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Sci Rep,
2019]
Charged-particle microbeams (CPMs) provide a unique opportunity to investigate the effects of ionizing radiation on living biological specimens with a precise control of the delivered dose, i.e. the number of particles per cell. We describe a methodology to manipulate and micro-irradiate early stage C. elegans embryos at a specific phase of the cell division and with a controlled dose using a CPM. To validate this approach, we observe the radiation-induced damage, such as reduced cell mobility, incomplete cell division and the appearance of chromatin bridges during embryo development, in different strains expressing GFP-tagged proteins in situ after irradiation. In addition, as the dosimetry of such experiments cannot be extrapolated from random irradiations of cell populations, realistic three-dimensional models of 2 cell-stage embryo were imported into the Geant4 Monte-Carlo simulation toolkit. Using this method, we investigate the energy deposit in various chromatin condensation states during the cell division phases. The experimental approach coupled to Monte-Carlo simulations provides a way to selectively irradiate a single cell in a rapidly dividing multicellular model with a reproducible dose. This method opens the way to dose-effect investigations following targeted irradiation.
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Bioinformatics,
2003]
SUMMARY: FuncAssociate is a web-based tool to help researchers use Gene Ontology attributes to characterize large sets of genes derived from experiment. Distinguishing features of FuncAssociate include the ability to handle ranked input lists, and a Monte Carlo simulation approach that is more appropriate to determine significance than other methods, such as Bonferroni or idk p-value correction. FuncAssociate currently supports 10 organisms (Vibrio cholerae, Shewanella oneidensis, Saccharomyces cerevisiae, Schizosaccharomyces pombe, Arabidopsis thaliana, Caenorhaebditis elegans, Drosophila melanogaster, Mus musculus, Rattus norvegicus and Homo sapiens). AVAILABILITY: FuncAssociate is freely accessible at
http://llama.med.harvard.edu/Software.html. Source code (in Perl and C) is freely available to academic users ''as is''.
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Biometrics,
2006]
Time-course studies of gene expression are essential in biomedical research to understand biological phenomena that evolve in a temporal fashion. We introduce a functional hierarchical model for detecting temporally differentially expressed (TDE) genes between two experimental conditions for cross-sectional designs, where the gene expression profiles are treated as functional data and modeled by basis function expansions. A Monte Carlo EM algorithm was developed for estimating both the gene-specific parameters and the hyperparameters in the second level of modeling. We use a direct posterior probability approach to bound the rate of false discovery at a pre-specified level and evaluate the methods by simulations and application to microarray time-course gene expression data on Caenorhabditis elegans developmental processes. Simulation results suggested that the procedure performs better than the two-way ANOVA in identifying TDE genes, resulting in both higher sensitivity and specificity. Genes identified from the C. elegans developmental data set show clear patterns of changes between the two experimental conditions.
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Mem Inst Oswaldo Cruz,
2008]
Despite massive losses of primary forest, the Amazonian rainforest remains an extremely rich source of biodiversity. In recent years, entomopathogenic nematodes (EPNs) have been isolated from soil in various parts of the world and used successfully as biological control agents against numerous insect pests. Therefore, a sampling in the rainforest of Monte Negro, Rondonia, Brazil was conducted with the aim of discovering new strains and/or species of EPNs for future development as biological control agents. From 156 soil samples taken at nine collecting sites, 19 isolates were obtained, all of them belonging to the genus Heterorhabditis. Four strains were subjected to detailed morphological and molecular evaluation. Based on morphometrics and internal transcribed spacer (ITS) sequence data, the strains LPP1, LPP2 and LPP4 were identified as Heterorhabditis indica, whereas LPP7 was considered Heterorhabditis baujardi. Comparative analysis of the ITS1 sequence of H. indica and H. baujardi isolates showed a polymorphic site for the restriction enzyme Tth 111 that could be used to distinguish the two species. Consequently, strains LPP1, LPP2, LPP3, LPP4, and LPP9 were identified as H. indica, whereas LPP5, LPP7, LPP8 and LPP10 were identified as H. baujardi.
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Bioinformatics,
2000]
MOTIVATION: Several results in the literature suggest that biologically interesting RNAs have secondary structures that are more stable than expected by chance. Based on these observations, we developed a scanning algorithm for detecting noncoding RNA genes in genome sequences, using a fully probabilistic version of the Zuker minimum-energy folding algorithm. RESULTS: Preliminary results were encouraging, but certain anomalies led us to do a carefully controlled investigation of this class of methods. Ultimately, our results argue that for the probabilistic model there is indeed a statistical effect, but it comes mostly from local base-composition bias and not from RNA secondary structure. For the thermodynamic implementation (which evaluates statistical significance by doing Monte Carlo shuffling in fixed-length sequence windows, thus eliminating the base-composition effect) the signals for noncoding RNAs are still usually indistinguishable from noise, especially when certain statistical artifacts resulting from local base-composition inhomogeneity are taken into account. We conclude that although a distinct, stable secondary structure is undoubtedly important in most noncoding RNAs, the stability of most noncoding RNA secondary structures is not sufficiently different from the predicted stability of a random sequence to be useful as a general genefinding approach.
