Structural variations (SVs) are one of the major sources of genomic diversity and are closely associated with human disease. Existing short-read-based SV detection tools often rely on limited alignment features, which restricts their ability to fully capture variation signals. While many multi-source fusion methods have improved recall rates, they also introduce a large number of false positives. To address these issues, we present SVScope, an integrated SV detection tool that fuses multi-source signals, structure-sensitive image encoding, and deep visual filtering. It first consolidates candidate variations derived from complementary alignment signals across tools into a standardized candidate set via a harmonized detection and merging pipeline. To further improve specificity, SVScope utilizes a dynamic window to extract alignment features from candidate regions, then encodes them into a seven-channel image with CIGAR operations and read pair orientations to capture local structural details. It also combines a convolutional neural network with an embedded attention mechanism to enhance effective signals and suppress redundant noise, thereby achieving precise filtering of false positives. Benchmarking on real datasets demonstrates that SVScope consistently improves recall compared to individual detection tools while substantially enhancing overall precision through deep learning-based filtering. These results highlight SVScope's capacity to balance sensitivity and specificity, offering a precise and robust solution for SV analysis in large-scale short-read sequencing studies. The code and documentation of SVScope are publicly available at https://github.com/nudt-bioinfo/SVScope

Modern genomics faces challenges from “reference bias” in linear genomes, prompting the adoption of pangenomic graphs to integrate multi-allelic variations. Sequence-to-graph alignment is a fundermental procedure in many pangenomic analyses. However, the alignment in complex topologies like cyclic graphs and highly polymorphic regions remains difficult due to path branch explosion and computational complexity. In this paper, we propose GESA, a sequence-to-graph alignment framework for sequences in highly divergent genomic regions. GESA adopts a hybrid strategy integrating haplotype-guided path linearization to organize topological information, thereby reducing information loss and potential path branch explosion. It employs a Transformer-CNN contrastive learning strategy to further capture global and local genomic features, enabling the identification of genetic characteristics in complex regions across the entire genome. Finally, a hierarchical vector-space retrieval technique is used to simplify the complex graph alignment computation into linear alignments on multiple sequences through vector similarity retrieval algorithms. GESA achieves an alignment ratio of 0.79 in cyclic graphs within the complex MHC region, outperforming Minigraph and GraphAligner by$4.3 \times$and$3.3 \times$, respectively. GESA lays a foundation for the future development of deep learning model applications in the field of pangenome graph alignment. The GESA code is available at https://github.com/nudt-bioinfo/GESA.

Structural variations (SVs) play a critical role in species diversity, biological evolution, and human diseases. Although third-generation sequencing technology has enhanced the ability to detect long and complex SVs through long reads that can span complex genomic regions, it still suffers from high false-positive (FP) SV calls due to three factors: the complexity of SVs, limitations of detection algorithms, and relatively high single-base error rates in long reads. To address this challenge, we propose FusionSVFilter, a deep learning-based algorithm for SV filtering. It first transforms genomic sequence features into multi-level grayscale images via sequence-to-image encoding, which captures the structural complexity of SVs. Moreover, the encoder is accelerated with parallel computing to improve speed. Afterward, these grayscale images are enhanced to retain key features and converted into RGB format to meet the input requirements of the model. In addition, FusionSVFilter further leverages transfer learning, initializing with a pre-trained ResNet50 model that is fine-tuned on our curated dataset to recognize SV-specific patterns. Experimental results show that FusionSVFilter sub-stantially reduces FP calls while maintaining true-positive (TP) detection at near-constant levels. The code and documentation are available at: https://github.com/nudt-bioinfo/FusionSVFilter.

Structural variant (SV) calling plays a critical role in understanding genome diversity and disease mechanisms. Although deep learning techniques have been increasingly applied to SV identification, existing general-purpose models still face significant challenges, including incomplete extraction of alignment signals, limited accuracy and efficiency, and poor performance in highly polymorphic or structurally complex genomic regions. These limitations lead to suboptimal detection accuracy in current SV callers. In this work, we present MMF-SV, a multi-modal feature fusion-based model (MMF) for SV calling. MMF-SV integrates matching patterns and statistical information from CIGAR signals with textual features extracted from alignment information, enabling comprehensive representation of diverse SV signals. We trained MMF-SV using CLIP, and the trained model achieved over 96% F1 score for classifying various types of variations. We validated the stability and robustness of the MMF-SV model through 5-fold cross-validation. Compared to existing long-read SV callers, MMF-SV achieves higher accuracy and can be effectively integrated with them to significantly reduce the number of false positives in the calling results.

