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PubMed2006

Gene networks as a tool to understand transcriptional regulation.

Gene regulatory networks, or simply gene networks (GNs), have shown to be a promising approach that the bioinformatics community has been developing for studying regulatory mechanisms in biological systems. GNs are built from the genome-wide high-throughput gene expression data that are often available from DNA microarray experiments. Conceptually, GNs are (un)directed graphs, where the nodes correspond to the genes and a link between a pair of genes denotes a regulatory interaction that occurs at transcriptional level. In the present study, we had two objectives: 1) to develop a framework for GN reconstruction based on a Bayesian network model that captures direct interactions between genes through nonparametric regression with B-splines, and 2) to demonstrate the potential of GNs in the analysis of expression data of a real biological system, the yeast pheromone response pathway. Our framework also included a number of search schemes to learn the network. We present an intuitive notion of GN theory as well as the detailed mathematical foundations of the model. A comprehensive analysis of the consistency of the model when tested with biological data was done through the analysis of the GNs inferred for the yeast pheromone pathway. Our results agree fairly well with what was expected based on the literature, and we developed some hypotheses about this system. Using this analysis, we intended to provide a guide on how GNs can be effectively used to study transcriptional regulation. We also discussed the limitations of GNs and the future direction of network analysis for genomic data. The software is available upon request.

Bayes Theorem↗

PubMed1992

MEPS parameters and graph analysis for the use of recombination to construct ordered sets of overlapping clones.

Homologous recombination can provide a basis for the construction of an ordered set of overlapping clones. The principle is to make two libraries, each in a vector that has a different selectable marker flanking the insert site. Recombination between the flanking markers, leading to a selectable phenotype, can only occur as the consequence of crossing over between inserts. The two libraries are crossed in a matrix, allowing the construction of an ordered set. The logic, akin to S. Benzer's (1961, Genetics 47:403-415) for the arrangement of deletion and point mutations, has a graph theoretic formulation, which helps to cope with the complex and noisy data inherent in the physical mapping of genomes rich in repeated sequences. The minimum length of identity required for homologous recombination is called the MEPS (minimum efficient processing segment) and is a property of each recombination pathway. The amount and the type of sequence similarity required for two sequences to recombine is different from that implied by either the conservation of restriction sites or by most procedures of hybridization.

Chromosome Deletion↗

PubMed2025

Fast and flexible minimizer digestion with digest.

SUMMARY: Minimizer digestion is an increasingly common component of bioinformatics tools, including tools for de Bruijn graph assembly and sequence classification. We describe a new open source tool and library to facilitate efficient digestion of genomic sequences. It can produce digests based on the related ideas of minimizers, modimizers or syncmers. Digest uses efficient data structures, scales well to many threads, and produces digests with expected spacings between digested elements. AVAILABILITY AND IMPLEMENTATION: Digest is implemented in C++17 with a Python API, and is available open-source at https://github.com/VeryAmazed/digest. The python library is available on Bioconda. Rust bindings are available as a public crate at https://crates.io/crates/digest-rs.

Software↗

PubMed2025

Fast and flexible minimizer digestion with digest.

Minimizer digestion is an increasingly common component of bioinformatics tools, including tools for De Bruijn-Graph assembly and sequence classification. We describe a new open source tool and library to facilitate efficient digestion of genomic sequences. It can produce digests based on the related ideas of minimizers, modimizers or syncmers. Digest uses efficient data structures, scales well to many threads, and produces digests with expected spacings between digested elements. Digest is implemented in C++17 with a Python API, and is available open-source at https://github.com/VeryAmazed/digest.

digestion↗

PubMed2026

Rawsamble: overlapping raw nanopore signals using a hash-based seeding mechanism.

