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At least 1,027 records · Page 57Linked to original sources
PubMed1998

A strategy for finding regions of similarity in complete genome sequences.

MOTIVATION: Complete genomic sequences will become available in the future. New methods to deal with very large sequences (sizes beyond 100 kb) efficiently are required. One of the main aims of such work is to increase our understanding of genome organization and evolution. This requires studies of the locations of regions of similarity. RESULTS: We present here a new tool, ASSIRC ('Accelerated Search for SImilarity Regions in Chromosomes'), for finding regions of similarity in genomic sequences. The method involves three steps: (i) identification of short exact chains of fixed size, called 'seeds', common to both sequences, using hashing functions; (ii) extension of these seeds into putative regions of similarity by a 'random walk' procedure; (iii) final selection of regions of similarity by assessing alignments of the putative sequences. We used simulations to estimate the proportion of regions of similarity not detected for particular region sizes, base identity proportions and seed sizes. This approach can be tailored to the user's specifications. We looked for regions of similarity between two yeast chromosomes (V and IX). The efficiency of the approach was compared to those of conventional programs BLAST and FASTA, by assessing CPU time required and the regions of similarity found for the same data set. AVAILABILITY: Source programs are freely available at the following address: ftp://ftp.biologie.ens. fr/pub/molbio/assirc.tar.gz CONTACT: vincens@biologie.ens.fr, hazout@urbb.jussieu.fr

Algorithms↗
PubMed1999

Polymer chromosome models and Monte Carlo simulations of radiation breaking DNA.

MOTIVATION: Chromatin breakage by ionizing radiation is relevant to studies of carcinogenesis, tumor radiotherapy, biodosimetry and molecular biology. This article focuses on computer analysis of chromosome irradiation in mammlian cells. METHODS: Polymer physics and Monte Carlo numerical methods are used to develop a coarse-grained computational approach. Chromatin is modeled as a random walk on a cubic lattice, and the radiation tracks hitting the chromatin are modeled as straight lines hitting lattice sites. Each track can make a cluster of DSBs on a chromosome. RESULTS: The results obtained replace conjectured DNA fragment-size distribution functions in the recently developed RLC formalism by more mechanistically motivated distributions. The discrete lattice algorithm reproduces features of current radiation experiments relevant to chromatin on large scales. It approximates the continuous formalism and experimental data with adequate precision. It was also found that assuming either fixed chromatin with correlations among different clusters of DSBs or moving chromatin with no such correlations gives virtually identical numerical predictions.

Algorithms↗
PubMed2026

VirBinn improves viral genome binning from metagenomic Hi-C through graph diffusion.

MOTIVATION: Metagenomic Hi-C provides in situ proximity signals that can improve genome binning and enable virus-host-association analysis. However, viral genome recovery remains difficult because virus-virus Hi-C contact matrices are extremely sparse. Viral genomes are small, often low-abundance, and frequently assemble into short contigs, leaving many true within-genome links unobserved and causing viral bins to fragment. RESULTS: We present VirBinn, a graph-diffusion framework for viral binning from metagenomic Hi-C. VirBinn enhances virus-virus connectivity through two complementary mechanisms: random-walk-with-restart enhancement on the sparse virus-virus contact graph and host-guided diffusion that propagates viral seeds through the host network to infer indirect virus-virus associations. The enhanced views are integrated and clustered using Leiden community detection to produce viral metagenome-assembled genomes (vMAGs). On dataset-specific simulation benchmarks with ground truth, VirBinn consistently recovers more high-quality vMAGs than Hi-C-based and shotgun-based baselines and substantially increases the number of near-complete genomes. On four real metagenomic Hi-C datasets spanning human gut, pig gut, sheep gut (long-read assembly), and wastewater, VirBinn yields more high-completeness vMAGs under CheckV and produces bins with strong within-cluster contact support. Finally, host linkage analysis using reconstructed host MAGs reveals habitat-specific host-association patterns and plausible host taxonomic profiles. AVAILABILITY AND IMPLEMENTATION: VirBinn is available at https://github.com/dyxstat/VirBinn. The scripts to reproduce the results and figures in this article are available at https://github.com/dyxstat/Reproduce_VirBinn.

Genome, Viral↗
PubMed2026

Quantifying uncertainty of predictions from cancer progression models.

