Alignment Algorithm Research Articles

Two-Cloud Private Read Alignment to a Public Reference Genome

The human genome is the ultimate identifier of an individual even though most of it is identical across human beings. Biological differences between two individuals are encoded in a set of base pair variations called Single Nucleotide Polymorphisms (SNPs), which may be indicative of an individual's personal information such as skin color and susceptibility to diseases. The large-scale nature of human genome necessitates outsourcing of genomic computations to public clouds. However, this raises some serious privacy concerns. The fact that the human reference template is public poses additional challenges. In this paper, we propose a two-cloud private read alignment algorithm using the Burrows-Wheeler Transform and the FM-Index. Our algorithm runs in the same order of complexity as the core FM-Index alignment algorithm without privacy. Our proposed scheme is able to achieve accuracy comparable to modern alignment algorithms such as Bowtie with complete privacy.

HackGAN: Harmonious Cross-Network Mapping Using CycleGAN With Wasserstein–Procrustes Learning for Unsupervised Network Alignment

Network alignment (NA) that identifies equivalent nodes across networks is an effective tool for integrating knowledge from multiple networks. The state-of-the-art NA methods learn inter-network node similarities based on labeled anchor links, which are costly, time-consuming, and difficult to acquire. Therefore, a few unsupervised network alignment (UNA) methods propose solving NA problems without anchor links. However, most existing UNA methods rely on discriminative attributes to capture nodes’ similarities and are hard to obtain optimal one-to-one alignments. Toward these issues, this article proposes a novel method named HackGAN to solve the UNA problem solely based on the structural information. Specifically, HackGAN represents nodes with embeddings based on an unsupervised graph neural network (GNN) to capture their global and local structural features. After that, it initializes mapping functions to transform the embedding spaces of different networks into the same vector space by iteratively solving the Wasserstein–Procrustes problem. The mapping functions are then refined by an adversarial model with cycle-consistency and Sinkhorn distance losses to obtain optimized one-to-one mappings. Based on the distances between mapped embeddings, accurate and robust results are obtained with a collective alignment algorithm. Experimental comparisons on both synthetic and real-world datasets demonstrate the superiority of HackGAN.

Rapid Initial Heading Alignment for MEMS Land Vehicular GNSS/INS Navigation System

For the low-cost microelectromechanical system (MEMS) inertial navigation system (INS) and global navigation satellite system (GNSS) integrated system in land vehicular applications, quick and accurate coarse alignment is still a challenge, especially for a host vehicle that moves at low speed under low dynamics. In this paper, we propose an in-motion coarse initial heading alignment algorithm for the low-cost MEMS INS aided by the GNSS, which is specifically designed for land vehicular conditions. The principle of the proposed method is straightforward when considered in the context of trajectory matching: the MEMS can compute a relative vehicle trajectory through a dead reckoning (DR) calculator using the gyro-derived attitude solution and the travel distance, and the GNSS can provide an absolute vehicle trajectory through its positioning solution. The initial heading is then computed by comparing these DR-indicated and GNSS-indicated trajectories. The proposed algorithm is verified using a civilian vehicle, a wheeled robot, and an agricultural tractor under different motion and dynamic conditions. The results show that the initial heading could be quickly determined with an accuracy of 0.25°, 0.6°, and 1.6° at a 98.6% confidence level within 5 s for the car, robot and tractor tests, respectively.

A Python-based educational software tool for visualizing bioinformatics alignment algorithms.

Bioinformatics education can be defined as the teaching and learning of how to use software tools, along with mathematical and statistical analysis, to solve biological problems. Although many resources are available, most students still struggle to understand even the simplest sequence alignment algorithms. Applying visualizations to these topics benefits both lecturers and students. Unfortunately, educational software for visualizing step-bystep processes in the user experience of sequence alignment algorithms is rare. In this article, an educational visualization tool for biological sequence alignment is presented, and the source code is released in order to encourage the collaborative power of open-source software, with the expectation of further contributions from the community in the future. Two different modules are integrated to enable a student to investigate the characteristics of alignment algorithms.

