1,721,000 research outputs found
SWIMmeR: An R-based software to unveiling crucial nodes in complex biological networks
No full textWe present SWIMmeR, an open-source version of its predecessor SWIM (SWitchMiner) that is a network-based tool for mining key (switch) genes that are associated with intriguing patterns of molecular coabundance and may play a crucial role in phenotypic transitions in various biological settings. SWIM was originally written in MATLABVR , a proprietary programming language that requires the purchase of a license to install, manipulate, operate and run the software. Over the last years, SWIM has sparked a widespread interest within the scientific community thanks to the promising results obtained through its application in a broad range of phenotype-specific scenarios, spanning from complex diseases to grapevine berry maturation. This success has created the call for it to be distributed in a freely accessible, open-source, runtime environment, such as R, aimed at a general audience of non-expert users that cannot afford the leading proprietary solution. SWIMmeR is provided as a comprehensive collection of R functions and it also includes several additional features that make it less intensive in terms of computer time and more efficient in terms of usability and further implementation and extension
SAveRUNNER: an R-based tool for drug repurposing
Full text linkBackground: Currently, no proven effective drugs for the novel coronavirus disease COVID-19 exist and despite widespread vaccination campaigns, we are far short from herd immunity. The number of people who are still vulnerable to the virus is too high to hamper new outbreaks, leading a compelling need to find new therapeutic options devoted to combat SARS-CoV-2 infection. Drug repurposing represents an effective drug discovery strategy from existing drugs that could shorten the time and reduce the cost compared to de novo drug discovery. Results: We developed a network-based tool for drug repurposing provided as a freely available R-code, called SAveRUNNER (Searching off-lAbel dRUg aNd NEtwoRk), with the aim to offer a promising framework to efficiently detect putative novel indications for currently marketed drugs against diseases of interest. SAveRUNNER predicts drug–disease associations by quantifying the interplay between the drug targets and the disease-associated proteins in the human interactome through the computation of a novel network-based similarity measure, which prioritizes associations between drugs and diseases located in the same network neighborhoods. Conclusions: The algorithm was successfully applied to predict off-label drugs to be repositioned against the new human coronavirus (2019-nCoV/SARS-CoV-2), and it achieved a high accuracy in the identification of well-known drug indications, thus revealing itself as a powerful tool to rapidly detect potential novel medical indications for various drugs that are worth of further investigation. SAveRUNNER source code is freely available at https://github.com/giuliafiscon/SAveRUNNER.git, along with a comprehensive user guide
String-matching and alignment algorithms for finding motifs in NGS data
No full textThe development of high-throughput Next Generation Sequencing (NGS) technologies allows to massively extract at low cost an extremely large amount of biological sequences in the form of reads, i.e., short fragments of an organism’s genome. The advent of NGS poses new issues for computer scientists and bioinformaticians, leading to the design of algorithms for aligning and merging the reads in order to obtain an efficient and effective reconstruction of the genome. In this chapter, we focus on methods that can quickly and precisely establish whether two reads are similar or not and that allow to analyze biological sequences extracted with NGS technologies. In particular, the most widespread string-matching, alignment-based, and alignment-free algorithms are summarized and discussed
SPINNAKER: an R-based tool to highlight key RNA interactions in complex biological networks
Full text linkBACKGROUND: Recently, we developed a mathematical model for identifying putative competing endogenous RNA (ceRNA) interactions. This methodology has aroused a broad acknowledgment within the scientific community thanks to the encouraging results achieved when applied to breast invasive carcinoma, leading to the identification of PVT1, a long non-coding RNA functioning as ceRNA for the miR-200 family. The main shortcoming of the model is that it is no freely available and implemented in MATLAB®, a proprietary programming platform requiring a paid license for installing, operating, manipulating, and running the software. RESULTS: Breaking through these model limitations demands to distribute it in an open-source, freely accessible environment, such as R, designed for an ordinary audience of users that are not able to afford a proprietary solution. Here, we present SPINNAKER (SPongeINteractionNetworkmAKER), the open-source version of our widely established mathematical model for predicting ceRNAs crosstalk, that is released as an exhaustive collection of R functions. SPINNAKER has been even designed for providing many additional features that facilitate its usability, make it more efficient in terms of further implementation and extension, and less intense in terms of computational execution time. CONCLUSIONS: SPINNAKER source code is freely available at https://github.com/sportingCode/SPINNAKER.git together with a thoroughgoing PPT-based guideline. In order to help users get the key points more conveniently, also a practical R-styled plain-text guideline is provided. Finally, a short movie is available to help the user to set the own directory, properly. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-022-04695-x
Integro-differential approach for modeling the COVID-19 dynamics - Impact of confinement measures in Italy
