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.

Gene expression during brain development or abnormal development is a biological process that is highly dynamic in spatio and temporal. Previous studies have mainly focused on individual brain regions or a certain developmental stage. Our motivation is to address this gap by incorporating spatio-temporal information to gain a more complete understanding of brain development or abnormal brain development, such as Alzheimer's disease (AD), and to identify potential determinants of response. In this study, we propose a novel two-step framework based on spatial-temporal information weighting and multi-step decision trees. This framework can effectively exploit the spatial similarity and temporal dependence between different stages and different brain regions, and facilitate differential gene analysis in brain regions with high heterogeneity. We focus on two datasets: the AD dataset, which includes gene expression data from early, middle and late stages, and the brain development dataset, spanning fetal development to adulthood. Our findings highlight the advantages of the proposed framework in discovering gene classes and elucidating their impact on brain development and AD progression across diverse brain regions and stages. These findings align with existing studies and provide insights into the processes of normal and abnormal brain development.

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.