Homology-based scaffolding orders contigs based on conserved collinearity of homologous sequences across related species. Existing methods often rely on costly whole-genome alignments or show limited robustness when integrating multiple references. Here, we introduce an anchor-based scaffolding framework that adapts synteny anchors to efficiently infer contig order and orientation relative to one or more reference genomes. Our approach leverages precomputed, sufficiently unique anchors and their respective high-confidence homology matches in a greedy approach, combining single-reference to multi-reference scaffolds using a maximum matching. Across simulated and real datasets, anchor-based scaffolding achieves accuracy comparable to state-of-the-art methods. Notably, the approach shows particular strengths in multi-reference settings. These results demonstrate that synteny-anchorbased scaffolding provides an additional tool for homology-based scaffolding with robust accuracy and uperior performance in multi-reference scenarios.
Copy number variation (CNV), as a major type of DNA structural variations (SVs), plays a key role in causing human diseases and contributing to genetic diversity. Accurate identification of CNVs is significant for disease mechanism analysis, personalized diagnosis and treatment, and drug development. Although next-generation sequencing (NGS) technology has greatly promoted the development of CNV detection methods, the existing methods generally have problems such as high false positives and inaccurate boundaries. Therefore, a new method is proposed for detecting CNVs in a single sample of NGS data, called CNV-ECOD. The method first employs the empirical-cumulative-distribution-based outlier detection (ECOD) algorithm to identify abnormal signals of read depth (RD) for preliminary detection of CNVs. To correct false positives and refine CNV boundaries further, it integrates paired-end mapping (PEM) and split read (SR) strategies. The integration of the RD-PEM-SR hierarchical progressive framework and the anomaly scoring mechanism based on ECOD can effectively improve the accuracy of CNV detection. Comparing our approach to four peer methods, simulation results demonstrate that it achieves the best balance between precision and sensitivity. Also, the proposed method has the best F1-scores and the highest overlap density scores (ODSs) in real-sample experiments. Therefore, CNV-ECOD is expected to develop into an efficient and robust CNV detection tool.
Accurate protein structure prediction is critical for rational enzyme engineering, which requires high-fidelity models. This study benchmarks three distinct structure prediction paradigms against the experimental crystal structure of IsPETase, serving as a diagnostic case study. The evaluated approaches include classical homology modeling (SWISS-MODEL), MSA-conditioned diffusion (AlphaFold 3), and generative language modeling (ESM-3). Predicted models were evaluated using stereochemical validation, molecular docking with a PET dimer analogue, and molecular dynamics simulations. While all approaches reproduced the overall fold and preserved the catalytic triad geometry, notable differences were observed in atomic clashes and hydrogen bonding patterns. ESM-3 showed elevated steric clashes and reduced hydrogen bond counts. Molecular dynamics indicated that the experimental structure maintained the highest stability, with SWISS-MODEL closely following, while ESM-3 displayed greater fluctuations, particularly in loop regions. Crucially, blind docking simulations revealed that the ESM-3 active site was sterically occluded, rendering it inaccessible to the PET dimer. This inaccessibility persisted even after targeted energy minimization. These findings suggest that while generative language models represent a powerful capability for rapid scaffold exploration, they do not yet achieve the thermodynamic precision of established homology and evolutionary approaches required for functional active site engineering.
Boolean networks provide a qualitative framework for modelling regulatory systems when kinetic parameters are unavailable, with cellular phenotypes represented as attractors of the induced dynamics [J. D. Schwab et al., Concepts in Boolean network modeling, Comput. Struct. Biotechnol. J. 18:571-582, 2020]. A central challenge is phenotypic reachability: determining whether asynchronous dynamics can connect invariant regions of the state space, a problem that becomes computationally intractable in large networks [L. Cifuentes-Fontanals, M. Noual, and E. Remy, Revisiting trap spaces in Boolean networks, Theor. Comput. Sci. 915:1-20, 2022; K. Perrot and C. Paulevé, Complexity of asynchronous reachability in Boolean networks, Theor. Comput. Sci. 1000:114650, 2024.]. We develop a structural theory of reachability in which trap spaces are identified with labelled order ideals of SCC-posets. The SCC-poset determines the order of commitment events, while admissible evaluations encode branching within regulatory modules, so that multistability appears as an intrinsic feature of the theory. Within this framework, we establish necessary and sufficient conditions for reachability, introduce the commitment depth, and show that deciding non-trivial branching is computationally intractable. We further demonstrate that effective interaction structure is jointly determined by topology and Boolean logic. We validate the framework on a Boolean model of CD4[Formula: see text] T-cell differentiation, where refinement chains recover the observed ordering of cytokine response, lineage commitment, and phenotypic branching. In the absence of multistability the structure collapses to a distributive lattice, a non-generic limiting regime.
Copy number variation (CNV) is one of the most imperative forms of structural variations that can span over the coding and non-coding regulatory regions of an individual's genome. Copy number variations (CNV) can significantly impact the genotype and phenotype traits by altering the gene dosage, consequently affecting the gene expression landscape concerning various cellular functions and are the cause behind complex diseases in an individual. Exceptionally fast advancement in Next Generation Sequencing (NGS) technology has led to massive growth of DNA-seq data, which contains both Whole Genome Sequence (WGS) and targeted Exome Sequence data of various species including H.sapiens, and precise detection of the DNA region affected by CNV enables the copy number profiling of a genome, thereby understanding our genome. This work has proposed a methodology named ReinVar, which can accurately determine and analyze the underlying copy number profile of the whole genome by adopting a model-free reinforcement learning paradigm. The methodology involves a novel approach to model the problem of identifying CNV as a Markov decision process (MDP), followed by determination of CNV under Reinforcement Learning framework. ReinVar also adopted a Map-Reduce programming paradigm to provide a big data solution to address the issue of exponential growth of NGS read sequence data. ReinVar has shown strong performance in detecting CNV gains and losses across diverse ethnic groups, with a high number of shared variant calls. ReinVar's ability to accurately identify both CNV gains and losses, coupled with consistent detection across ethnic groups and strong ROC characteristics, underscores ReinVar's effectiveness as a robust and sensitive CNV detection method.
