375 research outputs found
Alexandros Papadiamantis: Easter chanter
No full textTitle: Λαμπριάτικος Ψάλτης (Easter chanter) Originally published: newspaper ’Aκρόπολις, 1893 Language: Greek The excerpt used is from Panayotis Moullas, Α.Παπαδιαμάντης Αυτοβιογραφούμενος (Athens: Εστία 1999), pp. 100–103. About the author Alexandros Papadiamantis: [Skiathos (central Greece) 1851 – Skiathos 1911]: short story writer and translator. He was the third son of the priest Adamantios, hence the family name (papa-Diamantis). His mother was the offspring of a well-off family from the ..
Alexandros Papadiamantis: Easter chanter
No full textTitle: Λαμπριάτικος Ψάλτης (Easter chanter) Originally published: newspaper ’Aκρόπολις, 1893 Language: Greek The excerpt used is from Panayotis Moullas, Α.Παπαδιαμάντης Αυτοβιογραφούμενος (Athens: Εστία 1999), pp. 100–103. About the author Alexandros Papadiamantis: [Skiathos (central Greece) 1851 – Skiathos 1911]: short story writer and translator. He was the third son of the priest Adamantios, hence the family name (papa-Diamantis). His mother was the offspring of a well-off family from the ..
omiXcore: a web server for prediction of protein interactions with large RNA
No full textSUMMARY: Here we introduce omiXcore, a server for calculations of protein binding to large RNAs (> 500 nucleotides). Our webserver allows (i) use of both protein and RNA sequences without size restriction, (ii) pre-compiled library for exploration of human long intergenic RNAs interactions and (iii) prediction of binding sites. RESULTS: omiXcore was trained and tested on enhanced UV Cross-Linking and ImmunoPrecipitation data. The method discriminates interacting and non-interacting protein-RNA pairs and identifies RNA binding sites with Areas under the ROC curve > 0.80, which suggests that the tool is particularly useful to prioritize candidates for further experimental validation.
AVAILABILITY AND IMPLEMENTATION: omiXcore is freely accessed on the web at http://service.tartaglialab.com/grant_submission/omixcore. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.We acknowledge support of the Spanish Ministry of Economy and Competitiveness, ‘Centro de Excelencia Severo Ochoa 2013-2017’ and the CERCA Programme / Generalitat de Catalunya. This work was supported by the European Union Seventh Framework Programme [FP7/2007-13], European Research Council RIBOMYLOME_309545 (Gian Gaetano Tartaglia) and Spanish Ministry of Economy and Competitiveness BFU2014-5505-P (Gian Gaetano Tartaglia
RNAct: Protein-RNA interaction predictions for model organisms with supporting experimental data
No full textProtein-RNA interactions are implicated in a number of physiological roles as well as diseases, with molecular mechanisms ranging from defects in RNA splicing, localization and translation to the formation of aggregates. Currently, ∼1400 human proteins have experimental evidence of RNA-binding activity. However, only ∼250 of these proteins currently have experimental data on their target RNAs from various sequencing-based methods such as eCLIP. To bridge this gap, we used an established, computationally expensive protein-RNA interaction prediction method, catRAPID, to populate a large database, RNAct. RNAct allows easy lookup of known and predicted interactions and enables global views of the human, mouse and yeast protein-RNA interactomes, expanding them in a genome-wide manner far beyond experimental data (http://rnact.crg.eu).European Research Council [RIBOMYLOME_309545]; European Union's Horizon 2020 research and innovation programme [727658, IASIS]; Spanish Ministry of Economy and Competitiveness [BFU2014-55054-P, BFU2017-86970-P]; Spanish Ministry of Economy and Competitiveness; ‘Centro de Excelencia Severo Ochoa 2013-2017’; CERCA Programme of the Generalitat de Catalunya; Marie Sklodowska-Curie Individual Fellowship from the European Union's Horizon 2020 research and innovation programme (793135, ‘DeepRNA’ to B.L.). Funding for open access charge: European Research Council [RIBOMYLOME_309545], European Union [793135]
A high-throughput approach to profile RNA structure
