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PubMed2026

Accelerated long-read variant calling with Clair3 for whole-genome sequencing.

SUMMARY: The rapid growth of genomic data and increasing adoption of long-read sequencing technologies have rendered variant calling one of the most computationally demanding tasks in genomic analysis. Although deep learning-based methods currently outperform conventional approaches in distinguishing true variants from complex sequencing noise, they impose prohibitive computational and time requirements. To address this limitation, we present a computational framework based on Clair3 that integrates parallelized feature generation, enhanced variant phasing, in-memory read haplotagging, and GPU-accelerated neural network inference to accelerate variant calling. By dynamically optimizing the use of both GPU and CPU resources, our method achieves substantial runtime improvements without compromising accuracy. We evaluated our framework across a range of sequencing depths, diverse samples, and multiple hardware configurations. Our results demonstrate that the optimized pipeline completes variant calling for a 30× whole-genome sequence in 12-20 minutes using standard computational resources (32 CPU threads and one NVIDIA GPU), and in 12-15 minutes on an Apple Mac Studio (32 threads), which is ∼10-20-fold speedup compared with its initial release. In addition to exceptional efficiency, our method maintains state-of-the-art accuracy, achieving SNP F1-scores of 99.32% and 99.70% on 30× ONT and PacBio GIAB HG003 datasets, respectively. This work introduces a rapid, accurate, and scalable variant calling framework that effectively supports large-cohort genomic studies and time-sensitive clinical applications. AVAILABILITY AND IMPLEMENTATION: The accelerated implementation of Clair3 is open source and available at: https://github.com/HKU-BAL/Clair3/tree/gpu.

Whole Genome Sequencing↗

PubMed1989

An 'electronic' extramural course in epidemiology and medical statistics.

This article describes an extramural university course in epidemiology and medical statistics taught using a computer conferencing system, microcomputers and data communications. Computer conferencing was shown to be a powerful, yet quite easily mastered, vehicle for distance education. It allows health personnel unable to attend regular classes due to geographical or time constraints, to take part in an interactive learning environment at low cost. This overcomes part of the intellectual and social isolation associated with traditional correspondence courses. Teaching of epidemiology and medical statistics is well suited to computer conferencing, even if the asynchronicity of the medium makes discussion of the most complex statistical concepts a little cumbersome. Computer conferencing may also prove to be a useful tool for teaching other medical and health related subjects.

Computer Communication Networks↗

PubMedDate not supplied

[Nonspecific brain structures and specialization in the central nervous system].

It has been demonstrated that corticofugal influences may exert selective control of single sensory signals which converge on the neurons of the striatum and other non-specific structures of the brain. This finding suggests that complex sensory image may be formed not by assembling separate elementary signals, but rather by a selective control of these signals into a total sensory inflow within the network brain structures. This conclusion is confirmed by comparative anatomical data. In the phylogenesis of vertebrates, brain structures differentiate presumably into both the specific ones which perform the analysis of only the given kind of information, and those (conventionally described as non-specific) which are specialized on sensory integration and exhibit functional polymodality.

Animals↗

PubMed2026

Episode clustering in phylogenetic networks.

MOTIVATION: The classical duplication episode clustering (EC) model introduced by Guigó et al. in the 1990s provides a foundational approach for inferring genomic duplication events crucial to understanding genome evolution. This model clusters single gene duplications from a collection of gene trees at locations in the species tree to minimize the total number of such locations, called duplication episodes. However, it does not capture reticulate evolutionary histories. RESULTS: Here, we introduce NetEC, a novel extension of this problem to phylogenetic networks. To solve NetEC, we first develop a polynomial-time dynamic programming (DP) algorithm for testing whether a given set of network nodes can serve as episode locations. We then propose a main inference algorithm that utilizes this DP component to optimize the episode count; while the feasibility test runs in polynomial time, the full optimization has exponential worst-case complexity, and an optional heuristic mode is provided for larger instances. We also propose an extended episode analysis procedure that identifies additional genomic duplication candidates below reticulation nodes, complementing the main algorithm by resolving potential upward clustering of duplications induced by reticulation. We evaluate our method on simulated data and on an empirical Pandanales dataset comprising over 29 000 gene trees, demonstrating exact and accurate inference of genomic duplication events even in the presence of multiple reticulations. AVAILABILITY AND IMPLEMENTATION: All experiments were conducted using the NetEC tool (https://github.com/ppgorecki/netec), with all input data, scripts, and parameter settings for reproduction available in the same repository.