BACKGROUND: The advent of Single Molecule Real-Time (SMRT) sequencing has overcome many limitations of second-generation sequencing, such as limited read lengths, PCR amplification biases. However, longer reads increase data volume exponentially and high error rates make many existing alignment tools inapplicable. Additionally, a single CPU's performance bottleneck restricts the effectiveness of alignment algorithms for SMRT sequencing. RESULTS: To address these challenges, we introduce ParaHAT, a parallel alignment algorithm for noisy long reads. ParaHAT utilizes vector-level, thread-level, process-level, and heterogeneous parallelism. We redesign the dynamic programming matrices layouts to eliminate data dependency in the base-level alignment, enabling effective vectorization. We further enhance computational speed through heterogeneous parallel technology and implement the algorithm for multi-node computing using MPI, overcoming the computational limits of a single node. CONCLUSIONS: Performance evaluations show that ParaHAT got a 10.03x speedup in base-level alignment, with a parallel acceleration ratio and weak scalability metric of 94.61 and 98.98% on 128 nodes, respectively.

Sequence alignment is the core of many bioinformatics tasks such as read mapping, genome assembly, variant detection and so on. With the advent of the third generation sequencing, classical dynamic programming-based alignment algorithms face challenges in efficiently handling these long reads. To address this issue, we present WFA-vect, a SIMD-based fast sequence alignment algorithm based on WFA. In WFA-vect, we introduce load synchronous and mask-based branch strategies to make the algorithm more suitable for vectorization. The load synchronous equalizes the load across different vector units to facilitate vectorization. The mask-based branch uses branch masking to bypass branch, avoiding pipeline hazards. To avoid binding the SIMD algorithm to specific hardware, we design a universal vectorization framework, which allows researchers to quickly port WFA-vect to other platforms without needing to understand the details of the algorithm. WFA-vect attains a peak speedup of 3.87× and 3.98× for data with error rates of 1% and 20%, respectively, compared to the scalar algorithm, while maintaining the alignment result consistent. The code and documentation of WFA-vect are publicly available at https://github.com/nudt-bioinfo/WFA-vect.

Third-generation sequencing techniques have achieved major breakthroughs in sequencing long reads and speed. Continuous improvements in sequencing techniques have reduced sequencing costs, and the number of sequencing data files has shown explosive growth. In terms of sequence alignment, to deal with these high numbers and large-scale data, the conventional serial alignment method can no longer effectively meet the research requirements, therefore, it is of great importance to develop a faster, low-load, and compatible parallel alignment program. In this paper, we propose a parallel task pool algorithm based on the minimap2, a sequence alignment tool, and develop the task pool parallel alignment program based on this algorithm. We compare the program’s work with the average segmentation parallel alignment program. The results show that the task pool parallel alignment program has significant improvement in speedup, memory load, segmentation flexibility, and computational efficiency, it also has good scalability and computational stability. MinimapPool is available at https://github.com/krkrcc/MinimapPool.

MOTIVATION: Structural variants (SVs) play an important role in genetic research and precision medicine. As existing SV detection methods usually contain a substantial number of false positive calls, approaches to filter the detection results are needed. RESULTS: We developed a novel deep learning-based SV filtering tool, CSV-Filter, for both short and long reads. CSV-Filter uses a novel multi-level grayscale image encoding method based on CIGAR strings of the alignment results and employs image augmentation techniques to improve SV feature extraction. CSV-Filter also utilizes self-supervised learning networks for transfer as classification models, and employs mixed-precision operations to accelerate training. The experiments showed that the integration of CSV-Filter with popular SV detection tools could considerably reduce false positive SVs for short and long reads, while maintaining true positive SVs almost unchanged. Compared with DeepSVFilter, a SV filtering tool for short reads, CSV-Filter could recognize more false positive calls and support long reads as an additional feature. AVAILABILITY AND IMPLEMENTATION: https://github.com/xzyschumacher/CSV-Filter.

Read alignment is a basic and important task in genomic data analysis. The popularity of the third—generation sequencing technology has brought the need of sequence alignment algorithms to analyze long-read sequences with longer read length and high error rate. Moreover, the rapid growth of sequence data has also presented challenges for read alignment. To improve the ability to process large volume of sequencing reads, we developed a long-read sequence alignment algorithm MTMap on the heterogeneous processor FT-m7032. MTMap utilizes multi-level parallel technologies: firstly, we tailored the data structure for the wide vector processing units of DSP to speedup the score matrix computation. Secondly, we developed multithread parallelization for base-level alignment on each DSP cluster. Finally, we implemented multi-process parallelization between DSP clusters to fully exploit the computing power of FT-m7032. Experiments show that, MTMap achieves up to 16 times of parallel acceleration performance compared with the original algorithm under the condition of ensuring accuracy.