Research on Chinese Audio and Text Alignment Algorithm Based on AIC-FCM and Doc2Vec

‘‘Audiobook” is a multimedia-based reading technology that has emerged in recent years. Realizing the alignment of e-book text and book audio is the most important part of its processing. This article describes an audio and text alignment algorithm using deep learning and neural network technology to improve the efficiency and quality of audiobook production. The algorithm first uses dual-threshold endpoint detection technology to segment long audio into short audio with sentence dimensions and recognizes it as short text. The threshold is calculated by AIC-FCM optimized based on simulated annealing genetic algorithm. Then the algorithm uses Doc2vec optimized by the threshold prediction method based on the average length of the short text to calculate the text similarity. Finally, proofread and output the text sequence and audio segment aligned in the time dimension to meet the needs of audiobook production. Experiments show that compared to traditional audio and text alignment algorithms, the proposed algorithm is closer to the ideal segmentation result in long audio segmentation, and the alignment effect is basically the same as Doc2vec and the time complexity is reduced by about 35%.

Adapting the GACT-X Aligner to Accelerate Minimap2 in an FPGA Cloud Instance

In genomic analysis, long reads are an emerging type of data processed by assembly algorithms to recover the complete genome sample. They are, on average, one or two orders of magnitude longer than short reads from the previous generation, which provides important advantages in information quality. However, longer sequences bring new challenges to computer processing, undermining the performance of assembly algorithms developed for short reads. This issue is amplified by the exponential growth of genetic data generation and by the slowdown of transistor technology progress, illustrated by Moore’s Law. Minimap2 is the current state-of-the-art long-read assembler and takes dozens of CPU hours to assemble a human genome with clinical standard coverage. One of its bottlenecks, the alignment stage, has not been successfully accelerated on FPGAs in the literature. GACT-X is an alignment algorithm developed for FPGA implementation, suitable for any size input sequence. In this work, GACT-X was adapted to work as the aligner of Minimap2, and these are integrated and implemented in an FPGA cloud platform. The measurements for accuracy and speed-up are presented for three different datasets in different combinations of numbers of kernels and threads. The integrated solution’s performance limitations due to data transfer are also analyzed and discussed.

A SE(2)-based transfer alignment for large installation misalignment angle

Transfer alignment is the optimal solution for initial alignment of shipborne or airborne weapons. The need for transfer alignment is increasing at large installation misalignment angles. Traditional linear models have large attitude errors at large installation misalignment angles, nonlinear algorithms are computationally intensive and difficult to be widely used, and quaternion optimization algorithms are sensitive to noise. In this paper, the Lie group theory is introduced for the first time in the study of transfer alignment. The attitude and velocity are represented as the Group of Direct Planar Isometries (SE(2)), and then the SE(2)-based error vectors are constructed by log-linearization. The SE(2)-based linear transfer alignment algorithm is developed for large installation misalignment conditions. The computational effort of the proposed algorithm is very small due to its linear structure, and it has strong engineering application value. The simulation and field test are conducted to illustrate the effectiveness of the proposed transfer alignment algorithm.

Study of the error correction capability of multiple sequence alignment algorithm (MAFFT) in DNA storage

Synchronization (insertions–deletions) errors are still a major challenge for reliable information retrieval in DNA storage. Unlike traditional error correction codes (ECC) that add redundancy in the stored information, multiple sequence alignment (MSA) solves this problem by searching the conserved subsequences. In this paper, we conduct a comprehensive simulation study on the error correction capability of a typical MSA algorithm, MAFFT. Our results reveal that its capability exhibits a phase transition when there are around 20% errors. Below this critical value, increasing sequencing depth can eventually allow it to approach complete recovery. Otherwise, its performance plateaus at some poor levels. Given a reasonable sequencing depth (≤ 70), MSA could achieve complete recovery in the low error regime, and effectively correct 90% of the errors in the medium error regime. In addition, MSA is robust to imperfect clustering. It could also be combined with other means such as ECC, repeated markers, or any other code constraints. Furthermore, by selecting an appropriate sequencing depth, this strategy could achieve an optimal trade-off between cost and reading speed. MSA could be a competitive alternative for future DNA storage.