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Brief Bioinform,
2024]
Constructing gene regulatory networks is a widely adopted approach for investigating gene regulation, offering diverse applications in biology and medicine. A great deal of research focuses on using time series data or single-cell RNA-sequencing data to infer gene regulatory networks. However, such gene expression data lack either cellular or temporal information. Fortunately, the advent of time-lapse confocal laser microscopy enables biologists to obtain tree-shaped gene expression data of Caenorhabditis elegans, achieving both cellular and temporal resolution. Although such tree-shaped data provide abundant knowledge, they pose challenges like non-pairwise time series, laying the inaccuracy of downstream analysis. To address this issue, a comprehensive framework for data integration and a novel Bayesian approach based on Boolean network with time delay are proposed. The pre-screening process and Markov Chain Monte Carlo algorithm are applied to obtain the parameter estimates. Simulation studies show that our method outperforms existing Boolean network inference algorithms. Leveraging the proposed approach, gene regulatory networks for five subtrees are reconstructed based on the real tree-shaped datatsets of Caenorhabditis elegans, where some gene regulatory relationships confirmed in previous genetic studies are recovered. Also, heterogeneity of regulatory relationships in different cell lineage subtrees is detected. Furthermore, the exploration of potential gene regulatory relationships that bear importance in human diseases is undertaken. All source code is available at the GitHub repository https://github.com/edawu11/BBTD.git.
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J Theor Biol,
2008]
The nematode Caenorhabditis elegans has been reported to exhibit thermotaxis, a sophisticated behavioral response to temperature. However, there appears to be some inconsistency among previous reports. The results of population-level thermotaxis investigations suggest that C. elegans can navigate to the region of its cultivation temperature from nearby regions of higher or lower temperature. However, individual C. elegans nematodes appear to show only cryophilic tendencies above their cultivation temperature. A Monte-Carlo style simulation using a simple individual model of C. elegans provides insight into clarifying apparent inconsistencies among previous findings. The simulation using the thermotaxis model that includes the cryophilic tendencies, isothermal tracking and thermal adaptation was conducted. As a result of the random walk property of locomotion of C. elegans, only cryophilic tendencies above the cultivation temperature result in population-level thermophilic tendencies. Isothermal tracking, a period of active pursuit of an isotherm around regions of temperature near prior cultivation temperature, can strengthen the tendencies of these worms to gather around near-cultivation-temperature regions. A statistical index, the thermotaxis (TTX) L-skewness, was introduced and was useful in analyzing the population-level thermotaxis of model worms.
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G3 (Bethesda),
2013]
Advances in microscopy and fluorescent reporters have allowed us to detect the onset of gene expression on a cell-by-cell basis in a systemic fashion. This information, however, is often encoded in large repositories of images, and developing ways to extract this spatiotemporal expression data is a difficult problem that often uses complex domain-specific methods for each individual data set. We present a more unified approach that incorporates general previous information into a hierarchical probabilistic model to extract spatiotemporal gene expression from 4D confocal microscopy images of developing Caenorhabditis elegans embryos. This approach reduces the overall error rate of our automated lineage tracing pipeline by 3.8-fold, allowing us to routinely follow the C. elegans lineage to later stages of development, where individual neuronal subspecification becomes apparent. Unlike previous methods that often use custom approaches that are organism specific, our method uses generalized linear models and extensions of standard reversible jump Markov chain Monte Carlo methods that can be readily extended to other organisms for a variety of biological inference problems relating to cell fate specification. This modeling approach is flexible and provides tractable avenues for incorporating additional previous information into the model for similar difficult high-fidelity/low error tolerance image analysis problems for systematically applied genomic experiments.
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Dose Response
]
Microdosimetry is a tool for the investigation of microscopic energy deposition of ionizing radiation. This work used <i>Caenorhabditis</i> elegans as a model to estimate the microdosimetric deposition level at the <sup>60</sup>Co gamma radiation. Monte Carlo software PHITS was employed to establish irradiated nematodes model. The dose deposition of the entire body and gonad irradiated to 100 Gy was calculated. The injury levels of radiation were evaluated by the detection of biological indicators. The result of microdosimetric experiment suggested that the dose of whole body of nematodes was estimated to be 99.9 +/- 57.8 Gy, ranging from 19.6 to 332.2 Gy. The dose of gonad was predicted to be 129.4 +/- 558.8 Gy (9.5-6597 Gy). The result of biological experiment suggested that there were little changes in the length of nematodes after irradiation. However, times of head thrash per minute and the spawning yield in 3 consecutive days decreased 27.1% and 94.7%, respectively. Nematodes in the irradiated group displayed heterogeneity. Through contour analysis, trends of behavior kinematics and reproductive capacity of irradiated nematodes proved to be consistent with the dose distribution levels estimated by microdosimetric model. Finally, <i>C</i>. elegans presented a suitable combined model of microdosimetry and biology for studying radiation.
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Nucleic Acids Res,
2017]
Ribosome profiling via high-throughput sequencing (ribo-seq) is a promising new technique for characterizing the occupancy of ribosomes on messenger RNA (mRNA) at base-pair resolution. The ribosome is responsible for translating mRNA into proteins, so information about its occupancy offers a detailed view of ribosome density and position which could be used to discover new translated open reading frames (ORFs), among other things. In this work, we propose Rp-Bp, an unsupervised Bayesian approach to predict translated ORFs from ribosome profiles. We use state-of-the-art Markov chain Monte Carlo techniques to estimate posterior distributions of the likelihood of translation of each ORF. Hence, an important feature of Rp-Bp is its ability to incorporate and propagate uncertainty in the prediction process. A second novel contribution is automatic Bayesian selection of read lengths and ribosome P-site offsets (BPPS). We empirically demonstrate that our read length selection technique modestly improves sensitivity by identifying more canonical and non-canonical ORFs. Proteomics- and quantitative translation initiation sequencing-based validation verifies the high quality of all of the predictions. Experimental comparison shows that Rp-Bp results in more peptide identifications and proteomics-validated ORF predictions compared to another recent tool for translation prediction.