MOTIVATION: Raw nanopore signal analysis is a common approach in genomics to provide fast and resource-efficient analysis without translating the signals to bases (i.e. without basecalling). However, existing solutions cannot interpret raw signals directly if a reference genome is unknown due to a lack of accurate mechanisms to handle increased noise in pairwise raw signal comparison. Our goal is to enable the direct analysis of raw signals without a reference genome. To this end, we propose Rawsamble, the first mechanism that can identify regions of similarity between all raw signal pairs, known as all-vs-all overlapping, using a hash-based search mechanism. RESULTS: We use these overlaps to construct de novo assembly graphs with an existing assembler, miniasm, off-the-shelf. To our knowledge, these are the first de novo assemblies ever constructed directly from raw signals without basecalling. Our extensive evaluations across multiple genomes of varying sizes show that Rawsamble provides a significant speedup (on average by 5.01× and up to 23.10×) and reduces peak memory usage (on average by 5.74× and up to by 22.00×) compared to a conventional genome assembly pipeline using the state-of-the-art tools for basecalling (Dorado's fastest mode) and overlapping (minimap2) on a CPU. We find that around one-third of Rawsamble's overlapping pairs are also found by minimap2. We find that when we use overlapping reads from Rawsamble, we can construct unitigs that are (i) as accurate as those built from minimap2's overlaps and (ii) up to half a chromosome in length (e.g. 2.3 million bases for E. coli). AVAILABILITY AND IMPLEMENTATION: Rawsamble is available at https://github.com/CMU-SAFARI/RawHash. We also provide the scripts to fully reproduce our results on our GitHub page.

Nanopores↗

PubMed2026

GraphyloVar: predicting the impact of non-coding variants using a multi-species sequence model.

MOTIVATION: Understanding the functional impact of genetic variants is a key problem for precision medicine. Tools like CADD, PhyloP, and PhastCons are useful, but they often look at each position in the genome in isolation. This means they can miss important information from the evolutionary history that connects different species. In this paper, we extend our previous model, Graphylo, to predict the effects of variants. Our new model, GraphyloVar, is built to directly utilize the phylogenetic tree that relates the species. RESULTS: GraphyloVar is a deep learning model that considers both DNA sequence and evolutionary patterns from many species. It uses two main components: Graph Convolutional Networks (GCNs) to process the phylogenetic tree, and Transformer encoders to extract features from the DNA sequences. Pre-trained to predict population-level allele frequencies on the TOPMed whole-genome sequencing cohort, GraphyloVar achieves an AUROC of 0.6246 zero-shot on ∼149M held-out variants, and an ensemble with CADD reaches 0.6442 (+0.020, P<10-15). Fine-tuned GraphyloVar achieves the highest AUROC across all 13 MPRA benchmark datasets. By integrating deep learning with explicit phylogenetic input, GraphyloVar offers a powerful and complementary approach to variant effect prediction that utilizes the full evolutionary history from many species to better identify and prioritize important non-coding variants. AVAILABILITY AND IMPLEMENTATION: Code and datasets are available at https://github.com/DongjoonLim/GraphyloVar under DOI: 10.5281/zenodo.20616818.

Phylogeny↗

PubMed1995

Four strikes against physical mapping of DNA.

Physical mapping is a central problem in molecular biology and the human genome project. The problem is to reconstruct the relative position of fragments of DNA along the genome from information on their pairwise overlaps. We show that four simplified models of the problem lead to NP-complete decision problems: Colored unit interval graph completion, the maximum interval (or unit interval) subgraph, the pathwidth of a bipartite graph, and the k-consecutive ones problem for k > or = 2. These models have been chosen to reflect various features typical in biological data, including false-negative and positive errors, small width of the map, and chimericism.

Base Sequence↗

PubMed2026

Graph neural network-based risk stratification of prostate cancer using gene expression and SHAP interpretability.

