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Artículos de revistas sobre el tema "Nucleosome positioning prediction"

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Publicado: 4 de junio de 2021
Última modificación: 2 de febrero de 2022

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1

van der Heijden, Thijn, Joke J. F. A. van Vugt, Colin Logie y John van Noort. "Sequence-based prediction of single nucleosome positioning and genome-wide nucleosome occupancy". Proceedings of the National Academy of Sciences 109, n.º 38 (20 de agosto de 2012): E2514—E2522. http://dx.doi.org/10.1073/pnas.1205659109.

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Nucleosome positioning dictates eukaryotic DNA compaction and access. To predict nucleosome positions in a statistical mechanics model, we exploited the knowledge that nucleosomes favor DNA sequences with specific periodically occurring dinucleotides. Our model is the first to capture both dyad position within a few base pairs, and free binding energy within 2 kBT, for all the known nucleosome positioning sequences. By applying Percus’s equation to the derived energy landscape, we isolate sequence effects on genome-wide nucleosome occupancy from other factors that may influence nucleosome positioning. For both in vitro and in vivo systems, three parameters suffice to predict nucleosome occupancy with correlation coefficients of respectively 0.74 and 0.66. As predicted, we find the largest deviations in vivo around transcription start sites. This relatively simple algorithm can be used to guide future studies on the influence of DNA sequence on chromatin organization.
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2

Saxton, Daniel S. y Jasper Rine. "Nucleosome Positioning Regulates the Establishment, Stability, and Inheritance of Heterochromatin inSaccharomyces cerevisiae". Proceedings of the National Academy of Sciences 117, n.º 44 (19 de octubre de 2020): 27493–501. http://dx.doi.org/10.1073/pnas.2004111117.

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Heterochromatic domains are complex structures composed of nucleosome arrays that are bound by silencing factors. This composition raises the possibility that certain configurations of nucleosome arrays facilitate heterochromatic silencing. We tested this possibility inSaccharomyces cerevisiaeby systematically altering the distance between heterochromatic nucleosome-depleted regions (NDRs), which is predicted to affect local nucleosome positioning by limiting how nucleosomes can be packed between NDRs. Consistent with this prediction, serial deletions that altered the distance between heterochromatic NDRs revealed a striking oscillatory relationship between inter-NDR distance and defects in nucleosome positioning. Furthermore, conditions that caused poor nucleosome positioning also led to defects in both heterochromatin stability and the ability of cells to generate and inherit epigenetic transcriptional states. These findings strongly suggest that nucleosome positioning can contribute to formation and maintenance of functional heterochromatin and point to previously unappreciated roles of NDR positioning within heterochromatic domains.
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3

Widom, J. "Role of DNA sequence in nucleosome stability and dynamics". Quarterly Reviews of Biophysics 34, n.º 3 (agosto de 2001): 269–324. http://dx.doi.org/10.1017/s0033583501003699.

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1. Introduction 2701.1 Overview of nucleosome structure 2712. Relative equilibrium stability (affinity) of histone–DNA interactions in nucleosomes 2722.1 Relative affinity equals relative equilibrium stability 2722.2 Competition assays for relative free-energy measurements 2732.3 Technical issues in relative free-energy measurements 2752.4 Range of affinities 2783. Relation of nucleosome stability to nucleosome positioning 2793.1 Translational nucleosome positioning 2793.2 Rotational positioning 2803.3 Unfavorable positioning 2813.4 Experiments 2814. Physical basis of DNA sequence preferences 2824.1 Free-energy cost of DNA bending 2834.2 Molecular mechanics of DNA bending and bendability 2844.3 Bent and bendable DNA sequences 2864.4 Parameter sets for prediction of DNA bending and bendability 2884.5 DNA twisting 2904.6 Energetics of nucleosomal DNA packaging 2915. DNA sequence motifs for nucleosome packaging 2925.1 Natural and designed nucleosomal DNAs 2935.2 New rules and reagents from physical selection studies 2945.3 Molecular basis of DNA sequence preferences 2995.4 Special properties of the TA step 3005.5 Unfavorable sequences 3025.6 Natural genomes 3035.7 Evolutionary approach toward an optimal sequence 3055.8 Optimization by design 3056. Dynamic nucleosome instability 3086.1 Site-exposure equilibria 3086.2 DNA sequence-dependence to site-exposure equilibria 3126.3 Nucleosome translocation 3156.4 Action of processive enzymes 3197. Conclusions 3198. Acknowledgements 3209. References 320The nucleosome core particle is the fundamental repeating subunit of chromatin. It consists of two molecules each of the four ‘core histone’ proteins, H2A, H2B, H3 and H4, and a 147 bp stretch of DNA. The lowest level of chromatin organization consists of a repeated array of nucleosome core particles separated by variable lengths of ‘linker DNA’. In many, but not all, cases, each core particle plus its linker DNA is associated with one molecule of a fifth ‘linker’ histone protein, H1. The complex of the core particle plus its linker DNA and H1 (when present) is called a ‘nucleosome’.
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4

