Sequence Length Research Articles

Multi-label text classification via secondary use of large clinical real-world data sets.

Procedural coding presents a taxing challenge for clinicians. However, recent advances in natural language processing offer a promising avenue for developing applications that assist clinicians, thereby alleviating their administrative burdens. This study seeks to create an application capable of predicting procedure codes by analysing clinicians' operative notes, aiming to streamline their workflow and enhance efficiency. We downstreamed an existing and a native German medical BERT model in a secondary use scenario, utilizing already coded surgery notes to model the coding procedure as a multi-label classification task. In comparison to the transformer-based architecture, we were levering the non-contextual model fastText, a convolutional neural network, a support vector machine and logistic regression for a comparative analysis of possible coding performance. About 350,000 notes were used for model adaption. By considering the top five suggested procedure codes from medBERT.de, surgeryBERT.at, fastText, a convolutional neural network, a support vector machine and a logistic regression, the mean average precision achieved was 0.880, 0.867, 0.870, 0.851, 0.870and0.805 respectively. Support vector machines performed better for surgery reports with a sequence length greater than 512, achieving a mean average precision of 0.872 in comparison to 0.840 for fastText, 0.837 for medBERT.de and 0.820 for surgeryBERT.at. A prototypical front-end application for coding support was additionally implemented. The problem of predicting procedure codes from a given operative report can be successfully modelled as a multi-label classification task, with a promising performance. Support vector machines as a classical machine learning method outperformed the non-contextual fastText approach. FastText with less demanding hardware resources has reached a similar performance to BERT-based models and has shown to be more suitable for explaining the predictions efficiently.

TSTA: thread and SIMD-based trapezoidal pairwise/multiple sequence-alignment method

The rapid advancements in sequencing length necessitate the adoption of increasingly efficient sequence alignment algorithms. The Needleman–Wunsch method introduces the foundational dynamic-programming matrix calculation for global alignment, which evaluates the overall alignment of sequences. However, this method is known to be highly time-consuming. The proposed TSTA algorithm leverages both vector-level and thread-level parallelism to accelerate pairwise and multiple sequence alignments. Availability and implementation Source codes are available at https://github.com/bxskdh/TSTA.

A novel carrier index M‐ary differential chaos shift keying modulation scheme

AbstractTo obtain better spectral efficiency and higher bit rate, a novel carrier index M‐ary differential chaos shift keying modulation scheme is proposed. In this system, part of subcarriers is assigned to the chaotic reference so that it can carry extra data bits through carrier index modulation, while one of the remaining subcarriers is activated to transmit the data‐bearing signal. In addition, M‐ary DCSK modulation is also applied to data‐bearing signals based on the constellation theory and Walsh codes. Theoretical bit error rate expressions are derived over the multipath Rayleigh fading and additive white Gaussian noise channels. Simulations and comparisons are performed with various combinations of chaotic sequence lengths, subcarrier numbers and constellation sizes. Results show that the proposed scheme can outperform other counterparts in both spectral efficiency and bit error rate performances.

DsRNAPredictor-II: An improved predictor of identifying dsRNA and its silencing efficiency for Tribolium castaneum based on sequence length distribution

RNA interference (RNAi) has been widely utilized to investigate gene functions and has significant potential for control of pest insects. However, recent studies have revealed that the target insect species, dsRNA molecule length, target genes, and other experimental factors can affect the efficiency of RNAi mediated control, restricting the further development and application of this technology. Therefore, the aim of this study was to establish a deep learning model using bioinformatics to help researchers identify dsRNA fragments with the highest RNAi efficiency. In this study, we optimized an existing model, namely, dsRNAPredictor, by designing sub-models based on different sequence lengths. Accordingly, the data were divided into two groups: 130–399 bp and 400–616 bp long sequences. Then, one-hot encoding was employed to extract sequence information. The convolutional neural network framework comprising three convolutional layers, three average pooling layers, a flattened layer, and three dense layers was employed as the classifier. By adjusting the parameters, we established two sub-models for different sequence distributions. Using multiple independent test datasets and conducting hypothesis testing, we demonstrated that our model exhibits superior performance and strong robustness to dsRNAPredictor, respectively. Therefore, our model may help design dsRNAs with pre-screening potential and facilitate further research and applications.

