sequences and not their protein products or epitopes. These results appear to be the first description of molecular mimicry of immune genes in humans by a protozoan parasite.

RNAMotifProfile: a graph-based approach to build RNA structural motif profiles.

RNA structural motifs are the recurrent segments in RNA three-dimensional structures that play a crucial role in the functional diversity of RNAs. Understanding the similarities and variations within these recurrent motif groups is essential for gaining insights into RNA structure and function. While recurrent structural motifs are generally assumed to be composed of the same isosteric base interactions, this consistent pattern is not observed across all examples of these motifs. Existing methods for analyzing and comparing RNA structural motifs may overlook variations in base interactions and associated nucleotides. RNAMotifProfile is a novel profile-to-profile alignment algorithm that generates a comprehensive profile from a group of structural motifs, incorporating all base interactions and associated nucleotides at each position. By structurally aligning input motif instances using a guide-tree-based approach, RNAMotifProfile captures the similarities and variations within recurrent motif groups. Additionally, RNAMotifProfile can function as a motif search tool, enabling the identification of instances of a specific motif family by searching with the corresponding profile. The ability to generate accurate and comprehensive profiles for RNA structural motif families, and to search for these motifs, facilitates a deeper understanding of RNA structure-function relationships and potential applications in RNA engineering and therapeutic design.