Fulnecek, Jaroslav, Roman Matyasek y Ales Kovarik. "Plant 5S rDNA has multiple alternative nucleosome positions". Genome 49, n.º 7 (1 de julio de 2006): 840–50. http://dx.doi.org/10.1139/g06-039.

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In plants, 5S ribosomal DNA (5S rDNA) is typically found in hundreds of copies of tandemly arranged units. Nucleotide database searches revealed that the majority of 5S genes (>90%) have repeat lengths that are not simple multiples of a plant nucleosomal unit, ranging in plants from 175–185 bp. To get insight into the chromatin structure, we have determined positions of nucleosomes in the Nicotiana sylvestris and Nicotiana tomentosiformis 5S rDNA units with repeat lengths of about 430 and 645 bp, respectively. Mapping experiments carried out on isolated nucleo somal DNA revealed many (>50) micrococcal nuclease cleavage sites in each class of repeats. Permutation analysis and theoretical computer prediction showed multiple DNA bend sites, mostly located in the nontranscribed spacer region. The distance between bend sites, however, did not correspond to the average spacing of nucleosomes in 5S chromatin (~180 bp). These data indicate that 5S rDNA does not have fixed nucleosomal positioning sites and that units can be wrapped in a number of alternative nucleosome frames. Consequently, accessibility of transcription factors to cognate motifs might vary across the tandem array, potentially influencing gene expression.Key words: Nicotiana, 5S rDNA, heterochromatin, tandem repeats, nucleosomes, DNA curvature.
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5

胡, 世赛. "Prediction of Nucleosome Positioning Sequence for Yeast Genome". Biophysics 06, n.º 01 (2018): 1–6. http://dx.doi.org/10.12677/biphy.2018.61001.

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6

Boffelli, D., P. De Santis, A. Palleschi y M. Savino. "The curvature vector in nucleosomal DNAs and theoretical prediction of nucleosome positioning". Biophysical Chemistry 39, n.º 2 (febrero de 1991): 127–36. http://dx.doi.org/10.1016/0301-4622(91)85014-h.

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7

Wang, Jia, Shuai Liu y Weina Fu. "Nucleosome Positioning with Set of Key Positions and Nucleosome Affinity". Open Biomedical Engineering Journal 8, n.º 1 (31 de diciembre de 2014): 166–70. http://dx.doi.org/10.2174/1874120701408010166.

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The formation and precise positioning of nucleosome in chromatin occupies a very important role in studying life process. Today, there are many researchers who discovered that the positioning where the location of a DNA sequence fragment wraps around a histone octamer in genome is not random but regular. However, the positioning is closely relevant to the concrete sequence of core DNA. So in this paper, we analyzed the relation between the affinity and sequence structure of core DNA, and extracted the set of key positions. In these positions, the nucleotide sequences probably occupy mainly action in the binding. First, we simplified and formatted the experimental data with the affinity. Then, to find the key positions in the wrapping, we used neural network to analyze the positive and negative effects of nucleosome generation for each position in core DNA sequences. However, we reached a class of weights with every position to describe this effect. Finally, based on the positions with high weights, we analyzed the reason why the chosen positions are key positions, and used these positions to construct a model for nucleosome positioning prediction. Experimental results show the effectiveness of our method.
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8

Liu, H., R. Zhang, W. Xiong, J. Guan, Z. Zhuang y S. Zhou. "A comparative evaluation on prediction methods of nucleosome positioning". Briefings in Bioinformatics 15, n.º 6 (10 de septiembre de 2013): 1014–27. http://dx.doi.org/10.1093/bib/bbt062.

