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Efficient parameterized algorithms for biopolymer structure-sequence alignment.

Computational alignment of a biopolymer sequence (e.g., an RNA or a protein) to a structure is an effective approach to predict and search for the structure of new sequences. To identify the structure of remote homologs, the structure-sequence alignment has to consider not only sequence similarity, but also spatially conserved conformations caused by residue interactions and, consequently, is computationally intractable. It is difficult to cope with the inefficiency without compromising alignment accuracy, especially for structure search in genomes or large databases. This paper introduces a novel method and a parameterized algorithm for structure-sequence alignment. Both the structure and the sequence are represented as graphs, where, in general, the graph for a biopolymer structure has a naturally small tree width. The algorithm constructs an optimal alignment by finding in the sequence graph the maximum valued subgraph isomorphic to the structure graph. It has the computational time complexity O[k(t)N(2)] for the structure of N residues and its tree decomposition of width t. Parameter k, small in nature, is determined by a statistical cutoff for the correspondence between the structure and the sequence. This paper demonstrates a successful application of the algorithm to RNA structure search used for noncoding RNA identification. An application to protein threading is also discussed.

Algorithms↗

PubMed2006

Divergent evolution of a structural proteome: phenomenological models.

We develop models of the divergent evolution of genomes; the elementary object of sequence dynamics is the protein structural domain. To identify patterns of organization that reflect mechanisms of evolution, we consider the individual genomes of many procaryote species, studying the arrangement of protein structural domains in the space of all polypeptide structures. We view the network of structural similarities as a graph, called the organismal Protein Domain Universe Graph (oPDUG); vertices represent types of structural domains and edges represent strong structural similarity. As observed before, each oPDUG is a highly nonrandom graph, as evidenced in the vertex degree distribution, which resembles a Pareto law (which has a power-law asymptotic). To explain this and other peculiar properties of the oPDUGs, we construct an evolving-graph model for the long-timescale evolutionary dynamics of oPDUGs, containing only divergent mechanisms of domain discovery. The model generates degree distributions (resembling Pareto laws) and clustering-coefficient distributions that are characteristic of the oPDUGs. In the infinite-graph limit, we analytically compute the exponent for specific biological parameters, as well as the complete phase diagram of the model, finding two distinct regimes of domain innovation dynamics. Thus, divergent evolutionary dynamics quantitatively explains the nonrandom organization of oPDUGs.

Bacterial Proteins↗

PubMed2000

Compositional genomes: prebiotic information transfer in mutually catalytic noncovalent assemblies.

Mutually catalytic sets of simple organic molecules have been suggested to be capable of self-replication and rudimentary chemical evolution. Previous models for the behavior of such sets have analyzed the global properties of short biopolymer ensembles by using graph theory and a mean field approach. In parallel, experimental studies with the autocatalytic formation of amphiphilic assemblies (e.g., lipid vesicles or micelles) demonstrated self-replication properties resembling those of living cells. Combining these approaches, we analyze here the kinetic behavior of small heterogeneous assemblies of spontaneously aggregating molecules, of the type that could form readily under prebiotic conditions. A statistical formalism for mutual rate enhancement is used to numerically simulate the detailed chemical kinetics within such assemblies. We demonstrate that a straightforward set of assumptions about kinetically enhanced recruitment of simple amphiphilic molecules, as well as about the spontaneous growth and splitting of assemblies, results in a complex population behavior. The assemblies manifest a significant degree of homeostasis, resembling the previously predicted quasi-stationary states of biopolymer ensembles (Dyson, F. J. (1982) J. Mol. Evol. 18, 344-350). Such emergent catalysis-driven, compositionally biased entities may be viewed as having rudimentary "compositional genomes." Our analysis addresses the question of how mutually catalytic metabolic networks, devoid of sequence-based biopolymers, could exhibit transfer of chemical information and might undergo selection and evolution. This computed behavior may constitute a demonstration of natural selection in populations of molecules without genetic apparatus, suggesting a pathway from random molecular assemblies to a minimal protocell.