ICP Algorithm: Theory, Practice and Its SLAM-oriented Taxonomy

The Iterative Closest Point (ICP) algorithm is one of the most important algorithms for ge-ometric alignment of three-dimensional surface registration, which is frequently used in computer vision tasks, including the Simultaneous Localization And Mapping (SLAM) tasks. In this paper, we illustrate the theoretical principles of the ICP algorithm, how it can be used in surface registration tasks, and the traditional taxonomy of the variants of the ICP algorithm. As SLAM is becoming a popular topic, we also introduce a SLAM-oriented taxonomy of the ICP algorithm, based on the characteristics of each type of SLAM task, including whether the SLAM task is online or not and whether the landmarks are present as features in the SLAM task. We make a synthesis of each type of SLAM task by compar-ing several up-to-date research papers and analyzing their implementation details.

ICP Algorithm: Theory, Practice and Its SLAM-oriented Taxonomy

The Iterative Closest Point (ICP) algorithm is one of the most important algorithms for ge-ometric alignment of three-dimensional surface registration, which is frequently used in computer vision tasks, including the Simultaneous Localization And Mapping (SLAM) tasks. In this paper, we illustrate the theoretical principles of the ICP algorithm, how it can be used in surface registration tasks, and the traditional taxonomy of the variants of the ICP algorithm. As SLAM is becoming a popular topic, we also introduce a SLAM-oriented taxonomy of the ICP algorithm, based on the characteristics of each type of SLAM task, including whether the SLAM task is online or not and whether the landmarks are present as features in the SLAM task. We make a synthesis of each type of SLAM task by compar-ing several up-to-date research papers and analyzing their implementation details.

A Polar Integrated Alignment Assisted by DVL Under Large Azimuth Misalignment

The gravity tends to coincide with the earth’s rotation vector in the polar region, degrading the coarse alignment azimuth of the strapdown inertial navigation system (SINS) to a large misalignment. Under a large azimuth misalignment, the linear error model inevitably renders unacceptable theoretical errors in the integrated alignment. In addition, polar region challenge such sensors as a star sensor of celestial navigation, which fails to provide high-precision attitude reference for integrated alignment. Therefore, in allusion to the misalignment mentioned above based on SINS for the polar region, an integrated alignment algorithm is proposed, assisted by the Doppler Velocity Log (DVL). Notably, the work first investigates the extent to which azimuth accuracy deteriorates in the polar region. On the premise of large azimuth misalignment, the nonlinear error model under the transverse frame is derived from the ellipsoid earth model. Additionally, the state and measurement models are constructed, combined with the velocity of DVL as an observation. An adaptive measurement variance matrix is designed for the unscented Kalman filter (UKF) to estimate the attitude error. Eventually, the semi-physical experiments demonstrate that the proposed algorithm significantly improves the initial alignment accuracy of the polar region.

Using Markov chains and temporal alignment to identify clinical patterns in Dementia