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9

Liu, Guoqing, Fen Feng, Xiujuan Zhao y Lu Cai. "Nucleosome Organization around Pseudogenes in the Human Genome". BioMed Research International 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/821596.

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Pseudogene, disabled copy of functional gene, plays a subtle role in gene expression and genome evolution. The first step in deciphering RNA-level regulation of pseudogenes is to understand their transcriptional activity. So far, there has been no report on possible roles of nucleosome organization in pseudogene transcription. In this paper, we investigated the effect of nucleosome positioning on pseudogene transcription. For transcribed pseudogenes, the experimental nucleosome occupancy shows a prominent depletion at the regions both upstream of pseudogene start positions and downstream of pseudogene end positions. Intriguingly, the same depletion is also observed for nontranscribed pseudogenes, which is unexpected since nucleosome depletion in those regions is thought to be unnecessary in light of the nontranscriptional property of those pseudogenes. The sequence-dependent prediction of nucleosome occupancy shows a consistent pattern with the experimental data-based analysis. Our results indicate that nucleosome positioning may play important roles in both the transcription initiation and termination of pseudogenes.
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10

Yi, Xianfu, Yu-Dong Cai, Zhisong He, WeiRen Cui y Xiangyin Kong. "Prediction of Nucleosome Positioning Based on Transcription Factor Binding Sites". PLoS ONE 5, n.º 9 (1 de septiembre de 2010): e12495. http://dx.doi.org/10.1371/journal.pone.0012495.

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11

Alharbi, Bader A., Thamir H. Alshammari, Nathan L. Felton, Victor B. Zhurkin y Feng Cui. "nuMap: A Web Platform for Accurate Prediction of Nucleosome Positioning". Genomics, Proteomics & Bioinformatics 12, n.º 5 (octubre de 2014): 249–53. http://dx.doi.org/10.1016/j.gpb.2014.08.001.

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12

Ogawa, Ryu, Noriyuki Kitagawa, Hiroki Ashida, Rintaro Saito y Masaru Tomita. "Computational prediction of nucleosome positioning by calculating the relative fragment frequency index of nucleosomal sequences". FEBS Letters 584, n.º 8 (3 de marzo de 2010): 1498–502. http://dx.doi.org/10.1016/j.febslet.2010.02.067.

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13

Xing, Yong-qiang, Guo-qing Liu, Xiu-juan Zhao y Lu Cai. "An analysis and prediction of nucleosome positioning based on information content". Chromosome Research 21, n.º 1 (22 de febrero de 2013): 63–74. http://dx.doi.org/10.1007/s10577-013-9338-z.

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14

Zhao, Xiujuan, Zhiyong Pei, Jia Liu, Sheng Qin y Lu Cai. "Prediction of nucleosome DNA formation potential and nucleosome positioning using increment of diversity combined with quadratic discriminant analysis". Chromosome Research 18, n.º 7 (16 de octubre de 2010): 777–85. http://dx.doi.org/10.1007/s10577-010-9160-9.

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15

De Santis, P., M. Fuà, A. Palleschi y M. Savino. "Relationships between intrinsic and induced curvature in DNAs: Theoretical prediction of nucleosome positioning". Biophysical Chemistry 46, n.º 2 (abril de 1993): 193–204. http://dx.doi.org/10.1016/0301-4622(93)85027-f.

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16

De Santis, Pasquale, Stefano Morosetti y Anita Scipioni. "Prediction of Nucleosome Positioning in Genomes: Limits and Perspectives of Physical and Bioinformatic Approaches". Journal of Biomolecular Structure and Dynamics 27, n.º 6 (junio de 2010): 747–64. http://dx.doi.org/10.1080/07391102.2010.10508583.

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17

Mitra, R. y M. Gupta. "A continuous-index Bayesian hidden Markov model for prediction of nucleosome positioning in genomic DNA". Biostatistics 12, n.º 3 (30 de diciembre de 2010): 462–77. http://dx.doi.org/10.1093/biostatistics/kxq077.