Fine-tuning the evolutionary stability of recombinant herpesviral transmissible vaccines

Spillover of infectious diseases from wild animal populations constitutes a long-standing threat to human health for which few globally viable solutions have been developed. The use of oral baits laden with conventional vaccines distributed en masse represents one success story but is costly and practicable primarily for rabies risk reduction in North American and European carnivores. Efforts to expand vaccination to control pathogens within less accessible wildlife populations have raised interest in a new kind of vaccine capable of spreading pathogen-specific immunity through autonomous spread. However, such ‘transmissible’ vaccines raise concerns about the irrevocable release of genetically modified viruses into the environment. Herein, we explore the feasibility of an intrinsic strategy for transgene control within these vaccines based on the genetic destabilizing effect of cis -acting sequences flanking the heterologous transgene of interest. While suitable for the control of transgene stability within all types of DNA-viral vectored vaccines, this strategy has particular applicability to transmissible vaccines. Using a combination of experiments, mathematical modelling and whole-genome sequencing, we show that the rate of transgene loss can be controlled by varying the lengths of the direct repeat sequences. This opens a way for fine-tuning the lifespan of a transmissible vaccine in the wild.

A DNA base-specific sequence interposed between CRX and NRL contributes to RHODOPSIN expression

Gene expression emerges from DNA sequences through the interaction of transcription factors (TFs) with DNA cis-regulatory sequences. In eukaryotes, TFs bind to transcription factor binding sites (TFBSs) with differential affinities, enabling cell-specific gene expression. In this view, DNA enables TF binding along a continuum ranging from low to high affinity depending on its sequence composition; however, it is not known whether evolution has entailed a further level of entanglement between DNA-protein interaction. Here we found that the composition and length (22 bp) of the DNA sequence interposed between the CRX and NRL retinal TFs in the proximal promoter of RHODOPSIN (RHO) largely controls the expression levels of RHO. Mutagenesis of CRX-NRL DNA linking sequences (here termed “DNA-linker”) results in uncorrelated gene expression variation. In contrast, mutual exchange of naturally occurring divergent human and mouse Rho cis-regulatory elements conferred similar yet species-specific Rho expression levels. Two orthogonal DNA-binding proteins targeted to the DNA-linker either activate or repress the expression of Rho depending on the DNA-linker orientation relative to the CRX and NRL binding sites. These results argue that, in this instance, DNA itself contributes to CRX and NRL activities through a code based on specific base sequences of a defined length, ultimately determining optimal RHO expression levels.

Physiological Time-Series Fusion With Hybrid Attention for Adaptive Recognition of Pain.

Automatic pain assessment is an application in healthcare serving personalized pain care, and patients cannot self-report pain. Pain at the present is inferred from physiological dynamics at the present and in the near past. However, heterogeneous pain responses cross-subject and cross-type hinder accurate recognition of pain. This work solves the adaptive pain recognition problem across pain types. We concrete the adaptivity problem into recognizing both phasic/short and tonic/long pain from the physiological sequences of the same length. The adaptivity of the proposed solution (TCAtt-PainNet) was ensured by hybrid temporal-channel attention when fusing multivariate time-series of electrocardiogram (ECG) and galvanic skin response (GSR) features. The attention was obtained by learning the dependencies between the point at present and the sequence in the near past, where sequence point temporal attention was constructed via modified self-attention, and the following feature channel attention was constructed by squeeze-and-excitation temporal attention weighted deep feature sequence. The proposed solution successfully enhanced recognition adaptivity by addressing relevant information only from long input sequences when testing with tonic and phasic pain databases, making progress towards automatic pain assessment for real application scenarios with attributes unknown pain.

