75,322 research outputs found
Detection of regulator genes and eQTLs in gene networks
Genetic differences between individuals associated to quantitative phenotypic
traits, including disease states, are usually found in non-coding genomic
regions. These genetic variants are often also associated to differences in
expression levels of nearby genes (they are "expression quantitative trait
loci" or eQTLs for short) and presumably play a gene regulatory role, affecting
the status of molecular networks of interacting genes, proteins and
metabolites. Computational systems biology approaches to reconstruct causal
gene networks from large-scale omics data have therefore become essential to
understand the structure of networks controlled by eQTLs together with other
regulatory genes, and to generate detailed hypotheses about the molecular
mechanisms that lead from genotype to phenotype. Here we review the main
analytical methods and softwares to identify eQTLs and their associated genes,
to reconstruct co-expression networks and modules, to reconstruct causal
Bayesian gene and module networks, and to validate predicted networks in
silico.Comment: minor revision with typos corrected; review article; 24 pages, 2
figure
Quantitative Genetics and Functional-Structural Plant Growth Models: Simulation of Quantitative Trait Loci Detection for Model Parameters and Application to Potential Yield Optimization
Background and Aims: Prediction of phenotypic traits from new genotypes under
untested environmental conditions is crucial to build simulations of breeding
strategies to improve target traits. Although the plant response to
environmental stresses is characterized by both architectural and functional
plasticity, recent attempts to integrate biological knowledge into genetics
models have mainly concerned specific physiological processes or crop models
without architecture, and thus may prove limited when studying genotype x
environment interactions. Consequently, this paper presents a simulation study
introducing genetics into a functional-structural growth model, which gives
access to more fundamental traits for quantitative trait loci (QTL) detection
and thus to promising tools for yield optimization. Methods: The GreenLab model
was selected as a reasonable choice to link growth model parameters to QTL.
Virtual genes and virtual chromosomes were defined to build a simple genetic
model that drove the settings of the species-specific parameters of the model.
The QTL Cartographer software was used to study QTL detection of simulated
plant traits. A genetic algorithm was implemented to define the ideotype for
yield maximization based on the model parameters and the associated allelic
combination. Key Results and Conclusions: By keeping the environmental factors
constant and using a virtual population with a large number of individuals
generated by a Mendelian genetic model, results for an ideal case could be
simulated. Virtual QTL detection was compared in the case of phenotypic traits
- such as cob weight - and when traits were model parameters, and was found to
be more accurate in the latter case. The practical interest of this approach is
illustrated by calculating the parameters (and the corresponding genotype)
associated with yield optimization of a GreenLab maize model. The paper
discusses the potentials of GreenLab to represent environment x genotype
interactions, in particular through its main state variable, the ratio of
biomass supply over demand
Statistical Modeling of Epistasis and Linkage Decay using Logic Regression
Logic regression has been recognized as a tool that can identify and model non-additive genetic interactions using Boolean logic groups. Logic regression, TASSEL-GLM and SAS-GLM were compared for analytical precision using a previously characterized model system to identify the best genetic model explaining epistatic interaction for vernalization-sensitivity in barley. A genetic model containing two molecular markers identified in vernalization response in barley was selected using logic regression while both TASSEL-GLM and SAS-GLM included spurious associations in their models. The results also suggest the logic regression can be used to identify dominant/recessive relationships between epistatic alleles through its use of conjugate operators
DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences.
Modeling the properties and functions of DNA sequences is an important, but challenging task in the broad field of genomics. This task is particularly difficult for non-coding DNA, the vast majority of which is still poorly understood in terms of function. A powerful predictive model for the function of non-coding DNA can have enormous benefit for both basic science and translational research because over 98% of the human genome is non-coding and 93% of disease-associated variants lie in these regions. To address this need, we propose DanQ, a novel hybrid convolutional and bi-directional long short-term memory recurrent neural network framework for predicting non-coding function de novo from sequence. In the DanQ model, the convolution layer captures regulatory motifs, while the recurrent layer captures long-term dependencies between the motifs in order to learn a regulatory 'grammar' to improve predictions. DanQ improves considerably upon other models across several metrics. For some regulatory markers, DanQ can achieve over a 50% relative improvement in the area under the precision-recall curve metric compared to related models. We have made the source code available at the github repository http://github.com/uci-cbcl/DanQ
Perfect Sampling of the Master Equation for Gene Regulatory Networks
We present a Perfect Sampling algorithm that can be applied to the Master
Equation of Gene Regulatory Networks (GRNs). The method recasts Gillespie's
Stochastic Simulation Algorithm (SSA) in the light of Markov Chain Monte Carlo
methods and combines it with the Dominated Coupling From The Past (DCFTP)
algorithm to provide guaranteed sampling from the stationary distribution. We
show how the DCFTP-SSA can be generically applied to genetic networks with
feedback formed by the interconnection of linear enzymatic reactions and
nonlinear Monod- and Hill-type elements. We establish rigorous bounds on the
error and convergence of the DCFTP-SSA, as compared to the standard SSA,
through a set of increasingly complex examples. Once the building blocks for
GRNs have been introduced, the algorithm is applied to study properly averaged
dynamic properties of two experimentally relevant genetic networks: the toggle
switch, a two-dimensional bistable system, and the repressilator, a
six-dimensional genetic oscillator.Comment: Minor rewriting; final version to be published in Biophysical Journa
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