1,721,069 research outputs found
Mining genetic, transcriptomic, and imaging data in Parkinson’s disease
Full text linkParkinson’s disease (PD) is a brain disorder that leads to shaking, stiffness and difficulties with walking, balance, and coordination. Affected people may also have mental and behavioral changes, sleep problems, depression, memory difficulties and fatigue. PD is an age-related disease, with an increased prevalence in populations of subjects over the age of 60. About 5 to 10% of PD patients have an "early-onset" variant and it is often, but not always, inherited. PD is characterized by the loss of groups of neurons involved in the control of voluntary movements. Here we present a novel imaging-genetics workflow on Parkinson’s disease aimed to discover some new potential candidate biomarkers for Parkinson’s disease onset, by interpolating genotyping, transcriptomic, functional (Dopamine Transporter Scan) and morphological (Magnetic Resonance Imaging) imaging data. The proposed tutorial has the aim to encourage and stimulate the attendees on the biomedical research with the advantage of integration of heterogenous data. In the last decade the use of images together with genetics data has become widespread among the bioinformatics researchers. This has allowed to inspect and investigate in detail different specific diseases, to better understand their origin and cause. While in recent years many imaging genetics analyses have been developed and successfully applied to characterize brain functioning and neurodegenerative diseases such as Alzheimer’s disease, to our knowledge, no standard imaging genetics workflow has been proposed for PD. The novelty of our workflow can be summarized as follows: • We propose a domain free and easy-to-use workflow, integrating heterogenous data, such as genotyping, transcriptomic, and imaging data. • The workflow addresses the complexity of integrating real multi-source data when a limited number of data are available by proposing three step-based method, where the first step integrates genotyping and imaging features considering each feature individually, the second step summarizes imaging features in a single measure, and the last step focuses on linking potential functional effects caused by the biomarkers found during the two previous phases. • We propose a validation of the method on genetic and imaging data related to PD, showing our new results. The data used for this tutorial were obtained from the Parkinson’s Progression Marker Initiative (PPMI) data portal. Currently, PPMI is the most complete and comprehensive collection of PD-related data. The dataset that will be used in the tutorial consists in a set of polymorphisms, more specifically insertions and deletions (indels) or Single Nucleotide Polymorphisms (SNPs), and transcriptomic data retrieved by RNA sequencing. In addition, DaTSCAN and MRI data are used, which have been shown to be effective in providing potential biomarkers for PD onset and progression. The attendees will acquire an experience on how to conduct a complete imaging-genetics workflow, in a specific case study of Parkinsonian subjects. After the tutorial session the attendees will be able to conduct themselves an imaging-genetics pipeline, which could also be applied to study other neurological diseases. The tutorial will introduce the partecipants to the biological background, especially with the notion of DNA, RNA, Single-nucleotide polymorphism (SNP) and Genome-Wide Association Study (GWAS). The participants will have the opportunity to get familiar with PLINK, a free, open-source whole genome association analysis toolset, designed to perform a range of basic, large-scale analyzes in a computationally efficient manner. It provides a large range of functionalities designed for data management, summary statistics, quality control, population stratification detection, association analysis, etc. for genotyping data analysis. The audience will also learn how to run code on the widely used R programming environment for statistical computing and graphics. They will also learn some notions about Python, especially how to deal efficiently, with genotyping data using Pandas library, which was designed for data manipulation and analysis. The tutorial code is wrapped in different Jupyter notebooks (formerly IPython Notebooks), that is a web-based and system-independent interactive computational environment for easy analysis reproducibility
Network propagation of rare variants in Alzheimer's disease reveals tissue-specific hub genes and communities
Full text linkState-of-the-art rare variant association testing methods aggregate the contribution of rare variants in biologically relevant genomic regions to boost statistical power. However, testing single genes separately does not consider the complex interaction landscape of genes, nor the downstream effects of non-synonymous variants on protein structure and function. Here we present the NETwork Propagation-based Assessment of Genetic Events (NETPAGE), an integrative approach aimed at investigating the biological pathways through which rare variation results in complex disease phenotypes. We applied NETPAGE to sporadic, late-onset Alzheimer's disease (AD), using whole-genome sequencing from the AD Neuroimaging Initiative (ADNI) cohort, as well as whole-exome sequencing from the AD Sequencing Project (ADSP). NETPAGE is based on network propagation, a framework that models information flow on a graph and simulates the percolation of genetic variation through tissue-specific gene interaction networks. The result of network propagation is a set of smoothed gene scores that can be tested for association with disease status through sparse regression. The application of NETPAGE to AD enabled the identification of a set of connected genes whose smoothed variation profile was robustly associated to case-control status, based on gene interactions in the hippocampus. Additionally, smoothed scores significantly correlated with risk of conversion to AD in Mild Cognitive Impairment (MCI) subjects. Lastly, we investigated tissue-specific transcriptional dysregulation of the core genes in two independent RNA-seq datasets, as well as significant enrichments in terms of gene sets with known connections to AD. We present a framework that enables enhanced genetic association testing for a wide range of traits, diseases, and sample sizes
Multi view based imaging genetics analysis on Parkinson disease
Full text linkLongitudinal studies integrating imaging and genetic data have recently become widespread among bioinformatics researchers. Combining such heterogeneous data allows a better understanding of complex diseases origins and causes. Through a multi-view based workflow proposal, we show the common steps and tools used in imaging genetics analysis, interpolating genotyping, neuroimaging and transcriptomic data. We describe the advantages of existing methods to analyze heterogeneous datasets, using Parkinson’s Disease (PD) as a case study. Parkinson's disease is associated with both genetic and neuroimaging factors, however such imaging genetics associations are at an early investigation stage. Therefore it is desirable to have a free and open source workflow that integrates different analysis flows in order to recover potential genetic biomarkers in PD, as in other complex diseases
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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