1,720,968 research outputs found
Stress Mice Portal. A gene expression atlas for different kinds of stress in mice brains http://hpc-bioinformatics.cineca.it/stress_mice/
No full textStressful experiences are part of everyday life and organisms have evolved physiological and behavioural responses aimed at coping with stress and maintain homeostasis. However, repeated or intense stress can induce maladaptive reactions leading to depression, anxiety or post-traumatic stress disorder. Functional and structural adaptations in the brain mediated by changes in gene expression, have a crucial role in the stress response. Recent years have seen a tremendous increase in scientific publications studying the transcriptional effects of several stressors on different areas of the brain. The input raw data of these studies are freely available on public repositories, such as Sequence Read Archive (SRA) operated by the National Center for Biotechnology Information (NCBI). These data represent a wealth of information for further global and integrative retrospective analyses. In order to identify convergent biological processes and signalling pathways affected by stress, and reveal possible novel candidate genes, we analyzed the RNA-seq transcriptomes obtained from the brain of mice exposed to different kinds of stress protocols. Thus we surveyed 18 published RNA-seq transcriptomic experiments obtained from SRA, whose detailed information are available here. Since each of the laboratories producing these 18 datasets analyzed their own data by following different protocols (e.g. in terms of adopted bioinformatic workflows, software, genomic versions, thresholds of significance), we re-analyzed the data by applying the same up-to-date transcriptomic computational workflow to the whole dataset in order to obtain a more comparable set of results. The results were systematized into this web portal for allowing an easy visualization, comparison, interrogation and downloading. Stress Mice Portal is a free, open-source, curated web server able to query stress-related differentially expressed genes (DEGs) using different criteria (please refer to the Help page or the paper for further information). This portal provides new insights into the transcriptional landscape of the mouse brain under several stress conditions and is useful for identifying factors contributing to vulnerability or resilience to stress
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
DROPA: DRIP-seq optimized peak annotator
Full text linkBackground: R-loops are three-stranded nucleic acid structures that usually form during transcription and that may lead to gene regulation or genome instability. DRIP (DNA:RNA Immunoprecipitation)-seq techniques are widely used to map R-loops genome-wide providing insights into R-loop biology. However, annotation of DRIP-seq peaks to genes can be a tricky step, due to the lack of strand information when using the common basic DRIP technique.
Results: Here, we introduce DRIP-seq Optimized Peak Annotator (DROPA), a new tool for gene annotation of Rloop peaks based on gene expression information. DROPA allows a full customization of annotation options, ranging from the choice of reference datasets to gene feature definitions. DROPA allows to assign R-loop peaks to the DNA template strand in gene body with a false positive rate of less than 7%. A comparison of DROPA performance with three widely used annotation tools show that it identifies less false positive annotations than the others.
Conclusions: DROPA is a fully customizable peak-annotation tool optimized for co-transcriptional DRIP-seq peaks, which allows a finest gene annotation based on gene expression information. Its output can easily be integrated into pipelines to perform downstream analyses, while useful and informative summary plots and statistical enrichment tests can be produced
Massive NGS Data Analysis Reveals Hundreds Of Potential Novel Gene Fusions in Human Cell Lines
Full text linkBackground: gene fusions derive from chromosomal rearrangements. The resulting chimeric transcripts are often endowed
with oncogenic potential. Furthermore, they serve as diagnostic tools for the clinical classification of cancer subgroups with
different prognosis and, in some cases, they can provide specific drug targets. To date, many efforts have been carried out to
study gene fusion events occurring in tumor samples. In recent years, the availability of a comprehensive next-generation
sequencing dataset for all existing human tumor cell lines has provided the opportunity to further investigate these data in
order to identify novel and still uncharacterized gene fusion events. Results: In our work, we have extensively reanalyzed
935 paired-end RNA-sequencing experiments downloaded from the Cancer Cell Line Encyclopedia repository, aiming at
addressing novel putative cell-line specific gene fusion events in human malignancies. The bioinformatics analysis has
been performed by the execution of four gene fusion detection algorithms. The results have been further prioritized by
running a Bayesian classifier that makes an in silico validation. The collection of fusion events supported by all of the
predictive software results in a robust set of ∼1,700 in silico predicted novel candidates suitable for downstream analyses.
Given the huge amount of data and information produced, computational results have been systematized in a database
named LiGeA. The database can be browsed through a dynamic and interactive web portal, further integrated with
validated data from other well-known repositories. Taking advantage of the intuitive query forms, the users can easily
access, navigate, filter, and select the putative gene fusions for further validations and studies. They can also find suitable
experimental models for a given fusion of interest. Conclusions: We believe that the LiGeA resource can represent not only
the first compendium of both known and putative novel gene fusion events in the catalog of all of the human malignant
cell lines but it can also become a handy starting point for wet-lab biologists who wish to investigate novel cancer
biomarkers and specific drug targets
DROPA: DRIP-seq optimized peak annotator
Full text linkBackground: R-loops are three-stranded nucleic acid structures that usually form during transcription and that may lead to gene regulation or genome instability. DRIP (DNA:RNA Immunoprecipitation)-seq techniques are widely used to map R-loops genome-wide providing insights into R-loop biology. However, annotation of DRIP-seq peaks to genes can be a tricky step, due to the lack of strand information when using the common basic DRIP technique. Results: Here, we introduce DRIP-seq Optimized Peak Annotator (DROPA), a new tool for gene annotation of R-loop peaks based on gene expression information. DROPA allows a full customization of annotation options, ranging from the choice of reference datasets to gene feature definitions. DROPA allows to assign R-loop peaks to the DNA template strand in gene body with a false positive rate of less than 7%. A comparison of DROPA performance with three widely used annotation tools show that it identifies less false positive annotations than the others. Conclusions: DROPA is a fully customizable peak-annotation tool optimized for co-transcriptional DRIP-seq peaks, which allows a finest gene annotation based on gene expression information. Its output can easily be integrated into pipelines to perform downstream analyses, while useful and informative summary plots and statistical enrichment tests can be produced
A gene expression atlas for different kinds of stress in the mouse brain
Full text linkStressful experiences are part of everyday life and animals have evolved physiological and behavioral responses aimed at coping with stress and maintaining homeostasis. However, repeated or intense stress can induce maladaptive reactions leading to behavioral disorders. Adaptations in the brain, mediated by changes in gene expression, have a crucial role in the stress response. Recent years have seen a tremendous increase in studies on the transcriptional effects of stress. The input raw data are freely available from public repositories and represent a wealth of information for further global and integrative retrospective analyses. We downloaded from the Sequence Read Archive 751 samples (SRA-experiments), from 18 independent BioProjects studying the effects of different stressors on the brain transcriptome in mice. We performed a massive bioinformatics re-analysis applying a single, standardized pipeline for computing differential gene expression. This data mining allowed the identification of novel candidate stress-related genes and specific signatures associated with different stress conditions. The large amount of computational results produced was systematized in the interactive “Stress Mice Portal”
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
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