Metagenomic sequence data from defined mock communities is crucial for the assessment of sequencing platform performance and downstream analyses, including assembly, binning and taxonomic assignment. We report a comparison of shotgun metagenome sequencing and assembly metrics of a defined microbial mock community using the Oxford Nanopore Technologies (ONT) MinION, PacBio and Illumina sequencing platforms. Our synthetic microbial community BMock12 consists of 12 bacterial strains with genome sizes spanning 3.2–7.2 Mbp, 40–73% GC content, and 1.5–7.3% repeats. Size selection of both PacBio and ONT sequencing libraries prior to sequencing was essential to yield comparable relative abundances of organisms among all sequencing technologies. While the Illumina-based metagenome assembly yielded good coverage with few misassemblies, contiguity was greatly improved by both, Illumina + ONT and Illumina + PacBio hybrid assemblies but increased misassemblies, most notably in genomes with high sequence similarity to each other. Our resulting datasets allow evaluation and benchmarking of bioinformatics software on Illumina, PacBio and ONT platforms in parallel.
Single-cell microscopy and computational modeling offer novel mechanistic insight into the G1/S switch that initiates DNA replication in budding yeast, revealing a Clb5/6-Cdk1 and Sic1 feedback loop and new rules of biochemical circuit design.
Motivation: Long arrays of near-identical tandem repeats are a common feature of centromeric and subtelomeric regions in complex genomes. These sequences present a source of repeat structure diversity that is commonly ignored by standard genomic tools. Unlike reads shorter than the underlying repeat structure that rely on indirect inference methods, e.g. assembly, long reads allow direct inference of satellite higher order repeat structure. To automate characterization of local centromeric tandem repeat sequence variation we have designed Alpha-CENTAURI (ALPHA satellite CENTromeric AUtomated Repeat Identification), that takes advantage of Pacific Bioscience long-reads from whole-genome sequencing datasets. By operating on reads prior to assembly, our approach provides a more comprehensive set of repeat-structure variants and is not impacted by rearrangements or sequence underrepresentation due to misassembly.Results: We demonstrate the utility of Alpha-CENTAURI in characterizing repeat structure for alpha satellite containing reads in the hydatidiform mole (CHM1, haploid-like) genome. The pipeline is designed to report local repeat organization summaries for each read, thereby monitoring rearrangements in repeat units, shifts in repeat orientation and sites of array transition into non-satellite DNA, typically defined by transposable element insertion. We validate the method by showing consistency with existing centromere high order repeat references. Alpha-CENTAURI can, in principle, run on any sequence data, offering a method to generate a sequence repeat resolution that could be readily performed using consensus sequences available for other satellite families in genomes without high-quality reference assemblies.Availability and implementation: Documentation and source code for Alpha-CENTAURI are freely available at http://github.com/volkansevim/alpha-CENTAURI.Contact: ali.bashir@mssm.eduSupplementary information: Supplementary data are available at Bioinformatics online.
We study an individual-based predator-prey model of biological coevolution, using linear stability analysis and large-scale kinetic Monte Carlo simulations. The model exhibits approximate 1/f noise in diversity and population-size fluctuations, and it generates a sequence of quasisteady communities in the form of simple food webs. These communities are quite resilient toward the loss of one or a few species, which is reflected in different power-law exponents for the durations of communities and the lifetimes of species. The exponent for the former is near -1 , while the latter is close to -2 . Statistical characteristics of the evolving communities, including degree (predator and prey) distributions and proportions of basal, intermediate, and top species, compare reasonably with data for real food webs.
A recently published transcriptional oscillator associated with the yeast cell cycle provides clues and raises questions about the mechanisms underlying autonomous cyclic processes in cells. Unlike other biological and synthetic oscillatory networks in the literature, this one does not seem to rely on a constitutive signal or positive auto-regulation, but rather to operate through stable transmission of a pulse on a slow positive feedback loop that determines its period. We construct a continuous-time Boolean model of this network, which permits the modeling of noise through small fluctuations in the timing of events, and show that it can sustain stable oscillations. Analysis of simpler network models shows how a few building blocks can be arranged to provide stability against fluctuations. Our findings suggest that the transcriptional oscillator in yeast belongs to a new class of biological oscillators.
Abstract. Models of biological coevolution have recently been proposed and studied, in which a species is defined by a genome in the form of a finite bitstring, and the interactions between species i and j are given by a fixed matrix with independent, randomly distributed elements M ij . A consequence of the stochastic independence is that species whose genotypes differ even by a single bit may have completely different phenotypes, as defined by their interactions with the other species. This is clearly unrealistic, as closely related species should be similar in their interactions with the rest of the ecosystem. Here we therefore study a model, in which the M ij are correlated to a controllable degree by means of a local averaging scheme. We calculate, both analytically and numerically, the correlation function for matrix elements M ij and M kl versus the Hamming distance between the bitstrings representing the species. The agreement between the analytical and numerical calculations is excellent for correlations of limited range, but explainable differences arise for correlation ranges that are a significant fraction of the length of the bitstring. We compare long kinetic Monte Carlo simulations of coevolution models with uncorrelated and correlated interactions, respectively. In particular, we consider the probability density for the lifetimes of individual species. The species-lifetime distribution is close to a power law with an exponent near −2 over eight decades in time in both uncorrelated and correlated cases. The durations of quasi-steady states and power spectral densities for the diversity indices display noticeable differences. However, some qualitative features, like 1/f behaviour in power spectral densities for the diversity indices, are not affected by the correlations in the interaction matrix.
We study the growth of a directed network, in which the growth is constrained by the cost of adding links to the existing nodes. We propose a preferential-attachment scheme, in which a new node attaches to an existing node i with probability II(k(i)) approximately k(-1), where k(i) is the number of outgoing links at i. We calculate the degree distribution for the outgoing links in the asymptotic regime t --> infinity, n(k) both analytically and by Monte Carlo simulations. The distribution decays like kmu(k)/Tau(k) for large k, where is a constant. We investigate the effect of this preferential-attachment scheme, by comparing the results to an equivalent growth model with a degree-independent probability of attachment, which gives an exponential outdegree distribution. Also, we relate this mechanism to simple food-web models by implementing it in the cascade model. We show that the low-degree preferential-attachment mechanism breaks the symmetry between in- and outdegree distributions in the cascade model. It also causes a faster decay in the tails of the outdegree distributions for both our network growth model and the cascade model.
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