With the exponential growth of biomedical knowledge in unstructured text repositories such as PubMed, it is imminent to establish a knowledge graph-style, efficient searchable and targeted database that can support the need of information retrieval from researchers and clinicians. To mine knowledge from graph databases, most previous methods view a triple in a graph (see Fig. 1) as the basic processing unit and embed the triplet element (i.e. drugs/chemicals, proteins/genes and their interaction) as separated embedding matrices, which cannot capture the semantic correlation among triple elements. To remedy the loss of semantic correlation caused by disjoint embeddings, we propose a novel approach to learn triple embeddings by combining entities and interactions into a unified representation. Furthermore, traditional methods usually learn triple embeddings from scratch, which cannot take advantage of the rich domain knowledge embedded in pre-trained models, and is also another significant reason for the fact that they cannot distinguish the differences implied by the same entity in the multi-interaction triples. In this paper, we propose a novel fine-tuning based approach to learn better triple embeddings by creating weakly supervised signals from pre-trained knowledge graph embeddings. The method automatically samples triples from knowledge graphs and estimates their pairwise similarity from pre-trained embedding models. The triples are then fed pairwise into a Siamese-like neural architecture, where the triple representation is fine-tuned in the manner bootstrapped by triple similarity scores. Finally, we demonstrate that triple embeddings learned with our method can be readily applied to several downstream applications (e.g. triple classification and triple clustering). We evaluated the proposed method on two open-source drug-protein knowledge graphs constructed from PubMed abstracts, as provided by BioCreative. Our method achieves consistent improvement in both triple classification and triple clustering tasks when compared to other state-of-the-art triple embedding methods, with an average 35% improvement of F1 score for the multi-interaction triples.

The single molecule real-time sequencing technologies, such as PacBio and Nanopore, have higher throughput and produce longer reads, which promote the discovery of more structure variations that cannot be discovered by the second-generation sequencing data. However, compared with the second-generation sequencing data, the PacBio data lacks paired-end sequencing information, making traditional structure variations filter fail to process the new data. To solve this problem, this paper proposes a universal multi-tasking structure variation filtering model MSVF. MSVF adopts the CIGAR string defined in SAM format. CIGAR is not limited by sequencing technology or alignment algorithms, so MSVF is suitable for not only the second-generation but also the third-generation sequencing data. Moreover, CIGAR string preserves the complete sequence alignment information, which makes MSVF a highly precise model. Besides, MSVF uses deep learning methods, making it supports more structure variation types, including deletion and insertion. We trained and tested the models on the open-access NCBI datasets. The experiments proved that ShuffleNet, MobileNet, ResNet transfer learning models achieve better classification results on SVs task. The average AUC reaches more than 90% and the AUC of each category reach more than 87%. The accuracy and AUC of deletion and insertion structure variations were above 90% and above 92%, respectively. The code and data can be obtained at https://github.con weimingxiang/MSVF.

CRISPR/Cas9-based gene editing technology has been widely used in various cells and organisms. However, the off-target effects will bring unpredictable consequences to the organism edited. One of the main obstacles to predict CRISPR/Cas9 off-target is the imbalance of the number of positive and negative samples, which puts forward a challenge for the training of traditional deep learning algorithms. In this paper, we proposed H-VAE, a hybrid variational autoencoder model with data augmentation. This model can extract more abundant sgRNA-DNA base pair matching information, and reduce the risk of overfitting. Moreover, the sample imbalance is resolved. H-VAE can make use of underlying information of training sample, extracted by VAE, to alleviate data-imbalance problem. In view of the weak ability to extract base pair matching information of existing models, a different encoding scheme based on pair encoding is proposed, which enables the model to make full use of sgRNA-DNA base pair matching information. On the Mismatch data set, compared with DeepCRISPR, the ROC-AUC and PR-AUC increased by 0.6% and 41.9%, respectively. In the new Indels data set test scenario, compared with CRISPR-Net, the ROC-AUC and PR-AUC were increased by 1.5% and 133.4% respectively. This proves that H-VAE can improve off-target prediction in various scenarios. The improvement of PR-AUC shows that H-VAE can significantly improve the effect of unbalanced classification. The experimental results demonstrate that H-VAE could achieve a better effect compared with state-of-the-art CRISPR/Cas9 off-target methods on various types of data sets. The code and data can be obtained at https://github.com/weimingxiang/H-VAE.