Engineered FnCas9 mediated mutation profiling for clarithromycin resistance in Helicobacter pylori strains isolated from Indian patients with gastrointestinal disorders

Helicobacter pylori is a highly prevalent gut pathogen with reported implications in a wide range of gastrointestinal disorders. Antibiotic based therapy, especially with clarithromycin is one of most effective treatment strategies against H. pylori. However, rising global prevalence of clarithromycin resistance in certain H. pylori strains, primarily attributed to point mutations in the 23 s ribosomal RNA coding gene, pose a major challenge in the effective eradication of this pathogen. There are a number of established methodologies devised for H. pylori mutation detection, so as to provide a tailored treatment plan to the patients and resist further transmissions of antibiotic resistant strains. However, there is no ‘gold standard’ method available to detect mutation status in clinical isolates of H. pylori from infected patients. CRISPR-Cas9 based technologies have revolutionized the field of mutation detection in biological samples, particularly during the recent COVID-19 pandemic. Although multiple assays have been reported for detection of H. pylori in clinical samples including CRISPR diagnostics (CRISPRDx) platforms, there is no such assay reported till date to detect specific mutations that confer antibiotic resistance to this pathogen. In this study, we have developed an assay using engineered FnCas9 (en31-FnCas9) protein to effectively detect the A2142G and A2143G mutations in the 23 s rDNA of H. pylori strains isolated from gastric biopsy samples. The data from in vitro cleavage assays and strip-based lateral flow tests using en31-FnCas9 and guide RNAs targeting the conserved and mutated loci of H. pylori 23 s rDNA are in perfect congruence with the data from Sanger sequencing and restriction fragment length polymorphism (RFLP) analysis. Our results indicate that en31-FnCas9 based mutation analysis can be deployed as an efficient diagnostic methodology to detect clarithromycin susceptibility in patients with suspected H. pylori infections.

From Ground Up: Increasing Pitching Velocity through Lower-Limb Training

Pitching velocity is a crucial factor in effective baseball pitching performance. Upper-body-focused training is prevalent in baseball training to increase pitching velocity. However, significant pitchers, including youth league baseball players, suffer from upper body injuries due to the heavy training load of the throwing arm. This article emphasizes lower-body training to increase baseball pitching velocity. Three step-by-step recommended activities, with accompanying research support, include power training in the lower limbs, exploring and maintaining optimized stride length, and timing and mechanics of pitching sequences. The suggested lower body focused training will be beneficial in increasing pitching velocity and reducing the risk of injuries in the throwing arm. These recommendations are crucial for young baseball players, youth baseball coaches, strength coaches, and parents who aim to improve pitching performance while addressing concerns about overtraining and injury prevention.

Reference Sequence Browser: An R application with a user-friendly GUI to rapidly query sequence databases.

Land managers, researchers, and regulators increasingly utilize environmental DNA (eDNA) techniques to monitor species richness, presence, and absence. In order to properly develop a biological assay for eDNA metabarcoding or quantitative PCR, scientists must be able to find not only reference sequences (previously identified sequences in a genomics database) that match their target taxa but also reference sequences that match non-target taxa. Determining which taxa have publicly available sequences in a time-efficient and accurate manner currently requires computational skills to search, manipulate, and parse multiple unconnected DNA sequence databases. Our team iteratively designed a Graphic User Interface (GUI) Shiny application called the Reference Sequence Browser (RSB) that provides users efficient and intuitive access to multiple genetic databases regardless of computer programming expertise. The application returns the number of publicly accessible barcode markers per organism in the NCBI Nucleotide, BOLD, or CALeDNA CRUX Metabarcoding Reference Databases. Depending on the database, we offer various search filters such as min and max sequence length or country of origin. Users can then download the FASTA/GenBank files from the RSB web tool, view statistics about the data, and explore results to determine details about the availability or absence of reference sequences.

Speech Isolation and Recognition in Crowded Noise Using a Dual-Path Recurrent Neural Network

Speech separation is crucial for effective speech processing in multi-talker conditions, especially in real-time, low-latency applications. In this study, the Time-Domain Audio Separation Network (TasNet) and Dual-Path Recurrent Neural Network were used to perform a time-domain multiple-speaker speech separation challenge. One-dimensional conventional recurrent neural networks (RNNs) are not capable of accurately simulating long sequences. When their receptive length exceeds the sequence field, 1-D CNNs cannot recreate utterance-level sequences. Dual-Path Recurrent Neural Network (DPRNN) breaks up the lengthy sequential input that progressively performs intra- and inter-chunk operations with input lengths proportional to the square root of the beginning sequence length. Model outputs are more efficient than earlier systems, improving performance on the Libri Mix dataset. Investigations show that the DPRNN, sample-level modeling, and time-domain audio separation network can replace present methods. EEND-SS and other separation algorithms perform worse than DPRNN. The suggested model was able to achieve (12.376) SI-SDR, (0.969) STOI (short-time objective intelligibility), (12.363) SDR, (9.363) DER, and (97.193) SCA.