Catalysis↗

PubMed2003

Two notes on genome rearrangement.

A central problem in genome rearrangement is finding a most parsimonious rearrangement scenario using certain rearrangement operations. An important problem of this type is sorting a signed genome by reversals and translocations (SBRT). Hannenhalli and Pevzner presented a duality theorem for SBRT which leads to a polynomial time algorithm for sorting a multi-chromosomal genome using a minimum number of reversals and translocations. However, there is one case for which their theorem and algorithm fail. We describe that case and suggest a correction to the theorem and the polynomial algorithm. The solution of SBRT uses a reduction to the problem of sorting a signed permutation by reversals (SBR). The best extant algorithms for SBR require quadratic time. The common approach to solve SBR is by finding a safe reversal using the overlap graph or the interleaving graph of a permutation. We describe a family of signed permutations which proves a quadratic lower bound on the number of affected vertices in the overlap/interleaving graph during any optimal sorting scenario. This implies, in particular, an Omega(n3) lower bound for Bergeron's algorithm.

Algorithms↗

PubMed2004

Identification of alternatively spliced mRNA variants related to cancers by genome-wide ESTs alignment.

Several databases have been published to predict alternative splicing of mRNAs by analysing the exon linkage relationship by alignment of expressed sequence tags (ESTs) to the genome sequence; however, little effort has been made to investigate the relationship between cancers and alternative splicing. We developed a program, Alternative Splicing Assembler (ASA), to look for splicing variants of human gene transcripts by genome-wide ESTs alignment. Using ASA, we constructed the biosino alternative splicing database (BASD), which predicted splicing variants for reference sequences from the reference sequence database (RefSeq) and presented them in both graph and text formats. EST clusters that differ from the reference sequences in at least one splicing site were counted as splicing variants. Of 4322 genes screened, 3490 (81%) were observed with at least one alternative splicing variants. To discover the variants associated with cancers, tissue sources of EST sequences were extracted from the UniLib database and ESTs from the same tissue type were counted. These were regarded as the indicators for gene expression level. Using Fisher's exact test, alternative splicing variants, of which EST counts were significantly different between cancer tissues and their counterpart normal tissues, were identified. It was predicted that 2149 variants, or 383 variants after Bonferroni correction, of 26 812 variants were likely tumor-associated. By reverse transcription-PCR, 11 of 13 novel alternative splicing variants and eight of nine variants' tissue specificity were confirmed in hepatocellular carcinoma and in lung cancer. The possible involvement of alternative splicing in cancer is discussed.

Alternative Splicing↗

PubMed2006

Dense subgraph computation via stochastic search: application to detect transcriptional modules.

MOTIVATION: In a tri-partite biological network of transcription factors, their putative target genes, and the tissues in which the target genes are differentially expressed, a tightly inter-connected (dense) subgraph may reveal knowledge about tissue specific transcription regulation mediated by a specific set of transcription factors-a tissue-specific transcriptional module. This is just one context in which an efficient computation of dense subgraphs in a multi-partite graph is needed. RESULT: Here we report a generic stochastic search based method to compute dense subgraphs in a graph with an arbitrary number of partitions and an arbitrary connectivity among the partitions. We then use the tool to explore tissue-specific transcriptional regulation in the human genome. We validate our findings in Skeletal muscle based on literature. We could accurately deduce biological processes for transcription factors via the tri-partite clusters of transcription factors, genes, and the functional annotation of genes. Additionally, we propose a few previously unknown TF-pathway associations and tissue-specific roles for certain pathways. Finally, our combined analysis of Cardiac, Skeletal, and Smooth muscle data recapitulates the evolutionary relationship among the three tissues.

Algorithms↗

PubMed2002

Efficiently detecting polymorphisms during the fragment assembly process.