Accurate risk stratification is essential for guiding treatment decisions and preventing over treatment of prostate cancer, which remains one of the most prevalent cancers among adult men. While the Gleason score, obtained from prostate biopsies, is routinely used to assess tumor aggressiveness, the biopsy procedure carries risks such as pain, infection, and, in some cases, serious complications such as sepsis. In this study, we proposed an artificial intelligence-based framework that integrates mRNA expression profiles with functional interaction networks to classify prostate cancer patients into low-, medium-, and high-risk groups defined by Gleason scores. The pipeline comprised five steps: (1) data collection from The Cancer Genome Atlas (TCGA), (2) preprocessing of gene expression data, (3) two-stage feature selection to identify informative biomarkers, (4) risk classification using a dual-branch graph neural network (GNN) that combines gene-gene interaction graphs with sample-level expression features, and (5) model interpretation using SHAP to quantify feature contributions. Differentially expressed genes were identified in the High (ASPN, GMNN, PEBP4, C2, KNCK17), Medium (C2, IGSF1, ASPN, CDKN3, AMH), and Low (TNMD, VWA5B2, ST6GALNAC5, CYP3A5, PHGR1) risk groups, underscoring the molecular heterogeneity of disease progression. On an independent held-out test set, the model achieved AUCs of 0.86, 0.88, and 0.95 for the low-, medium-, and high-risk groups, respectively, with an overall accuracy of 80%. These results suggest that combining GNN-based modeling with explainable AI can capture both global and local molecular patterns relevant to tumor aggressiveness. However, as the model was developed and evaluated solely on the TCGA cohort, the findings should be regarded as exploratory, and external validation will be required to establish generalizability. Within these limitations, the proposed framework highlights the potential of molecular profiling and graph-based deep learning to support more precise, potentially less invasive, risk assessment and individualized treatment planning in prostate cancer.

Prostatic Neoplasms↗

PubMed2004

Decomposition of metabolic network into functional modules based on the global connectivity structure of reaction graph.

MOTIVATION: Metabolic networks are organized in a modular, hierarchical manner. Methods for a rational decomposition of the metabolic network into relatively independent functional subsets are essential to better understand the modularity and organization principle of a large-scale, genome-wide network. Network decomposition is also necessary for functional analysis of metabolism by pathway analysis methods that are often hampered by the problem of combinatorial explosion due to the complexity of metabolic network. Decomposition methods proposed in literature are mainly based on the connection degree of metabolites. To obtain a more reasonable decomposition, the global connectivity structure of metabolic networks should be taken into account. RESULTS: In this work, we use a reaction graph representation of a metabolic network for the identification of its global connectivity structure and for decomposition. A bow-tie connectivity structure similar to that previously discovered for metabolite graph is found also to exist in the reaction graph. Based on this bow-tie structure, a new decomposition method is proposed, which uses a distance definition derived from the path length between two reactions. An hierarchical classification tree is first constructed from the distance matrix among the reactions in the giant strong component of the bow-tie structure. These reactions are then grouped into different subsets based on the hierarchical tree. Reactions in the IN and OUT subsets of the bow-tie structure are subsequently placed in the corresponding subsets according to a 'majority rule'. Compared with the decomposition methods proposed in literature, ours is based on combined properties of the global network structure and local reaction connectivity rather than, primarily, on the connection degree of metabolites. The method is applied to decompose the metabolic network of Escherichia coli. Eleven subsets are obtained. More detailed investigations of the subsets show that reactions in the same subset are really functionally related. The rational decomposition of metabolic networks, and subsequent studies of the subsets, make it more amenable to understand the inherent organization and functionality of metabolic networks at the modular level. SUPPLEMENTARY INFORMATION: http://genome.gbf.de/bioinformatics/

Algorithms↗

PubMed2005

Mapping genes of complex psychiatric diseases in Daghestan genetic isolates.

