15,651 research outputs found
Languages of lossless seeds
Several algorithms for similarity search employ seeding techniques to quickly
discard very dissimilar regions. In this paper, we study theoretical properties
of lossless seeds, i.e., spaced seeds having full sensitivity. We prove that
lossless seeds coincide with languages of certain sofic subshifts, hence they
can be recognized by finite automata. Moreover, we show that these subshifts
are fully given by the number of allowed errors k and the seed margin l. We
also show that for a fixed k, optimal seeds must asymptotically satisfy l ~
m^(k/(k+1)).Comment: In Proceedings AFL 2014, arXiv:1405.527
Protein Sequencing with an Adaptive Genetic Algorithm from Tandem Mass Spectrometry
In Proteomics, only the de novo peptide sequencing approach allows a partial
amino acid sequence of a peptide to be found from a MS/MS spectrum. In this
article a preliminary work is presented to discover a complete protein sequence
from spectral data (MS and MS/MS spectra). For the moment, our approach only
uses MS spectra. A Genetic Algorithm (GA) has been designed with a new
evaluation function which works directly with a complete MS spectrum as input
and not with a mass list like the other methods using this kind of data. Thus
the mono isotopic peak extraction step which needs a human intervention is
deleted. The goal of this approach is to discover the sequence of unknown
proteins and to allow a better understanding of the differences between
experimental proteins and proteins from databases
A backward procedure for change-point detection with applications to copy number variation detection
Change-point detection regains much attention recently for analyzing array or
sequencing data for copy number variation (CNV) detection. In such
applications, the true signals are typically very short and buried in the long
data sequence, which makes it challenging to identify the variations
efficiently and accurately. In this article, we propose a new change-point
detection method, a backward procedure, which is not only fast and simple
enough to exploit high-dimensional data but also performs very well for
detecting short signals. Although motivated by CNV detection, the backward
procedure is generally applicable to assorted change-point problems that arise
in a variety of scientific applications. It is illustrated by both simulated
and real CNV data that the backward detection has clear advantages over other
competing methods especially when the true signal is short
Optimum Search Schemes for Approximate String Matching Using Bidirectional FM-Index
Finding approximate occurrences of a pattern in a text using a full-text
index is a central problem in bioinformatics and has been extensively
researched. Bidirectional indices have opened new possibilities in this regard
allowing the search to start from anywhere within the pattern and extend in
both directions. In particular, use of search schemes (partitioning the pattern
and searching the pieces in certain orders with given bounds on errors) can
yield significant speed-ups. However, finding optimal search schemes is a
difficult combinatorial optimization problem.
Here for the first time, we propose a mixed integer program (MIP) capable to
solve this optimization problem for Hamming distance with given number of
pieces. Our experiments show that the optimal search schemes found by our MIP
significantly improve the performance of search in bidirectional FM-index upon
previous ad-hoc solutions. For example, approximate matching of 101-bp Illumina
reads (with two errors) becomes 35 times faster than standard backtracking.
Moreover, despite being performed purely in the index, the running time of
search using our optimal schemes (for up to two errors) is comparable to the
best state-of-the-art aligners, which benefit from combining search in index
with in-text verification using dynamic programming. As a result, we anticipate
a full-fledged aligner that employs an intelligent combination of search in the
bidirectional FM-index using our optimal search schemes and in-text
verification using dynamic programming outperforms today's best aligners. The
development of such an aligner, called FAMOUS (Fast Approximate string Matching
using OptimUm search Schemes), is ongoing as our future work
Qualitative Analysis of POMDPs with Temporal Logic Specifications for Robotics Applications
We consider partially observable Markov decision processes (POMDPs), that are
a standard framework for robotics applications to model uncertainties present
in the real world, with temporal logic specifications. All temporal logic
specifications in linear-time temporal logic (LTL) can be expressed as parity
objectives. We study the qualitative analysis problem for POMDPs with parity
objectives that asks whether there is a controller (policy) to ensure that the
objective holds with probability 1 (almost-surely). While the qualitative
analysis of POMDPs with parity objectives is undecidable, recent results show
that when restricted to finite-memory policies the problem is EXPTIME-complete.
While the problem is intractable in theory, we present a practical approach to
solve the qualitative analysis problem. We designed several heuristics to deal
with the exponential complexity, and have used our implementation on a number
of well-known POMDP examples for robotics applications. Our results provide the
first practical approach to solve the qualitative analysis of robot motion
planning with LTL properties in the presence of uncertainty
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