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Structural variants (SVs) significantly contribute to human genome diversity and play a crucial role in precision medicine. Although advancements in single-molecule long-read sequencing offer a groundbreaking resource for SV detection, identifying SV breakpoints and sequences accurately and robustly remains challenging. We introduce VolcanoSV, an innovative hybrid SV detection pipeline that utilizes both a reference genome and local de novo assembly to generate a phased diploid assembly. VolcanoSV uses phased SNPs and unique k -mer similarity analysis, enabling precise haplotype-resolved SV discovery. VolcanoSV is adept at constructing comprehensive genetic maps encompassing SNPs, small indels, and all types of SVs, making it well-suited for human genomics studies. Our extensive experiments demonstrate that VolcanoSV surpasses state-of-the-art assembly-based tools in the detection of insertion and deletion SVs, exhibiting superior recall, precision, F1 scores, and genotype accuracy across a diverse range of datasets, including low-coverage (10x) datasets. VolcanoSV outperforms assembly-based tools in the identification of complex SVs, including translocations, duplications, and inversions, in both simulated and real cancer data. Moreover, VolcanoSV is robust to various evaluation parameters and accurately identifies breakpoints and SV sequences. Detecting genomic structural variants (SVs) using long-read sequencing remains challenging. Here the authors introduce a hybrid pipeline for precise haplotype-resolved SV discovery that outperforms current tools across diverse long-read sequencing datasets.

Long-read sequencing offers long contiguous DNA fragments, facilitating diploid genome assembly and structural variant (SV) detection. Efficient and robust algorithms for SV identification are crucial with increasing data availability. Alignment-based methods, favored for their computational efficiency and lower coverage requirements, are prominent. Alternative approaches, relying solely on available reads for de novo genome assembly and employing assembly-based tools for SV detection via comparison to a reference genome, demand significantly more computational resources. However, the lack of comprehensive benchmarking constrains our comprehension and hampers further algorithm development. Here we systematically compare 14 read alignment-based SV calling methods (including 4 deep learning-based methods and 1 hybrid method), and 4 assembly-based SV calling methods, alongside 4 upstream aligners and 7 assemblers. Assembly-based tools excel in detecting large SVs, especially insertions, and exhibit robustness to evaluation parameter changes and coverage fluctuations. Conversely, alignment-based tools demonstrate superior genotyping accuracy at low sequencing coverage (5-10×) and excel in detecting complex SVs, like translocations, inversions, and duplications. Our evaluation provides performance insights, highlighting the absence of a universally superior tool. We furnish guidelines across 31 criteria combinations, aiding users in selecting the most suitable tools for diverse scenarios and offering directions for further method development. Long-read sequencing can greatly improve detection of genomic structural variants (SVs), and numerous methods have been developed to identify SVs using long-read data. Here the authors compare the performance of these methods and provide guidelines to aid users in selecting the most suitable tools for various scenarios.

Recent advances in spatial transcriptomics (ST) highlight the need to integrate multiple slices for joint analysis. A key challenge is generating interpretable embeddings that preserve spatial geometry while correcting batch effects. We present MaskGraphene, a graph neural network that integrates ST data via masked self-supervised learning, triplet loss, and cluster-wise local alignment. By establishing indirect “soft-links” and direct “hard-links” across slices, MaskGraphene yields joint embeddings with high geometric fidelity. Benchmarks against eight methods demonstrate superior alignment and interpretability. MaskGraphene enhances downstream applications, including domain identification, trajectory reconstruction, biomarker discovery, and brain-layer mapping, enabling robust ST integration and biological insight.

Accurate detection of genetic variants, including single nucleotide polymorphisms (SNPs), small insertions and deletions (INDELs), and structural variants (SVs), is critical for comprehensive genomic analysis. While traditional short-read sequencing performs well for SNP and INDEL detection, it struggles to resolve SVs, especially in complex genomic regions, due to inherent read length limitations. Linked-read sequencing technologies, such as single-tube Long Fragment Read sequencing (stLFR), overcome these challenges by employing molecular barcodes, providing crucial long-range information. This study investigates traditional pair-end linked-reads and a conceptual extension of linked-read technology: barcoded single-end reads of 500 bp (SE500_stLFR) and 1000 bp (SE1000_stLFR), generated using the single-tube Long Fragment Read (stLFR) platform. Unlike conventional paired-end (PE100_stLFR) linked reads, these longer single-end reads could offer improved resolution for variant detection by leveraging extended read lengths per barcode. We simulated a diverse set of datasets for the HG002 sample using T2T-based realistic genome simulation. Variant detection performance was then systematically assessed across three_stLFR configurations: standard PE100_stLFR, SE500_stLFR, and SE1000_stLFR. Benchmarking against the Genome in a Bottle (GIAB) gold standard reveals distinct strengths of each configuration. Extended single-end reads (SE500_stLFR and SE1000_stLFR) significantly enhance SV detection, with SE1000_stLFR providing the best balance between precision and recall. In contrast, the shorter PE100_stLFR reads exhibit higher precision for SNP and INDEL calling, particularly within high-confidence regions, though with reduced performance in low-mappability contexts. To explore optimization strategies, we constructed hybrid libraries combining paired-end and single-end barcoded reads. These hybrid approaches integrate the complementary advantages of different read types, consistently outperforming single libraries across small variant types and genomic contexts. Collectively, our findings offer a robust comparative framework for evaluating stLFR sequencing strategies, highlight the promise of barcoded single-end reads for improving SV detection, and provide practical guidance for tailoring sequencing designs to the complexities of the genome.