Full text linkThe COVID-19 pandemic has overwhelmed the life and security of most of the world countries, and especially of the Western countries, without similar experiences in the recent past. In a first phase, the response of health systems and governments was disorganized, but then incisive, also driven by the fear of a new and dramatic phenomenon. In the second phase, several governments, including Italy, accepted the doctrine of “coexistence with the virus” by putting into practice a series of containment measures aimed at limiting the dramatic sanitary consequences while not jeopardizing the economic and social stability of the country. Here, we present a new mathematical approach to modeling the COVID-19 dynamics that accounts for typical evolution parameters (i.e., virus variants, vaccinations, containment measurements). Reproducing the COVID-19 epidemic spread is an extremely challenging task due to the low reliability of the available data, the lack of recurrent patterns, and the considerable amount and variability of the involved parameters. However, the adoption of fairly uniform criteria among the Italian regions enabled to test and optimize the model in various conditions leading to robust and interesting results. Although the regional variability is quite large and difficult to predict, we have retrospectively obtained reliable indications on which measures were the most appropriate to limit the transmissibility coefficients within detectable ranges for all the regions. To complicate matters further, the rapid spread of the English variant has upset contexts where the propagation of contagion was close to equilibrium conditions, decreeing success or failure of a certain measure. Finally, we assessed the effectiveness of the zone assignment criteria, highlighting how the reactivity of the measures plays a fundamental role in limiting the spread of the infection and thus the total number of deaths, the most important factor in assessing the success of epidemic management
A network-based algorithm for identifying drug repurposing opportunities for complex diseases
Full text linkSAveRUNNER is a recently developed network-based tool to efficiently identify novel medical indications for currently approved drugs (known as drug repurposing strategy). Up to now, SAveRUNNER has been gainfully applied to unveil repurposable solutions for several diseases, including viral infection (e.g., SARS, COVID-19, HIV, Malaria, Ebola), breast cancer, progressive disorders of central nervous system (e.g., Amyotrophic Lateral Sclerosis, Multiple Sclerosis), and other neurodegenerative diseases (e.g., Alzheimer's Disease). Here, SAveRUNNER algorithm and its main applications are described
Circular RNA mediated gene regulation in human breast cancer: A bioinformatics analysis
Full text linkCircular RNAs (circRNAs) are a new acknowledged class of RNAs that has been shown to play a major role in several biological functions both in physiological and pathological conditions, operating as critical part of regulatory processes, like competing endogenous RNA (ceRNA) networks. The ceRNA hypothesis is a recently discovered molecular mechanism that adds a new key layer of post-transcriptional regulation, whereby various types of RNAs can reciprocally influence each other’s expression competing for binding the same pool of microRNAs, even affecting disease development. In this study, we build a network of circRNA-miRNA-mRNA interactions in human breast cancer, called CERNOMA, that is a bipartite graph with one class of nodes corresponding to differentially expressed miRNAs (DEMs) and the other one corresponding to differentially expressed circRNAs (DEC) and mRNAs (DEGs). A link between a DEC (or DEG) and DEM is placed if it is predicted to be a target of the DEM and shows an opposite expression level trend with respect to the DEM. Within the CERNOMA, we highlighted an interesting deregulated circRNA-miRNA-mRNA triplet, including the up-regulated hsa_circRNA_102908 (BRCA1 associated RING domain 1), the down-regulated miR-410-3p, and the up-regulated ESM1, whose overexpression has been already shown to promote tumor dissemination and metastasis in breast cancer
A network-based bioinformatic analysis for identifying potential repurposable active molecules in different types of human cancers
No full textDrug repurposing, also known as drug repositioning, is the process of identifying novel therapeutic indications for existing drugs, offering a cost-effective and time-efficient strategy to drug discovery. In this context, we developed a network-based algorithm, named SAveRUNNER (Searching off-lAbel dRUg aNd NEtwoRk), which predicts drug-disease associations by accounting for the interaction between the drug targets and disease-associated genes in the human interactome, implementing a novel network-based similarity measure that prioritizes associations between drugs and diseases locating in the same network neighborhoods. Following its successful applications to different disorders (such as viral infections and neurological diseases), in this study, we applied SAveRUNNER on a panel of 13 types of cancers using both disease-associated genes downloaded from widely-used databases and from gene expression data
Bioinformatics analyses to identify molecular gene signatures associated with breast cancer phenotypes
Full text linkBreast cancer is a heterogeneous and complex disease as witnessed by the existence of different subtypes with distinct morphologies and clinical implications. Despite the remarkable advances in understanding the mechanisms underlying breast cancer, this disease is still a major public health problem worldwide and poses significant open challenges. Here, we show how a multi-omics data integration analysis may provide useful insights in the identification of promising molecular signatures associated with the different breast cancer subtypes
A perspective on the algorithms predicting and evaluating the RNA secondary structure
Full text linkInvestigating the RNA structure contributes greatly to understand
RNA roles in cellular processes. Indeed, functional RNAs show
specific instrumental sub-structures for their interaction with other
molecules. The RNA structure prediction will provide fundamental
insights into developing hypothesis connecting function to structure,
but it is a challenging and unsolved task yet.
We aim at discussing the current status of the widespread RNA
folding tools and comparing their performances on RNA families
with known structure, in order to estimate how much the predictions
are close to the experimental folding.
A comprehensive understanding of RNA folding could highlight
further roles of long non-coding RNA in the gene expression
regulation and in the epigenetic regulatory pathways in physiological
and pathological conditions of a living cell
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