Phylogeny↗

PubMed2025

N6-methyladenine identification using deep learning and discriminative feature integration.

N6-methyladenine (6 mA) is a pivotal DNA modification that plays a crucial role in epigenetic regulation, gene expression, and various biological processes. With advancements in sequencing technologies and computational biology, there is an increasing focus on developing accurate methods for 6 mA site identification to enhance early detection and understand its biological significance. Despite the rapid progress of machine learning in bioinformatics, accurately detecting 6 mA sites remains a challenge due to the limited generalizability and efficiency of existing approaches. In this study, we present Deep-N6mA, a novel Deep Neural Network (DNN) model incorporating optimal hybrid features for precise 6 mA site identification. The proposed framework captures complex patterns from DNA sequences through a comprehensive feature extraction process, leveraging k-mer, Dinucleotide-based Cross Covariance (DCC), Trinucleotide-based Auto Covariance (TAC), Pseudo Single Nucleotide Composition (PseSNC), Pseudo Dinucleotide Composition (PseDNC), and Pseudo Trinucleotide Composition (PseTNC). To optimize computational efficiency and eliminate irrelevant or noisy features, an unsupervised Principal Component Analysis (PCA) algorithm is employed, ensuring the selection of the most informative features. A multilayer DNN serves as the classification algorithm to identify N6-methyladenine sites accurately. The robustness and generalizability of Deep-N6mA were rigorously validated using fivefold cross-validation on two benchmark datasets. Experimental results reveal that Deep-N6mA achieves an average accuracy of 97.70% on the F. vesca dataset and 95.75% on the R. chinensis dataset, outperforming existing methods by 4.12% and 4.55%, respectively. These findings underscore the effectiveness of Deep-N6mA as a reliable tool for early 6 mA site detection, contributing to epigenetic research and advancing the field of computational biology.

Deep Learning↗

PubMed1988

Analysis of lettuce necrotic yellows virus structural proteins with monoclonal antibodies and concanavalin A.

Three major structural proteins of lettuce necrotic yellow virus (LNYV) were identified by discontinuous polyacrylamide gel electrophoresis (PAGE) to have Mr approximately 78,000 (G), 57,000 (N), and 19,000 (M). Unreduced G and M proteins had faster mobilities in PAGE indicating the presence of disulfide bonds. The G protein was shown to be glycosylated with a complex network of oligosaccharides containing beta-N-acetylchitobiose N-linked to asparagine residues of the protein. Up to 17 additional minor bands were also detected in silver-stained electrophoretograms. In Western immunoblots, 9 of these (Mr approximately 27,000-220,000) were recognized by a monoclonal antibody to the N protein and another 6 (Mr approximately 58,000-180,000) with a monoclonal antibody to the G protein, indicating that they were degradation products or aggregates of these two viral proteins. Two minor silver-stained bands failed to react with either of the monoclonal antibodies, but were recognized by polyclonal anti-LNYV serum and are probably the L (Mr approximately 190,000) and NS (Mr approximately 38,000) viral proteins.

Antibodies, Monoclonal↗

PubMed1987

Immunocytochemical localization of cathepsins B and H in rat liver.

Light and electron microscopic localization of cathepsins B and H in rat liver was investigated by immunoenzyme and protein A-gold techniques. For light microscopy (LM), semi-thin sections of the Epon-embedded material were stained by the immunoenzyme technique after removal of epoxy resin. For electron microscopy (EM), ultra-thin sections of the Lowicryl K4M-embedded material were stained by the protein A-gold technique. By LM, reaction deposits for cathepsins B and H were present in the cytoplasmic granules of parenchymal cells and endothelial cells, and Kupffer cells. The sinus-lining cells and the parenchymal cells showed the similar staining intensity. By EM, gold particles were present exclusively in lysosomes of all the cell types cited above. The same results were obtained from quantitative analysis. In addition, Golgi complexes themselves were mostly negative but some small vesicles on the trans side of them were labeled for these proteinases. The results indicate that cathepsins B and H are present in the lysosomes of rat liver and that these enzymes seem to be transported by small vesicles from endoplasmic reticulum to lysosomes via tubuloreticular network of the trans Golgi region.

Animals↗

PubMed2025

A neural network model enables worm tracking in challenging conditions and increases signal-to-noise ratio in phenotypic screens.

