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PubMed2026

Causal circuit tracing reveals distinct computational architectures in single-cell foundation models: inhibitory dominance, biological coherence, and cross-model convergence.

MOTIVATION: Sparse autoencoders (SAEs) decompose foundation-model activations into interpretable features, but the model-internal causal interactions between those features (i.e. what ablating one feature does to the others, as distinct from the biological causal structure of the underlying cells)-and how those model-internal relationships relate to biological structure-are uncharacterized in single-cell foundation models. RESULTS: We introduce model-internal causal circuit tracing-zeroing one SAE feature at a source layer and measuring the resulting change in all downstream SAE features, for each of 120 source features-and apply it to Geneformer V2-316M and scGPT whole-human across four conditions (96 892 ablation-derived edges, 80 191 forward passes). On annotation-selected source features, edges share GO/KEGG/Reactome/STRING/TRRUST ontology terms at 50.9%-68.5%, a 2.9-6.2× enrichment over a configuration-preserving permutation null (P<.002); on 20 randomly sampled source features this attenuates to 21.5%-26.3%-still 2.5-3.1× above null-quantifying the annotation-selection contribution. Inhibitory dominance (fraction of ablation edges with d<0, i.e. source activation supports downstream target) is 65.5%-89.4%. scGPT produces larger raw per-edge effects (mean |d|=1.40 versus 1.05); after feature-share normalization, Geneformer is stronger (paired gene-pair ratio 0.64 on 33 301 shared pairs). Cross-model consensus yields 1142 architecture-invariant domain pairs (ordered pairs of GO biological-process categories "A→B" each connected by at least one ablation edge in both models; 10.6× enrichment over permutation null; P<.001). Circuit edge magnitude explains <1% of the variance in marginal driver-gene coexpression on the same cells (R2=0.010, n=31 176): the graph encodes structure beyond bivariate correlation. Against a matched-cell-type ENCODE ChIP-seq prior, circuit-predicted transcription factor (TF)→target pairs are enriched 2.06× (Fisher OR 5.84), markedly higher than 1.12× against TRRUST; direct ChIP-seq-supported target pairs show 10-30× larger CRISPRi sign-bias-corrected excess than indirect pairs. Gene-level CRISPRi validation on Replogle K562 and the noncancer RPE1 arm (and a true primary-T-cell control from Shifrut E, Carnevale J, Tobin V et al. Genome-wide CRISPR screens in primary human T cells reveal key regulators of immune function. Cell 2018; 175: 1958-71.e15) after sign-bias correction shows excess over baseline of +0.03 and +0.35 percentage points on K562 and RPE1, respectively (baseline already 52%-56% from sign marginals); effect-magnitude Spearman correlations ρ≈0. Bootstrap and per-cell-type stability (N∈{50,100,200}; B cell, CD4 + T, macrophage) give Pearson r≥0.97 on shared edges with 100% sign agreement; edge Jaccard grows monotonically with sample size. The circuit graph is therefore highly reproducible as an effect-size map, cell type specific in edge identity, consistent with coexpression encoding, and weakly but detectably enriched for ChIP-seq-supported direct regulatory edges. AVAILABILITY AND IMPLEMENTATION: https://github.com/Biodyn-AI/bio-sae-circuits (Python). Archival DOI: 10.5281/zenodo.19,633,166 (Zenodo).

Humans↗

PubMed2005

Structured motifs search.

In this paper, we describe an algorithm for the localization of structured models, i.e. sequences of (simple) motifs and distance constraints. It basically combines standard pattern matching procedures with a constraint satisfaction solver, and it has the ability, not present in similar tools, to search for partial matches. A significant feature of our approach, especially in terms of efficiency for the application context, is that the (potentially) exponentially many solutions to the considered problem are represented in compact form as a graph. Moreover, the time and space necessary to build the graph are linear in the number of occurrences of the component patterns.

Algorithms↗

PubMed2002

Maximal power tests for detecting defects in meiotic recombination.

We have determined the marker separations (genetic distances) that maximize the probability, or power, of detecting meiotic recombination deficiency when only a limited number of meiotic progeny can be assayed. We find that the optimal marker separation is as large as 30-100 cM in many cases. Provided the appropriate marker separation is used, small reductions in recombination potential (as little as 50%) can be detected by assaying a single interval in as few as 100 progeny. If recombination is uniformly altered across the genomic region of interest, the same sensitivity can be obtained by assaying multiple independent intervals in correspondingly fewer progeny. A reduction or abolition of crossover interference, with or without a reduction of recombination proficiency, can be detected with similar sensitivity. We present a set of graphs that display the optimal marker separation and the number of meiotic progeny that must be assayed to detect a given recombination deficiency in the presence of various levels of crossover interference. These results will aid the optimal design of experiments to detect meiotic recombination deficiency in any organism.