In the healthcare sector, resorting to big data and advanced analytics is a great advantage when dealing with complex groups of patients in terms of comorbidities, representing a significant step towards personalized targeting. In this work, we focus on understanding key features and clinical pathways of patients with multimorbidity suffering from Dementia. This disease can result from many heterogeneous factors, potentially becoming more prevalent as the population ages. We present a set of methods that allow us to identify medical appointment patterns within a cohort of 1924 patients followed from January 2007 to August 2021 in Hospital da Luz (Lisbon), and to stratify patients into subgroups that exhibit similar patterns of interaction. With Markov Chains, we are able to identify the most prevailing medical appointments attended by Dementia patients, as well as recurring transitions between these. To perform patient stratification, we applied AliClu, a temporal sequence alignment algorithm for clustering longitudinal clinical data, which allowed us to successfully identify patient subgroups with similar medical appointment activity. A feature analysis per cluster obtained allows the identification of distinct patterns and characteristics. This pipeline provides a tool to identify prevailing clinical pathways of medical appointments within the dataset, as well as the most common transitions between medical specialities within Dementia patients. This methodology, alongside demographic and clinical data, has the potential to provide early signalling of the most likely clinical pathways and serve as a support tool for health providers in deciding the best course of treatment, considering a patient as a whole.

Point cloud registration based on concave hull association

ABSTRACT This letter presents a point cloud coarse registration method based on concave hull association. The assumption is that some internal points may be variable in different scans due to self-occlusion, but the hull vertices in overlap regions are consistent. Therefore, we only use the vertices of the concave hulls to find feature associations and reduce the number of wrong correspondences. Firstly, the method extracts the concave hulls and calculatecalculates the unique feature descriptors for each vertex of the hulls. Then, we use the sample consensus initial alignment algorithm to achieve feature matching and find the coarse registration parameters. The proposed method has been verified on different datasets and compared with state-of-the-art algorithms. The experiments demonstrate that the proposed algorithm eliminates unnecessary correspondences and obtains high efficiency and robustness.

An Adaptive Multiple Backtracking UKF Method Based on Krein Space Theory for Marine Vehicles Alignment Process

The large misalignment errors of horizontal and azimuthal angle cannot be converged rapidly on the sea surface due to extra disturbance of linear and angular motions caused by wind and waves, which increases the time cost of marine vehicles alignment process and ultimately limits the SINS quick working capability. In order to solve the above problem, an adaptive multiple backtracking UKF method based on Krein space theory is proposed in this paper. Firstly, considering time-varying calibration errors of inertial sensors, a novel robust posterior filtering equation in Krein space is designed to improve the matching degree between the physical process and the mathematical model. Moreover, combined with the multiple forward and reverse navigation processes, an adaptive calculation times control method in backtracking UKF based on the innovation is constructed to solve the nonlinear fast alignment problem in the case of large misalignment angle errors with the purpose of further intelligently shortening the alignment time and ensuring alignment accuracy simultaneously in different periods and working environments. The physical experiment results demonstrate that the horizontal and azimuthal alignment accuracy of the proposed algorithm are improved compared with standard CKF alignment algorithm.

A Coarse Alignment Method Based on Vector Observation and Truncated Vectorized $\boldsymbol{\kappa}$-Matrix for Underwater Vehicle

This paper focuses on the coarse alignment problem for the strapdown inertial navigation system (SINS) applied to the underwater vehicle. The coarse alignment is one of the crucial technologies of underwater navigation and determines whether SINS can work normally. However, the traditional coarse alignment methods suffer from the complicated noises hidden in the inertial sensors’ outputs. To solve this problem, a new coarse alignment algorithm based on vector observation and truncated vectorized <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{\kappa}$</tex-math></inline-formula> -matrix is proposed in this paper. Different from previous methods based on the optimal quaternion, the proposed algorithm makes the matrix state-space formulae of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{\kappa}$</tex-math></inline-formula> -matrix vectorized, and truncates the resulting state vectors by implementing the linear dependence within the components of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{\kappa}$</tex-math></inline-formula> -matrix. On one hand, based on the vectorized and truncated <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{\kappa}$</tex-math></inline-formula> -matrix, a linear reduced model of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{\kappa}$</tex-math></inline-formula> -matrix can be applied to the estimation process, and the measurement errors of inertial sensors can be easily restrained using the estimation algorithm. On the other hand, the proposed approach makes up for the drawbacks of the Optimal-REQUEST algorithm which has a conservative covariance matrix and scalar gain. The simulation test and practical experiment demonstrate that the proposed approach can reduce random noise effectively. Therefore, the performance of coarse alignment algorithm can be improved more enormously than the traditional algorithms.