MOTIVATION: Current genomic sequence assemblers assume that the input data is derived from a single, homogeneous source. However, recent whole-genome shotgun sequencing projects have violated this assumption, resulting in input fragments covering the same region of the genome whose sequences differ due to polymorphic variation in the population. While single-nucleotide polymorphisms (SNPs) do not pose a significant problem to state-of-the-art assembly methods, these methods do not handle insertion/deletion (indel) polymorphisms of more than a few bases. RESULTS: This paper describes an efficient method for detecting sequence discrepencies due to polymorphism that avoids resorting to global use of more costly, less stringent affine sequence alignments. Instead, the algorithm uses graph-based methods to determine the small set of fragments involved in each polymorphism and performs more sophisticated alignments only among fragments in that set. Results from the incorporation of this method into the Celera Assembler are reported for the D. melanogaster, H. sapiens, and M. musculus genomes.

Algorithms↗

PubMed2004

Discovery of meaningful associations in genomic data using partial correlation coefficients.

MOTIVATION: A major challenge of systems biology is to infer biochemical interactions from large-scale observations, such as transcriptomics, proteomics and metabolomics. We propose to use a partial correlation analysis to construct approximate Undirected Dependency Graphs from such large-scale biochemical data. This approach enables a distinction between direct and indirect interactions of biochemical compounds, thereby inferring the underlying network topology. RESULTS: The method is first thoroughly evaluated with a large set of simulated data. Results indicate that the approach has good statistical power and a low False Discovery Rate even in the presence of noise in the data. We then applied the method to an existing data set of yeast gene expression. Several small gene networks were inferred and found to contain genes known to be collectively involved in particular biochemical processes. In some of these networks there are also uncharacterized ORFs present, which lead to hypotheses about their functions. AVAILABILITY: Programs running in MS-Windows and Linux for applying zeroth, first, second and third order partial correlation analysis can be downloaded at: http://mendes.vbi.vt.edu/tiki-index.php?page=Software. SUPPLEMENTARY INFORMATION: Supplementary information can be found at: URL to be decided.

Algorithms↗

PubMed2001

Birth of scale-free molecular networks and the number of distinct DNA and protein domains per genome.

MOTIVATION: Current growth in the field of genomics has provided a number of exciting approaches to the modeling of evolutionary mechanisms within the genome. Separately, dynamical and statistical analyses of networks such as the World Wide Web and the social interactions existing between humans have shown that these networks can exhibit common fractal properties-including the property of being scale-free. This work attempts to bridge these two fields and demonstrate that the fractal properties of molecular networks are linked to the fractal properties of their underlying genomes. RESULTS: We suggest a stochastic model capable of describing the evolutionary growth of metabolic or signal-transduction networks. This model generates networks that share important statistical properties (so-called scale-free behavior) with real molecular networks. In particular, the frequency of vertices connected to exactly k other vertices follows a power-law distribution. The shape of this distribution remains invariant to changes in network scale: a small subgraph has the same distribution as the complete graph from which it is derived. Furthermore, the model correctly predicts that the frequencies of distinct DNA and protein domains also follow a power-law distribution. Finally, the model leads to a simple equation linking the total number of different DNA and protein domains in a genome with both the total number of genes and the overall network topology. AVAILABILITY: MatLab (MathWorks, Inc.) programs described in this manuscript are available on request from the authors. CONTACT: ar345@columbia.edu.

Biological Evolution↗

PubMed2026

Artificial Intelligence for Natural Products Discovery and Development.

