MOTIVATION: Integrating Large Language Models (LLMs) with research tools presents technical and reproducibility challenges for biomedical research. While commercial artificial intelligence (AI) systems are easy to adopt, they obscure data provenance, lack transparency, and can generates false information, making them unfit for many research problems. To address these challenges, we developed the Bioinformatics Retrieval Augmented Digital (BRAD) agent software system. RESULTS: Here, we introduce BRAD, an agentic system that integrates LLMs with external tools and data to streamline research workflows. BRAD's modular agents retrieve information from literature, custom software, and online databases while maintaining transparent protocols to increase the reliability of AI generated results. We apply BRAD to a biomarker discovery pipeline, automating both execution and the generation of enrichment reports. This workflow contextualizes user data within the literature, enabling a level of interpretation and automation that surpasses conventional research tools. Beyond the workflow we highlight here, BRAD is a flexible system that has been deployed in other applications including a chatbot, video RAG, and analysis of single cell data. AVAILABILITY AND IMPLEMENTATION: The source code for BRAD is available at https://github.com/Jpickard1/BRAD; Information for pip installation, tutorials, documentation, and further information can be found at: ReadTheDocs.

Recent advances in biological technologies, such as multi-way chromosome conformation capture (3C), require development of methods for analysis of multi-way interactions. Hypergraphs are mathematically tractable objects that can be utilized to precisely represent and analyze multi-way interactions. Here we present the Hypergraph Analysis Toolbox (HAT), a software package for visualization and analysis of multi-way interactions in complex systems.

We propose a method for simultaneously detecting shared and unshared communities in heterogeneous multilayer weighted and undirected networks. The multilayer network is assumed to follow a generative probabilistic model that takes into account the similarities and dissimilarities between the communities. We make use of a variational Bayes approach for jointly inferring the shared and unshared hidden communities from multilayer network observations. We show that our approach outperforms state-of-the-art algorithms in detecting disparate (shared and private) communities on synthetic data as well as on real genome-wide fibroblast proliferation dataset.

The von Neumann graph entropy (VNGE) facilitates measurement of information divergence and distance between graphs in a graph sequence. It has been successfully applied to various learning tasks driven by network-based data. While effective, VNGE is computationally demanding as it requires the full eigenspectrum of the graph Laplacian matrix. In this paper, we propose a new computational framework, Fast Incremental von Neumann Graph EntRopy (FINGER), which approaches VNGE with a performance guarantee. FINGER reduces the cubic complexity of VNGE to linear complexity in the number of nodes and edges, and thus enables online computation based on incremental graph changes. We also show asymptotic equivalence of FINGER to the exact VNGE, and derive its approximation error bounds. Based on FINGER, we propose efficient algorithms for computing Jensen-Shannon distance between graphs. Our experimental results on different random graph models demonstrate the computational efficiency and the asymptotic equivalence of FINGER. In addition, we apply FINGER to two real-world applications and one synthesized anomaly detection dataset, and corroborate its superior performance over seven baseline graph similarity methods.

In this paper, we explore how network centrality and network entropy can be used to identify a bifurcation network event. A bifurcation often occurs when a network undergoes a qualitative change in its structure as a response to internal changes or external signals. In this paper, we show that network centrality allows us to capture important topological properties of dynamic networks. By extracting multiple centrality features from a network for dimensionality reduction, we are able to track the network dynamics underlying an intrinsic low-dimensional manifold. Moreover, we employ von Neumann graph entropy (VNGE) to measure the information divergence between networks over time. In particular, we propose an asymptotically consistent estimator of VNGE so that the cubic complexity of VNGE is reduced to quadratic complexity that scales more gracefully with network size. Finally, the effectiveness of our approaches is demonstrated through a real-life application of cyber intrusion detection.

Motivation: The availability of powerful analysis tools will further understanding of genome organization and its relationship to phenotype in dynamical settings. Results: The 4D Nucleome Analysis Toolbox (NAT) is a user-friendly and powerful MATLAB toolbox for time series analysis of genome-wide chromosome conformation capture (Hi-C) data and gene expression (RNA-seq). NAT can load and normalize data, define topologically associating domains, analyse translocations, produce visualization, and study time course data. We provide examples that include time series data sets and karyotypically abnormal cell lines demonstrating the flexibility of NAT. Availability and implementation: https://github.com/laseaman/4D_Nucleome_Analysis_Toolbox. Contact: [email protected].

