42,230 research outputs found
The Parallelism Motifs of Genomic Data Analysis
Genomic data sets are growing dramatically as the cost of sequencing
continues to decline and small sequencing devices become available. Enormous
community databases store and share this data with the research community, but
some of these genomic data analysis problems require large scale computational
platforms to meet both the memory and computational requirements. These
applications differ from scientific simulations that dominate the workload on
high end parallel systems today and place different requirements on programming
support, software libraries, and parallel architectural design. For example,
they involve irregular communication patterns such as asynchronous updates to
shared data structures. We consider several problems in high performance
genomics analysis, including alignment, profiling, clustering, and assembly for
both single genomes and metagenomes. We identify some of the common
computational patterns or motifs that help inform parallelization strategies
and compare our motifs to some of the established lists, arguing that at least
two key patterns, sorting and hashing, are missing
Spectral Sequence Motif Discovery
Sequence discovery tools play a central role in several fields of
computational biology. In the framework of Transcription Factor binding
studies, motif finding algorithms of increasingly high performance are required
to process the big datasets produced by new high-throughput sequencing
technologies. Most existing algorithms are computationally demanding and often
cannot support the large size of new experimental data. We present a new motif
discovery algorithm that is built on a recent machine learning technique,
referred to as Method of Moments. Based on spectral decompositions, this method
is robust under model misspecification and is not prone to locally optimal
solutions. We obtain an algorithm that is extremely fast and designed for the
analysis of big sequencing data. In a few minutes, we can process datasets of
hundreds of thousand sequences and extract motif profiles that match those
computed by various state-of-the-art algorithms.Comment: 20 pages, 3 figures, 1 tabl
Efficient Algorithms for the Closest Pair Problem and Applications
The closest pair problem (CPP) is one of the well studied and fundamental
problems in computing. Given a set of points in a metric space, the problem is
to identify the pair of closest points. Another closely related problem is the
fixed radius nearest neighbors problem (FRNNP). Given a set of points and a
radius , the problem is, for every input point , to identify all the
other input points that are within a distance of from . A naive
deterministic algorithm can solve these problems in quadratic time. CPP as well
as FRNNP play a vital role in computational biology, computational finance,
share market analysis, weather prediction, entomology, electro cardiograph,
N-body simulations, molecular simulations, etc. As a result, any improvements
made in solving CPP and FRNNP will have immediate implications for the solution
of numerous problems in these domains. We live in an era of big data and
processing these data take large amounts of time. Speeding up data processing
algorithms is thus much more essential now than ever before. In this paper we
present algorithms for CPP and FRNNP that improve (in theory and/or practice)
the best-known algorithms reported in the literature for CPP and FRNNP. These
algorithms also improve the best-known algorithms for related applications
including time series motif mining and the two locus problem in Genome Wide
Association Studies (GWAS)
Delay Parameter Selection in Permutation Entropy Using Topological Data Analysis
Permutation Entropy (PE) is a powerful tool for quantifying the
predictability of a sequence which includes measuring the regularity of a time
series. Despite its successful application in a variety of scientific domains,
PE requires a judicious choice of the delay parameter . While another
parameter of interest in PE is the motif dimension , Typically is
selected between and with or giving optimal results for the
majority of systems. Therefore, in this work we focus solely on choosing the
delay parameter. Selecting is often accomplished using trial and error
guided by the expertise of domain scientists. However, in this paper, we show
that persistent homology, the flag ship tool from Topological Data Analysis
(TDA) toolset, provides an approach for the automatic selection of . We
evaluate the successful identification of a suitable from our TDA-based
approach by comparing our results to a variety of examples in published
literature
Probabilistic Approach to Structural Change Prediction in Evolving Social Networks
We propose a predictive model of structural
changes in elementary subgraphs of social network based on
Mixture of Markov Chains. The model is trained and verified
on a dataset from a large corporate social network analyzed
in short, one day-long time windows, and reveals distinctive
patterns of evolution of connections on the level of local
network topology. We argue that the network investigated in
such short timescales is highly dynamic and therefore immune
to classic methods of link prediction and structural analysis,
and show that in the case of complex networks, the dynamic
subgraph mining may lead to better prediction accuracy. The
experiments were carried out on the logs from the Wroclaw
University of Technology mail server
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