Natural products (NPs) remain a cornerstone of modern drug discovery, offering stereochemical complexity and diverse bioactivities that precisely modulate therapeutic targets, refined through billions of years of evolution. However, their research has long been hindered by inefficient, empirical workflows, high resource consumption, structural complexity, and the "multicomponent, multi-target" nature of their mechanisms. The exponential growth of genomic, metabolomic, and spectral data has overwhelmed conventional analytical methods, exposing critical bottlenecks in handling high-dimensional, heterogeneous datasets that exceed human interpretive capacity. Artificial intelligence (AI) is emerging as a transformative paradigm to address these challenges, integrating multi-omics and chemical data to shift NP research from fragmented empiricism toward mechanism-driven, precision-oriented development. By leveraging deep learning architectures- including graph neural networks, Transformers, and diffusion-based generative models-AI enables systematic decoding of NP biosynthesis, automated structure elucidation, rational target identification, knowledge extraction from vast unstructured scientific literature, and de novo molecular design. This review comprehensively surveys recent advances in AI applications across the full NP discovery and development pipeline, encompassing genome mining, structure-based and ligand-based virtual screening, multimodal structural characterization, lead optimization, and biosynthetic pathway engineering. We further examine the emerging roles of protein-centric, molecule- centric, and multimodal foundation models, as well as large language models, in bridging genotype-to-chemotype gaps and unlocking unstructured scientific knowledge. Finally, we discuss critical challenges including data scarcity, representational limitations for complex stereochemistry, physical plausibility in generative models, and the urgent need for experimental validation, while outlining future directions toward autonomous experimentation, closed-loop optimization, and human-AI collaborative discovery.

Artificial intelligence↗

PubMed2003

Predicting protein function from protein/protein interaction data: a probabilistic approach.

MOTIVATION: The development of experimental methods for genome scale analysis of molecular interaction networks has made possible new approaches to inferring protein function. This paper describes a method of assigning functions based on a probabilistic analysis of graph neighborhoods in a protein-protein interaction network. The method exploits the fact that graph neighbors are more likely to share functions than nodes which are not neighbors. A binomial model of local neighbor function labeling probability is combined with a Markov random field propagation algorithm to assign function probabilities for proteins in the network. RESULTS: We applied the method to a protein-protein interaction dataset for the yeast Saccharomyces cerevisiae using the Gene Ontology (GO) terms as function labels. The method reconstructed known GO term assignments with high precision, and produced putative GO assignments to 320 proteins that currently lack GO annotation, which represents about 10% of the unlabeled proteins in S. cerevisiae.

Algorithms↗

PubMed2026

PaNDA: Efficient Optimization of Phylogenetic Diversity in Networks.

Phylogenetic diversity (PD) plays an important role in biodiversity, conservation, and evolutionary studies by measuring the diversity of a set of taxa based on their phylogenetic relationships. In phylogenetic trees, a subset of k taxa with maximum PD can be found by a simple and efficient greedy algorithm. However, this algorithmic tractability is lost when considering phylogenetic networks, which incorporate reticulate evolutionary events such as hybridization and horizontal gene transfer. To address this challenge, we introduce PaNDA (Phylogenetic Network Diversity Algorithms), the first software package and interactive graphical user-interface for exploring, visualizing, and maximizing diversity in phylogenetic networks. PaNDA includes a novel algorithm to find a subset of k taxa with maximum diversity, running in polynomial time for networks of bounded scanwidth, a measure of tree-likeness of a network that grows slower than the well-known level measure. This algorithm considers the variant of PD on networks in which the branch lengths of all paths from the root to the selected taxa contribute towards their diversity. We demonstrate the scalability of this algorithm on simulated networks, successfully analyzing level-15 networks with up to 200 taxa in seconds. We also provide a proof-of-concept analysis using a phylogenetic network on Xiphophorus species, illustrating how the tool can support diversity studies based on real genomic data. The software is easily installable and freely available at https://github.com/nholtgrefe/panda. Additionally, we extend the definition of PD to semi-directed phylogenetic networks, which are mixed graphs increasingly used in phylogenetic analysis to model uncertainty of the root location. We prove that finding a subset of k taxa with maximum diversity remains NP-hard on semi-directed networks, but do present a polynomial-time algorithm for networks with bounded level.

network↗

PubMed2004

Estimating genomic coexpression networks using first-order conditional independence.