No full textHere we introduce the Computational Recognition of Secondary Structure (CROSS) method to calculate the structural profile of an RNA sequence (single- or double-stranded state) at single-nucleotide resolution and without sequence length restrictions. We trained CROSS using data from high-throughput experiments such as Selective 2΄-Hydroxyl Acylation analyzed by Primer Extension (SHAPE; Mouse and HIV transcriptomes) and Parallel Analysis of RNA Structure (PARS; Human and Yeast transcriptomes) as well as high-quality NMR/X-ray structures (PDB database). The algorithm uses primary structure information alone to predict experimental structural profiles with >80% accuracy, showing high performances on large RNAs such as Xist (17 900 nucleotides; Area Under the ROC Curve AUC of 0.75 on dimethyl sulfate (DMS) experiments). We integrated CROSS in thermodynamics-based methods to predict secondary structure and observed an increase in their predictive power by up to 30%.The research leading to these results has received funding from European Union Seventh Framework Programme [FP7/2007-2013]; European Research Council [RIBOMYLOME_309545 to GGT]; Spanish Ministry of Economy and Competitiveness [BFU2014-55054-P to GGT]; AGAUR [2014 SGR 00685 to GGT]; Spanish Ministry of Economy and Competitiveness, European Research Development Fund ERDF, 'Centro de Excelencia Severo Ochoa 2013-2017' [SEV-2012-0208]. Funding for open access charge: European Research Council [RIBOMYLOME_309545 to GGT]; Spanish Ministry of Economy and Competitiveness [BFU2014-55054-P to GGT]. The authors also thank the CRG fellowship to SM
Aggregation is a Context-Dependent Constraint on Protein Evolution
No full textSolubility is a requirement for many cellular processes. Loss of solubility and aggregation can lead to the partial or complete abrogation of protein function. Thus, understanding the relationship between protein evolution and aggregation is an important goal. Here, we analysed two deep mutational scanning experiments to investigate the role of protein aggregation in molecular evolution. In one data set, mutants of a protein involved in RNA biogenesis and processing, human TAR DNA binding protein 43 (TDP-43), were expressed in S. cerevisiae. In the other data set, mutants of a bacterial enzyme that controls resistance to penicillins and cephalosporins, TEM-1 beta-lactamase, were expressed in E. coli under the selective pressure of an antibiotic treatment. We found that aggregation differentiates the effects of mutations in the two different cellular contexts. Specifically, aggregation was found to be associated with increased cell fitness in the case of TDP-43 mutations, as it protects the host from aberrant interactions. By contrast, in the case of TEM-1 beta-lactamase mutations, aggregation is linked to a decreased cell fitness due to inactivation of protein function. Our study shows that aggregation is an important context-dependent constraint of molecular evolution and opens up new avenues to investigate the role of aggregation in the cell
Prediction of protein-RNA interactions from single-cell transcriptomic data
No full textProteins are crucial in regulating every aspect of RNA life, yet understanding their interactions with coding and noncoding RNAs remains limited. Experimental studies are typically restricted to a small number of cell lines and a limited set of RNA-binding proteins (RBPs). Although computational methods based on physico-chemical principles can predict protein-RNA interactions accurately, they often lack the ability to consider cell-type-specific gene expression and the broader context of gene regulatory networks (GRNs). Here, we assess the performance of several GRN inference algorithms in predicting protein-RNA interactions from single-cell transcriptomic data, and propose a pipeline, called scRAPID (single-cell transcriptomic-based RnA Protein Interaction Detection), that integrates these methods with the catRAPID algorithm, which can identify direct physical interactions between RBPs and RNA molecules. Our approach demonstrates that RBP-RNA interactions can be predicted from single-cell transcriptomic data, with performances comparable or superior to those achieved for the well-established task of inferring transcription factor-target interactions. The incorporation of catRAPID significantly enhances the accuracy of identifying interactions, particularly with long noncoding RNAs, and enables the identification of hub RBPs and RNAs. Additionally, we show that interactions between RBPs can be detected based on their inferred RNA targets. The software is freely available at https://github.com/tartaglialabIIT/scRAPID
Fight for Faith and Motherland!