High-resolution posture tracking of C. elegans has applications in genetics, neuroscience, and drug screening. While classic methods can reliably track isolated worms on uniform backgrounds, they fail when worms overlap, coil, or move in complex environments. Model-based tracking and deep learning approaches have addressed these issues to an extent, but there is still significant room for improvement in tracking crawling worms. Here we train a version of the DeepTangle algorithm developed for swimming worms using a combination of data derived from Tierpsy tracker and hand-annotated data for more difficult cases. DeepTangleCrawl (DTC) outperforms existing methods, reducing failure rates and producing more continuous, gap-free worm trajectories that are less likely to be interrupted by collisions between worms or self-intersecting postures (coils). We show that DTC enables the analysis of previously inaccessible behaviours and increases the signal-to-noise ratio in phenotypic screens, even for data that was specifically collected to be compatible with legacy trackers including low worm density and thin bacterial lawns. DTC broadens the applicability of high-throughput worm imaging to more complex behaviours that involve worm-worm interactions and more naturalistic environments including thicker bacterial lawns.

Caenorhabditis elegans↗

PubMedDate not supplied

A critical analysis of the current interpretation of immune diseases. Arguments for a more pathological classification.

Based on the new acquisitions of knowledge on the immune network and on the regulating mechanisms of the immune system, the paper presents an original and at the same time a more pathophysiological classification of immune diseases according to: 1. the functionality stage (input--or antigen presentation, central--integrative, and output--effector), and 2. the immune ligand involved (immunoglobulin or Ti complex). From these new concepts it results that the main feature of the immune system reactivity is the antigen recognition. The authors bring arguments in favour of the assertion that immunity--characterized by recognition--is equal with autoimmunity--characterized by autorecognition, i.e., recognition of the self antigens. From this statement it ensues that: 1. an immune disease is a disease of internal regulation of the immune network, i.e., of the relationships between idiotypes and anti-idiotypes, and 2. any immune disease is an autoimmune disease, meaning that the pathogenetic significance of the so-called "autoimmune diseases" and of the "hypersensitivity diseases" is the same.

Autoimmune Diseases↗

PubMed2025

Comprehensive discovery and functional characterization of the noncanonical proteome.

The systematic identification and functional characterization of noncanonical translation products, such as novel peptides, will facilitate the understanding of the human genome and provide new insights into cell biology. Here, we constructed a high-coverage peptide sequencing reference library with 11,668,944 open reading frames and employed an ultrafiltration tandem mass spectrometry assay to identify novel peptides. Through these methods, we discovered 8945 previously unannotated peptides from normal gastric tissues, gastric cancer tissues and cell lines, nearly half of which were derived from noncoding RNAs. Moreover, our CRISPR screening revealed that 1161 peptides are involved in tumor cell proliferation. The presence and physiological function of a subset of these peptides, selected based on screening scores, amino acid length, and various indicators, were verified through Flag-knockin and multiple other methods. To further characterize the potential regulatory mechanisms involved, we constructed a framework based on artificial intelligence structure prediction and peptide‒protein interaction network analysis for the top 100 candidates and revealed that these cancer-related peptides have diverse subcellular locations and participate in organelle-specific processes. Further investigation verified the interacting partners of pep1-nc-OLMALINC, pep5-nc-TRHDE-AS1, pep-nc-ZNF436-AS1 and pep2-nc-AC027045.3, and the functions of these peptides in mitochondrial complex assembly, energy metabolism, and cholesterol metabolism, respectively. We showed that pep5-nc-TRHDE-AS1 and pep2-nc-AC027045.3 had substantial impacts on tumor growth in xenograft models. Furthermore, the dysregulation of these four peptides is closely correlated with clinical prognosis. Taken together, our study provides a comprehensive characterization of the noncanonical proteome, and highlights critical roles of these previously unannotated peptides in cancer biology.

Humans↗

PubMed1974

Ultrastructural analysis during germination and outgrowth of Bacillus subtilis spores.

