50,841 research outputs found
Web-enabled knowledge-based analysis of genetic data
We present a web-based implementation of GenePath, an intelligent assistant tool for data analysis in functional genomics. GenePath considers mutant data and uses expert-defined patterns to find gene-to-gene or gene-to-outcome relations. It presents the results of analysis as genetic networks, wherein a set of genes has various influence on one another and on a biological outcome. In the paper, we particularly focus on its web-based interface and explanation mechanisms
Population genetic data for 17 Y STR markers from Benghazi (East Libya)
The seventeen Y-STR loci included in the AmpF‘STR1 YfilerTM PCR Amplification kit (DYS19, DYS389I,DYS389II, DYS390, DYS391, DYS392, DYS393, DYS385a/b, DYS438, DYS439, DYS437, DYS448, DYS458,DYS456, DYS635, and Y-GATA-H4) were used to type a sample population of 238 males from eastern Libya (Benghazi region). Of 238 observed haplotypes, 214 were unique (90%) and 24 (10%) were found more than once. The 17 loci gave a discriminating power of 0.999. DYS458 showed the highest diversity as a single-locus marker (0.73). Allelic frequencies and gene diversities for each Y-STR locus were determined. The high haplotype diversity and discrimination capacity (0.996) demonstrate the utility of
these loci for human identification in forensic applications. Comparative analysis with Y-STR datasets of
relevant populations and submission of the haplotypes to the Y-STR Haplotype Reference Database (YHRD) was undertaken
A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks
An explosion of high-throughput DNA sequencing in the past decade has led to
a surge of interest in population-scale inference with whole-genome data.
Recent work in population genetics has centered on designing inference methods
for relatively simple model classes, and few scalable general-purpose inference
techniques exist for more realistic, complex models. To achieve this, two
inferential challenges need to be addressed: (1) population data are
exchangeable, calling for methods that efficiently exploit the symmetries of
the data, and (2) computing likelihoods is intractable as it requires
integrating over a set of correlated, extremely high-dimensional latent
variables. These challenges are traditionally tackled by likelihood-free
methods that use scientific simulators to generate datasets and reduce them to
hand-designed, permutation-invariant summary statistics, often leading to
inaccurate inference. In this work, we develop an exchangeable neural network
that performs summary statistic-free, likelihood-free inference. Our framework
can be applied in a black-box fashion across a variety of simulation-based
tasks, both within and outside biology. We demonstrate the power of our
approach on the recombination hotspot testing problem, outperforming the
state-of-the-art.Comment: 9 pages, 8 figure
A Bayesian inference framework to reconstruct transmission trees using epidemiological and genetic data
The accurate identification of the route of transmission taken by an infectious agent through a host population is critical to understanding its epidemiology and informing measures for its control. However, reconstruction of transmission routes during an epidemic is often an underdetermined problem: data about the location and timings of infections can be incomplete, inaccurate, and compatible with a large number of different transmission scenarios. For fast-evolving pathogens like RNA viruses, inference can be strengthened by using genetic data, nowadays easily and affordably generated. However, significant statistical challenges remain to be overcome in the full integration of these different data types if transmission trees are to be reliably estimated. We present here a framework leading to a bayesian inference scheme that combines genetic and epidemiological data, able to reconstruct most likely transmission patterns and infection dates. After testing our approach with simulated data, we apply the method to two UK epidemics of Foot-and-Mouth Disease Virus (FMDV): the 2007 outbreak, and a subset of the large 2001 epidemic. In the first case, we are able to confirm the role of a specific premise as the link between the two phases of the epidemics, while transmissions more densely clustered in space and time remain harder to resolve. When we consider data collected from the 2001 epidemic during a time of national emergency, our inference scheme robustly infers transmission chains, and uncovers the presence of undetected premises, thus providing a useful tool for epidemiological studies in real time. The generation of genetic data is becoming routine in epidemiological investigations, but the development of analytical tools maximizing the value of these data remains a priority. Our method, while applied here in the context of FMDV, is general and with slight modification can be used in any situation where both spatiotemporal and genetic data are available
A novel spectral method for inferring general diploid selection from time series genetic data
The increased availability of time series genetic variation data from
experimental evolution studies and ancient DNA samples has created new
opportunities to identify genomic regions under selective pressure and to
estimate their associated fitness parameters. However, it is a challenging
problem to compute the likelihood of nonneutral models for the population
allele frequency dynamics, given the observed temporal DNA data. Here, we
develop a novel spectral algorithm to analytically and efficiently integrate
over all possible frequency trajectories between consecutive time points. This
advance circumvents the limitations of existing methods which require
fine-tuning the discretization of the population allele frequency space when
numerically approximating requisite integrals. Furthermore, our method is
flexible enough to handle general diploid models of selection where the
heterozygote and homozygote fitness parameters can take any values, while
previous methods focused on only a few restricted models of selection. We
demonstrate the utility of our method on simulated data and also apply it to
analyze ancient DNA data from genetic loci associated with coat coloration in
horses. In contrast to previous studies, our exploration of the full fitness
parameter space reveals that a heterozygote advantage form of balancing
selection may have been acting on these loci.Comment: Published in at http://dx.doi.org/10.1214/14-AOAS764 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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