scholarly journals An Exploration into Fern Genome Space

2015 ◽  
Vol 7 (9) ◽  
pp. 2533-2544 ◽  
Author(s):  
Paul G. Wolf ◽  
Emily B. Sessa ◽  
Daniel Blaine Marchant ◽  
Fay-Wei Li ◽  
Carl J. Rothfels ◽  
...  
Keyword(s):  
Genome Space

scholarly journals Geometric construction of viral genome space and its applications

2021 ◽  
Author(s):  
Nan Sun ◽  
Shaojun Pei ◽  
Lily He ◽  
Changchuan Yin ◽  
Rong Lucy He ◽  
...  
Keyword(s):  
Viral Genome ◽  
Geometric Construction ◽  
Genome Space

scholarly journals The fog of genetics: Known unknowns and unknown unknowns in the genetics of complex traits and diseases

2019 ◽  
Author(s):  
João Pedro de Magalhães ◽  
Jingwei Wang
Keyword(s):  
Genetic Variability ◽  
Genetic Variants ◽  
Genetic Information ◽  
Complex Traits ◽  
Drug Targets ◽  
Molecular Mechanisms ◽  
Genetic Associations ◽  
Missing Heritability ◽  
Genome Space ◽  
Known Unknowns

AbstractAssociating genetic variants with phenotypes is not only important to understand the underlying biology but also to identify potential drug targets for treating diseases. It is widely accepted that for most complex traits many associations remain to be discovered, the so-called “missing heritability.” Yet missing heritability can be estimated, it is a known unknown, and we argue is only a fraction of the unknowns in genetics. The majority of possible genetic variants in the genome space are either too rare to be detected or even entirely absent from populations, and therefore do not contribute to estimates of phenotypic or genetic variability. We call these unknown unknowns in genetics the “fog of genetics.” Using data from the 1000 Genomes Project we then show that larger genes with greater genetic diversity are more likely to be associated with human traits, demonstrating that genetic associations are biased towards particular types of genes and that the genetic information we are lacking about traits and diseases is potentially immense. Our results and model have multiple implications for how genetic variability is perceived to influence complex traits, provide insights on molecular mechanisms of disease and for drug discovery efforts based on genetic information.

Dynamic Evolution of Genomes and the Concept of Genome Space

1999 ◽  
Vol 870 (1 MOLECULAR STR) ◽  
pp. 293-300 ◽  
Author(s):  
MATTHEW I. BELLGARD ◽  
TAKESHI ITOH ◽  
HIDEMI WATANABE ◽  
TADASHI IMANISHI ◽  
TAKASHI GOJOBORI
Keyword(s):  
Dynamic Evolution ◽  
Genome Space ◽  
Evolution Of Genomes

scholarly journals Are there ergodic limits to evolution? Ergodic exploration of genome space and convergence

2015 ◽  
Vol 5 (6) ◽  
pp. 20150041 ◽  
Author(s):  
Tom C. B. McLeish
Keyword(s):  
Time Scale ◽  
Evolutionary Dynamics ◽  
Compound Eye ◽  
Biological Structure ◽  
Evolutionary Time ◽  
Structural Space ◽  
Interesting Class ◽  
Equivalent Systems ◽  
Genome Space ◽  
Time Required

We examine the analogy between evolutionary dynamics and statistical mechanics to include the fundamental question of ergodicity —the representative exploration of the space of possible states (in the case of evolution this is genome space). Several properties of evolutionary dynamics are identified that allow a generalization of the ergodic dynamics, familiar in dynamical systems theory, to evolution. Two classes of evolved biological structure then arise, differentiated by the qualitative duration of their evolutionary time scales. The first class has an ergodicity time scale (the time required for representative genome exploration) longer than available evolutionary time, and has incompletely explored the genotypic and phenotypic space of its possibilities. This case generates no expectation of convergence to an optimal phenotype or possibility of its prediction. The second, more interesting, class exhibits an evolutionary form of ergodicity—essentially all of the structural space within the constraints of slower evolutionary variables have been sampled; the ergodicity time scale for the system evolution is less than the evolutionary time. In this case, some convergence towards similar optima may be expected for equivalent systems in different species where both possess ergodic evolutionary dynamics. When the fitness maximum is set by physical, rather than co-evolved, constraints, it is additionally possible to make predictions of some properties of the evolved structures and systems. We propose four structures that emerge from evolution within genotypes whose fitness is induced from their phenotypes. Together, these result in an exponential speeding up of evolution, when compared with complete exploration of genomic space. We illustrate a possible case of application and a prediction of convergence together with attaining a physical fitness optimum in the case of invertebrate compound eye resolution.