Genetic isolates, which provide outstanding opportunities for identification of susceptibility genes for complex diseases, can be classified as primary (having an ancient demographic history in a stable environment) or secondary (having a younger demographic history) Neel [1992: Minority populations: Genetics, demography, and health, pp. 1-13]. Daghestan contains 26 out of 50 indigenous Caucasus ethnicities that have been in existence for hundreds of generations in the same highland region. The ethnic groups are subdivided into numerous primary isolates. The founder effect and gene drift in these primary isolates may have caused aggregation of specific haplotypes with limited numbers of pathogenic alleles and loci in some isolates relative to others. These are expressed as inter-population differences in lifetime prevalence and features of certain complex clinical phenotypes and in patterns of genetic linkage and linkage disequilibrium (LD). Stable highland and ethnic-cultural environments have led to increased penetrance and a reduced number of phenocopies, which typically hamper the identification of any susceptibility genes for complex diseases. Owing to these characteristics of the primary isolates, a comparative linkage study in the primary isolates allows us to define the number of susceptibility genes for any complex disease and to identify the source of variability and non-replication of linkage analysis results. As part of an ongoing study, seven extended schizophrenia and one nonspecific mental retardation kindreds have been ascertained from Daghestan isolates. Lifetime morbid risk for schizophrenia in the isolates varied from 0 to 5%. A genome scan with markers spaced 10 cM apart was carried out on these pedigrees and linkage analysis was performed using descent graph methods, as implemented in Simwalk2. To identify regions containing susceptibility genes within these kindreds, we followed up those regions with non-parametric and parametric linkage analyses, with the choice of genetic model guided by the results obtained in the NPL. While the analyses are ongoing, the most positive findings were made in different isolated pedigrees on chromosomes 17p11, 3q24, and 22q for schizophrenia and on chromosome 12q for nonspecific mental retardation.

Chromosome Mapping↗

PubMed2026

plinkQC: an integrated tool for ancestry inference, sample selection, and quality control in population genetics.

MOTIVATION: Population genetic analyses rely on high quality datasets that pass rigorous controls for sample and marker quality. Many analyses also require additional processing including identification of ancestry and sample relatedness. A software package that addresses all these common, yet crucial tasks is missing. RESULTS: We have developed plinkQC, an R/CRAN package that combines these functionalities into a single software package with detailed vignettes for example applications. plinkQC determines the ancestry of study samples via a pre-trained random forest classifier that reaches 98% performance accuracy with just 5% of marker overlap between reference and user data. To obtain the maximal set of unrelated study samples, we developed a graph-based pruning method, taking both relationship estimates and sample quality into account. We demonstrate optimal sample selection on the 1000 Genomes project, where we retain an additional 71 samples compared to publicly available exclusion lists. Finally, plinkQC bundles these results together with per-individual and per-marker quality control checks into three simple functions and returns both the quality controlled dataset and quality control report about each step of the analysis. AVAILABILITY AND IMPLEMENTATION: plinkQC is available as an R/CRAN package. The documentation and code are available on github: https://meyer-lab-cshl.github.io/plinkQC/ and https://github.com/meyer-lab-cshl/plinkQC_manuscript.

Software↗

PubMed2025

High-resolution metagenome assembly for modern long reads with myloasm.

Long-read metagenome assembly promises complete genomic recovery from microbiomes. However, the complexity of metagenomes poses challenges. We present myloasm, a metagenome assembler for PacBio HiFi and Oxford Nanopore Technologies (ONT) R10.4 long reads. Myloasm uses polymorphic k-mers to construct a high-resolution string graph and then leverages differential abundance for graph simplification. On real-world ONT metagenomes, myloasm assembled three times more complete circular contigs than the next-best assembler. Myloasm can make ONT and HiFi comparable for assembly: for a jointly sequenced gut metagenome, myloasm with ONT assembled more complete circular genomes than any assembler with HiFi. Myloasm recovers previously inaccessible within-species diversity; we recovered six complete Prevotella copri single-contig genomes from a gut metagenome and eight complete TM7 (Saccharibacteria) contigs with > 93% similarity from an oral metagenome. With this improved resolution, we resolved two 98% similar ermF antibiotic resistance genes spreading through distinct strain-specific mobile genetic elements in a human gut.