Meiosis↗

PubMed2005

Scale-free networks in cell biology.

A cell's behavior is a consequence of the complex interactions between its numerous constituents, such as DNA, RNA, proteins and small molecules. Cells use signaling pathways and regulatory mechanisms to coordinate multiple processes, allowing them to respond to and adapt to an ever-changing environment. The large number of components, the degree of interconnectivity and the complex control of cellular networks are becoming evident in the integrated genomic and proteomic analyses that are emerging. It is increasingly recognized that the understanding of properties that arise from whole-cell function require integrated, theoretical descriptions of the relationships between different cellular components. Recent theoretical advances allow us to describe cellular network structure with graph concepts and have revealed organizational features shared with numerous non-biological networks. We now have the opportunity to describe quantitatively a network of hundreds or thousands of interacting components. Moreover, the observed topologies of cellular networks give us clues about their evolution and how their organization influences their function and dynamic responses.

Animals↗

PubMedDate not supplied

Evolution of metabolisms: a new method for the comparison of metabolic pathways using genomics information.

The abundance of information provided by completely sequenced genomes defines a starting point for new insights in the multilevel organization of organisms and their evolution. At the lowest level enzymes and other protein complexes are formed by aggregating multiple polypeptides. At a higher level enzymes group conceptually into metabolic pathways as part of a dynamic information-processing system, and substrates are processed by enzymes yielding other substrates. A method based on a combination of sequence information with graph topology of the underlying pathway is presented. With this approach pathways of different organisms are related to each other by phylogenetic analysis, extending conventional phylogenetic analysis of individual enzymes. The new method is applied to pathways related to electron transfer and to the Krebs citric acid cycle. In addition to providing a more comprehensive understanding of similarities and differences between organisms, this method indicates different evolutionary rates between substrates and enzymes.

Animals↗

PubMed2004

A fast algorithm for determining the best combination of local alignments to a query sequence.

BACKGROUND: Existing sequence alignment algorithms assume that similarities between DNA or amino acid sequences are linearly ordered. That is, stretches of similar nucleotides or amino acids are in the same order in both sequences. Recombination perturbs this order. An algorithm that can reconstruct sequence similarity despite rearrangement would be helpful for reconstructing the evolutionary history of recombined sequences. RESULTS: We propose a graph-based algorithm for combining multiple local alignments to a query sequence into the single combination of alignments that either covers the maximal portion of the query or results in the single highest alignment score to the query. This algorithm can help study the process of genome rearrangement, improve functional gene annotation, and reconstruct the evolutionary history of recombined proteins. The algorithm takes O(n2) time, where n is the number of local alignments considered. CONCLUSIONS: We discuss two example applications of the algorithm. The algorithm is able to provide useful reconstructions of the metazoan mitochondrial genome. It is also able to increase the percentage of a query sequence's amino acid residues for which similar stretches of amino acids can be found in sequence databases.

Algorithms↗

PubMedDate not supplied

The footprint sorting problem.

Phylogenetic footprints are short pieces of noncoding DNA sequence in the vicinity of a gene that are conserved between evolutionary distant species. A seemingly simple problem is to sort footprints in their order along the genomes. It is complicated by the fact that not all footprints are collinear: they may cross each other. The problem thus becomes the identification of the crossing footprints, the sorting of the remaining collinear cliques, and finally the insertion of the noncollinear ones at "reasonable" positions. We show that solving the footprint sorting problem requires the solution of the "Minimum Weight Vertex Feedback Set Problem", which is known to be NP-complete and APX-hard. Nevertheless good approximations can be obtained for data sets of interest. The remaining steps of the sorting process are straightforward: computation of the transitive closure of an acyclic graph, linear extension of the resulting partial order, and finally sorting w.r.t. the linear extension. Alternatively, the footprint sorting problem can be rephrased as a combinatorial optimization problem for which approximate solutions can be obtained by means of general purpose heuristics. Footprint sortings obtained with different methods can be compared using a version of multiple sequence alignment that allows the identification of unambiguously ordered sublists. As an application we show that the rat has a slightly increased insertion/deletion rate in comparison to the mouse genome.

Journal Article↗

PubMed2025

MaxComp: Predicting single-cell chromatin compartments from 3D chromosome structures.