Electron microscopy of thin sections of dormant and germinating spores of Bacillus subtilis 168 revealed a progressive change in the structure of the cortex, outer spore coat, and inner spore coat. The initial changes were observed in the cortex region, which showed a loose fibrous network within 10 min of germination, and in the outer spore coat, which began to be sloughed off. The permeability of the complex outer spore layers was modified within 10 min, since, at this time, the internal structures of the spore coat were readily stainable. A nicking degradation action of the laminated inner spore coat began at 20 min, and this progressed for the next 20 min leading to the loosening of the inner spore coat. By 30 min, the outer spore coat showed signs of disintegration, and at 40 min, both the outer and inner spore coats were degraded extensively. At 30 to 40 min, a period just preceding net deoxyribonucleic acid synthesis, mesosomes became very prominent in the inner spore core and the cell wall began to thicken around the spore core. At 50 min, an emerging cell was observed, and by 60 min, there was clear evidence for elongation of the emerging cell and the presence of two nuclear bodies. At 90 min, elongation had been followed by the first cell division. There was evidence for spore coat fragments at the opposite poles of the dividing cell.

Bacillus subtilis↗

PubMed1991

Quantitative analysis of electroencephalograms: is there chaos in the future?

The history of quantitative, computerized electroencephalogram (EEG) analysis is reviewed. It is shown that, until very recently, the basic approach to EEG analysis involved the assumption that the EEG is stochastic. Consequently, statistical pattern recognition techniques, segmentation procedures, syntactic methods, knowledge-based approaches, and even artificial neural network methods have been developed with different levels of success. A fundamentally different approach to computerized EEG analysis, however, is making its way into the laboratories. The basic idea, inspired by recent advances in the area of non-linear dynamics, and especially the theory of chaos, is to view an EEG as the output of a deterministic system of relatively simple complexity, but containing non-linearities. This suggests that studying the geometrical dynamics of EEGs, and the development of neurophysiologically realistic models of EEG generation may produce more successful automated EEG analysis techniques than the classical, stochastic methods. Evidence supporting the non-linear dynamics paradigm is reviewed, and possible research paths are indicated.

Algorithms↗

PubMed1992

Refinement of an enzyme complex with inhibitor bound at partial occupancy. Hen egg-white lysozyme and tri-N-acetylchitotriose at 1.75 A resolution.

The structure of the tri-N-acetylchitotriose inhibitor complex of hen egg-white lysozyme has been refined at 1.75 A resolution, using data collected from a complex crystal with ligand bound at less than full occupancy. To determine the exact value of the inhibitor occupancy, a model comprising unliganded and sugar-bound protein molecules was generated and refined against the 1.75 A data, using a modified version of the Hendrickson & Konnert least-squares procedure. The crystallographic R-factor for the model was found to fall to a minimum at 55% bound sugar. Conventional refinement assuming unit occupancy was found to yield incorrect thermal and positional parameters. Application of the same refinement procedures to an earlier 2.0 A data set, collected independently on different complex crystals by Blake et al. gave less consistent results than the 1.75 A refinement. From an analysis of the high resolution structure a detailed picture of the protein-carbohydrate interactions in the non-productive complex has emerged, together with the conformation and mobility changes that accompany ligand binding. The specificity of interaction between the protein and inhibitor, bound in subsites A to C of the active site, is seen to be generated primarily by an extensive network of hydrogen bonds, both to the protein itself and to bound solvent molecules. The latter also play an important role in maintaining the structural integrity of the active site cleft in the apo-protein.

Binding Sites↗

PubMed1991

Axon-Schwann cell networks are regular components of nodal regions in normal large nerve fibres of cat spinal roots.

The paranodal occurrence of axon-Schwann cell networks (ASNs), which are entities assumed to take part in the removal of degenerate axonal material, was examined quantitatively by electron microscopical serial section analysis in normal cat ventral and dorsal spinal roots. In nerve fibres greater than or equal to 10 microns in diameter 88% of the nodal regions in the ventral roots and 97% in the dorsal roots showed ASN complexes, which especially in the ventral roots often consisted of many segregated axoplasmic portions. The corresponding frequencies in fibres less than 10 microns were 28% and 62% in the ventral and the dorsal roots, respectively. ASN complexes were rare in fibres less than 5 microns. The results show that the ASN is a part of the normal paranodal architecture in large myelinated nerve fibres. The ASN occurrence seems to differ with neurone type.

Animals↗

PubMed2026

Dissecting spatial patterning and signaling with directional diffusion in spatial multi-omics.

