59,485 research outputs found
Social Fingerprinting: detection of spambot groups through DNA-inspired behavioral modeling
Spambot detection in online social networks is a long-lasting challenge
involving the study and design of detection techniques capable of efficiently
identifying ever-evolving spammers. Recently, a new wave of social spambots has
emerged, with advanced human-like characteristics that allow them to go
undetected even by current state-of-the-art algorithms. In this paper, we show
that efficient spambots detection can be achieved via an in-depth analysis of
their collective behaviors exploiting the digital DNA technique for modeling
the behaviors of social network users. Inspired by its biological counterpart,
in the digital DNA representation the behavioral lifetime of a digital account
is encoded in a sequence of characters. Then, we define a similarity measure
for such digital DNA sequences. We build upon digital DNA and the similarity
between groups of users to characterize both genuine accounts and spambots.
Leveraging such characterization, we design the Social Fingerprinting
technique, which is able to discriminate among spambots and genuine accounts in
both a supervised and an unsupervised fashion. We finally evaluate the
effectiveness of Social Fingerprinting and we compare it with three
state-of-the-art detection algorithms. Among the peculiarities of our approach
is the possibility to apply off-the-shelf DNA analysis techniques to study
online users behaviors and to efficiently rely on a limited number of
lightweight account characteristics
Fundamental principles in drawing inference from sequence analysis
Individual life courses are dynamic and can be represented as a sequence of states for some portion of their experiences. More generally, study of such sequences has been made in many fields around social science; for example, sociology, linguistics, psychology, and the conceptualisation of subjects progressing through a sequence of states is common. However, many models and sets of data allow only for the treatment of aggregates or transitions, rather than interpreting whole sequences. The temporal aspect of the analysis is fundamental to any inference about the evolution of the subjects but assumptions about time are not normally made explicit. Moreover, without a clear idea of what sequences look like, it is impossible to determine when something is not seen whether it was not actually there. Some principles are proposed which link the ideas of sequences, hypothesis, analytical framework, categorisation and representation; each one being underpinned by the consideration of time. To make inferences about sequences, one needs to: understand what these sequences represent; the hypothesis and assumptions that can be derived about sequences; identify the categories within the sequences; and data representation at each stage. These ideas are obvious in themselves but they are interlinked, imposing restrictions on each other and on the inferences which can be draw
Back-translation for discovering distant protein homologies
Frameshift mutations in protein-coding DNA sequences produce a drastic change
in the resulting protein sequence, which prevents classic protein alignment
methods from revealing the proteins' common origin. Moreover, when a large
number of substitutions are additionally involved in the divergence, the
homology detection becomes difficult even at the DNA level. To cope with this
situation, we propose a novel method to infer distant homology relations of two
proteins, that accounts for frameshift and point mutations that may have
affected the coding sequences. We design a dynamic programming alignment
algorithm over memory-efficient graph representations of the complete set of
putative DNA sequences of each protein, with the goal of determining the two
putative DNA sequences which have the best scoring alignment under a powerful
scoring system designed to reflect the most probable evolutionary process. This
allows us to uncover evolutionary information that is not captured by
traditional alignment methods, which is confirmed by biologically significant
examples.Comment: The 9th International Workshop in Algorithms in Bioinformatics
(WABI), Philadelphia : \'Etats-Unis d'Am\'erique (2009
Information profiles for DNA pattern discovery
Finite-context modeling is a powerful tool for compressing and hence for
representing DNA sequences. We describe an algorithm to detect genomic
regularities, within a blind discovery strategy. The algorithm uses information
profiles built using suitable combinations of finite-context models. We used
the genome of the fission yeast Schizosaccharomyces pombe strain 972 h- for
illustration, unveilling locations of low information content, which are
usually associated with DNA regions of potential biological interest.Comment: Full version of DCC 2014 paper "Information profiles for DNA pattern
discovery
Extending colonic mucosal microbiome analysis - Assessment of colonic lavage as a proxy for endoscopic colonic biopsies
This study was supported through GI Research funds and MRC Grant Ref: MR/M00533X/1 to GH.Peer reviewedPublisher PD
A MOSAIC of methods: Improving ortholog detection through integration of algorithmic diversity
Ortholog detection (OD) is a critical step for comparative genomic analysis
of protein-coding sequences. In this paper, we begin with a comprehensive
comparison of four popular, methodologically diverse OD methods: MultiParanoid,
Blat, Multiz, and OMA. In head-to-head comparisons, these methods are shown to
significantly outperform one another 12-30% of the time. This high
complementarity motivates the presentation of the first tool for integrating
methodologically diverse OD methods. We term this program MOSAIC, or Multiple
Orthologous Sequence Analysis and Integration by Cluster optimization. Relative
to component and competing methods, we demonstrate that MOSAIC more than
quintuples the number of alignments for which all species are present, while
simultaneously maintaining or improving functional-, phylogenetic-, and
sequence identity-based measures of ortholog quality. Further, we demonstrate
that this improvement in alignment quality yields 40-280% more confidently
aligned sites. Combined, these factors translate to higher estimated levels of
overall conservation, while at the same time allowing for the detection of up
to 180% more positively selected sites. MOSAIC is available as python package.
MOSAIC alignments, source code, and full documentation are available at
http://pythonhosted.org/bio-MOSAIC
Understanding diversity of human innate immunity receptors: analysis of surface features of leucine-rich repeat domains in NLRs and TLRs.
BackgroundThe human innate immune system uses a system of extracellular Toll-like receptors (TLRs) and intracellular Nod-like receptors (NLRs) to match the appropriate level of immune response to the level of threat from the current environment. Almost all NLRs and TLRs have a domain consisting of multiple leucine-rich repeats (LRRs), which is believed to be involved in ligand binding. LRRs, found also in thousands of other proteins, form a well-defined "horseshoe"-shaped structural scaffold that can be used for a variety of functions, from binding specific ligands to performing a general structural role. The specific functional roles of LRR domains in NLRs and TLRs are thus defined by their detailed surface features. While experimental crystal structures of four human TLRs have been solved, no structure data are available for NLRs.ResultsWe report a quantitative, comparative analysis of the surface features of LRR domains in human NLRs and TLRs, using predicted three-dimensional structures for NLRs. Specifically, we calculated amino acid hydrophobicity, charge, and glycosylation distributions within LRR domain surfaces and assessed their similarity by clustering. Despite differences in structural and genomic organization, comparison of LRR surface features in NLRs and TLRs allowed us to hypothesize about their possible functional similarities. We find agreement between predicted surface similarities and similar functional roles in NLRs and TLRs with known agonists, and suggest possible binding partners for uncharacterized NLRs.ConclusionDespite its low resolution, our approach permits comparison of molecular surface features in the absence of crystal structure data. Our results illustrate diversity of surface features of innate immunity receptors and provide hints for function of NLRs whose specific role in innate immunity is yet unknown
The impact of different DNA extraction kits and laboratories upon the assessment of human gut microbiota composition by 16S rRNA gene sequencing
Peer reviewedPublisher PD
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