MOTIVATION: Cancer progresses through the accumulation of genomic events. Cancer progression models such as Mutual Hazard Networks (MHNs) describe this dynamic, enabling prediction of temporal event positions and patient-specific risks of acquiring mutations. However, current MHN analyses rely on single most likely models and do not quantify the uncertainty inherent to parameter estimation. Assessing forecast stability is essential before using them to anticipate treatment-relevant mutations, adapt targeted therapies, or prioritize monitoring of patients at elevated progression risk. RESULTS: We address a key prerequisite for the responsible clinical use of cancer progression models by making MHN-derived predictions uncertainty-aware. We present a Bayesian framework for MHN that uses Markov Chain Monte Carlo to sample from the posterior distributions of model parameters and derived predictions. For practical use we implemented the Random-Walk Metropolis, Metropolis-Adjusted Langevin Algorithm (MALA), and simplified manifold MALA samplers as part of the existing mhn Python package. Only MALA and smMALA were successful in sampling from MHN posteriors, with MALA performing best. While most MHN parameters and predictions showed low posterior variance, a small subset displayed greater variability across the posterior distribution. This differentiation cannot be obtained from a single most likely model, emphasizing the need for uncertainty quantification, especially in clinical contexts. As an illustrative example, posterior sampling identified a subgroup of STK11$-$, KRAS$+$ lung adenocarcinoma patients with a high predicted short-term risk-with low variance across posterior samples-to develop an STK11 mutation. This subgroup exhibited poorer survival under immunotherapy, resembling patterns observed in STK11+ patients. AVAILABILITY AND IMPLEMENTATION: Our implementation is part of version 1.2.0 of the mhn package (https://github.com/spang-lab/LearnMHN). All analyses including the code to produce all figures in this article can be found under https://github.com/huy29433/MCMC-sampling-for-MHN (https://doi.org/10.5281/zenodo.21160219).

Humans↗
PubMed2005

Improving protein structure prediction with model-based search.

MOTIVATION: De novo protein structure prediction can be formulated as search in a high-dimensional space. One of the most frequently used computational tools to solve such search problems is the Monte Carlo method. We present a novel search technique, called model-based search. This method samples the high-dimensional search space to build an approximate model of the underlying function. This model is incrementally refined in areas of interest, whereas areas that are not of interest are excluded from further exploration. Model-based search derives its efficiency from the fact that the information obtained during the exploration of the search space is used to guide further exploration. In contrast, Monte Carlo-based techniques lack memory and exploration is performed based on random walks, ignoring the information obtained in previous steps. RESULTS: Model-based search is applied to protein structure prediction, where search is employed to find the global minimum of the protein's energy landscape. We show that model-based search uses computational resources more efficiently to find lower-energy conformations of proteins than one of the leading protein structure prediction methods, which relies on a tailored Monte Carlo method to perform a search. The performance improvements become more pronounced as the dimensionality of the search problem increases. We argue that model-based search will enable more accurate protein structure prediction than was previously possible. Furthermore, we believe that similar performance improvements can be expected in other problems that are currently solved using Monte Carlo-based search methods. AVAILABILITY: An implementation of model-based search can be obtained by contacting the authors.

Amino Acids↗
PubMed2001

STAT1 from the cell membrane to the DNA.

The binding of interferons (IFNs) to their receptors leads to the phosphorylation and activation of signal transducers and activators of transcription (STATs), and their translocation from the cytoplasm to the nucleus. The mechanisms by which the STATs move to the nuclear pore are not, however, known. Here it is shown that IFN-alpha and -gamma signalling and STAT1 translocation are independent of the actin cytoskeleton or microtubules. Using fluorescence loss in photobleaching (FLIP) and fluorescence recovery after photobleaching (FRAP) experiments, the mobility of a fusion protein of STAT1 with green fluorescent protein (STAT1-GFP) was compared with that of GFP and protein kinase C-GFP. In IFN-gamma-treated and control cells, cytoplasmic STAT1-GFP shows high, energy-independent, mobility comparable to that of freely diffusible GFP. A random walk model for movement of STAT1 from the plasma membrane to the nuclear pore is, therefore, indicated. Nuclear STAT1-GFP showed similar high mobility, with exclusion from nucleoli, consistent with high rates of association and dissociation of STAT1-DNA and/or STAT1-protein complexes in the nucleoplasm of the cell.

Biological Transport↗
PubMed1995

Dependence of frequency of homologous recombination on the homology length.