Spatial multi-omics sequencing enables the simultaneous profiling of transcriptomics, proteomics, and epigenomics at a spatial resolution, offering insights into complex tissue organization and molecular regulation. However, the effective integration of multiple omics modalities in a spatial context remains a major challenge. Here, we present SpaDDM, a spatial multi-omics integration framework based on directional diffusion models (DDMs), which supports spatial pattern identification, cross-omics alignment, and inter-and intracellular signaling flow analysis. SpaDDM employs DDM-based graph networks to learn omics-specific representations by jointly incorporating spatial coordinates and molecular measurements within each modality, followed by an attention mechanism to align features across modalities. We benchmarked SpaDDM on diverse spatial multi-omics datasets, including transcriptomics-epigenomics and transcriptomics-proteomics combinations across multiple tissues and species. SpaDDM consistently outperformed existing methods by more accurately deciphering spatial tissue patterns and effectively reducing the boundary noise between spatial regions. Moreover, the learned low-dimensional coembedded representations of individual cells serve as integral mediators for inferring the signaling flows that underlie spatial patterning. Finally, we demonstrated that SpaDDM alignment of complementary information across multi-omics layers facilitates cross-omics translation and significantly improves the prediction of cell state alignments.

Multiomics↗

PubMed1989

Quantitative analysis of ventral denticular patterns of Drosophila melanogaster larvae and the regulation of the bithorax complex.

A quantitative model of the effect of the bithorax complex on segmentation is presented which could explain the known data of the spatiotemporal regulation of key gene complex during early Drosophila development, in relation to their effects on some of the segmentation landmarks. The model tries to put together the two different genetic levels, the genotypic and the phenotypic. At the genotypic level, a minimal cross-regulatory network of the different genes involved, Antp, Ubx, abd-A and Abd-B which explains the reported levels of expressions of these genes. At the phenotypic level, the pattern of the ventral denticle belts across the larva which are characteristics of the different segments have been compared by calculating a value of the degree of similarity in the case of the wild-type and several mutant combinations. Finally the two parts of the model are combined, showing that a satisfactory agreement between the two can be achieved. Therefore, this work is a first attempt to develop a method which will provide an explanatory solution of the old question in morphogenesis of how the phenotype is directed by the genotype of a cell or organism.

Animals↗

PubMed1985

Evidence for an involvement of actin in the positioning and motility of centrosomes.

Cultured human polymorphonuclear leukocytes exposed to the tumor promoter 12-O-tetradecanoylphorbol-13-acetate (TPA) spread on the substratum and undergo centrosome splitting. The two centrioles may separate by a distance of several micrometers, each being surrounded by an aster of microtubules. Here we show that the centriole/aster complexes are in constant, rapid motion through the cytoplasm, carrying with them some of the cytoplasmic granules while pushing aside others, or deforming and displacing the nucleus. An analysis of this unique motility phenomenon was undertaken. We show that intact microtubules are required for TPA-induced centrosome splitting and aster motility, but not for cell spreading. More importantly, disruption of the actin network inhibits both centrosome splitting and cell spreading, and even reverses splitting (induces convergence and fusion of asters) in polymorphonuclear leukocytes pretreated with TPA alone. These observations indicate the existence of a dynamic relationship between microtubules and actin networks and provide evidence for a role of actin in determining the position of the centrosome by way of interaction with the microtubules radiating from it.

Actins↗

PubMed2025

MPAC: a computational framework for inferring pathway activities from multi-omic data.

Fully capturing cellular state requires examining genomic, epigenomic, transcriptomic, proteomic, and other assays for a biological sample and comprehensive computational modeling to reason with the complex and sometimes conflicting measurements. Modeling these so-called multi-omic data is especially beneficial in disease analysis, where observations across omic data types may reveal unexpected patient groupings and inform clinical outcomes and treatments. We present Multi-omic Pathway Analysis of Cells (MPAC), a computational framework that interprets multi-omic data through prior knowledge from biological pathways. MPAC leverages network relationships encoded in pathways through a factor graph to infer consensus activity levels for proteins and associated pathway entities from multi-omic data, runs permutation testing to eliminate spurious activity predictions, and groups biological samples by pathway activities to allow identifying and prioritizing proteins with potential clinical relevance, e.g., associated with patient prognosis. Using DNA copy number alteration and RNA-seq data from head and neck squamous cell carcinoma patients from The Cancer Genome Atlas as an example, we demonstrate that MPAC predicts a patient subgroup related to immune responses not identified by analysis with either input omic data type alone. Key proteins identified via this subgroup have pathway activities related to clinical outcome as well as immune cell compositions. Our MPAC R package, available at https://bioconductor.org/packages/MPAC, enables similar multi-omic analyses on new datasets.

Journal Article↗