Improving Systematic Generalization of Linear Transformer Using Normalization Layers and Orthogonality Loss Function

A Linear Transformer linearizes the attention mechanism of the vanilla Transformer architecture, significantly improving efficiency and achieving linear theoretical complexity with respect to sequence length. However, few studies have explored the capabilities of the Linear Transformer beyond its efficiency. In this work, we investigate the systematic generalization capability of the Linear Transformer, a crucial property for strong generalization to unseen data. Through preliminary experiments, we identify two major issues contributing to its unstable systematic generalization performance: (i) unconstrained norms of Queries and Keys, and (ii) high correlation among Values across the sequence. To address these issues, we propose two simple yet effective methods: normalization layers for Queries and Keys, and an orthogonality loss function applied to Values during training. In experiments, we demonstrate that applying these methods to the Linear Transformer significantly improves its stability and systematic generalization performance across several well-known tasks. Furthermore, our proposed methods outperform the vanilla Transformer on specific systematic generalization tasks, such as the sort-of-CLEVR and SCAN tasks.

CMMamba: channel mixing Mamba for time series forecasting

Transformer-based methods have achieved excellent results in the field of time series forecasting due to their powerful ability to model sequences and capture their long-term dependencies. However, Transformer-based methods are still computationally inefficient and have high costs when modeling long sequences. In addition, previous work ignored the destruction of the original in-channel features by too many mixed features when performing channel mixing. To address the above issues, we introduce Mamba into the field of time series forecasting. The selective state-space model Mamba models long sequences more efficiently by expanding linearly according to the sequence length. Based on this, we propose Channel Mixing Mamba (CMMamba), which uses bidirectional Mamba to model sequences and uses a channel mixing mechanism to select appropriate mixed features to enhance the feature representation within the original channel. By introducing Mamba, we achieve efficient modeling of sequences. We also propose Deep Convolutional Structure (DCS) to learn cross-channel dependencies and temporal order information. Extensive experimental results on six real-world public datasets demonstrate the effectiveness of CMMamba.

Full-length transcriptome characterization and analysis of Carrizo Citrange and molecular insights into pathogen defense.

Citrus huanglongbing (HLB) is a major challenge that impacts the flourishing of the citrus industry. Therefore, analyzing the genomic information of HLB-resistant or tolerant citrus resources is crucial for breeding HLB-resistant citrus varieties. The Carrizo citrange, a hybrid of Citrus sinensis and Poncirus trifoliata, plays a pivotal role in citrus cultivation. However, its genetic explorations are difficult due to the absence of a reference genome or full-length transcriptome. In order to enhance our understanding of the genetic information of citrange, we conducted a full-length transcriptomic sequencing of multiple tissues from the Carrizo citrange using the PacBio Sequel II platform. Moreover, we performed gene ontology (GO) annotation, gene functional annotation, simple sequence repeats (SSR) types analysis, as well as identification of lncRNAs, alternative splicing events, and analysis of pathogen defense-related genes. Results showed that a total of 43,452 isoforms were generated, with 43,307 of them being annotated. GO annotation indicated the involvement of these isoforms in various biological processes, cellular components, and molecular functions. The coding sequence length of the isoforms ranged from 1,000 to 4,000 base pairs (bp). Moreover, we have discovered 54 varieties of transcription factors and regulators, along with 16 classifications of genes associated with resistance. Among all types of SSRs, trimer type SSRs were the most abundant. 130 lncRNAs were predicted to be highly reliable in the isoforms of the Carrizo citrange, with alternative splicing events identified, and the most frequent being retained intron. The analysis of gene family expansion and contraction revealed a significant increase in pathogen defense-related genes within the Carrizo citrange. The results of this study will be of great value for future investigations into gene function in citrange and for expanding the genetic pool for breeding citrus varieties resistant or tolerant to HLB.

