NES<sup>2</sup>RA

Asnicar, Francesco; Masera, Luca; Coller, E.; Gallo, Caterina; Sella, Nadir; Tolio, T.; Morettin, Paolo; Erculiani, Luca; Galante, Francesca; Semeniuta, Stanislau; Malacarne, G.; Engelen, Kristof; Argentini, Andrea; Cavecchia, Valter; Moser, C.; Blanzieri, Enrico

doi:10.1177/1094342016662508

Cited by 7 publications

(3 citation statements)

References 51 publications

(42 reference statements)

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“…Ranked aggregation for metaanalysis can also be modified to change the outcomes of GCN by buffering the effect of sample heterogeneity (Zhong et al, 2014;Wang et al, 2015a;Asnicar et al, 2016). Aggregated rank standardized correlation/MI matrices were calculated from separate experiments to determine if this approach enhanced GCN performance.…”

Section: Ranked Aggregation Of Network Improved Performance Of Gcnsmentioning

confidence: 99%

Construction and Optimization of a Large Gene Coexpression Network in Maize Using RNA-Seq Data

et al. 2017

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ORCID IDs: 0000-0002-6182-800X (J.H.); 0000-0001-6612-3570 (S.V.); 0000-0002-9564-8146 (K.M.M.).With the emergence of massively parallel sequencing, genomewide expression data production has reached an unprecedented level. This abundance of data has greatly facilitated maize research, but may not be amenable to traditional analysis techniques that were optimized for other data types. Using publicly available data, a gene coexpression network (GCN) can be constructed and used for gene function prediction, candidate gene selection, and improving understanding of regulatory pathways. Several GCN studies have been done in maize (Zea mays), mostly using microarray datasets. To build an optimal GCN from plant materials RNA-Seq data, parameters for expression data normalization and network inference were evaluated. A comprehensive evaluation of these two parameters and a ranked aggregation strategy on network performance, using libraries from 1266 maize samples, were conducted. Three normalization methods and 10 inference methods, including six correlation and four mutual information methods, were tested. The three normalization methods had very similar performance. For network inference, correlation methods performed better than mutual information methods at some genes. Increasing sample size also had a positive effect on GCN. Aggregating single networks together resulted in improved performance compared to single networks.Maize (Zea mays) is the most widely produced crop in United States, and U.S. agriculture accounted for 36% of world maize production in 2015 (USDA, 2016). Maize has also been in the center of genetics research for more than 100 years, including McClintock's pioneering work with transposable elements (reviewed by McClintock, 1983;Fedoroff, 2012). Due to recent technological advances in nucleic acid sequencing and the availability of the maize genome sequence (Schnable et al., 2009), maize genomics research has been greatly expedited.RNA-sequencing (RNA-Seq) has become the favored technique for detecting genomewide expression patterns. RNA-Seq has some advantages over microarray analysis of gene expression, including single base-pair resolution, detection of novel transcripts, and the ability to analyze transcript abundance without existing genome information (reviewed by Wang et al., 2009;Han et al., 2015;Conesa et al., 2016). RNA-Seq data provides information about single nucleotide polymorphisms, which facilitates genomewide association studies (Fu et al., 2013;Li et al., 2013a;Lonsdale et al., 2013;Fadista et al., 2014). Because of its widespread adaptability, greater than 5000 Illumina platform Maize RNA-Seq libraries (Fig. 1A) are available in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) database (Leinonen et al., 2010), adding to the body of data that can be used to study the maize genome.The maize genome is large and heterogeneous, and the genome annotation is still far from complete (Cigan et al., 2005;Ficklin and Feltus, 2011). Although recent work has...

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Section: Ranked Aggregation Of Network Improved Performance Of Gcnsmentioning

confidence: 99%

Construction and Optimization of a Large Gene Coexpression Network in Maize Using RNA-Seq Data

et al. 2017

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“…Finally, the interface is the other pivotal point towards seamless integration with other services and tools and has been designed to adapt to users' needs, as well as to simplify the implementation of other tools on top of it. One example of such means is the NES 2 RA algorithm (Asnicar et al 2018), a mining tool for transcriptomic data used to expand a known local gene network (LGN) by finding new related genes. This method has been applied to the grapevine transcriptomic dataset using VESPUCCI as data source to expand LGNs related to the secondary metabolic pathways for anthocyanin and stilbenoid synthesis and signaling networks related to the hormones abscisic acid and ethylene (Malacarne et al 2018).…”

Section: Resourcesmentioning

confidence: 99%

Status and Prospects of Systems Biology in Grapevine Research

Matus

Ruggieri

Romero

et al. 2019

Compendium of Plant Genomes

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The cultivated grapevine, Vitis vinifera L., has gathered a vast amount of omics data throughout the last two decades, driving the imperative use of computational resources for its analysis and integration. Molecular systems biology arises from this need allowing to model and predict the emergence of phenotypes or responses in biological systems. Beyond single omics networks, integrative approaches associate the molecular components of an organism and combine them into higher order networks to model dynamic behaviors. Application of network-based methods in multi-omics data is providing additional resources to address important questions regarding grapevine fruit quality and composition. Here we review the recent history of systems biology in this species. We highlight the most relevant aspects of the discipline and describe important integrative studies that have helped in the global understanding of how this species responds to the environment and how it triggers the fruit ripening developmental program. We also highlight the latest resources that are available for the grapevine community to exploit and take advantage of all the omics data that its being generated.

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“…The fifth paper, entitled 'NES 2 RA: Network expansion by stratified variable subsetting and ranking aggregation', by F Asnicar, L Masera, E Coller, C Gallo, N Sella, T Tolio, P Morettin, L Erculiani, F Galante, S Semeniuta, G Malacarne, K Engelen, A Argentini, V Cavecchia, C Moser and E Blanzieri (see Asnicar et al, 2016), presents and extensively discusses the computational aspects of NES 2 RA, a novel approach for generating ranked candidate genes lists, which expands known local gene networks (LGNs) starting from gene expression data. NES 2 RA relies on the computational power provided by the gene@home BOINC project, hosted by the TN-Grid platform.…”

Section: Detailed Contentmentioning

confidence: 99%

Parallelism in computational biology

Vega‐Rodríguez

Rubio-Largo

2016

The International Journal of High Performance Computing Applica

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Computational biology allows and encourages the application of many different parallelism-based technologies. This special issue brings together high-quality state-of-the-art contributions about parallelism-based technologies in computational biology, from different points of view or perspectives, that is, from diverse high-performance computing applications. The special issue collects considerably extended and improved versions of the best papers, accepted and presented in PBio 2015 (the Third International Workshop on Parallelism in Bioinformatics, and part of IEEE ISPA 2015). The domains and topics covered in these seven papers are timely and important, and the authors have done an excellent job of presenting the material.

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NES²RA

Cited by 7 publications

References 51 publications

Construction and Optimization of a Large Gene Coexpression Network in Maize Using RNA-Seq Data

Construction and Optimization of a Large Gene Coexpression Network in Maize Using RNA-Seq Data

Status and Prospects of Systems Biology in Grapevine Research

Parallelism in computational biology

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NES2RA

Cited by 7 publications

References 51 publications

Construction and Optimization of a Large Gene Coexpression Network in Maize Using RNA-Seq Data

Construction and Optimization of a Large Gene Coexpression Network in Maize Using RNA-Seq Data

Status and Prospects of Systems Biology in Grapevine Research

Parallelism in computational biology

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NES²RA