The genome is organized into distinct chromatin compartments with at least two main classes, a transcriptionally active A and an inactive B compartment, broadly corresponding to euchromatin and heterochromatin. Chromatin regions within the same compartment preferentially interact with each other over regions in the opposite compartment. A/B compartments are traditionally identified from ensemble Hi-C contact frequency matrices using principal component analysis of their covariance matrices. However, defining compartments at the single-cell level from sparse single-cell Hi-C data is challenging, especially since homologous copies are often not resolved. To address this, we present MaxComp, an unsupervised method, for inferring single-cell A/B compartments based on 3D geometric considerations in single-cell chromosome structures-derived either from multiplexed FISH-omics imaging or 3D structure models derived from Hi-C data. By representing each 3D chromosome structure as an undirected graph with edge-weights encoding structural information, MaxComp reformulates compartment prediction as a variant of the Max-cut problem, solved using semidefinite graph programming (SPD) to optimally partition the graph into two structural compartments. Our results show that the population average of MaxComp single-cell compartment annotations closely matches those derived from ensemble Hi-C principal component analysis, demonstrating that compartmentalization can be recovered from geometric principles alone, using only the 3D coordinates and nuclear microenvironment of chromatin regions. Our approach reveals widespread cell-to-cell variability in compartment organization, with substantial heterogeneity across genomic loci. When applied to multiplexed FISH imaging data, MaxComp also uncovers relationships between compartment annotations and transcriptional activity at the single-cell level. In summary, MaxComp offers a new framework for understanding chromatin compartmentalization in single cells, connecting 3D genome architecture, and transcriptional activity with the cell-to-cell variations of chromatin compartments.

Chromatin↗

PubMed2004

Global protein function annotation through mining genome-scale data in yeast Saccharomyces cerevisiae.

As we are moving into the post genome-sequencing era, various high-throughput experimental techniques have been developed to characterize biological systems on the genomic scale. Discovering new biological knowledge from the high-throughput biological data is a major challenge to bioinformatics today. To address this challenge, we developed a Bayesian statistical method together with Boltzmann machine and simulated annealing for protein functional annotation in the yeast Saccharomyces cerevisiae through integrating various high-throughput biological data, including yeast two-hybrid data, protein complexes and microarray gene expression profiles. In our approach, we quantified the relationship between functional similarity and high-throughput data, and coded the relationship into 'functional linkage graph', where each node represents one protein and the weight of each edge is characterized by the Bayesian probability of function similarity between two proteins. We also integrated the evolution information and protein subcellular localization information into the prediction. Based on our method, 1802 out of 2280 unannotated proteins in yeast were assigned functions systematically.

Bayes Theorem↗

PubMed1999

Inferring tree models for oncogenesis from comparative genome hybridization data.

Comparative genome hybridization (CGH) is a laboratory method to measure gains and losses of chromosomal regions in tumor cells. It is believed that DNA gains and losses in tumor cells do not occur entirely at random, but partly through some flow of causality. Models that relate tumor progression to the occurrence of DNA gains and losses could be very useful in hunting cancer genes and in cancer diagnosis. We lay some mathematical foundations for inferring a model of tumor progression from a CGH data set. We consider a class of tree models that are more general than a path model that has been developed for colorectal cancer. We derive a tree model inference algorithm based on the idea of a maximum-weight branching in a graph, and we show that under plausible assumptions our algorithm infers the correct tree. We have implemented our methods in software, and we illustrate with a CGH data set for renal cancer.

Chromosome Aberrations↗

PubMedDate not supplied

[Model of genes expression regulation in bacteria by means of formation of secondary RNA structures].

In this article a model, first, classical attenuation RNA regulation of gene expression by means of transcription termination is offered. The model bases on representation about a macrostate of secondary structure in RNA regulatory region between a ribosome and a RNA polymerase, on the formulas of a resonant type defining the value of deceleration of a RNA polymerase by a set of hairpins in the same region. The special attention is given to selection of parameters of model. To check of model the computer simulation is carried out and the dependences of transcription termination probability from the value of concentration charged tRNA are obtained, in particular, and from concentration of amino acid for many regulatory regions in genomes of bacteria (here data are presented for trpE genes in Streptomyces spp., Bradyrhizobium japonicum and Escherichia coli) and at various values of three parameters, which authors consider as the main. The obtained dependences are compounded with the accessible experimental data; including, under the form of the graphs concerning to activity of an enzyme depending on concentration of amino acid (for example, anthranilate synthase from tryptophan in S. venezuela). One possible usage: now attenuation is predicted usually by means of multiple alignment, it needs some sequences; the obtaining with the help of model on an individual sequence characteristic for attenuation or its absence of a curve at approaching parameters could be considered as argument for the benefit of presence or absence of attenuation.

