Transcription factors (TFs) are essential players in the regulation of gene expression and thus have been the subject of interest in the context of diseases and the manipulation of specific cell functions or pathways. The complex interplay of TFs and genes can be modeled as a network, in which each edge represents a regulatory influence. A challenge in such networks is the identification of a set of TFs with the maximum regulatory influence, which is of particular importance in the context of perturbation studies. We model the task of finding a set of TFs to maximize the influence on a set of genes as a probing problem in a bipartite graph. We test different adaptive and non-adaptive algorithms on simulated graphs and discuss their properties and adaptivity gap. Then, we apply the algorithms on real-life data to find TFs regulating genes involved in T-cell mediated immunity and lymphoid leukemia. The approach has minimal data requirements and can be readily applied to all other types of bipartite networks.
Cancer phylogenies are key to understanding tumor evolution. However, due to the uncertainty in phylogenetic estimation, one typically infers many, equally-plausible phylogenies from bulk DNA sequencing data of tumors, hindering downstream analysis that relies on correct phylogenies. To resolve this challenge, we introduce Sapling, a method to solve two variants of the Backbone Tree Inference from Reads problem, which seeks a small set of backbone trees on a subset of mutations that collectively summarize the space of plausible cancer phylogenies. We prove that the problems are NP-hard. On simulated and real data, we demonstrate that Sapling is capable of inferring high-quality backbone trees that adequately summarize the space of plausible cancer phylogenies. In addition, we demonstrate that Sapling is able to infer full-size trees with higher likelihoods than state-of-the-art methods.
BackgroundAtom-to-atom maps play an important role in many applications. However, they are often difficult to obtain. The KEGG reaction database does not provide atom-to-atom maps for its reactions and instead offers a description of local changes for pairs of reactant and product molecules in terms of so-called RCLASSes. Developed for classification purposes, RCLASS data are difficult to use for purposes such as the construction of atom-to-atom maps or reaction rules. DPO graph transformation rules, on the other hand, work as a convenient and efficient representation, particularly for these applications. The RCLASS data can be understood as collections of local graph patterns in the reactants and products of a reaction, together with partial correspondences of atoms. The problem of converting RCLASS data into DPO rules, therefore, is a special case of the graph reconstruction problem, which consists of inferring a graph from a collection of subgraphs.ResultsWe developed laveau, a tool that computes explicit DPO rules from KEGG reactions and RCLASS data. The algorithm proceeds stepwise, starting with a translation of individual RDM codes, specifically developed by the KEGG database, into equivalent RDM pattern graphs. Multiple RDM pattern graphs for the same RCLASS are then combined based on their embeddings into the reactant and product molecules, observing certain consistency conditions. In the final step, these combined pairwise patterns are merged into a pair of subgraphs of reactants and products, respectively. If RCLASSes connecting all pairs of reactant and product molecules are available, the complete reaction center(s) is/are contained in the union of these subgraphs. The atom-to-atom map inherited from the RDM codes then defines a DPO transformation rule. Application of these rules to the reactants then yields complete atom-to-atom maps (AAMs). Starting from 3195 RCLASSes, laveau generates a total of 1232 DPO rules and 1594 AAMs.ConclusionsThe laveau software makes it possible to extract local atom-to-atom maps from the RCLASSes of the KEGG database, covering a large set of enzyme-catalyzed reactions. The results are made available in the form of DPO rules for use in atom-level models of metabolic networks, filling a crucial gap in the available data.
Background: In the Nearest-Neighbor Thermodynamic Model, a standard approach for RNA secondary structure prediction, the energy of the multiloops is modeled using a linear entropic penalty governed by three branching parameters. Although these parameters are typically fixed, recent work has shown that reparametrizing the multiloop score and considering alternative branching conformations can lead to significantly better structure predictions. However, prior approaches for exploring the alternative branching structures were computationally inefficient for long sequences. Results: We present a novel algorithm that partitions the parameter space, identifying all distinct branching structures (optimal under different branching parameters) for a given RNA sequence using the fewest possible minimum free energy computations. Our method efficiently computes the full parameter-space partition and the associated optimal structures, enabling a comprehensive evaluation of the structural landscape across parameter choices. We apply this algorithm to the Archive II benchmarking dataset, assessing the maximum attainable prediction accuracy for each sequence under the reparameterized multiloop model. We find that the potential for improvement over default predictions is substantial in many cases, and that the optimal prediction accuracy is highly sensitive to auxiliary modeling decisions, such as the treatment of lonely base pairs and dangling ends. Conclusion: Our results support the hypothesis that the conventional choice of multiloop parameters may limit prediction accuracy and that exploring alternative parameterizations is both tractable and worthwhile. The efficient partitioning algorithm we introduce makes this exploration feasible for longer sequences and larger datasets. Furthermore, we identify several open challenges in identifying the optimal structure.
Chemical reaction databases typically report the molecular structures of reactant and product compounds, as well as their stoichiometry, but lack information, in particular, on the correspondence of reactant and product atoms. These atom-to-atom maps (AAM), however, are crucial for applications including chemical synthesis planning in organic chemistry and the analysis of isotope labeling experiments in modern metabolomics. AAMs therefore need to be reconstructed computationally. This situation is aggravated, furthermore, by the fact that chemically correct AAMs are, fundamentally, determined by quantum-mechanical phenomena and thus cannot be reliably computed by solving graph-theoretical optimization problems defined by the reactant and product structures. A viable solution for this problem is to shift the focus into first identifying a partial AAM containing the reaction center, i.e., covering the atoms incident with all bonds that change during a reaction. This then leads to the problem of extending the partial map to the full reaction. The AAM of a reaction is faithfully represented by the Imaginary Transition State (ITS) graph, providing a convenient graph-theoretic framework to address the questions of when and how a partial AAM can be extended. We show that an unique extension exists whenever, and only if, these partial AAMs cover the reaction center. Moreover, uniqueness results are generalized to partial AAMs in situations where hydrogen atoms are not represented explicitly. In this case their extension can be computed by solving a constrained graph-isomorphism search between specific subgraphs of ITS graphs. We close by benchmarking different tools for this task.
