1,721,164 research outputs found
Circular RNAs and Exosomes: The New Frontier of Cancer Diagnosis
No full textOver the last decade, the development of new technologies for genome-wide analyses of the eukaryotic transcriptome has allowed comprehensive studies of circRNA species. Although biologists identified circular transcripts more than 20 years ago, these circular molecules were long considered artefacts of aberrant splicing reactions or the prerogative of a few viral pathogens. The evidence from expression data indicates a wide range of more than thousands of endogenous circRNAs in mammalian cells, some of which are abundant, conserved, and stably accumulated within the cell. The functional analysis of these transcripts revealed that circRNAs might regulate microRNA (miRNA) function and suggest that they might act in the regulation of gene expression. Due to their emerging role as gene expression regulators, circRNAs are very likely to become important players in cancer development and pathologies like other types of noncoding RNAs. Moreover, they have shown a great potential as tissue-based markers for cancer classification and prognostication. Recently, it was discovered that circRNAs are secreted through exosomes vesicles; thus, the study of this class of non-coding RNAs has potential implications for therapeutic and research applications. © 2015 by Begell House, Inc
On the variable ordering in subgraph isomorphism algorithms.
No full textGraphs are mathematical structures to model several biological data. Applications to analyze them require to apply solutions for the subgraph isomorphism problem, which is NP-complete. Here, we investigate the existing strategies to reduce the subgraph isomorphism algorithm running time with emphasis on the importance of the order with which the graph vertices are taken into account during the search, called variable ordering, and its incidence on the total running time of the algorithms. We focus on two recent solutions, which are based on an effective variable ordering strategy. We discuss their comparison both with the variable ordering strategies reviewed in the paper and the other algorithms present in the ICPR2014 contest on graph matching algorithms for pattern search in biological databases
On the variable ordering in subgraph isomorphism algorithms
No full textGraphs are mathematical structures to model several biological data. Applications to analyze them require to apply solutions for the subgraph isomorphism problem, which is NP-complete. Here, we investigate the existing strategies to reduce the subgraph isomorphism algorithm running time with emphasis on the importance of the order with which the graph vertices are taken into account during the search, called variable ordering, and its incidence on the total running time of the algorithms. We focus on two recent solutions, which are based on an effective variable ordering strategy. We discuss their comparison both with the variable ordering strategies reviewed in the paper and the other algorithms present in the ICPR2014 contest on graph matching algorithms for pattern search in biological databases
GraphGrep: A Fast and Universal Method for Querying Graphs
No full textGraphGrep is an application-independent method for querying graphs, finding all the occurrences of a subgraph in a database of graphs. The interface to GraphGrep is a regular expression graph query language Glide that combines features from Xpath and Smart. Glide incorporates both single node and variable-length wildcards. Our algorithm uses hash-based fingerprinting to represent the graphs in an abstract form and to filter the database. GraphGrep has been tested on databases of size up to 16,000 molecules and performs well in this entire range
From translational bioinformatics computational methodologies to personalized medicine
No full textIn recent years, there has been a remarkable revolution in the theory and application of bioinformatics. The emergence of new technologies, along with the abundance of diverse data types to be integrated within the context of personalized medicine, presents various methodological challenges across different domains. These include, for instance, challenges in single-cell analysis, individual genome analysis, pan-genomes, multi-omics data analysis, and the seamless integration of such data with clinical information
Editorial: New Trends on Genome and Transcriptome Characterizations