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18

Liu, Guoqing, Hongyu Zhao, Hu Meng, Yongqiang Xing y Lu Cai. "A deformation energy model reveals sequence-dependent property of nucleosome positioning". Chromosoma 130, n.º 1 (16 de enero de 2021): 27–40. http://dx.doi.org/10.1007/s00412-020-00750-9.

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AbstractWe present a deformation energy model for predicting nucleosome positioning, in which a position-dependent structural parameter set derived from crystal structures of nucleosomes was used to calculate the DNA deformation energy. The model is successful in predicting nucleosome occupancy genome-wide in budding yeast, nucleosome free energy, and rotational positioning of nucleosomes. Our model also indicates that the genomic regions underlying the MNase-sensitive nucleosomes in budding yeast have high deformation energy and, consequently, low nucleosome-forming ability, while the MNase-sensitive non-histone particles are characterized by much lower DNA deformation energy and high nucleosome preference. In addition, we also revealed that remodelers, SNF2 and RSC8, are likely to act in chromatin remodeling by binding to broad nucleosome-depleted regions that are intrinsically favorable for nucleosome positioning. Our data support the important role of position-dependent physical properties of DNA in nucleosome positioning.
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19

Rube, H. Tomas y Jun S. Song. "Quantifying the role of steric constraints in nucleosome positioning". Nucleic Acids Research 42, n.º 4 (27 de noviembre de 2013): 2147–58. http://dx.doi.org/10.1093/nar/gkt1239.

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Abstract Statistical positioning, the localization of nucleosomes packed against a fixed barrier, is conjectured to explain the array of well-positioned nucleosomes at the 5′ end of genes, but the extent and precise implications of statistical positioning in vivo are unclear. We examine this hypothesis quantitatively and generalize the idea to include moving barriers as well as nucleosomes actively packed against a barrier. Early experiments noted a similarity between the nucleosome profile aligned and averaged across genes and that predicted by statistical positioning; however, we demonstrate that aligning random nucleosomes also generates the same profile, calling the previous interpretation into question. New rigorous results reformulate statistical positioning as predictions on the variance structure of nucleosome locations in individual genes. In particular, a quantity termed the variance gradient, describing the change in variance between adjacent nucleosomes, is tested against recent high-throughput nucleosome sequencing data. Constant variance gradients provide support for generalized statistical positioning in ∼50% of long genes. Genes that deviate from predictions have high nucleosome turnover and cell-to-cell gene expression variability. The observed variance gradient suggests an effective nucleosome size of 158 bp, instead of the commonly perceived 147 bp. Our analyses thus clarify the role of statistical positioning in vivo.
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20

Cui, Xu y Li. "ZCMM: A Novel Method Using Z-Curve Theory- Based and Position Weight Matrix for Predicting Nucleosome Positioning". Genes 10, n.º 10 (28 de septiembre de 2019): 765. http://dx.doi.org/10.3390/genes10100765.

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Nucleosomes are the basic units of eukaryotes. The accurate positioning of nucleosomes plays a significant role in understanding many biological processes such as transcriptional regulation mechanisms and DNA replication and repair. Here, we describe the development of a novel method, termed ZCMM, based on Z-curve theory and position weight matrix (PWM). The ZCMM was trained and tested using the nucleosomal and linker sequences determined by support vector machine (SVM) in Saccharomyces cerevisiae (S. cerevisiae), and experimental results showed that the sensitivity (Sn), specificity (Sp), accuracy (Acc), and Matthews correlation coefficient (MCC) values for ZCMM were 91.40%, 96.56%, 96.75%, and 0.88, respectively, and the average area under the receiver operating characteristic curve (AUC) value was 0.972. A ZCMM predictor was developed to predict nucleosome positioning in Homo sapiens (H. sapiens), Caenorhabditis elegans (C. elegans), and Drosophila melanogaster (D. melanogaster) genomes, and the accuracy (Acc) values were 77.72%, 85.34%, and 93.62%, respectively. The maximum AUC values of the four species were 0.982, 0.861, 0.912 and 0.911, respectively. Another independent dataset for S. cerevisiae was used to predict nucleosome positioning. Compared with the results of Wu's method, it was found that the Sn, Sp, Acc, and MCC of ZCMM results for S. cerevisiae were all higher, reaching 96.72%, 96.54%, 94.10%, and 0.88. Compared with the Guo’s method ‘iNuc-PseKNC’, the results of ZCMM for D. melanogaster were better. Meanwhile, the ZCMM was compared with some experimental data in vitro and in vivo for S. cerevisiae, and the results showed that the nucleosomes predicted by ZCMM were highly consistent with those confirmed by these experiments. Therefore, it was further confirmed that the ZCMM method has good accuracy and reliability in predicting nucleosome positioning.
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21

