4,491 research outputs found
Models and information-theoretic bounds for nanopore sequencing
Nanopore sequencing is an emerging new technology for sequencing DNA, which
can read long fragments of DNA (~50,000 bases) in contrast to most current
short-read sequencing technologies which can only read hundreds of bases. While
nanopore sequencers can acquire long reads, the high error rates (20%-30%) pose
a technical challenge. In a nanopore sequencer, a DNA is migrated through a
nanopore and current variations are measured. The DNA sequence is inferred from
this observed current pattern using an algorithm called a base-caller. In this
paper, we propose a mathematical model for the "channel" from the input DNA
sequence to the observed current, and calculate bounds on the information
extraction capacity of the nanopore sequencer. This model incorporates
impairments like (non-linear) inter-symbol interference, deletions, as well as
random response. These information bounds have two-fold application: (1) The
decoding rate with a uniform input distribution can be used to calculate the
average size of the plausible list of DNA sequences given an observed current
trace. This bound can be used to benchmark existing base-calling algorithms, as
well as serving a performance objective to design better nanopores. (2) When
the nanopore sequencer is used as a reader in a DNA storage system, the storage
capacity is quantified by our bounds
Write Channel Model for Bit-Patterned Media Recording
We propose a new write channel model for bit-patterned media recording that
reflects the data dependence of write synchronization errors. It is shown that
this model accommodates both substitution-like errors and insertion-deletion
errors whose statistics are determined by an underlying channel state process.
We study information theoretic properties of the write channel model, including
the capacity, symmetric information rate, Markov-1 rate and the zero-error
capacity.Comment: 11 pages, 12 figures, journa
Gaussian Belief Propagation Based Multiuser Detection
In this work, we present a novel construction for solving the linear
multiuser detection problem using the Gaussian Belief Propagation algorithm.
Our algorithm yields an efficient, iterative and distributed implementation of
the MMSE detector. We compare our algorithm's performance to a recent result
and show an improved memory consumption, reduced computation steps and a
reduction in the number of sent messages. We prove that recent work by
Montanari et al. is an instance of our general algorithm, providing new
convergence results for both algorithms.Comment: 6 pages, 1 figures, appeared in the 2008 IEEE International Symposium
on Information Theory, Toronto, July 200
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