6,154 research outputs found
Ensemble Analysis of Adaptive Compressed Genome Sequencing Strategies
Acquiring genomes at single-cell resolution has many applications such as in
the study of microbiota. However, deep sequencing and assembly of all of
millions of cells in a sample is prohibitively costly. A property that can come
to rescue is that deep sequencing of every cell should not be necessary to
capture all distinct genomes, as the majority of cells are biological
replicates. Biologically important samples are often sparse in that sense. In
this paper, we propose an adaptive compressed method, also known as distilled
sensing, to capture all distinct genomes in a sparse microbial community with
reduced sequencing effort. As opposed to group testing in which the number of
distinct events is often constant and sparsity is equivalent to rarity of an
event, sparsity in our case means scarcity of distinct events in comparison to
the data size. Previously, we introduced the problem and proposed a distilled
sensing solution based on the breadth first search strategy. We simulated the
whole process which constrained our ability to study the behavior of the
algorithm for the entire ensemble due to its computational intensity. In this
paper, we modify our previous breadth first search strategy and introduce the
depth first search strategy. Instead of simulating the entire process, which is
intractable for a large number of experiments, we provide a dynamic programming
algorithm to analyze the behavior of the method for the entire ensemble. The
ensemble analysis algorithm recursively calculates the probability of capturing
every distinct genome and also the expected total sequenced nucleotides for a
given population profile. Our results suggest that the expected total sequenced
nucleotides grows proportional to of the number of cells and
proportional linearly with the number of distinct genomes
Sparse Iterative Learning Control with Application to a Wafer Stage: Achieving Performance, Resource Efficiency, and Task Flexibility
Trial-varying disturbances are a key concern in Iterative Learning Control
(ILC) and may lead to inefficient and expensive implementations and severe
performance deterioration. The aim of this paper is to develop a general
framework for optimization-based ILC that allows for enforcing additional
structure, including sparsity. The proposed method enforces sparsity in a
generalized setting through convex relaxations using norms. The
proposed ILC framework is applied to the optimization of sampling sequences for
resource efficient implementation, trial-varying disturbance attenuation, and
basis function selection. The framework has a large potential in control
applications such as mechatronics, as is confirmed through an application on a
wafer stage.Comment: 12 pages, 14 figure
Bacterial Community Reconstruction Using A Single Sequencing Reaction
Bacteria are the unseen majority on our planet, with millions of species and
comprising most of the living protoplasm. While current methods enable in-depth
study of a small number of communities, a simple tool for breadth studies of
bacterial population composition in a large number of samples is lacking. We
propose a novel approach for reconstruction of the composition of an unknown
mixture of bacteria using a single Sanger-sequencing reaction of the mixture.
This method is based on compressive sensing theory, which deals with
reconstruction of a sparse signal using a small number of measurements.
Utilizing the fact that in many cases each bacterial community is comprised of
a small subset of the known bacterial species, we show the feasibility of this
approach for determining the composition of a bacterial mixture. Using
simulations, we show that sequencing a few hundred base-pairs of the 16S rRNA
gene sequence may provide enough information for reconstruction of mixtures
containing tens of species, out of tens of thousands, even in the presence of
realistic measurement noise. Finally, we show initial promising results when
applying our method for the reconstruction of a toy experimental mixture with
five species. Our approach may have a potential for a practical and efficient
way for identifying bacterial species compositions in biological samples.Comment: 28 pages, 12 figure
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