Bacteria↗

PubMed2025

MO-GCAN: multi-omics integration based on graph convolutional and attention networks.

MOTIVATION: Cancer subtypes play a critical role in disease progression, prognosis, and treatment, making their detection essential for tailoring precision medicine. Studies have shown that multi-omics integration outperforms single-omics approaches in cancer subtyping tasks. However, due to the high-dimensionality of multi-omics data, many existing studies either fail to capture the correlation between true labels and learned features, or lack sufficient capacity to model complex biological representations. These limitations hinder the full potential of leveraging the rich and complementary information embedded in multi-omics datasets. RESULT: We propose a framework that leverages supervised feature learning and classification based on a graph-based learning approach with attention mechanism for cancer subtyping. More specifically, we train graph convolutional network models on each omics dataset to extract latent representations, which are then concatenated to form a comprehensive multi-omics feature embedding. We further develop sample fusion network based on the omics-specific graphs, incorporating the derived features and feeding them into a graph attention model for subtype classification. This two-stage multi-omics framework is applied to eight cancer types, with performance evaluated in terms of test accuracy, training time, macro-averaged precision, recall, and F-score. Experimental results show that the proposed method outperforms state-of-the-art approaches across various cancer types. Additionally, we provide empirical evidence supporting the hypothesis that retaining a limited number of high-confidence edges and utilizing enriched embeddings from intermediate graph neural network layers can improve predictive performance. AVAILABILITY AND IMPLEMENTATION: Data and the code are available at https://github.com/YD-00/MO-GCAN-Updated.git.

Neoplasms↗

PubMed2025

Integrative Genotyping and Analysis of Canine Structural Variation Using Long-read and Short-read Data.

Structural variation makes an important contribution to canine evolution and phenotypic differences. Although recent advances in long-read sequencing have enabled the generation of multiple canine genome assemblies, most prior analyses of structural variation have relied on short-read sequencing. To offer a more complete assessment of structural variation in canines, we performed an integrative analysis of structural variants present in 12 canine samples with available long-read and short-read sequencing data along with genome assemblies. Use of long-reads permits the discovery of heterozygous variation that is absent in existing haploid assembly representations while offering a marked increase in the ability to identify insertion variants relative to short-read approaches. Examination of the size spectrum of structural variants shows that dimorphic LINE-1 and SINE variants account for over 45% of all deletions and identified 1,410 LINE-1s with intact open reading frames that show presence-absence dimorphism. Using a graph-based approach, we genotype newly discovered structural variants in an existing collection of 1,879 resequenced dogs and wolves, generating a variant catalog containing a 56.5% increase in the number of deletions and 705% increase in the number of insertions previously found in the analyzed samples. Examination of allele frequencies across admixture components present across breed clades identified 283 structural variants evolving with a signature of selection.

Animals↗

PubMed2002

Identification of mixups among DNA sequencing plates.

MOTIVATION: During the process of high-throughput genome sequencing there are opportunities for mixups of reagents and data associated with particular projects. The sequencing templates or sequence data generated for an assembly may become contaminated with reagents or sequences from another project, resulting in poorer quality and inaccurate assemblies. RESULTS: We have developed a system to assess sequence assemblies and monitor for laboratory mixups. We describe several methods for testing the consistency of assemblies and resolving mixed ones. We use statistical tests to evaluate the distribution of sequencing reads from different plates into contigs, and a graph-based approach to resolve situations where data has been inappropriately combined. While these methods have been designed for use in a high-throughput DNA sequencing environment processing thousands of clones, they can be applied in any situation where distinct sequencing projects are performed at redundant coverage.

Algorithms↗

PubMed2025

CaXML: Chemistry-informed machine learning explains mutual changes between protein conformations and calcium ions in calcium-binding proteins using structural and topological features.

Proteins' flexibility is a feature in communicating changes in cell signaling instigated by binding with secondary messengers, such as calcium ions, associated with the coordination of muscle contraction, neurotransmitter release, and gene expression. When binding with the disordered parts of a protein, calcium ions must balance their charge states with the shape of calcium-binding proteins and their versatile pool of partners depending on the circumstances they transmit. Accurately determining the ionic charges of those ions is essential for understanding their role in such processes. However, it is unclear whether the limited experimental data available can be effectively used to train models to accurately predict the charges of calcium-binding protein variants. Here, we developed a chemistry-informed, machine-learning algorithm that implements a game theoretic approach to explain the output of a machine-learning model without the prerequisite of an excessively large database for high-performance prediction of atomic charges. We used the ab initio electronic structure data representing calcium ions and the structures of the disordered segments of calcium-binding peptides with surrounding water molecules to train several explainable models. Network theory was used to extract the topological features of atomic interactions in the structurally complex data dictated by the coordination chemistry of a calcium ion, a potent indicator of its charge state in protein. Our design created a computational tool of CaXML, which provided a framework of explainable machine learning model to annotate ionic charges of calcium ions in calcium-binding proteins in response to the chemical changes in an environment. Our framework will provide new insights into protein design for engineering functionality based on the limited size of scientific data in a genome space.