We describe a computationally efficient statistical framework for estimating networks of coexpressed genes. This framework exploits first-order conditional independence relationships among gene-expression measurements to estimate patterns of association. We use this approach to estimate a coexpression network from microarray gene-expression measurements from Saccharomyces cerevisiae. We demonstrate the biological utility of this approach by showing that a large number of metabolic pathways are coherently represented in the estimated network. We describe a complementary unsupervised graph search algorithm for discovering locally distinct subgraphs of a large weighted graph. We apply this algorithm to our coexpression network model and show that subgraphs found using this approach correspond to particular biological processes or contain representatives of distinct gene families.

Algorithms↗

PubMed2004

High-quality RNA preparation for transcript profiling of osteocytes from native human bone microdissections.

Osteocytes, the most abundant bone cell type with important roles in tissue maintenance and pathological aberrations such as observed in bone metastases, are enclosed within a highly compact, calcified extracellular matrix. This location complicates analysis in native bone, with the consequence that despite their importance their in vivo molecular physiology is only poorly understood. We have examined the possibility of isolating osteocyte RNA for transcript profiling from native, frozen bone instead of employing the formalin-fixed, paraffin-embedded, decalcified version routinely used in histology, providing chemically modified and highly disintegrated RNAs. Bone tissue was tape-assisted cryosectioned and fixed to glass slides by support of UV-flash-triggered adhesive polymerization followed by quick hematoxylin-eosin staining to generate a guidance image for microdissection. Using an UVa-nitrogen laser, matrix-enclosed osteocytes were either excised and catapulted into RNA preparation vials or freed of accompanying nonosteocyte cellular material. The influences of bone sectioning, staining, and osteocyte capturing procedures on the prepared osteocyte RNAs were analyzed and the method was optimized accordingly. The obtained osteocyte RNAs showed the expected expression pattern of marker genes (reverse transcriptase-polymerase chain reaction), and, following conversion into fluorescent-labeled cDNAs, led to transcript profiles (cDNAchips; 2600 genes) with scatter-graph geometries indicating suitability for high-confidence evaluation. With the approach described here we introduce a methodological way for the characterization of the in vivo molecular physiology of osteocytes by functional genomics.

Bone and Bones↗

PubMed2003

GoMiner: a resource for biological interpretation of genomic and proteomic data.

We have developed GoMiner, a program package that organizes lists of 'interesting' genes (for example, under- and overexpressed genes from a microarray experiment) for biological interpretation in the context of the Gene Ontology. GoMiner provides quantitative and statistical output files and two useful visualizations. The first is a tree-like structure analogous to that in the AmiGO browser and the second is a compact, dynamically interactive 'directed acyclic graph'. Genes displayed in GoMiner are linked to major public bioinformatics resources.

Computer Graphics↗

PubMed2005

Estimating the total mouse DNA methylation according to the B1 repetitive elements.

Measuring the degree of methylation of the B1 element in mouse may represent the global DNA methylation status because about 30,000 copies of the B1 element are randomly dispersed in the total mouse genome. Six CpG dinucleotides are located within each 163 bp size of B1 element, and each CpG dinucleotide was partially methylated. We quantitated the DNA methylation of the B1 repetitive elements by performing PCR for the methylation specific PCR (MSP) and also by the pyrosequencing. Each CpG dinucleotide was methylated at an average of 9% in the mouse genome by the pyrosequencing and MSP. Especially, we checked whether CpG methylation of the B1 element could respond to a treatment of the DNA methylation inhibitor, 5-azacytidine (5-AzaC). Consequently, the calibration graph resulting from measuring the relative CpG methylation percentage of the B1 element is linearly decreased with the increasing amount of 5-AzaC (up to 50 ng/ml concentration) in the NIH3T3 cells with a standard deviation of only 1.73% between three independent assays. Our methods can be applied to the routine analysis of the global DNA methylation changes in mouse in vivo and in vitro in pharmaceuticals and basic epigenetic research with efforts being less labor-intensive.

