21,650 research outputs found
A Fast Reconstruction Algorithm for Gene Networks
This paper deals with gene networks whose dynamics is assumed to be generated
by a continuous-time, linear, time invariant, finite dimensional system (LTI)
at steady state. In particular, we deal with the problem of network
reconstruction in the typical practical situation in which the number of
available data is largely insufficient to uniquely determine the network. In
order to try to remove this ambiguity, we will exploit the biologically a
priori assumption of network sparseness, and propose a new algorithm for
network reconstruction having a very low computational complexity (linear in
the number of genes) so to be able to deal also with very large networks (say,
thousands of genes). Its performances are also tested both on artificial data
(generated with linear models) and on real data obtained by Gardner et al. from
the SOS pathway in Escherichia coli.Comment: 12 pages, 3 figure
Inferring Regulatory Networks by Combining Perturbation Screens and Steady State Gene Expression Profiles
Reconstructing transcriptional regulatory networks is an important task in
functional genomics. Data obtained from experiments that perturb genes by
knockouts or RNA interference contain useful information for addressing this
reconstruction problem. However, such data can be limited in size and/or are
expensive to acquire. On the other hand, observational data of the organism in
steady state (e.g. wild-type) are more readily available, but their
informational content is inadequate for the task at hand. We develop a
computational approach to appropriately utilize both data sources for
estimating a regulatory network. The proposed approach is based on a three-step
algorithm to estimate the underlying directed but cyclic network, that uses as
input both perturbation screens and steady state gene expression data. In the
first step, the algorithm determines causal orderings of the genes that are
consistent with the perturbation data, by combining an exhaustive search method
with a fast heuristic that in turn couples a Monte Carlo technique with a fast
search algorithm. In the second step, for each obtained causal ordering, a
regulatory network is estimated using a penalized likelihood based method,
while in the third step a consensus network is constructed from the highest
scored ones. Extensive computational experiments show that the algorithm
performs well in reconstructing the underlying network and clearly outperforms
competing approaches that rely only on a single data source. Further, it is
established that the algorithm produces a consistent estimate of the regulatory
network.Comment: 24 pages, 4 figures, 6 table
Essential guidelines for computational method benchmarking
In computational biology and other sciences, researchers are frequently faced
with a choice between several computational methods for performing data
analyses. Benchmarking studies aim to rigorously compare the performance of
different methods using well-characterized benchmark datasets, to determine the
strengths of each method or to provide recommendations regarding suitable
choices of methods for an analysis. However, benchmarking studies must be
carefully designed and implemented to provide accurate, unbiased, and
informative results. Here, we summarize key practical guidelines and
recommendations for performing high-quality benchmarking analyses, based on our
experiences in computational biology.Comment: Minor update
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