1,553 research outputs found
SNPSTR: a database of compound microsatellite-SNP markers
There has been widespread and growing interest in genetic markers suitable for drawing population genetic inferences about past demographic events and to detect the effects of selection. In addition to single nucleotide polymorphisms (SNPs), microsatellites (or short tandem repeats, STRs) have received great attention in the analysis of human population history. In the SNPSTR database () we catalogue a relatively new type of compound genetic marker called SNPSTR which combines a microsatellite marker (STR) with one or more tightly linked SNPs. Here, the SNP(s) and the microsatellite are less than 250 bp apart so each SNPSTR can be considered a small haplotype with no recombination occurring between the two individual markers. Thus, SNPSTRs have the potential to become a very useful tool in the field of population genetics. The SNPSTR database contains all inferable human SNPSTRs as well as those in mouse, rat, dog and chicken, i.e. all model organisms for which extensive SNP datasets are available
Inference of Temporally Varying Bayesian Networks
When analysing gene expression time series data an often overlooked but
crucial aspect of the model is that the regulatory network structure may change
over time. Whilst some approaches have addressed this problem previously in the
literature, many are not well suited to the sequential nature of the data. Here
we present a method that allows us to infer regulatory network structures that
may vary between time points, utilising a set of hidden states that describe
the network structure at a given time point. To model the distribution of the
hidden states we have applied the Hierarchical Dirichlet Process Hideen Markov
Model, a nonparametric extension of the traditional Hidden Markov Model, that
does not require us to fix the number of hidden states in advance. We apply our
method to exisiting microarray expression data as well as demonstrating is
efficacy on simulated test data
Finite-temperature hole dynamics in the t-J model: Exact results for high dimensions
We discuss the dynamics of a single hole in the t-J model at finite
temperature, in the limit of large spatial dimensions. The problem is shown to
yield a simple and physically transparent solution, that exemplifies the
continuous thermal evolution of the underlying string picture from the T=0
string-pinned limit through to the paramagnetic phase.Comment: 6 pages, including 2 figure
Probability Models for Degree Distributions of Protein Interaction Networks
The degree distribution of many biological and technological networks has
been described as a power-law distribution. While the degree distribution does
not capture all aspects of a network, it has often been suggested that its
functional form contains important clues as to underlying evolutionary
processes that have shaped the network. Generally, the functional form for the
degree distribution has been determined in an ad-hoc fashion, with clear
power-law like behaviour often only extending over a limited range of
connectivities. Here we apply formal model selection techniques to decide which
probability distribution best describes the degree distributions of protein
interaction networks. Contrary to previous studies this well defined approach
suggests that the degree distribution of many molecular networks is often
better described by distributions other than the popular power-law
distribution. This, in turn, suggests that simple, if elegant, models may not
necessarily help in the quantitative understanding of complex biological
processes.
Sampling properties of random graphs: the degree distribution
We discuss two sampling schemes for selecting random subnets from a network:
Random sampling and connectivity dependent sampling, and investigate how the
degree distribution of a node in the network is affected by the two types of
sampling. Here we derive a necessary and sufficient condition that guarantees
that the degree distribution of the subnet and the true network belong to the
same family of probability distributions. For completely random sampling of
nodes we find that this condition is fulfilled by classical random graphs; for
the vast majority of networks this condition will, however, not be met. We
furthermore discuss the case where the probability of sampling a node depends
on the degree of a node and we find that even classical random graphs are no
longer closed under this sampling regime. We conclude by relating the results
to real {\it E.coli} protein interaction network data.Comment: accepted for publication in Phys.Rev.
Towards a first-principles theory of surface thermodynamics and kinetics
Understanding of the complex behavior of particles at surfaces requires
detailed knowledge of both macroscopic and microscopic processes that take
place; also certain processes depend critically on temperature and gas
pressure. To link these processes we combine state-of-the-art microscopic, and
macroscopic phenomenological, theories. We apply our theory to the O/Ru(0001)
system and calculate thermal desorption spectra, heat of adsorption, and the
surface phase diagram. The agreement with experiment provides validity for our
approach which thus identifies the way for a predictive simulation of surface
thermodynamics and kinetics.Comment: 4 pages including 3 figures. Related publications can be found at
http://www.fhi-berlin.mpg.de/th/paper.htm
- …