No full textTitle: Μάχου ὑπέρ πίστεως καὶ πατρίδος (Fight for Faith and Motherland!) Originally published: as a leaflet in Iaşi, 24 February 1822. Language: Greek The excerpts text used are from: Apostolos Daskalakis, Kείµενα-πηγαί τῆς ἱστορίας τῆς ἑλληνικῆς ἐπαναστάσεως (Αθήνα: 1966), pp. 141–144. About the author Alexandros Ypsilantis [1792, Bucharest – 1828, St. Petersburg]: military leader. He was the offspring of one of the most distinguished Phanariot families. His grandfather Alexandros and his fa..
Computational characterization of protein-RNA interactions and implications for phase separation
No full textDespite what was previously considered, the role of RNA is not only to carry the genetic
information from DNA to proteins. Indeed, RNA has proven to be implicated in more
complex cellular processes. Recent evidence suggests that transcripts have a regulatory
role on gene expression and contribute to the spatial and temporal organization of the
intracellular environment. They do so by interacting with RNA-binding proteins (RBPs)
to form complex ribonucleoprotein (RNP) networks, however the key determinants that
govern the formation of these complexes are still not well understood. In this work, I will
describe algorithms that I developed to estimate the ability of RNAs to interact with
proteins. Additionally, I will illustrate applications of computational methods to propose
an alternative model for the function of Xist lncRNA and its protein network.
Finally, I will show how computational predictions can be integrated with high
throughput approaches to elucidate the relationship between the structure of the RNA and
its ability to interact with proteins. I conclude by discussing open questions and future
opportunities for computational analysis of cell’s regulatory network.
Overall, the underlying goal of my work is to provide biologists with new insights into
the functional association between RNAs and proteins as well as with sophisticated tools
that will facilitate their investigation on the formation of RNP complexesA pesar de lo que se consideraba anteriormente, el papel del ARN no es solo transportar
la información genética del ADN a las proteínas. De hecho, el ARN ha demostrado estar
implicado en muchos procesos celulares más complejos. La evidencia reciente sugiere
que los transcriptos tienen un papel regulador en la expresión génica y contribuyen a la
organización espacial y temporal del entorno intracelular. Lo hacen interactuando con
proteínas de unión a ARN (RBP) para formar redes complejas de ribonucleoproteína
(RNP), sin embargo, los determinantes clave que rigen la formación de estos complejos
aún no se conocen bien. En este trabajo, describiré algoritmos que he desarrollado para
estimar la capacidad de los ARN de interactuar con las proteínas. Además, ilustraré
aplicaciones de métodos computacionales para proponer una maquinaria alternativa para
el Xist lncRNA y su red de interacciones.
Finalmente, mostraré cómo las predicciones computacionales pueden integrarse con
enfoques de alto rendimiento para dilucidar la relación entre la estructura del ARN y su
capacidad para interactuar con las proteínas. Concluyo discutiendo preguntas abiertas y
oportunidades futuras para el análisis computacional de la red reguladora de la célula.
En general, el objetivo subyacente de mi trabajo es proporcionar a los biólogos nuevas
ideas sobre la asociación funcional entre ARN y proteínas, así como herramientas
sofisticadas que facilitarán su investigación sobre la formación de complejos RNP.Programa de doctorat en Biomedicin
A method for RNA structure prediction shows evidence for structure in lncRNAs
No full textTo compare the secondary structure profiles of RNA molecules we developed the CROSSalign method. CROSSalign is based on the combination of the Computational Recognition Of Secondary Structure (CROSS) algorithm to predict the RNA secondary structure profile at single-nucleotide resolution and the Dynamic Time Warping (DTW) method to align profiles of different lengths. We applied CROSSalign to investigate the structural conservation of long non-coding RNAs such as XIST and HOTAIR as well as ssRNA viruses including HIV. CROSSalign performs pair-wise comparisons and is able to find homologs between thousands of matches identifying the exact regions of similarity between profiles of different lengths. In a pool of sequences with the same secondary structure CROSSalign accurately recognizes repeat A of XIST and domain D2 of HOTAIR and outperforms other methods based on covariance modeling. The algorithm is freely available at the webpage http://service.tartaglialab.com//new_submission/crossalign
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