Awazu, Akinori. "Prediction of nucleosome positioning by the incorporation of frequencies and distributions of three different nucleotide segment lengths into a general pseudo k-tuple nucleotide composition". Bioinformatics 33, n.º 1 (25 de agosto de 2016): 42–48. http://dx.doi.org/10.1093/bioinformatics/btw562.

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22

Tolstorukov, M. Y., V. Choudhary, W. K. Olson, V. B. Zhurkin y P. J. Park. "nuScore: a web-interface for nucleosome positioning predictions". Bioinformatics 24, n.º 12 (29 de abril de 2008): 1456–58. http://dx.doi.org/10.1093/bioinformatics/btn212.

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23

Bina, Minou. "Predicting nucleosome-positioning signalsComment on “Cracking the chromatin code: Precise rule of nucleosome positioning” by E.N. Trifonov". Physics of Life Reviews 8, n.º 1 (marzo de 2011): 59–61. http://dx.doi.org/10.1016/j.plrev.2011.01.012.

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24

Wu, Jing, Yusen Zhang y Zengchao Mu. "Predicting Nucleosome Positioning Based on Geometrically Transformed Tsallis Entropy". PLoS ONE 9, n.º 11 (7 de noviembre de 2014): e109395. http://dx.doi.org/10.1371/journal.pone.0109395.

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25

Xi, Liqun, Yvonne Fondufe-Mittendorf, Lei Xia, Jared Flatow, Jonathan Widom y Ji-Ping Wang. "Predicting nucleosome positioning using a duration Hidden Markov Model". BMC Bioinformatics 11, n.º 1 (2010): 346. http://dx.doi.org/10.1186/1471-2105-11-346.

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26

Scipioni, Anita y Pasquale De Santis. "Predicting nucleosome positioning in genomes: Physical and bioinformatic approaches". Biophysical Chemistry 155, n.º 2-3 (mayo de 2011): 53–64. http://dx.doi.org/10.1016/j.bpc.2011.03.006.

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27

Scipioni, Anita, Stefano Morosetti y Pasquale De Santis. "A statistical thermodynamic approach for predicting the sequence-dependent nucleosome positioning along genomes". Biopolymers 91, n.º 12 (diciembre de 2009): 1143–53. http://dx.doi.org/10.1002/bip.21276.

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28

Jia, Cangzhi, Qing Yang y Quan Zou. "NucPosPred: Predicting species-specific genomic nucleosome positioning via four different modes of general PseKNC". Journal of Theoretical Biology 450 (agosto de 2018): 15–21. http://dx.doi.org/10.1016/j.jtbi.2018.04.025.

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29

Guo, Shou-Hui, En-Ze Deng, Li-Qin Xu, Hui Ding, Hao Lin, Wei Chen y Kuo-Chen Chou. "iNuc-PseKNC: a sequence-based predictor for predicting nucleosome positioning in genomes with pseudo k-tuple nucleotide composition". Bioinformatics 30, n.º 11 (6 de febrero de 2014): 1522–29. http://dx.doi.org/10.1093/bioinformatics/btu083.

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30

Read, David F., Kate Cook, Yang Y. Lu, Karine G. Le Roch y William Stafford Noble. "Predicting gene expression in the human malaria parasite Plasmodium falciparum using histone modification, nucleosome positioning, and 3D localization features". PLOS Computational Biology 15, n.º 9 (11 de septiembre de 2019): e1007329. http://dx.doi.org/10.1371/journal.pcbi.1007329.