Comparison of time-series models for predicting physiological metrics under sedation.

This study presents a comprehensive comparison of multiple time-series models applied to physiological metric predictions. It aims to explore the effectiveness of both statistical prediction models and pharmacokinetic-pharmacodynamic prediction model and modern deep learning approaches. Specifically, the study focuses on predicting the bispectral index (BIS), a vital metric in anesthesia used to assess the depth of sedation during surgery, using datasets collected from real-life surgeries. The goal is to evaluate and compare model performance considering both univariate and multivariate schemes. Accurate BIS prediction is essential for avoiding under- or over-sedation, which can lead to adverse outcomes. The study investigates a range of models: The traditional mathematical models include the pharmacokinetic-pharmacodynamic model and statistical models such as autoregressive integrated moving average (ARIMA) and vector autoregression (VAR). The deep learning models encompass recurrent neural networks (RNNs), specifically Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), as well as Temporal Convolutional Networks (TCNs) and Transformer models. The analysis focuses on evaluating model performance in predicting the BIS using two distinct datasets of physiological metrics collected from actual surgical procedures. It explores both univariate and multivariate prediction schemes and investigates how different combinations of features and input sequence lengths impact model accuracy. The experimental findings reveal significant performance differences among the models: In univariate prediction scenarios for predicting BIS, the LSTM model demonstrates a 2.88% improvement over the second-best performing model. For multivariate predictions, the LSTM model outperforms others by 6.67% compared to the next best model. Furthermore, the addition of Electromyography (EMG) and Mean Arterial Pressure (MAP) brings significant accuracy improvement when predicting BIS. The study emphasizes the importance of selecting and building appropriate time-series models to achieve accurate predictions in biomedical applications. This research provides insights to guide future efforts in improving vital sign prediction methodologies for clinical and research purposes. Clinically, with improvements in the prediction of physiological parameters, clinicians can be informed of interventions if an anomaly is detected or predicted.

An attention fused sequence -to-sequence convolutional neural network for accurate solar irradiance forecasting and prediction using sky images

The challenge of accurately forecasting ultra-short-term solar irradiance for photovoltaic systems is complicated by rapidly changing weather, and while ground-based sky images offer potential improvements, effectively extracting spatiotemporal data from these images remains a significant hurdle for current computer vision models. A new hybrid model, "An Attention Fused Sequence-to-Sequence Convolutional Neural Network," is being developed to address this challenge. The model predicts intra-hour GHI, DNI, and DHI with a 10-min lead time by combining a Convolutional Neural Network (for spatial feature extraction from sky images), an attention mechanism (to focus on relevant regions), and a sequence-to-sequence model (for temporal feature extraction from time-series data). The proposed Model is trained using the NREL Solar Radiation Research Laboratory Dataset while evaluating the model with the Mean Bias Error, Mean Absolute Error, Root Mean Squared Error, R Squared, and forecasting skill score. The 10-min and sequence length 2 interval is considered to be the best performing across most of the evaluation metrics with an MBE value is 2.321 W/m2, MAE value of 39.490 W/m2, RMSE value of 62.086 W/m2, R2 value is 0.909 W/m2, FSS value of 23.589 W/m2 and an MBE of 4.876 W/m2, MAE of 56.887 W/m2, RMSE of 85.346 W/m2, R2 of 0.834 and FSS of 24.881 W/m2 respectively. Furthermore, the sensitivity analysis reveals that the proposed model's performance is influenced by both the sequence length and the lead time. The proposed framework outperforms other techniques for ultra-short-term PV generation forecasting, demonstrating its potential for practical deployment in PV systems to improve grid reliability and energy management.

RNA fold prediction by Monte Carlo in graph space and the statistical mechanics of tertiary interactions.