The frequency of homologous recombination is believed to be a linear function of the length (N bp) of homology between DNAs. Here, the N intercept is believed to be determined by a threshold length below which some physical constraint is effective. In the mammalian gene targeting systems, however, the frequency depends more steeply than linearly on the homology length. To explain both the linear dependence and the steeper dependence, we propose a model where the branch point of a reaction intermediate is assumed to "walk randomly" along the homologous region until it is processed. The intermediate is assumed to be destroyed if the branch point ever reaches either end of the homology. In this model, the length dependence is governed by a parameter, h, which is defined as efficiency of processing of the intermediate and reflects unlikelihood of the destruction at either end of the homology. We find that the frequency is proportional to N3 for smaller N and is a linear function of N for larger N. Where the shift from the N3 dependence to the linear dependence takes place is determined by the parameter h. The range of N showing the N3 dependence becomes narrower as h becomes larger. The dependence steeper than linear dependence, which is observed not only in the mammalian gene targeting system but also in bacteriophage T4, Escherichia coli and yeast systems, agrees well with the predicted N3 dependence. The N intercept is determined not by physical (or structural) constraints but only by the parameter h in this model.

Animals↗
PubMed1996

The probability distribution of the amount of an individual's genome surviving to the following generation.

The probability that at least p% of an individual's genome is passed on collectively to his children is calculated. With data availability the consideration of the chromosome as a whole rather than discrete loci becomes of increasing practical importance. Assuming the genomic continuum model, which allows for recombination, the crossover process in a chromosome pedigree is viewed as a continuous-time Markov random walk on the vertices of a hypercube with time parameter map distance along the chromosome. The desired probability corresponds to the probability of sojourn times of the process in a small set of vertices, which are well approximated via the Poisson clumping heuristic. Results are given for the human genome. It is very likely that an individual with at least four children passes on at least 90% of his genome. There exists no "equivalent" number of independently segregating loci for this distribution.

Genetics, Population↗
PubMed1997

Evolutionary dynamics of sporophytic self-incompatibility alleles in plants.

The stationary frequency distribution and allelic dynamics in finite populations are analyzed through stochastic simulations in three models of single-locus, multi-allelic sporophytic self-incompatibility. The models differ in the dominance relationships among alleles. In one model, alleles act codominantly in both pollen and style (SSIcod), in the second, alleles form a dominance hierarchy in pollen and style (SSIdom). In the third model, alleles interact codominantly in the style and form a dominance hierarchy in the pollen (SSIdomcod). The SSIcod model behaves similarly to the model of gametophytic self-incompatibility, but the selection intensity is stronger. With dominance, dominant alleles invade the population more easily than recessive alleles and have a lower frequency at equilibrium. In the SSIdom model, recessive alleles have both a higher allele frequency and higher expected life span. In the SSIdomcod model, however, loss due to drift occurs more easily for pollen-recessive than for pollen-dominant alleles, and therefore, dominant alleles have a higher expected life span than the more recessive alleles. The process of allelic turnover in the SSIdomcod and SSIdom models is closely approximated by a random walk on a dominance ladder. Implications of the results for experimental studies of sporophytic self-incompatibility in natural populations are discussed.

Alleles↗
PubMed1984

The master equation for neural interaction.

Based on neural interaction equations a random walk model for the stochastic dynamics of a single neuron is introduced. In this model the somatic potential corresponds to a state in the state space and action potentials provide the mechanism causing transitions. Time is made discrete, consisting of small finite increments delta t; assumptions are made about the transitions within such an increment and the associated probabilities are formulated. These quantities depend on delta t and on parameters derived from neural interaction equations. Moreover the model is chosen so that the sequence of somatic potentials is a Markov chain. By appropriately scaling the parameters, in the limit as delta t----0, a master equation for the probability in continuous time is obtained. Depending on the parameters, the master equation describes the evolution of a deterministic, a diffusion, or a discrete process. An interpretation for the diffusion and discrete processes is outlined. The conclusion is that the stochastic equations for neural interaction lead to a master equation representing a diffusion or a discrete process depending on the number, size of synaptic connectivity coefficients, and probability distribution of neural activity. An example is included describing how a master equation may be used to derive properties of the single neuron's output process.

Animals↗
PubMed1995

R-loop stability as a function of RNA structure and size.