BACKGROUND: VISPR is an interactive visualization and analysis framework for CRISPR screening experiments. However, it only supports the output of MAGeCK, and requires installation and manual configuration. Furthermore, VISPR is designed to run on a single computer, and data sharing between collaborators is challenging. RESULTS: To make the tool easily accessible to the community, we present VISPR-online, a web-based general application allowing users to visualize, explore, and share CRISPR screening data online with a few simple steps. VISPR-online provides an exploration of screening results and visualization of read count changes. Apart from MAGeCK, VISPR-online supports two more popular CRISPR screening analysis tools: BAGEL and JACKS. It provides an interactive environment for exploring gene essentiality, viewing guide RNA (gRNA) locations, and allowing users to resume and share screening results. CONCLUSIONS: VISPR-online allows users to visualize, explore and share CRISPR screening data online. It is freely available at http://vispr-online.weililab.org , while the source code is available at https://github.com/lemoncyb/VISPR-online .

Molecular Dynamics (MD) is the simulation of the dynamic behavior of atoms and molecules. As the most popular software for molecular dynamics, GROMACS cannot work on large-scale data because of limit computing resources. In this paper, we propose a CPU and Intel® Xeon Phi Many Integrated Core (MIC) collaborated parallel framework to accelerate GROMACS using the offload mode on a MIC coprocessor, with which the performance of GROMACS is improved significantly, especially with the utility of Tianhe-2 supercomputer. Furthermore, we optimize GROMACS so that it can run on both the CPU and MIC at the same time. In addition, we accelerate multi-node GROMACS so that it can be used in practice. Benchmarking on real data, our accelerated GROMACS performs very well and reduces computation time significantly. Source code: https://github.com/tianhe2/gromacs-mic.

BACKGROUND: Novel sequence motifs detection is becoming increasingly essential in computational biology. However, the high computational cost greatly constrains the efficiency of most motif discovery algorithms. RESULTS: In this paper, we accelerate MEME algorithm targeted on Intel Many Integrated Core (MIC) Architecture and present a parallel implementation of MEME called MIC-MEME base on hybrid CPU/MIC computing framework. Our method focuses on parallelizing the starting point searching method and improving iteration updating strategy of the algorithm. MIC-MEME has achieved significant speedups of 26.6 for ZOOPS model and 30.2 for OOPS model on average for the overall runtime when benchmarked on the experimental platform with two Xeon Phi 3120 coprocessors. CONCLUSIONS: Furthermore, MIC-MEME has been compared with state-of-arts methods and it shows good scalability with respect to dataset size and the number of MICs. Source code: https://github.com/hkwkevin28/MIC-MEME .

Single Nucleotide Polymorphism (SNP) detection is a fundamental procedure of whole genome analysis. SOAPsnp, a classic tool for detection, would take more than one week to analyze one typical human genome, which limits the efficiency of downstream analyses. In this paper, we present mSNP, an optimized version of SOAPsnp, which leverages Intel Xeon Phi coprocessors for large-scale SNP detection. Firstly, we redesigned the essential data structures of SOAPsnp, which significantly reduces memory footprint and improves computing efficiency. Then we developed a coordinated parallel framework for a higher hardware utilization of both CPU and Xeon Phi. Also, we tailored the data structures and operations to utilize the wide VPU of Xeon Phi to improve data throughput. Last but not the least, we proposed a read-based window division strategy to improve throughput and obtain better load balance. mSNP is the first SNP detection tool empowered by Xeon Phi. We achieved a 38x single thread speedup on CPU, without any loss in precision. Moreover, mSNP successfully scaled to 4,096 nodes on Tianhe-2. Our experiments demonstrate that mSNP is efficient and scalable for large-scale human genome SNP detection.

Whole genome re-sequencing plays a crucial role in biomedical studies. The emergence of genomic big data calls for an enormous amount of computing power. However, current computational methods are inefficient in utilizing available computational resources. In this paper, we address this challenge by optimizing the utilization of the fastest supercomputer in the world - TH-2 supercomputer. TH-2 is featured by its neo-heterogeneous architecture, in which each compute node is equipped with 2 Intel Xeon CPUs and 3 Intel Xeon Phi coprocessors. The heterogeneity and the massive amount of data to be processed pose great challenges for the deployment of the genome analysis software pipeline on TH-2. Runtime profiling shows that SOAP3-dp and SOAPsnp are the most time-consuming components (up to 70% of total runtime) in a typical genome-analyzing pipeline. To optimize the whole pipeline, we first devise a number of parallel and optimization strategies for SOAP3-dp and SOAPsnp, respectively targeting each node to fully utilize all sorts of hardware resources provided both by CPU and MIC. We also employ a few scaling methods to reduce communication between different nodes. We then scaled up our method on TH-2. With 8192 nodes, the whole analyzing procedure took 8.37 hours to finish the analysis of a 300 TB dataset of whole genome sequences from 2,000 human beings, which can take as long as 8 months on a commodity server. The speedup is about 700x.