46,634 research outputs found
Linking de novo assembly results with long DNA reads by dnaasm-link application
Currently, third-generation sequencing techniques, which allow to obtain much
longer DNA reads compared to the next-generation sequencing technologies, are
becoming more and more popular. There are many possibilities to combine data
from next-generation and third-generation sequencing.
Herein, we present a new application called dnaasm-link for linking contigs,
a result of \textit{de novo} assembly of second-generation sequencing data,
with long DNA reads. Our tool includes an integrated module to fill gaps with a
suitable fragment of appropriate long DNA read, which improves the consistency
of the resulting DNA sequences. This feature is very important, in particular
for complex DNA regions, as presented in the paper. Finally, our implementation
outperforms other state-of-the-art tools in terms of speed and memory
requirements, which may enable the usage of the presented application for
organisms with a large genome, which is not possible in~existing applications.
The presented application has many advantages as (i) significant memory
optimization and reduction of computation time (ii) filling the gaps through
the appropriate fragment of a specified long DNA read (iii) reducing number of
spanned and unspanned gaps in the existing genome drafts.
The application is freely available to all users under GNU Library or Lesser
General Public License version 3.0 (LGPLv3). The demo application, docker image
and source code are available at http://dnaasm.sourceforge.net.Comment: 16 pages, 5 figure
Interests Diffusion in Social Networks
Understanding cultural phenomena on Social Networks (SNs) and exploiting the
implicit knowledge about their members is attracting the interest of different
research communities both from the academic and the business side. The
community of complexity science is devoting significant efforts to define laws,
models, and theories, which, based on acquired knowledge, are able to predict
future observations (e.g. success of a product). In the mean time, the semantic
web community aims at engineering a new generation of advanced services by
defining constructs, models and methods, adding a semantic layer to SNs. In
this context, a leapfrog is expected to come from a hybrid approach merging the
disciplines above. Along this line, this work focuses on the propagation of
individual interests in social networks. The proposed framework consists of the
following main components: a method to gather information about the members of
the social networks; methods to perform some semantic analysis of the Domain of
Interest; a procedure to infer members' interests; and an interests evolution
theory to predict how the interests propagate in the network. As a result, one
achieves an analytic tool to measure individual features, such as members'
susceptibilities and authorities. Although the approach applies to any type of
social network, here it is has been tested against the computer science
research community.
The DBLP (Digital Bibliography and Library Project) database has been elected
as test-case since it provides the most comprehensive list of scientific
production in this field.Comment: 30 pages 13 figs 4 table
Complex networks and public funding: the case of the 2007-2013 Italian program
In this paper we apply techniques of complex network analysis to data sources
representing public funding programs and discuss the importance of the
considered indicators for program evaluation. Starting from the Open Data
repository of the 2007-2013 Italian Program Programma Operativo Nazionale
'Ricerca e Competitivit\`a' (PON R&C), we build a set of data models and
perform network analysis over them. We discuss the obtained experimental
results outlining interesting new perspectives that emerge from the application
of the proposed methods to the socio-economical evaluation of funded programs.Comment: 22 pages, 9 figure
Finding statistics and newspapers workbook
This resource supports the Understanding Educational Enquiry module DET050 of the PG Cert at the University of Birmingham. It details how to find statistics and newspapers, and could be used by participants as part of their action research. It also stands alone for any user wanting to find statistics or newspapers for their studies. It outlines resources and guidance specifically for the University of Birmingham, but it may be adapted for use elsewhere
Network Kriging
Network service providers and customers are often concerned with aggregate
performance measures that span multiple network paths. Unfortunately, forming
such network-wide measures can be difficult, due to the issues of scale
involved. In particular, the number of paths grows too rapidly with the number
of endpoints to make exhaustive measurement practical. As a result, it is of
interest to explore the feasibility of methods that dramatically reduce the
number of paths measured in such situations while maintaining acceptable
accuracy.
We cast the problem as one of statistical prediction--in the spirit of the
so-called `kriging' problem in spatial statistics--and show that end-to-end
network properties may be accurately predicted in many cases using a
surprisingly small set of carefully chosen paths. More precisely, we formulate
a general framework for the prediction problem, propose a class of linear
predictors for standard quantities of interest (e.g., averages, totals,
differences) and show that linear algebraic methods of subset selection may be
used to effectively choose which paths to measure. We characterize the
performance of the resulting methods, both analytically and numerically. The
success of our methods derives from the low effective rank of routing matrices
as encountered in practice, which appears to be a new observation in its own
right with potentially broad implications on network measurement generally.Comment: 16 pages, 9 figures, single-space
Using data network metrics, graphics, and topology to explore network characteristics
Yehuda Vardi introduced the term network tomography and was the first to
propose and study how statistical inverse methods could be adapted to attack
important network problems (Vardi, 1996). More recently, in one of his final
papers, Vardi proposed notions of metrics on networks to define and measure
distances between a network's links, its paths, and also between different
networks (Vardi, 2004). In this paper, we apply Vardi's general approach for
network metrics to a real data network by using data obtained from special data
network tools and testing procedures presented here. We illustrate how the
metrics help explicate interesting features of the traffic characteristics on
the network. We also adapt the metrics in order to condition on traffic passing
through a portion of the network, such as a router or pair of routers, and show
further how this approach helps to discover and explain interesting network
characteristics.Comment: Published at http://dx.doi.org/10.1214/074921707000000058 in the IMS
Lecture Notes Monograph Series
(http://www.imstat.org/publications/lecnotes.htm) by the Institute of
Mathematical Statistics (http://www.imstat.org
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