The sequence-specific formation of R-loops can be assayed using RNAs which overlap a HindIII cleavage site in a 3.5 kb plasmid. Chemical modification of the displaced DNA strand has permitted stabilization of these R-loops and allowed a systematic investigation of the dependence of these triple-stranded structures on the chain length and structure of the input RNA. RNAs as short as 50 nt form stable R-loops if 5-allylamine uridines (Uaa-RNA) are used in place of normal uridines; normal RNAs must be 100 nt long to form R-loops quantitatively. Since acetic anhydride decreases the hybridization efficiency of Uaa-RNAs, the positive charge of the RNAs must diminish the electrostatic repulsion of the three negatively charged phosphodiester backbones. The dependence of R-loop stability on the length of RNA can be stimulated with a random walk model, which also applies to strand migration within Holiday junctions. R-loop hybridization provides a versatile method to generate single-stranded DNA in a sequence-selective manner.

DNA↗
PubMed1996

Kinetics of spontaneous displacement of RNA from heteroduplexes by DNA.

We have used R-loop formation and direct hybridization techniques to analyze the kinetics by which RNA is displaced from a heteroduplex by DNA of identical sequence. Using random walk simulations we were able to calculate the step times for a single displacement reaction. For RNA with a GC content of 57-60% the data indicate an RNA exchange probability of 50.06%, which is indicative of a modest destabilization of the heteroduplex compared with a DNA duplex in the presence of magnesium. The average step time for the reversible exchange of a single nucleotide is 345.0 (+/- 1.3) ms/step. An acceleration of the displacement reaction was observed in the absence of magnesium. A comparison with step times for elongation shows that RNA displacement would not be rate limiting to transcription elongation under two conditions: (i) if magnesium is eliminated from the newly synthesized heteroduplex; (ii) if displacement is kept in a forward only exchange mode through binding of the emerging RNA. Distamycin, a minor groove binding drug, is very effective as a 'catalyst' of RNA displacement. This effect is likely to be due to preferential binding of distamycin to the minor groove of the DNA duplex as opposed to the heteroduplex. This kinetic assay could therefore serve as a convenient assay for the determination of binding preferences of nucleic acid ligands.

Base Sequence↗
PubMed2006

Accuracy of DNA methylation pattern preservation by the Dnmt1 methyltransferase.

DNA methyltransferase 1 (Dnmt1) has a central role in copying the pattern of DNA methylation after replication which is one manifestation of epigenetic inheritance. With oligonculeotide substrates we show that mouse Dnmt1 has a 30- to 40-fold preference for hemimethylated DNA that is almost lost after addition of fully methylated oligonucleotides. Using long hemimethylated DNA substrates that carry defined methylation patterns and bisulfite analysis of the methylation reaction products, we show a 15-fold preference for hemimethylated CG sites. Dnmt1 moves along the DNA in a random walk methylating hemimethylated substrates with high processivity (>50 sites are visited on average which corresponds to linear diffusion over 6000 bp). The frequency of skipping sites is very low (<0.3%) and there is no detectable flanking sequence preference. CGCTC sites tend to terminate the processive methylation of DNA by Dnmt1. Unmethylated DNA is modified non-processively with a preference for methylation at CCGG sites. We simulate the propagation of methylation patterns using a stochastic model with the specificity of Dnmt1 observed here and conclude that either methylation of several sites is required to propagate the methylation information over several cellular generations or additional epigenetic information must be used.

Animals↗
PubMed2002

A Fourier analysis of symmetry in protein structure.

The score matrix from a structure comparison program (SAP) was used to search for repeated structures using a Fourier analysis. When tested with artificial data, a simple Fourier transform of the smoothed matrix provided a clear signal of the repeat periodicity that could be used to extract the repeating units with the SAP program. The strength of the Fourier signal was calibrated against the signal from model proteins. The most useful of these was the novel random-walk approach employed to generate realistic 'fake' structures. On the basis of these it was possible to conclude that only a small proportion of protein structures have an unexpected degree of symmetry. Artificially generated 'ideal' folds provided an upper limit on the strength of signal that could be expected from a 'perfectly' repeating compact structure. Unexpectedly, some of the very regular beta-propellor folds attained the same strength but the majority of symmetric structures lay below this region. When native proteins were ranked by the power of their spectrum a wide variety of fold types were seen to score highly. In the betaalpha class, these included the globular betaalpha proteins and the more repetitive leucine-rich betaalpha folds. In the all-beta class; beta-propellors, beta-prisms and beta-helices were found as well as the more globular gamma-crystalin domains. When this ranked list was filtered to remove proteins that contained detectable internal sequence similarity (using the program REPRO), the list became exclusively composed of just globular betaalpha class proteins and in the top 50 re-ranked proteins, only a single 4-fold propellor structure remained.

