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Summary: In the perspective of energy‐efficient storage solutions supporting scientific computing, we investigate the possibility of using low‐power Systems‐On‐Chip (SoCs) as storage bricks of a BeeGFS file system in support of a realistic genome sequencing pipeline from Bioinformatics. Metadata and data performances of such a file system made of low‐power devices are assessed via various benchmarks for an increasing number of clients accessing the data, and compared to those achieved with traditional power‐hungry architectures with a similar BeeGFS setup. Then, we show how the sequencing information streamed by portable sequencing devices, such as the Oxford Nanopore MinION, can be managed by the underlying BeeGFS file system built with low‐power SoCs. [ABSTRACT FROM AUTHOR]

Computational intelligence methods for bioinformatics and biostatistics: 15th International Meeting, CIBB 2018, Caparica, Portugal, September 6-8, 2018, Revised Selected Papers. CIBB 2012 was organized in Houston (TX), then in Nice (France) in 2013, Cambridge (UK) in 2014, Naples (Italy) in 2015, Stirling (UK) in 2016, Cagliari (Italy) in 2017, Lisbon (Portugal) in 2018, and Bergamo (Italy) in 2019. This supplement contains seven revised and extended papers selected from CIBB 2018 and CIBB 2019, the 15th and 16th editions of the international conference on Computational Intelligence Methods for Bioinformatics and Biostatistics. [Extracted from the article]

In this paper we describe our experience in exploiting different cloud-based environments for an actual use case taken from the bioinformatics domain – the molecular surfaces analysis - that identifies similarities and possible complementarities in the protein surfaces. The analysis of macromolecular surfaces is important since protein surface conformations drive many biological reactions. We developed a workflow that performs the macromolecular surfaces analysis and provides interesting results from a scientific point of view. An important issue is represented by the fact that it is highly compute-intensive, therefore it cannot be run on a single CPU system for meaningful use cases and a parallel infrastructure is required to obtain reasonable execution time. For a decade grid infrastructures have represented suitable solutions to achieve cost effective computational power for Bioinformatics applications. However, these solutions do not offer an adequate customisation of the computational environment (e.g. installing databases and configuring virtual network) due to the rigid organisation of the storage and computational sites. Running applications on customised machines obtained by user-defined images simplifies the computing model, decreases the failure rates and therefore reduces waiting times for production analysis with respect to the canonical grid computations. For these reasons a cloud-based approach is more suitable than a pure grid paradigm. We experimented using two cloud-based approaches, based on the Worker Node On Demand Service and on OpenStack, to run the molecular surfaces analysis use case and we compared the results in terms of performance, efficiency and efforts to build the computing model with respect to grid computing. [ABSTRACT FROM AUTHOR]

Recent advances in molecular biology and bioinformatic techniques have brought about an explosion of information about the spatial organisation of the DNA in the nucleus of a cell. High-throughput molecular biology techniques provide a genome-wide capture of the spatial organisation of chromosomes at unprecedented scales, which permit one to identify physical interactions between genetic elements located throughout a genome. This important information is, however, hampered by the lack of biologist-friendly analysis and visualisation software: these disciplines are literally caught in a flood of data and are now facing many of the scale-out issues that high-performance computing has been addressing for years. Data must be managed, analysed and integrated, with substantial requirements of speed (in terms of execution time), application scalability and data representation. In this work, we present NuChart-II, an efficient and highly optimised tool for genomic data analysis that provides a gene-centric, graph-based representation of genomic information and which proposes an ex-post normalisation technique for Hi-C data. While designing NuChart-II, we addressed several common issues in the parallelisation of memory-bound algorithms for shared-memory systems. [ABSTRACT FROM AUTHOR]

The representation, integration, and interpretation of omic data is a complex task, in particular considering the huge amount of information that is daily produced in molecular biology laboratories all around the world. The reason is that sequencing data regarding expression profiles, methylation patterns, and chromatin domains is difficult to harmonize in a systems biology view, since genome browsers only allow coordinatebased representations, discarding functional clusters created by the spatial conformation of the DNA in the nucleus. In this context, recent progresses in high throughput molecular biology techniques and bioinformatics have provided insights into chromatin interactions on a larger scale and offer a formidable support for the interpretation of multi-omic data. In particular, a novel sequencing technique called Chromosome Conformation Capture allows the analysis of the chromosome organization in the cell's natural state. While performed genome wide, this technique is usually called Hi-C. Inspired by service applications such as Google Maps, we developed NuChart, an R package that integrates Hi-C data to describe the chromosomal neighborhood starting from the information about gene positions, with the possibility of mapping on the achieved graphs genomic features such as methylation patterns and histone modifications, along with expression profiles. In this paper we show the importance of the NuChart application for the integration of multi-omic data in a systems biology fashion, with particular interest in cytogenetic applications of these techniques. Moreover, we demonstrate how the integration of multi-omic data can provide useful information in understanding why genes are in certain specific positions inside the nucleus and how epigenetic patterns correlate with their expression. [ABSTRACT FROM AUTHOR]