Using a graph representation of RNA structures, we have studied the ensembles of secondary and tertiary graphs two sets of RNA with Monte Carlo simulations. The first consisted of 91 target ribozyme and riboswitch sequences of moderate lengths (< 150 nt) having a variety of secondary, H-type pseudoknots and kissing loop interactions. The second set consisted of 71 more diverse sequences across many RNA families. Using a simple empirical energy model for tertiary interactions and only sequence information for each target as input, the simulations examined how tertiary interactions impact the statistical mechanics of the fold ensembles. The results show that the graphs proliferate enormously when tertiary interactions are possible, producing an entropic driving force for the ensemble to access folds having tertiary structures even though they are overall energetically unfavorable in the energy model. For each of the targets in the two test sets, we assessed the quality of the model and the simulations by examining how well the simulated structures were able to predict the native fold and compared the results to fold predictions from ViennaRNA. Our model generated good or excellent predictions in a large majority of the targets. Overall, this method was able to produce predictions of comparable quality to Vienna, but it outperformed Vienna for structures with H-type pseudoknots. The results suggest that while tertiary interactions are predicated on real-space contacts, their impacts on the folded structure of RNA can be captured by graph space information for sequences of moderate lengths, using a simple tertiary energy model for the loops, the base pairs and base stacks.

Optimal 16S rRNA gene amplicon sequencing analysis for oral microbiota to avoid the potential bias introduced by trimming length, primer, and database.

16S rRNA gene amplicon sequencing analysis is used to investigate bacterial communities; however, the estimated bacterial composition can differ from the original due to experimental and analytical biases. Therefore, this study determines the optimal conditions to minimize the potential biases in 16S rRNA gene amplicon sequencing analysis using different DNA samples, trimming lengths, primers, and databases. The results of the mock1 community with 250 and 300 bp paired-end (PE), comprising 15 bacteria from various environments, showed the highest similarity between the theoretical value and the data using the Greengenes2 and the V3 region at the genus level and V5-V6 at the species level. In the 300 bp PE sequencing analysis of the mock2 community, comprising six major oral bacteria, the data using the V3-V4, V4, and V5-V6 regions with the SILVA, Greengenes2, and the Human Oral Microbiome Database (HOMD) showed the highest similarity to the theoretical values at the genus level. At the species level, the data using the V3-V4 and V4 regions with Greengenes2 and the data using the V1-V2 with HOMD exhibited the highest alignment with the theoretical values. In the species analysis of the dental calculus samples with 300 bp PE, the Shannon index value was higher with the V1-V2 region and HOMD than with others. Our results suggest that the optimal conditions for oral microbiome analysis are the combinations of 300 bp PE and the V3-V4 or V4 region with the Greengenes2 and the V1-V2 with HOMD.IMPORTANCEThe 16S rRNA gene amplicon sequencing analysis is frequently used in oral microbiome research. However, this method can have biases that distort the experimental data, which depend on the methodological steps, including sequencing length, trimming length, selected amplification regions, and referenced databases. In this study, a combination of 300 bp PE and the primer targeting the V3-V4 or V4 regions with the Greengenes2 and the V1-V2 region with the HOMD was the most bias-minimizing condition for oral microbiota analysis. In addition, this is the first report of such analyses in modern Japanese dental calculus. The methods used in this study will aid in setting appropriate conditions for sequence analysis of microbiota obtained not only from the oral cavity but also from any environment.

Music-induced emotion flow modeling by ENMI Network.

The relation between emotions and music is substantial because music as an art can evoke emotions. Music emotion recognition (MER) studies the emotions that music brings in the effort to map musical features to the affective dimensions. This study conceptualizes the mapping of music and emotion as a multivariate time series regression problem, with the aim of capturing the emotion flow in the Arousal-Valence emotional space. The Efficient Net-Music Informer (ENMI) Network was introduced to address this phenomenon. The ENMI was used to extract Mel-spectrogram features, complementing the time series data. Moreover, the Music Informer model was adopted to train on both time series music features and Mel-spectrogram features to predict emotional sequences. In our regression task, the model achieved a root mean square error (RMSE) of 0.0440 and 0.0352 in the arousal and valence dimensions, respectively, in the DEAM dataset. A comprehensive analysis of the effects of different hyperparameters tuning was conducted. Furthermore, different sequence lengths were predicted for the regression accuracy of the ENMI Network on three different datasets, namely the DEAM dataset, the Emomusic dataset, and the augmented Emomusic dataset. Additionally, a feature ablation on the Mel-spectrogram features and an analysis of the importance of the various musical features in the regression results were performed, establishing the effectiveness of the model presented herein.

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.