Using the Corpus of North American Spoken English to explore regional variation in millions of eɪ diphthongs

Increases in the quality of automatic speech recognition (ASR) systems and forced alignment algorithms have opened up new horizons for the study of acoustic properties of naturalistic speech (Coto-Solano et al., 2021). This paper describes the procedures used for the automatic extraction and alignment of audio and ASR transcript excerpts containing eɪ diphthongs from the Corpus of North American Spoken English, a 1.3-billion-word corpus of geolocated ASR transcripts of videos uploaded to YouTube channels of local government entities. In total, more than 2 million eɪ tokens were extracted from naturalistic speech in over 2000 locations in the US and Canada. The pipeline utilizes python code to extract audio segments from the DASH manifest of YouTube videos, then feeds them together with the corresponding transcript excerpt to the Montreal Forced Aligner. Targeted segments are then filtered using functions in Parselmouth, a python port of Praat. Because the underlying corpus is searchable and words are annotated with timing information, the extraction procedure is suitable for retrieving acoustic data from a range of naturalistic speech configurations (for example from discussions of particular topics or in the context of specific utterance types such as questions or replies). The corpus contains a variety of speech genres, making it suitable for studies of geophonetic and sociophonetic variation, and pipelines can be constructed to target specific phenomenona in YouTube videos, depending on the underlying research question.

A novel approach to analyze the association characteristics between post-spliced introns and their corresponding mRNA.

Studies have shown that post-spliced introns promote cell survival when nutrients are scarce, and intron loss/gain can influence many stages of mRNA metabolism. However, few approaches are currently available to study the correlation between intron sequences and their corresponding mature mRNA sequences. Here, based on the results of the improved Smith-Waterman local alignment-based algorithm method (SW method) and binding free energy weighted local alignment algorithm method (BFE method), the optimal matched segments between introns and their corresponding mature mRNAs in Caenorhabditis elegans (C.elegans) and their relative matching frequency (RF) distributions were obtained. The results showed that although the distributions of relative matching frequencies on mRNAs obtained by the BFE method were similar to the SW method, the interaction intensity in 5'and 3'untranslated regions (UTRs) regions was weaker than the SW method. The RF distributions in the exon-exon junction regions were comparable, the effects of long and short introns on mRNA and on the five functional sites with BFE method were similar to the SW method. However, the interaction intensity in 5'and 3'UTR regions with BFE method was weaker than with SW method. Although the matching rate and length distribution shape of the optimal matched fragment were consistent with the SW method, an increase in length was observed. The matching rates and the length of the optimal matched fragments were mainly in the range of 60%-80% and 20-30bp, respectively. Although we found that there were still matching preferences in the 5'and 3'UTR regions of the mRNAs with BFE, the matching intensities were significantly lower than the matching intensities between introns and their corresponding mRNAs with SW method. Overall, our findings suggest that the interaction between introns and mRNAs results from synergism among different types of sequences during the evolutionary process.

GRASP: Scalable Graph Alignment by Spectral Corresponding Functions

What is the best way to match the nodes of two graphs? This graph alignment problem generalizes graph isomorphism and arises in applications from social network analysis to bioinformatics. Some solutions assume that auxiliary information on known matches or node or edge attributes is available, or utilize arbitrary graph features. Such methods fare poorly in the pure form of the problem, in which only graph structures are given. Other proposals translate the problem to one of aligning node embeddings, yet, by doing so, provide only a single-scale view of the graph. In this article, we transfer the shape-analysis concept of functional maps from the continuous to the discrete case, and treat the graph alignment problem as a special case of the problem of finding a mapping between functions on graphs. We present GRASP, a method that first establishes a correspondence between functions derived from Laplacian matrix eigenvectors, which capture multiscale structural characteristics, and then exploits this correspondence to align nodes. We enhance the basic form of GRASP by altering two of its components, namely the embedding method and the assignment procedure it employs, leveraging its modular, hence adaptable design. Our experimental study, featuring noise levels higher than anything used in previous studies, shows that the enhanced form of GRASP outperforms scalable state-of-the-art methods for graph alignment across noise levels and graph types, and performs competitively with respect to the best non-scalable ones. We include in our study another modular graph alignment algorithm, CONE, which is also adaptable thanks to its modular nature, and show it can manage graphs with skewed power-law degree distributions.