SUMMARY A proper and efficient representation of possibly complex and large molecular surfaces is an important task in bioinformatics and biophysics. Molecular surfaces indeed are used for different aims, in particular, for computation, as visual support tools for biologists, in electrostatics problems involving implicit solvents (e.g. while solving the Poisson-Boltzmann equation) or for molecular dynamics simulations. This is the reason why, in literature, a multitude of algorithms that differ on the basis of the adopted representation and the approach/technology used were proposed. In this paper we present a CUDA-based software component able to produce high-resolution molecular surfaces based on the Van der Waals, solvent accessible, Richards-Connolly and blobby definitions. The component was designed to be used in heterogeneous visualization pipelines; therefore, the representation of the molecular surfaces suits both the direct visualization and the efficient storing for following processing steps. Experimental results show speedup figures between 39.3 and 88.4 considering molecules of different sizes and surface resolutions, resulting in meshes of up to 258.6 million triangles. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

miRNA target genes prediction represents a crucial step in miRNAs functional characterization. In this context, the challenging issue remains predictions accuracy and recognition of false positive results. In this article myMIR, a web based system for increasing reliability of miRNAs predicted targets lists, is presented. myMIR implements an integrated pipeline for computing ranked miRNA::target lists and provides annotations for narrowing them down. The system relies on knowledge base data, suitably integrated in order to extend the functional characterization of targeted genes to miRNAs, by highlighting the search on over-represented annotation terms. Validation results show a dramatic reduction in the quantity of predictions and an increase in the sensitivity, when compared to other methods. This improves the predictions accuracy and allows the formulation of novel hypotheses on miRNAs functional involvement. [ABSTRACT FROM PUBLISHER]

Background: The NCBI dbEST currently contains more than eight million human Expressed Sequenced Tags (ESTs). This wide collection represents an important source of information for gene expression studies, provided it can be inspected according to biologically relevant criteria. EST data can be browsed using different dedicated web resources, which allow to investigate library specific gene expression levels and to make comparisons among libraries, highlighting significant differences in gene expression. Nonetheless, no tool is available to examine distributions of quantitative EST collections in Gene Ontology (GO) categories, nor to retrieve information concerning library-dependent EST involvement in metabolic pathways. In this work we present the Human EST Ontology Explorer (HEOE) http://www.itb.cnr.it/ptp/human_est_explorer, a web facility for comparison of expression levels among libraries from several healthy and diseased tissues. Results: The HEOE provides library-dependent statistics on the distribution of sequences in the GO Direct Acyclic Graph (DAG) that can be browsed at each GO hierarchical level. The tool is based on large-scale BLAST annotation of EST sequences. Due to the huge number of input sequences, this BLAST analysis was performed with the aid of grid computing technology, which is particularly suitable to address data parallel task. Relying on the achieved annotation, library-specific distributions of ESTs in the GO Graph were inferred. A pathway-based search interface was also implemented, for a quick evaluation of the representation of libraries in metabolic pathways. EST processing steps were integrated in a semi-automatic procedure that relies on Perl scripts and stores results in a MySQL database. A PHP-based web interface offers the possibility to simultaneously visualize, retrieve and compare data from the different libraries. Statistically significant differences in GO categories among user selected libraries can also be computed. Conclusion: The HEOE provides an alternative and complementary way to inspect EST expression levels with respect to approaches currently offered by other resources. Furthermore, BLAST computation on the whole human EST dataset was a suitable test of grid scalability in the context of large-scale bioinformatics analysis. The HEOE currently comprises sequence analysis from 70 non-normalized libraries, representing a comprehensive overview on healthy and unhealthy tissues. As the analysis procedure can be easily applied to other libraries, the number of represented tissues is intended to increase. [ABSTRACT FROM AUTHOR]

Background: Tissue MicroArray technique is becoming increasingly important in pathology for the validation of experimental data from transcriptomic analysis. This approach produces many images which need to be properly managed, if possible with an infrastructure able to support tissue sharing between institutes. Moreover, the available frameworks oriented to Tissue MicroArray provide good storage for clinical patient, sample treatment and block construction information, but their utility is limited by the lack of data integration with biomolecular information. Results: In this work we propose a Tissue MicroArray web oriented system to support researchers in managing bio-samples and, through the use of ontologies, enables tissue sharing aimed at the design of Tissue MicroArray experiments and results evaluation. Indeed, our system provides ontological description both for pre-analysis tissue images and for post-process analysis image results, which is crucial for information exchange. Moreover, working on well-defined terms it is then possible to query web resources for literature articles to integrate both pathology and bioinformatics data. Conclusions: Using this system, users associate an ontology-based description to each image uploaded into the database and also integrate results with the ontological description of biosequences identified in every tissue. Moreover, it is possible to integrate the ontological description provided by the user with a full compliant gene ontology definition, enabling statistical studies about correlation between the analyzed pathology and the most commonly related biological processes. [ABSTRACT FROM AUTHOR]

BLAST is probably the most used application in bioinformatics teams. BLAST complexity tends to be a concern when the query sequence sets and reference databases are large. Here we present BGBlast: an approach for handling the computational complexity of large BLAST executions by porting BLAST to the Grid platform, leveraging the power of the thousands of CPUs which compose the EGEE infrastructure. BGBlast provides innovative features for efficiently managing BLAST databases in the distributed Grid environment. The system (1) keeps the databases constantly up to date while still allowing the user to regress to earlier versions, (2) stores the older versions of databases on the Grid with a time and space efficient delta encoding and (3) manages the number of replicas for each database over the Grid with an adaptive algorithm, dynamically balancing between execution parallelism and storage costs. [ABSTRACT FROM AUTHOR]

Emerging technologies for structure matching based on surface descriptions have demonstrated their effectiveness in many research fields. In particular, they can be successfully applied to in silico studies of structural biology. Protein activities, in fact, are related to the external characteristics of these macromolecules and the ability to match surfaces can be important to infer information about their possible functions and interactions. In this work, we present a surface-matching algorithm, based on encoding the outer morphology of proteins in images of local description, which allows us to establish point-to-point correlations among macromolecular surfaces using image-processing functions. Discarding methods relying on biological analysis of atomic structures and expensive computational approaches based on energetic studies, this algorithm can successfully be used for macromolecular recognition by employing local surface features. Results demonstrate that the proposed algorithm can be employed both to identify surface similarities in context of macromolecular functional analysis and to screen possible protein interactions to predict pairing capability. [ABSTRACT FROM PUBLISHER]