Journal Article↗

PubMed2004

A graph theoretical approach for predicting common RNA secondary structure motifs including pseudoknots in unaligned sequences.

MOTIVATION: RNA structure motifs contained in mRNAs have been found to play important roles in regulating gene expression. However, identification of novel RNA regulatory motifs using computational methods has not been widely explored. Effective tools for predicting novel RNA regulatory motifs based on genomic sequences are needed. RESULTS: We present a new method for predicting common RNA secondary structure motifs in a set of functionally or evolutionarily related RNA sequences. This method is based on comparison of stems (palindromic helices) between sequences and is implemented by applying graph-theoretical approaches. It first finds all possible stable stems in each sequence and compares stems pairwise between sequences by some defined features to find stems conserved across any two sequences. Then by applying a maximum clique finding algorithm, it finds all significant stems conserved across at least k sequences. Finally, it assembles in topological order all possible compatible conserved stems shared by at least k sequences and reports a number of the best assembled stem sets as the best candidate common structure motifs. This method does not require prior structural alignment of the sequences and is able to detect pseudoknot structures. We have tested this approach on some RNA sequences with known secondary structures, in which it is capable of detecting the real structures completely or partially correctly and outperforms other existing programs for similar purposes. AVAILABILITY: The algorithm has been implemented in C++ in a program called comRNA, which is available at http://ural.wustl.edu/softwares.html

Algorithms↗

PubMed2005

The cobweb of life revealed by genome-scale estimates of horizontal gene transfer.

With the availability of increasing amounts of genomic sequences, it is becoming clear that genomes experience horizontal transfer and incorporation of genetic information. However, to what extent such horizontal gene transfer (HGT) affects the core genealogical history of organisms remains controversial. Based on initial analyses of complete genomic sequences, HGT has been suggested to be so widespread that it might be the "essence of phylogeny" and might leave the treelike form of genealogy in doubt. On the other hand, possible biased estimation of HGT extent and the findings of coherent phylogenetic patterns indicate that phylogeny of life is well represented by tree graphs. Here, we reexamine this question by assessing the extent of HGT among core orthologous genes using a novel statistical method based on statistical comparisons of tree topology. We apply the method to 40 microbial genomes in the Clusters of Orthologous Groups database over a curated set of 297 orthologous gene clusters, and we detect significant HGT events in 33 out of 297 clusters over a wide range of functional categories. Estimates of positions of HGT events suggest a low mean genome-specific rate of HGT (2.0%) among the orthologous genes, which is in general agreement with other quantitative of HGT. We propose that HGT events, even when relatively common, still leave the treelike history of phylogenies intact, much like cobwebs hanging from tree branches.

Algorithms↗

PubMed2005

A graph theoretical approach for analysis of protein flexibility change at protein complex formation.

Hitherto analyses of protein complexes are frequently confined to the changes in the interface of the protein subunits undergoing interaction, while the holistic picture of the protein monomers' structure transformation, or the pervasive rigidity adopted by the newly formed complex are most often than not improperly evaluated in spite of the multiple and deep insights that they can yield about the interaction process itself at the molecular level, or at the higher level of genomic functional analyses for which relevant systems biological information can be obtained. To address this aspect of protein-protein interaction we propose in this work a newly developed algorithm that is based on graph theoretical instances and makes possible the evaluation of the changes in the flexibility of the interacting molecules and the rigidity adopted at complex formation. Since one can also figure out the opposite process, i.e. that in which the complex decomposes into its constituent subunits, each of which may accomplish another vital role in the organism, the methodology proposed here is also able to address such problem. The algorithm we propose performs a rigidity and/or flexibility evaluation of every node (atom) on the network constituted by the entire set of intra and inter-molecular inter-atomic interactions. Comparison of flexible or rigid molecular regions or domains within the complex with those in the respective isolated monomers leads to quantification of the loss (or gain) in the number of degrees of freedom at complex formation and their effects on protein complex formation mechanisms. This index is also valuable in the identification of collective motions within the protein that may play a critical role in the process of complex formation, and the influences they may have in the behavior and function of the complex (as well as the subunits constituting it) within the organism. Furthermore, the methodology, embedded in protein docking algorithms allows the development of a framework for categorizing and ranking decoys output by broadly used grid scoring type algorithms, one of which is the system for protein-protein interaction system MIAX that has been under continuous development in recent years.