Comparison between Two Time Synchronization and Data Alignment Methods for Multi-Channel Wearable Biosensor Systems Using BLE Protocol

Wireless wearable sensor systems for biomedical signal acquisition have developed rapidly in recent years. Multiple sensors are often deployed for monitoring common bioelectric signals, such as EEG (electroencephalogram), ECG (electrocardiogram), and EMG (electromyogram). Compared with ZigBee and low-power Wi-Fi, Bluetooth Low Energy (BLE) can be a more suitable wireless protocol for such systems. However, current time synchronization methods for BLE multi-channel systems, via either BLE beacon transmissions or additional hardware, cannot satisfy the requirements of high throughput with low latency, transferability between commercial devices, and low energy consumption. We developed a time synchronization and simple data alignment (SDA) algorithm, which was implemented in the BLE application layer without the need for additional hardware. We further developed a linear interpolation data alignment (LIDA) algorithm to improve upon SDA. We tested our algorithms using sinusoidal input signals at different frequencies (10 to 210 Hz in increments of 20 Hz-frequencies spanning much of the relevant range of EEG, ECG, and EMG signals) on Texas Instruments (TI) CC26XX family devices, with two peripheral nodes communicating with one central node. The analysis was performed offline. The lowest average (±standard deviation) absolute time alignment error between the two peripheral nodes achieved by the SDA algorithm was 384.3 ± 386.5 μs, while that of the LIDA algorithm was 189.9 ± 204.7 μs. For all sinusoidal frequencies tested, the performance of LIDA was always statistically better than that of SDA. These average alignment errors were quite low-well below one sample period for commonly acquired bioelectric signals.

Deep reinforcement learning-based pairwise DNA sequence alignment method compatible with embedded edge devices

Sequence alignment is an essential component of bioinformatics, for identifying regions of similarity that may indicate functional, structural, or evolutionary relationships between the sequences. Genome-based diagnostics relying on DNA sequencing have benefited hugely from the boom in computing power in recent decades, particularly due to cloud-computing and the rise of graphics processing units (GPUs) and other advanced computing platforms for running advanced algorithms. Translating the success of such breakthroughs in diagnostics to affordable solutions for low-cost healthcare requires development of algorithms that can operate on the edge instead of in the cloud, using low-cost and low-power electronic systems such as microcontrollers and field programmable gate arrays (FPGAs). In this work, we present EdgeAlign, a deep reinforcement learning based method for performing pairwise DNA sequence alignment on stand-alone edge devices. EdgeAlign uses deep reinforcement learning to train a deep Q-network (DQN) agent for performing sequence alignment on fixed length sub-sequences, using a sliding window that is scanned over the length of the entire sequence. The hardware resource-consumption for implementing this scheme is thus independent of the lengths of the sequences to be aligned, and is further optimized using a novel AutoML based method for neural network model size reduction. Unlike other algorithms for sequence alignment reported in literature, the model demonstrated in this work is highly compact and deployed on two edge devices (NVIDIA Jetson Nano Developer Kit and Digilent Arty A7-100T, containing Xilinx XC7A35T Artix-7 FPGA) for demonstration of alignment for sequences from the publicly available Influenza sequences at the National Center for Biotechnology Information (NCBI) Virus Data Hub.