MOTIVATION: Topological domains have been proposed as the backbone of interphase chromosome structure. They are regions of high local contact frequency separated by sharp boundaries. Genes within a domain often have correlated transcription. In this paper, we present a computational efficient spectral algorithm to identify topological domains from chromosome conformation data (Hi-C data). We consider the genome as a weighted graph with vertices defined by loci on a chromosome and the edge weights given by interaction frequency between two loci. Laplacian-based graph segmentation is then applied iteratively to obtain the domains at the given compactness level. Comparison with algorithms in the literature shows the advantage of the proposed strategy. RESULTS: An efficient algorithm is presented to identify topological domains from the Hi-C matrix. AVAILABILITY AND IMPLEMENTATION: The Matlab source code and illustrative examples are available at http://bionetworks.ccmb.med.umich.edu/ CONTACT: : [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Cell function manifests through intricately controlled dynamic processes that both interact amongst each other—in a network—and also respond to external signals, e.g., transcription factors, etc. Recently, controllability of complex networks has received considerable attention for its potential to influence the behavior of dynamically interacting nodes of interest in biological, social, and engineered networks. For example, Liu et al. recently used the notion of structural controllability to provide insights into the percentage of nodes required to control the evolution of the state of the nodes in a network in response to external stimuli [1]–[3]. However, when applying the notion of controllability in biology, we must consider the dynamical nature of biological systems as well as the timing of the external input. Here, we provide a parallel yet distinct approach to the problem of network controllability in the context of cell differentiation and highlight the importance of network controllability and its applications in dynamical biological systems [4]. Cellular differentiation is the process via which the nucleus achieves a new function. After many years of intensive investigation, we are just scratching the surface of how the cell nucleus functions on a global level to produce specialized cell types. The relationship between nuclear form and function will be critical to understanding the dynamics of the nucleus during cell differentiation [5], [6]. We argue that feedback between form and function during differentiation fine-tunes a cell-specific form, leading to the desired function [7]. In this venue, we first define form as a geometric network that reflects the physical configuration of chromosomes in the interphase nucleus and function as the transcriptional network. Adopting this geometric point of view allows us to consider the evolution of a concrete, physically realizable network during differentiation, and assess how essential features of this network evolve over time. The basic question is thus how network geometry with a particular initial configuration evolves toward a specific cell type with its own unique configuration and how essential features of the network evolve with this geometry. Two constructs form the basic premise in addressing this question. First, cell fate is guided by transcription factors that have broad influence on cell differentiation and reprogramming [8]. An example of this type of influence is the well-studied transcription factor MyoD, which can convert fibroblasts into skeletal muscle cells by activating the skeletal muscle differentiation program [9]. Another example is the recently discovered set of transcription factors that can reprogram cells to pluripotency [10]. Second, we pose that the essential features of the network that evolve to allow for efficient reprogramming fall in the realm of control theory. One of the basic concepts in control theory is—to no surprise—controllability. Essentially, controllability is a feature of a dynamic system with inputs, allowing its states to be steered towards desirable target states. If we now consider the physical configuration of the chromosomes, the form, as the dynamic state during differentiation, and transcription factors such as MyoD as the external signals, the natural idea is to examine the differentiation dynamics from the point of view of its controllability. What would such a point of view offer? It provides a systematic means of quantifying directions that the network can most efficiently be steered towards, given how the transcription factor binds and interacts with various parts of the chromosomal network. This can be accomplished by not only considering whether the network is controllable from the input, but also examining how controllable each direction in the configuration space of the chromosomal network is. And since the network is dynamic—after all, that is what differentiation is all about—a static, parameterized notion of controllability, such as structural controllability, turns out to be unsuitable for assessing network controllability. In the structural controllability approach, the weights on the interactions are of little consequence—as long as they are zero and non-zero—for assessing whether the network is steerable to a particular configuration or not. However, in networks such as the chromosomal network during cellular differentiation, there is intriguing evidence that two nodes interact more if their pairwise physical distance is less. Thus, as the nodes in the chromosomal network come closer, they have a higher interaction with each other, and it is well-conceivable that the network becomes less controllable by an external signal such as a transcription factor. We have proposed a framework to examine this notion of controllability via the controllability gramian, a matrix that captures how directions in the nodes' configuration space are attainable with a given external input [11]. The controllability gramian not only reveals the efficiency and time critical nature of steering the network, but also has a statistical significance in terms of its connection with measurement covariances and their determinant, the network entropy. Thus, we can estimate the entropy from the determinant of the covariance matrix, and interpret it as a measure of network controllability. Using this approach, we have proposed a diffusion-driven network with dynamics that is parameterized by the relative distances between the nodes, for which the evolution of the controllability gramian not only matches the experimentally determined covariances in terms of their algebraic properties, but also offers a control theoretic parallel for a highly entropic intermediate network as the cell transitions between two highly ordered states during differentiation (Figure 1). Figure 1 Dynamics of network controllability and entropy. The dynamical properties of a cell are hardwired in the genome, but both environmental and epigenetic factors influence how this information is accessed and applied [12], [13]. Thus, a cell is naturally receptive to external influence, and this gives us an opportunity by which to manipulate a cell to achieve a desired function or outcome. To best take advantage of this property, we require a deeper understanding of when and where to apply external influences. The application of network controllability theory may be the key to systematic reasoning about which nodes to target to achieve global impact toward a desired outcome, and when to target them in a perturbed system, such as cancer. This may lead to novel strategies for redirecting cancer cells along new trajectories that avoid further pathology. However, the dynamic nature of the network, as well as the efficiency and timing of the control mechanism, should be an integral part of such a network controllability research.