Machine Learning↗

PubMed2000

The NOESY jigsaw: automated protein secondary structure and main-chain assignment from sparse, unassigned NMR data.

High-throughput, data-directed computational protocols for Structural Genomics (or Proteomics) are required in order to evaluate the protein products of genes for structure and function at rates comparable to current gene-sequencing technology. This paper presents the JIGSAW algorithm, a novel high-throughput, automated approach to protein structure characterization with nuclear magnetic resonance (NMR). JIGSAW applies graph algorithms and probabilistic reasoning techniques, enforcing first-principles consistency rules in order to overcome a 5-10% signal-to-noise ratio. It consists of two main components: (1) graph-based secondary structure pattern identification in unassigned heteronuclear NMR data, and (2) assignment of spectral peaks by probabilistic alignment of identified secondary structure elements against the primary sequence. Deferring assignment eliminates the bottleneck faced by traditional approaches, which begin by correlating peaks among dozens of experiments. JIGSAW utilizes only four experiments, none of which requires 13C-labeled protein, thus dramatically reducing both the amount and expense of wet lab molecular biology and the total spectrometer time. Results for three test proteins demonstrate that JIGSAW correctly identifies 79-100% of alpha-helical and 46-65% of beta-sheet NOE connectivities and correctly aligns 33-100% of secondary structure elements. JIGSAW is very fast, running in minutes on a Pentium-class Linux workstation. This approach yields quick and reasonably accurate (as opposed to the traditional slow and extremely accurate) structure calculations. It could be useful for quick structural assays to speed data to the biologist early in an investigation and could in principle be applied in an automation-like fashion to a large fraction of the proteome.

Algorithms↗

PubMed2002

Feature subset selection for splice site prediction.

MOTIVATION: The large amount of available annotated Arabidopsis thaliana sequences allows the induction of splice site prediction models with supervised learning algorithms (see Haussler (1998) for a review and references). These algorithms need information sources or features from which the models can be computed. For splice site prediction, the features we consider in this study are the presence or absence of certain nucleotides in close proximity to the splice site. Since it is not known how many and which nucleotides are relevant for splice site prediction, the set of features is chosen large enough such that the probability that all relevant information sources are in the set is very high. Using only those features that are relevant for constructing a splice site prediction system might improve the system and might also provide us with useful biological knowledge. Using fewer features will of course also improve the prediction speed of the system. RESULTS: A wrapper-based feature subset selection algorithm using a support vector machine or a naive Bayes prediction method was evaluated against the traditional method for selecting features relevant for splice site prediction. Our results show that this wrapper approach selects features that improve the performance against the use of all features and against the use of the features selected by the traditional method. AVAILABILITY: The data and additional interactive graphs on the selected feature subsets are available at http://www.psb.rug.ac.be/gps

Arabidopsis↗

PubMed2004

Genogroup I and II noroviruses detected in stool samples by real-time reverse transcription-PCR using highly degenerate universal primers.

Genogroup I noroviruses from five genetic clusters and genogroup II noroviruses from eight genetic clusters were detected in stool extracts using degenerate primers and single-tube, real-time reverse transcription-PCR (RT-PCR) with SYBR Green detection. Two degenerate primer sets, designated MON 431-433 and MON 432-434, were designed from consensus sequences from the major clusters of norovirus based on the RNA-dependent RNA polymerase region of the norovirus genome. Viruses were extracted from stool samples within 20 min using a viral RNA extraction kit. Real-time RT-PCR for noroviruses generated semiquantitative results by means of the cycle threshold data and dilution endpoint standard curves. Presumptive product verification was achieved by evaluation of first-derivative melt graphs. Multiple clusters of noroviruses were identified simultaneously in a multiplex fashion by virtue of slight differences in melting temperature. The detection of 13 different genetic clusters suggests that the MON primers may serve as universal primers for most, if not all, of the noroviruses in a multiplex assay. Our technique provides a framework for broad application of real-time RT-PCR in clinical, environmental, and food testing laboratories for a wide range of noroviruses.

Caliciviridae Infections↗