Animals↗

PubMed2001

Generating protein interaction maps from incomplete data: application to fold assignment.

MOTIVATION: We present a framework to generate comprehensive overviews of protein-protein interactions. In the post-genomic view of cellular function, each biological entity is seen in the context of a complex network of interactions. Accordingly, we model functional space by representing protein-protein-interaction data as undirected graphs. We suggest a general approach to generate interaction maps of cellular networks in the presence of huge amounts of fragmented and incomplete data, and to derive representations of large networks which hide clutter while keeping the essential architecture of the interaction space. This is achieved by contracting the graphs according to domain-specific hierarchical classifications. The key concept here is the notion of induced interaction, which allows the integration, comparison and analysis of interaction data from different sources and different organisms at a given level of abstraction. RESULTS: We apply this approach to compute the overlap between the DIP compendium of interaction data and a dataset of yeast two-hybrid experiments. The architecture of this network is scale-free, as frequently seen in biological networks, and this property persists through many levels of abstraction. Connections in the network can be projected downwards from higher levels of abstraction down to the level of individual proteins. As an example, we describe an algorithm for fold assignment by network context. This method currently predicts protein folds at 30% accuracy without any requirement of detectable sequence similarity of the query protein to a protein of known structure. We used this algorithm to compile a list of structural assignments for previously unassigned genes from yeast. Finally we discuss ways forward to use interaction networks for the prediction of novel protein-protein interactions. AVAILABILITY: http://www.ebi.ac.uk/~lappe/FoldPred/.

Algorithms↗

PubMed2004

[Relationship between genetic lineage and pathotype of Pyricularia grisea in Yunnan Plateau].

Rice blast caused by Pyricularia grisea is the most destructive disease in Yunnan Plateau, China. In order to elucidate the relationship between genetic lineage and pathotype of P. grisea of Yunnan Plateau as well as the variability of the fungus at DNA level,the repetitive element-based PCR (rep-PCR) of Pot2, an element found in approximately 100 copies in the fungus genome,was exploited. Two hundred and thirty-six isolates of P. grisea collected from 15 main rice-growing counties of Yunnan Plateau were fingerprinted by using rep-PCR. A linkage graph of the rep-PCR fingerprints from 134 representative isolates was generated using an unweighted pair-grouped average program (UPGMA) of the STATISTICAL 5.0 software. The isolates were classified into 8 genetic lineages (G1 approximately G8) at the level of 1.75 genetic linkage distance, of which the G1, G2 and G4 were the dominant lineages. The isolates in a certain area generally belonged to one correspondent genetic lineage and the isolates from the same plot and host rice variety mostly shared one linkage group though different genetic lineages within one lesion. Furthermore, 29 isolates representing the eight genetic lineages were inoculated on 33 rice cultivars of Yunnan at the stage of 3 approximately 4 leaves in greenhouse. The isolates were divided into 6 pathotype groups (P1 approximately P6) according to its compatibility, which demonstrated that some isolates of one genetic lineage sharing two or three pathotype groups, alternatively, one or four pathotype groups. The isolates from each genetic lineage, however, may share one pathotype group such as P2. The preliminary results implicated that the relationship between genetic lineages and pathotype groups of P. grisea in Yunnan Plateau was complicated rather than simple. On the other hand,2 rice cultivars including HeXi 16 and JingGuo 92 were resistant to the 29 isolates but YunJing 20 and HeXi 30 both susceptible to all of them, which was helpful for deploying the blast-resistant genes in rice production of Yunnan Plateau. Therefore, the rice blast-resistance spectrum of the tentative new rice cultivars should be evaluated before its release considering the blast-resistant rice breeding and the practice of rice production in Yunnan Plateau.

Ascomycota↗