SPECIATION FROM EVOLUTION

1999 ◽  
Vol 10 (07) ◽  
pp. 1295-1302 ◽  
Author(s):  
SCOTT MCMANUS ◽  
D. L. HUNTER ◽  
NAEEM JAN ◽  
TANE RAY ◽  
LEO MOSELEY
Keyword(s):  
New Species ◽  
Single Species ◽  
Distinct Species ◽  
Genome Space ◽  
Mutation And Selection

Evolution, based on the principles of mutation and selection, is a powerful basis for microscopic changes which can account for the evolution of a species and macroscopic speciation where there is splitting of a species into two distinct new species. We show that a single species evolves into distinct species after several generations in an unrestricted genome space.

scholarly journals Epistasis can lead to fragmented neutral spaces and contingency in evolution

2011 ◽  
Vol 279 (1734) ◽  
pp. 1777-1783 ◽  
Author(s):  
Steffen Schaper ◽  
Iain G. Johnston ◽  
Ard A. Louis
Keyword(s):  
Deleterious Mutation ◽  
Single Point ◽  
Point Mutations ◽  
Evolutionary Process ◽  
Mutational Robustness ◽  
Rna Sequences ◽  
Epistatic Interactions ◽  
Phenotypic Structure ◽  
Single Point Mutations ◽  
Genome Space

In evolution, the effects of a single deleterious mutation can sometimes be compensated for by a second mutation which recovers the original phenotype. Such epistatic interactions have implications for the structure of genome space—namely, that networks of genomes encoding the same phenotype may not be connected by single mutational moves. We use the folding of RNA sequences into secondary structures as a model genotype–phenotype map and explore the neutral spaces corresponding to networks of genotypes with the same phenotype. In most of these networks, we find that it is not possible to connect all genotypes to one another by single point mutations. Instead, a network for a phenotypic structure with n bonds typically fragments into at least 2 n neutral components, often of similar size. While components of the same network generate the same phenotype, they show important variations in their properties, most strikingly in their evolvability and mutational robustness. This heterogeneity implies contingency in the evolutionary process.

scholarly journals A Novel Method of Characterizing Genetic Sequences: Genome Space with Biological Distance and Applications

PLoS ONE ◽  
2011 ◽  
Vol 6 (3) ◽  
pp. e17293 ◽  
Author(s):  
Mo Deng ◽  
Chenglong Yu ◽  
Qian Liang ◽  
Rong L. He ◽  
Stephen S.-T. Yau
Keyword(s):  
Biological Distance ◽  
Genetic Sequences ◽  
Novel Method ◽  
Genome Space

scholarly journals 3D Genome Contributes to Protein-Protein Interactome

2020 ◽  
Author(s):  
Yi Shi ◽  
Song Cao ◽  
Mingxuan Zhang ◽  
Xianbin Su ◽  
Zehua Guo ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Interacting Proteins ◽  
Protein Protein Interactions ◽  
3D Genome ◽  
Protein Interactome ◽  
Dna Elements ◽  
Genome Space ◽  
Ppi Prediction ◽  
Cellular Compartmentalization ◽  
Genome Information

AbstractNumerous computational methods have been proposed to predict protein-protein interactions, none of which however, considers the original DNA loci of the interacting proteins in the perspective of 3D genome. Here we retrospect the DNA origins of the interacting proteins in the context of 3D genome and discovered that 1) if a gene pair is more proximate in 3D genome, their corresponding proteins are more likely to interact. 2) signal peptide involvement of PPI affects the corresponding gene-gene proximity in 3D genome space. 3) by incorporating 3D genome information, existing PPI prediction methods can be further improved in terms of accuracy. Combining our previous discoveries, we conjecture the existence of 3D genome driven cellular compartmentalization, meaning the co-localization of DNA elements lead to increased probability of the co-localization of RNA elements and protein elements.

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