NuchaRt: Embedding High-Level Parallel Computing in R for Augmented Hi-C Data Analysis

Tordini, Fabio; Merelli, Ivan; Lió, Píetro; Milanesi, Luciano; Aldinucci, Marco

doi:10.1007/978-3-319-44332-4_20

Cited by 3 publications

(6 citation statements)

References 8 publications

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“…In previous works we proposed NuchaRt, a tool for Hi-C data analysis that produces a graph-based representation of the genes along the chromosome -a sort of topological map of the chromosome [2], [7]. These maps will be the ground for the integration and analysis of omics information (i.e., resulting from different biological experiments), such as RNA-Seq and ChIP-Seq, which can greatly benefit from analysis relying on the 3D maps of the DNA.…”

Section: Genomic Data Analysismentioning

confidence: 99%

“…Two widely used analysis pipelines for RNA-Seq and ChIP-Seq data exist, relying on Tophat [16] and Cufflinks [17] the former, and on Bowtie [18] and MACS [19] the latter. Concerning Hi-C data analysis, in [2] and [7] we discussed our approach for the analysis and representation of such data: we refer to those works for better explanations.…”

Section: Data Analysis Pipelinesmentioning

confidence: 99%

“…For instance, DNA sequences alignment is a highly parallel task, and the obtainable speedup is significant (though affected by memory overhead). Bowtie allows to execute a parallel search, where threads will run on separate processors/cores and synchronize when parsing reads 2 . Consequently, all tools based on Bowtie (e.g., TopHat and HiCUP) permit to exploit multi-threading during the alignment phase.…”

Section: Data Analysis Pipelinesmentioning

confidence: 99%

“…By exploiting NuchaRt, a software tool that produces a graph-based representation of the genes placed along the chromosome [2], we can integrate data resulting from Hi-C, RNA-Seq and ChIP-Seq experiments, in order to study the interactions among genetic elements that can reveal insights on biological mechanisms, such as genes regulation, translocations and epigenetic patterns.…”

Section: Introductionmentioning

confidence: 99%

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A Cloud Solution for Multi-omics Data Integration

Tordini

2016

2016 Intl IEEE Conferences on Ubiquitous Intelligence &Amp; Computing, Advanced and Trusted Computing, Scalable Computing and C

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Abstract-Recent advances in molecular biology and Bioinformatics techniques have brought to an explosion of the information about the spatial organisation of the DNA inside the nucleus. In particular, 3C-based techniques are revealing the genome folding for many different cell types, and permit to create a more effective representation of the disposition of genes in the three-dimensional space. This information can be used to re-interpret heterogeneous genomic data (multi-omic) relying on 3D maps of the chromosome.The storage and computational requirements needed to accomplish such operations on raw sequenced data have to be fulfilled using HPC solutions, and the the Cloud paradigm is a valuable and convenient mean for delivering HPC to Bioinformatics. In this work we describe a data analysis workflow that allows the integration and the interpretation of multiomic data on a sort of "topographical" nuclear map, capable of representing the effective disposition of genes in a graph-based representation. We propose a cloud-based task farm pattern to orchestrate the services needed to accomplish genomic data analysis, where each service represents a special-purpose tool, playing a part in well known data analysis pipelines.

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Section: Genomic Data Analysismentioning

confidence: 99%

Section: Data Analysis Pipelinesmentioning

confidence: 99%

Section: Data Analysis Pipelinesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Cloud Solution for Multi-omics Data Integration

Tordini

2016

2016 Intl IEEE Conferences on Ubiquitous Intelligence &Amp; Computing, Advanced and Trusted Computing, Scalable Computing and C

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“…On the other hand, graphbased models of Hi-C data can be very useful for creating representations where other omics data can be mapped, in order to characterise different spatially associated domains [17,18]. By exploiting their higher level of expressiveness, graphs permit the integration of multi-omic data and facilitate their statistical analysis [19,20].…”

Section: Introductionmentioning

confidence: 99%

Exploiting Docker containers over Grid computing for a comprehensive study of chromatin conformation in different cell types

Merelli

Fornari

Tordini

et al. 2019

Journal of Parallel and Distributed Computing

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Perspectives of using Cloud computing in integrative analysis of multi-omics data

Wyciślik

Mrozek

2021

Briefings in Functional Genomics

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Integrative analysis of multi-omics data is usually computationally demanding. It frequently requires building complex, multi-step analysis pipelines, applying dedicated techniques for data processing and combining several data sources. These efforts lead to a better understanding of life processes, current health state or the effects of therapeutic activities. However, many omics data analysis solutions focus only on a selected problem, disease, types of data or organisms. Moreover, they are implemented for general-purpose scientific computational platforms that most often do not easily scale the calculations natively. These features are not conducive to advances in understanding genotype–phenotypic relationships. Fortunately, with new technological paradigms, including Cloud computing, virtualization and containerization, these functionalities could be orchestrated for easy scaling and building independent analysis pipelines for omics data. Therefore, solutions can be re-used for purposes that they were not primarily designed. This paper shows perspectives of using Cloud computing advances and containerization approach for such a purpose. We first review how the Cloud computing model is utilized in multi-omics data analysis and show weak points of the adopted solutions. Then, we introduce containerization concepts, which allow both scaling and linking of functional services designed for various purposes. Finally, on the Bioconductor software package example, we disclose a verified concept model of a universal solution that exhibits the potentials for performing integrative analysis of multiple omics data sources.

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NuchaRt: Embedding High-Level Parallel Computing in R for Augmented Hi-C Data Analysis

Cited by 3 publications

References 8 publications

A Cloud Solution for Multi-omics Data Integration

A Cloud Solution for Multi-omics Data Integration

Exploiting Docker containers over Grid computing for a comprehensive study of chromatin conformation in different cell types

Perspectives of using Cloud computing in integrative analysis of multi-omics data

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