Animals↗

PubMed2026

PEARL: integrative multi-omics classification and omics feature discovery via deep graph learning.

MOTIVATION: Integrating multi-omics data provides valuable insights into biological processes by capturing information across multiple molecular layers, enabling a comprehensive understanding of complex diseases and driving advancements in precision medicine. However, existing computational methods for multi-omics integration face significant challenges, such as low reliability and poor generalizability, due to the high dimensionality and low sample size nature of omics data. RESULTS: To address these challenges, we present PEARL (Pearson-Enhanced spectrAl gRaph convoLutional networks), a novel deep graph learning method for biomedical classification and functional important omics features identification. PEARL leverages a simple yet effective learning architecture to achieve superior and robust performance in high-dimensional, low-sample-size multi-omics settings. Our results demonstrate that PEARL significantly outperforms existing state-of-the-art methods on both synthetic and real biomedical datasets. Furthermore, applied to Alzheimer's disease (AD) brain multi-omics data, features prioritized by PEARL lead to functionally important genes that demonstrate significant enrichment in AD-related pathways. These findings highlight PEARL's practical utility in biomedical research and its potential to enhance biological interpretability in multi-omics studies. AVAILABILITY AND IMPLEMENTATION: The source code of our computational framework is available at https://github.com/zqq121017/PEARL.

Multiomics↗

PubMed2026

ARGformer: learning on ancestral recombination graphs with transformers.

MOTIVATION: Recent advances in inference of the ancestral recombination graph (ARG), which describes how segments of chromosomes trace back through recombination and shared lineages, have made it possible to reconstruct genome-wide genealogies for large cohorts, but it remains difficult to summarize and use this information for population genetic analyses. RESULTS: We present ARGformer, an encoder-only transformer that learns context-dependent embeddings with a self-supervised masked objective finetuned with contrastive learning for downstream retrieval tasks. We train ARGformer on genealogies from coalescent simulations and on genealogies inferred from ancient and present-day Homo sapiens genomes. Using only these learned embeddings, without access to genotype matrices, ARGformer captures patterns of global population structure and supports ancestry inference through clustering and nearest-neighbor retrieval. On genealogies that include archaic hominins, ARGformer can highlight Denisovan-derived segments in Oceanian genomes and reveals Oceanian-like ancestry in South American Indigenous populations. AVAILABILITY AND IMPLEMENTATION: ARGformer is available at https://github.com/AI-sandbox/ARGformer.

Humans↗

PubMed2004

Essentiality and damage in metabolic networks.

Understanding the architecture of physiological functions from annotated genome sequences is a major task for postgenomic biology. From the annotated genome sequence of the microbe Escherichia coli, we propose a general quantitative definition of enzyme importance in a metabolic network. Using a graph analysis of its metabolism, we relate the extent of the topological damage generated in the metabolic network by the deletion of an enzyme to the experimentally determined viability of the organism in the absence of that enzyme. We show that the network is robust and that the extent of the damage relates to enzyme importance. We predict that a large fraction (91%) of enzymes causes little damage when removed, while a small group (9%) can cause serious damage. Experimental results confirm that this group contains the majority of essential enzymes. The results may reveal a universal property of metabolic networks.

Computer Simulation↗