1,721,058 research outputs found
A study of intrinsic disorder and its role in functional proteomics
Full text linkThesis (Ph.D.) - Indiana University, Informatics, 2009The last decade has witnessed the emergence of an alternate view on how protein function arises. This view attributes the functionality of many proteins to the presence of an ensemble of flexible regions popularly as `intrinsically disordered' or `unstructured'. Several proteomic studies have corroborated the existence of either wholly disordered proteins or proteins that contain regions of disorder in them. The purpose of this dissertation was to investigate the consistency of such regions across experiments, their mechanism of facilitating function via disorder-to-order transitions, their presence and significance in pathogenic versus non-pathogenic organisms and their promise of applicability towards the computational prediction of peptides involved in the most common class of post-translational modifications, phosphorylation. Besides these, a new algorithm exploiting the strong correlation between phosphorylation and intrinsic disorder has also been proposed to improve the detection of phosphorylated peptides via high-throughput methods such as tandem mass-spectrometry (LC-MS/MS). Results presented in this study, guide us in understanding the robustness of unstructured regions in proteins to sequence changes and environment, their role in facilitating molecular recognition as well as improving currently available methods for identification of post-translationally modified peptides. The findings and conclusions of this dissertation have the potential to impact ongoing structural genomics initiatives by suggesting alternative methods for determining structure for targets containing regions of disorder. Additional ramifications of results from this work include directing attention towards the possible use of regions of intrinsic disorder by pathogenic organisms for host cell invasion. We believe that unlike the traditional reductionist approach in a scientific method, this study gathers strength and utility by investigating the role of intrinsic disorder on more than one front in order to provide a novel perspective to the understanding of complex interactions within biological systems. Concluding arguments presented in this study pique one's curiosity regarding the evolution of disordered regions and proteins in general. On a technological side, the findings from this study unequivocally support the viable use of informatics methods in gaining new insights about a relatively young class of proteins known as intrinsically disordered proteins and its applicability to improve our present knowledge of cellular physiology
A large-scale evaluation of computational protein function prediction
No full textAutomated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of protein products can only be annotated computationally. If computational predictions are to be relied upon, it is crucial that the accuracy of these methods be high. Here we report the results from the first large-scale community-based critical assessment of protein function annotation (CAFA) experiment. Fifty-four methods representing the state of the art for protein function prediction were evaluated on a target set of 866 proteins from 11 organisms. Two findings stand out: (i) today's best protein function prediction algorithms substantially outperform widely used first-generation methods, with large gains on all types of targets; and (ii) although the top methods perform well enough to guide experiments, there is considerable need for improvement of currently available tools
A New Framework for the Use of Variant Interpretation Tools in Clinical Practice
Full text linkCurrent ACMG/AMP guidelines for the use of sequence variants for genetic diagnosis
and treatment permit the use of in silico predictors as Supporting evidence (PP3 and
BP4 criteria). These criteria, however, lack quantitative support and leave clinicians and
scientists without standards for applying these criteria, leading to large interpretation
variability. To address this challenge, our team built upon previous work and introduced
a novel criterion that can be used to calibrate any computational model or any other
continuous-scale evidence on any variant type. We used it to estimate score intervals
corresponding to the four strengths of evidence for pathogenicity and benignity for
fourteen missense variant interpretation tools on a carefully assembled data sets of
known pathogenic and benign variants. We found that most tools achieved the Supporting
evidence level for both pathogenic and benign classification using newly established datadriven
thresholds. Importantly, at appropriate score thresholds, several in silico methods
can also provide Moderate and Strong evidence levels for a limited number of variants.
Based on these findings, we provided recommendations for quantitative revisions of the
PP3 and BP4 criteria within ACMG/AMP guidelines and the future assessment of in silico
methods for clinical interpretation.Book of abstract: 4th Belgrade Bioinformatics Conference, June 19-23, 202
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
- …