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31

Cacchione, S., M. A. Cerone, P. De Santis y M. Savino. "Superstructural features of the upstream regulatory regions of two pea rbcS genes and nucleosomes positioning: theoretical prediction and experimental evaluation". Biophysical Chemistry 53, n.º 3 (febrero de 1995): 267–81. http://dx.doi.org/10.1016/0301-4622(94)00105-s.

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32

Warnecke, Tobias, Claudia C. Weber y Laurence D. Hurst. "Why there is more to protein evolution than protein function: splicing, nucleosomes and dual-coding sequence". Biochemical Society Transactions 37, n.º 4 (22 de julio de 2009): 756–61. http://dx.doi.org/10.1042/bst0370756.

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There is considerable variation in the rate at which different proteins evolve. Why is this? Classically, it has been considered that the density of functionally important sites must predict rates of protein evolution. Likewise, amino acid choice is usually assumed to reflect optimal protein function. In the present article, we briefly review evidence suggesting that this protein function-centred view is too simplistic. In particular, we concentrate on how selection acting during the protein's production history can also affect protein evolutionary rates and amino acid choice. Exploring the role of selection at the DNA and RNA level, we specifically address how the need (i) to specify exonic splice enhancer motifs in pre-mRNA, and (ii) to ensure nucleosome positioning on DNA have an impact on amino acid choice and rates of evolution. For both, we review evidence that sequence affected by more than one coding demand is particularly constrained. Strikingly, in mammals, splicing-related constraints are quantitatively as important as expression parameters in predicting rates of protein evolution. These results indicate that there is substantially more to protein evolution than protein functional constraints.
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33

Kato, Hiroaki, Mitsuhiro Shimizu y Takeshi Urano. "Chemical map-based prediction of nucleosome positioning using the Bioconductor package nuCpos". BMC Bioinformatics 22, n.º 1 (13 de junio de 2021). http://dx.doi.org/10.1186/s12859-021-04240-2.

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Abstract Background Assessing the nucleosome-forming potential of specific DNA sequences is important for understanding complex chromatin organization. Methods for predicting nucleosome positioning include bioinformatics and biophysical approaches. An advantage of bioinformatics methods, which are based on in vivo nucleosome maps, is the use of natural sequences that may contain previously unknown elements involved in nucleosome positioning in vivo. The accuracy of such prediction attempts reflects the genomic coordinate resolution of the nucleosome maps applied. Nucleosome maps are constructed using micrococcal nuclease digestion followed by high-throughput sequencing (MNase-seq). However, as MNase has a strong preference for A/T-rich sequences, MNase-seq may not be appropriate for this purpose. In addition to MNase-seq-based maps, base pair-resolution chemical maps of in vivo nucleosomes from three different species (budding and fission yeasts, and mice) are currently available. However, these chemical maps have yet to be integrated into publicly available computational methods. Results We developed a Bioconductor package (named nuCpos) to demonstrate the superiority of chemical maps in predicting nucleosome positioning. The accuracy of chemical map-based prediction in rotational settings was higher than that of the previously developed MNase-seq-based approach. With our method, predicted nucleosome occupancy reasonably matched in vivo observations and was not affected by A/T nucleotide frequency. Effects of genetic alterations on nucleosome positioning that had been observed in living yeast cells could also be predicted. nuCpos calculates individual histone binding affinity (HBA) scores for given 147-bp sequences to examine their suitability for nucleosome formation. We also established local HBA as a new parameter to predict nucleosome formation, which was calculated for 13 overlapping nucleosomal DNA subsequences. HBA and local HBA scores for various sequences agreed well with previous in vitro and in vivo studies. Furthermore, our results suggest that nucleosomal subsegments that are disfavored in different rotational settings contribute to the defined positioning of nucleosomes. Conclusions Our results demonstrate that chemical map-based statistical models are beneficial for studying nucleosomal DNA features. Studies employing nuCpos software can enhance understanding of chromatin regulation and the interpretation of genetic alterations and facilitate the design of artificial sequences.
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34

Han, Guo-Sheng, Qi Li y Ying Li. "Comparative analysis and prediction of nucleosome positioning using integrative feature representation and machine learning algorithms". BMC Bioinformatics 22, S6 (junio de 2021). http://dx.doi.org/10.1186/s12859-021-04006-w.