Recent advancements in spatial transcriptomics (ST) have underscored the importance of integrating data from multiple ST slices for joint analysis. A major challenge remains generating interpretable joint embeddings that preserve geometric information for downstream analyses. Here we introduce MaskGraphene, a graph neural network that combines self-supervised and self-contrastive training to integrate gene expression and spatial location into joint embeddings. By employing cluster-wise alignment and a graph attention autoencoder with masked self-supervised and triplet loss optimizations, MaskGraphene effectively preserves geometric structures while achieving batch correction. In benchmarks against seven state-of-the-art methods, MaskGraphene consistently demonstrated superior alignment accuracy and geometric fidelity across diverse ST datasets. Its interpretable embeddings significantly enhanced downstream applications, including domain identification, spatial trajectory reconstruction, biomarker discovery, and the creation of topographical maps of brain slices. Notably, MaskGraphene successfully recovered layer-wise brain structures with near-perfect accuracy. MaskGraphene provides a powerful and versatile framework for advancing ST data integration and analysis, unlocking valuable biological insights.Competing Interest StatementThe authors have declared no competing interest.Footnotes* More comprehensive analyses have been carried out on ST data, and some new benchmarking data has also been introduced.

ABSTRACT Working memory and response inhibition are functions that mature relatively late in life, after adolescence, paralleling the maturation of the prefrontal cortex. The link between behavioral and neural maturation is not obvious, however, making it challenging to understand how neural activity underlies the maturation of cognitive function. To gain insights into the nature of observed changes in prefrontal activity between adolescence and adulthood, we investigated the progressive changes in unit activity of Recurrent Neural Networks (RNNs) as they were trained to perform working memory and response inhibition tasks. These included increased delay period activity during working memory tasks, and increased activation in antisaccade tasks. These findings reveal universal properties underlying the neuronal computations behind cognitive tasks and explicate the nature of changes that occur as the result of developmental maturation. Competing Interest Statement The authors have declared no competing interest. Footnotes * https://github.com/maiziezhoulab/RNN_BrainMaturation

Abstract Motivation Identifying structural variants (SVs) is of critical importance in health and disease, however, detecting them remains a scientific and computing challenge. Several linked-read sequencing technologies, including 10X linked-read, TELL-Seq, and single tube long fragment read (stLFR), have been recently developed as cost-effective approaches to reconstruct multi-megabase haplotypes (phase blocks) from sequence data of a single sample. These technologies provide an optimal sequencing platform to characterize SVs, though few computational algorithms can utilize them. Thus, we developed Aquila_stLFR, an approach that resolves SVs through haplotype-based assembly of stLFR linked-reads. Results Aquila_stLFR first partitions LFRs into two haplotype-specific blocks, by taking advantage of the potential phasing ability of the linked-read itself. Each haplotype is then assembled independently, to achieve a complete diploid assembly to finally reconstruct the genome-wide SVs. We benchmarked Aquila_stLFR on a well-studied sample, NA24385, and showed Aquila_stLFR can detect medium to large size (50bp – 10kb) deletions with a high sensitivity and insertions with a high specificity. Availability Source code and documentation are available on https://github.com/maiziex/Aquila_stLFR. Contact maizie.zhou{at}vanderbilt.edu Supplementary information Supplementary data are available at Bioinformatics online. Competing Interest Statement The authors have declared no competing interest. Footnotes * We have revised the paper to a short paper version and reordered the authorship. We will submit it to a journal later.

Large Language Models (LLMs) have achieved unparalleled success across diverse language modeling tasks in recent years. However, this progress has also intensified ethical concerns, impacting the deployment of LLMs in everyday contexts. This paper provides a comprehensive survey of ethical challenges associated with LLMs, from longstanding issues such as copyright infringement, systematic bias, and data privacy, to emerging problems like truthfulness and social norms. We critically analyze existing research aimed at understanding, examining, and mitigating these ethical risks. Our survey underscores integrating ethical standards and societal values into the development of LLMs, thereby guiding the development of responsible and ethically aligned language models.

This work examines how head-mounted AR can be used to build an interactive sonic landscape to engage with a public sculpture. We describe a sonic artwork, \"Listening To Listening\", that has been designed to accompany a real-world sculpture with two prototype interaction schemes. Our artwork is created for the HoloLens platform so that users can have an individual experience in a mixed reality context. Personal head-mounted AR systems have recently become available and practical for integration into public art projects, however research into sonic sculpture works has yet to account for the affordances of current portable and mainstream AR systems. In this work, we take advantage of the HoloLens' spatial awareness to build sonic spaces that have a precise spatial relationship to a given sculpture and where the sculpture itself is modelled in the augmented scene as an \"invisible hologram\". We describe the artistic rationale for our artwork, the design of the two interaction schemes, and the technical and usability feedback that we have obtained from demonstrations during iterative development.