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Int J Mol Sci ; 22(15)2021 Jul 22.
Artigo em Inglês | MEDLINE | ID: mdl-34360576


Noncoding RNAs have been known to contribute to a variety of fundamental life processes, such as development, metabolism, and circadian rhythms. However, much remains unrevealed in the huge noncoding RNA datasets, which require further bioinformatic analysis and experimental investigation-and in particular, the coding potential of lncRNAs and the functions of lncRNA-encoded peptides have not been comprehensively studied to date. Through integrating the time-course experimentation with state-of-the-art computational techniques, we studied tens of thousands of zebrafish lncRNAs from our own experiments and from a published study including time-series transcriptome analyses of the testis and the pineal gland. Rhythmicity analysis of these data revealed approximately 700 rhythmically expressed lncRNAs from the pineal gland and the testis, and their GO, COG, and KEGG pathway functions were analyzed. Comparative and conservative analyses determined 14 rhythmically expressed lncRNAs shared between both the pineal gland and the testis, and 15 pineal gland lncRNAs as well as 3 testis lncRNAs conserved among zebrafish, mice, and humans. Further, we computationally analyzed the conserved lncRNA-encoded peptides, and revealed three pineal gland and one testis lncRNA-encoded peptides conserved among these three species, which were further investigated for their three-dimensional (3D) structures and potential functions. Our computational findings provided novel annotations and regulatory mechanisms for hundreds of rhythmically expressed pineal gland and testis lncRNAs in zebrafish, and set the stage for their experimental studies in the near future.

Ritmo Circadiano , Glândula Pineal/metabolismo , RNA Longo não Codificante/genética , Testículo/metabolismo , Transcriptoma , Proteínas de Peixe-Zebra/metabolismo , Peixe-Zebra/genética , Animais , Biologia Computacional , Perfilação da Expressão Gênica , Masculino , Fragmentos de Peptídeos/química , Fragmentos de Peptídeos/genética , Fragmentos de Peptídeos/metabolismo , Peixe-Zebra/fisiologia , Proteínas de Peixe-Zebra/química , Proteínas de Peixe-Zebra/genética
Biology (Basel) ; 10(5)2021 Apr 26.
Artigo em Inglês | MEDLINE | ID: mdl-33925925


Recent studies have demonstrated that numerous long noncoding RNAs (ncRNAs having more than 200 nucleotide base pairs (lncRNAs)) actually encode functional micropeptides, which likely represents the next regulatory biology frontier. Thus, identification of coding lncRNAs from ever-increasing lncRNA databases would be a bioinformatic challenge. Here we employed the Coding Potential Alignment Tool (CPAT), Coding Potential Calculator 2 (CPC2), LGC web server, Coding-Non-Coding Identifying Tool (CNIT), RNAsamba, and MicroPeptide identification tool (MiPepid) to analyze approximately 21,000 zebrafish lncRNAs and computationally to identify 2730-6676 zebrafish lncRNAs with high coding potentials, including 313 coding lncRNAs predicted by all the six bioinformatic tools. We also compared the sensitivity and specificity of these six bioinformatic tools for identifying lncRNAs with coding potentials and summarized their strengths and weaknesses. These predicted zebrafish coding lncRNAs set the stage for further experimental studies.

Sci Rep ; 6: 35652, 2016 10 24.
Artigo em Inglês | MEDLINE | ID: mdl-27774993


Modeling of signaling pathways is crucial for understanding and predicting cellular responses to drug treatments. However, canonical signaling pathways curated from literature are seldom context-specific and thus can hardly predict cell type-specific response to external perturbations; purely data-driven methods also have drawbacks such as limited biological interpretability. Therefore, hybrid methods that can integrate prior knowledge and real data for network inference are highly desirable. In this paper, we propose a knowledge-guided fuzzy logic network model to infer signaling pathways by exploiting both prior knowledge and time-series data. In particular, the dynamic time warping algorithm is employed to measure the goodness of fit between experimental and predicted data, so that our method can model temporally-ordered experimental observations. We evaluated the proposed method on a synthetic dataset and two real phosphoproteomic datasets. The experimental results demonstrate that our model can uncover drug-induced alterations in signaling pathways in cancer cells. Compared with existing hybrid models, our method can model feedback loops so that the dynamical mechanisms of signaling networks can be uncovered from time-series data. By calibrating generic models of signaling pathways against real data, our method supports precise predictions of context-specific anticancer drug effects, which is an important step towards precision medicine.

Redes Reguladoras de Genes/fisiologia , Transdução de Sinais/fisiologia , Algoritmos , Calibragem , Lógica Fuzzy , Humanos , Modelos Biológicos , Proteômica/métodos