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Abstract Background Nucleosome plays an important role in the process of genome expression, DNA replication, DNA repair and transcription. Therefore, the research of nucleosome positioning has invariably received extensive attention. Considering the diversity of DNA sequence representation methods, we tried to integrate multiple features to analyze its effect in the process of nucleosome positioning analysis. This process can also deepen our understanding of the theoretical analysis of nucleosome positioning. Results Here, we not only used frequency chaos game representation (FCGR) to construct DNA sequence features, but also integrated it with other features and adopted the principal component analysis (PCA) algorithm. Simultaneously, support vector machine (SVM), extreme learning machine (ELM), extreme gradient boosting (XGBoost), multilayer perceptron (MLP) and convolutional neural networks (CNN) are used as predictors for nucleosome positioning prediction analysis, respectively. The integrated feature vector prediction quality is significantly superior to a single feature. After using principal component analysis (PCA) to reduce the feature dimension, the prediction quality of H. sapiens dataset has been significantly improved. Conclusions Comparative analysis and prediction on H. sapiens, C. elegans, D. melanogaster and S. cerevisiae datasets, demonstrate that the application of FCGR to nucleosome positioning is feasible, and we also found that integrative feature representation would be better.
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35

Feng, Jihua, Jianping Xiao, Ying Lu y Qiufu Shan. "Prediction of Nucleosome Positioning Based on Support Vector Machine". International Journal of Bioscience, Biochemistry and Bioinformatics, 2013, 449–51. http://dx.doi.org/10.7763/ijbbb.2013.v3.253.

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36

Di Gangi, Mattia, Giosuè Lo Bosco y Riccardo Rizzo. "Deep learning architectures for prediction of nucleosome positioning from sequences data". BMC Bioinformatics 19, S14 (noviembre de 2018). http://dx.doi.org/10.1186/s12859-018-2386-9.

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37

"Correction for van der Heijden et al., Sequence-based prediction of single nucleosome positioning and genome-wide nucleosome occupancy". Proceedings of the National Academy of Sciences 110, n.º 15 (12 de marzo de 2013): 6240. http://dx.doi.org/10.1073/pnas.1301591110.

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38

Cui, Feng, Linlin Chen, Peter R. LoVerso y Victor B. Zhurkin. "Prediction of nucleosome rotational positioning in yeast and human genomes based on sequence-dependent DNA anisotropy". BMC Bioinformatics 15, n.º 1 (22 de septiembre de 2014). http://dx.doi.org/10.1186/1471-2105-15-313.

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39

Park, Bongsoo, Rasheda Khanam, Vinesh Vinayachandran, Abdullah H. Baqui, Stephanie J. London y Shyam Biswal. "Epigenetic biomarkers and preterm birth". Environmental Epigenetics 6, n.º 1 (1 de enero de 2020). http://dx.doi.org/10.1093/eep/dvaa005.

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Abstract Preterm birth (PTB) is a major public health challenge, and novel, sensitive approaches to predict PTB are still evolving. Epigenomic markers are being explored as biomarkers of PTB because of their molecular stability compared to gene expression. This approach is also relatively new compared to gene-based diagnostics, which relies on mutations or single nucleotide polymorphisms. The fundamental principle of epigenome diagnostics is that epigenetic reprogramming in the target tissue (e.g. placental tissue) might be captured by more accessible surrogate tissue (e.g. blood) using biochemical epigenome assays on circulating DNA that incorporate methylation, histone modifications, nucleosome positioning, and/or chromatin accessibility. Epigenomic-based biomarkers may hold great potential for early identification of the majority of PTBs that are not associated with genetic variants or mutations. In this review, we discuss recent advances made in the development of epigenome assays focusing on its potential exploration for association and prediction of PTB. We also summarize population-level cohort studies conducted in the USA and globally that provide opportunities for genetic and epigenetic marker development for PTB. In addition, we summarize publicly available epigenome resources and published PTB studies. We particularly focus on ongoing genome-wide DNA methylation and epigenome-wide association studies. Finally, we review the limitations of current research, the importance of establishing a comprehensive biobank, and possible directions for future studies in identifying effective epigenome biomarkers to enhance health outcomes for pregnant women at risk of PTB and their infants.
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