Full text linkWorldwide, National Health Systems are investing to fit the requirements of “precision medicine.” This term refers to the prevention and treatment of diseases that take into account the individual characteristics of patients, from their genetic variability to their different life style. However, the aims of precision medicine can only be fully realized when the internal mechanisms of diseases are understood and a deep knowledge of their individual variability is reached. Such a high level of comprehension can not only allow physicians to maximize the benefit against dangerous side effects, but can also promote the discovery of new treatments and prevention procedures. The same attitude, which in medicine can be synthetized as a shift from pathologies to patients, can be applied in the field of agriculture, when general principles are adapted and regulated according to the specific environments and local situations where cultures are realized. These objectives require financial and intellectual resources to collect large amounts of genome and transcriptome data that are specifically related to the phenomena of interest in the different fields of applications (diseases in many contexts of related pathologies, or agricultural settings at the production level). However, a second methodological aspect is the powerful combination of information theoretical concepts with specific algorithmic and computational tools. This informational perspective is intended to extract deep biological meaning that often escape from simple statistical analyses of macroscopic phenomena. In fact, long range correlations or deep mathematical regularities, surprisingly enough, seem to relate with biological structures and functions that are encoded in a multilevel organization of genomes and of their expression. The search for new information-based categories will provide novel interpretation of classical biological concepts using this new informational approach. The results of this methodological innovation are going to have a wide range of applications in the public health and in many economic sectors that contribute positively to human lifestyle and to progress of countries. In particular, in the agri-food sector, the study of the variability of genomes and transcriptomes implies the improvement of productivity and quality of products. In this field the combination of plant biotechnology with bioinformatics, in comparison with the traditional techniques of phenotypic analysis of plants, will provide a remarkable increase of speed and efficiency in the selection of progenies with superior characteristics
Editorial. Special section on: Advances in Similarity Search (SISAP)
No full textEditorial of the Special Issue dedicated to The InternationalConference on Similarity Search and Applications(SISAP
Editorial
No full textNETTAB workshops are a series of international events on ‘Network Tools and Applications in Biology’ that are aimed at presenting and discussing emerging Information and Communication Technologies whose adoption in support of biology appear to be of particular interest. Since 2001, many different topics were discussed including: XML standardization for data integration (2001), multi agent systems (2002), scientific workflows (2005), Web Services (2006) and the Semantic Web (2007).
The NETTAB 2009 workshop that was held at the Mathematics and Computer Science Department of the University of Catania, Italy, 10–13 June 2009, was focused on ‘Collaborative Bioinformatics Research and Development’ and on ‘Tools for RNA Analysis’. The workshop included many original contributions devoted to these innovative research domains, the best of which have been carefully peer reviewed and included in this Special Issue on ‘Collaborative Bioinformatics and RNA Analysis’. There is a clear switch from previous focus themes since these are aimed at different aspects of data integration, ranging from standardization, to automation of procedures. In this case, the accent is on human collaboration and ways to implement it
PANPROVA: PANgenomic PROkaryotic eVolution of full Assemblies
Full text linkComputational tools for pangenonic analysis have gained increasing interest over the past two decades in various applications such as evolutionary studies and vaccine development. Synthetic benchmarks are essential for the systematic evaluation of their performance. Currently, benchmarking tools represent a genome as a set of genetic sequences and fail to simulate the complete information of the genomes, which is essential for evaluating pangenomic detection between fragmented genomes.
We present PANPROVA, a benchmark tool to simulate prokaryotic pangenomic evolution by evolving the complete genomic sequence of an ancestral isolate. In this way the possibility of operating in the pre-assembly phase is enabled. Gene set variations, sequence variation and horizontal acquisition from a pool of external genomes are the evolutionary features of the tool
From translational bioinformatics computational methodologies to personalized medicine
No full textThe last years have seen an impressive revolution in the theory and application of bioinformatics since new technologies and the abundance of different types of data to be integrated in a personalized medicine perspective are posing various methodological challenges in different fields such as, for example, single cell analysis [1], individual genome analysis and pan-genomes [2], Multi-omics data analysis and integration with clinical data [3]. In this special issue, we invite researchers to submit primary research focusing on translational bioinformatics methods that, starting from population-based molecular profiling, clinical data, epidemiological data, and other types of data make healthcare decisions tailored to groups of patients or individual patients
- …