Fourier Analysis↗
PubMed1990

Distance-constrained molecular docking by simulated annealing.

An optimized method based on the principle of simulated annealing is presented for determining the relative position and orientation of interacting molecules. The spatial relationships of these molecules are described by intermolecular distance constraints between specific pairs of atoms, such as found in hydrogen bonds or from experimentally determined data. The method makes use of a random walk through six rotational and translational degrees of freedom where the constituent molecules are treated as rigid bodies. Van der Waals repulsions are used only to define a lower bound on distances between constrained atom pairs within the docking procedure. A cost function comprised of purely geometric constraints is optimized via simulated annealing, in order to search for the best orientation and position of the two molecules. Our docking procedure is applied to eight serine proteinase complexes from the Brookhaven Protein Data Bank. For each simulation 100 computations were performed. A typical docking computation requires only a few seconds of CPU time on a VAXserver 3500. The influence of the number of constraints on the final docked positions was studied. The sensitivity of the docking procedure to a ligand structure which is not well defined is also addressed. Possible applications of this method include using approximate distances incorporating complete energy functions.

Aspartic Acid Endopeptidases↗
PubMed2004

Kalman filtration of radiation monitoring data from atmospheric dispersion of radioactive materials.

A Kalman filter method using off-site radiation monitoring data is proposed as a tool for on-line estimation of the source term for short-range atmospheric dispersion of radioactive materials. The method is based on the Gaussian plume model, in which the plume parameters including the source term exhibit a 'random walk' process. The embedded parameters of the Kalman filter are determined through maximum-likelihood estimation making the filter essentially free of external parameters. The method is tested using both real and simulated radiation monitoring data. For simulated data, the method is shown to retrieve the embedded parameters employed in generating the data and to reconstruct the plume model parameters, including the source term. When tested against experimental radiation monitoring data the method is found accurately to uncover the known source term.

Air Movements↗
PubMed2006

A Monte-Carlo code for the detailed simulation of electron and light-ion tracks in condensed matter.

In an effort to understand the basic mechanism of the action of charged particles in solid radiation dosimeters, we extend our Monte-Carlo code (MC4) to condensed media (liquids/solids) and present new track-structure calculations for electrons and protons. Modeling the energy dissipation process is based on a model dielectric function, which accounts in a semi-empirical and self-consistent way for condensed-phase effects which are computationally intractable. Importantly, these effects mostly influence track-structure characteristics at the nanometer scale, which is the focus of radiation action models. Since the event-by-event scheme for electron transport is impractical above several kilo-electron volts, a condensed-history random-walk scheme has been implemented to transport the energetic delta rays produced by energetic ions. Based on the above developments, new track-structure calculations are presented for two representative dosimetric materials, namely, liquid water and silicon. Results include radial dose distributions in cylindrical and spherical geometries, as well as, clustering distributions, which, among other things, are important in predicting irreparable damage in biological systems and prompt electric-fields in microelectronics.

Algorithms↗
PubMed1997

Pattern formation triggered by rare events: lessons from the spread of rabies.

Understanding of large-scale spatial pattern formation is a key to successful management in ecology and epidemiology. Neighbourhood interactions between local units are known to contribute to large-scale patterns, but how much do they contribute and what is the role of regional interactions caused by long-distance processes? How much long-distance dispersal do we need to explain the patterns that we observe in nature? There seems to be no way to answer these questions empirically. Therefore, we present a modelling approach that is a combination of a grid-based model describing local interactions and an individual-based model describing dispersal. Applying our approach to the spread of rabies, we show that in addition to local rabies dynamics, one long-distance infection per 14000 km2 per year is sufficient to reproduce the wave-like spread of this disease. We conclude that even rare ecological events that couple local dynamics on a regional scale may have profound impacts on large-scale patterns and, in turn, dynamics. Furthermore, the following results emerge: (i) Both neighbourhood infection and long-distance infection are needed to generate the wave-like dispersal pattern of rabies; (ii) randomly walking rabid foxes are not sufficient to generate the wave pattern; and (iii) on a scale of less than 100 km x 100 km, temporal oscillations emerge that are independent from long-distance dispersal.

Animals↗