OneGenE: Regulatory Gene Network Expansion via Distributed Volunteer Computing on BOINC

Asnicar, Francesco; Masera, Luca; Pistore, Davide; Valentini, Samuel; Cavecchia, Valter; Blanzieri, Enrico

doi:10.1109/empdp.2019.8671629

Cited by 4 publications

(5 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…OneGenE [15] is a method to compute ranked candidate gene lists that expands known local gene networks given gene expression data. As its predecessor NES 2 RA, OneGenE (Algorithms 1 and 3) is based on the systematic and iterative application of the skeleton function of the PC algorithm (Algorithm 2) on subsets of the input data.…”

Section: Methodsmentioning

confidence: 99%

“…LGNs, by pre-computing partial results, namely single-gene NES 2 RA expansions, on the BOINC platform. Figure 1 shows the block scheme of the OneGenE architecture [15], highlighting the different platforms and the two computational stages. First, a pre-computation step: candidate expansion lists are pre-computed for each gene (or more generally transcript) of the target organism exploiting the BOINC platform.…”

Section: Methodsmentioning

confidence: 99%

“…LGN be the set of transcripts in an input LGN and l g,θ is the candidate expansion list of the gene g with the parameter tuple θ. The final candidate gene expansion list is obtained by combining the set of partial results L = {l g,θ | g ∈ S LGN , θ ∈ Θ} by means of a ranking aggregator [15]. Algorithm 3 shows possible alternatives (threshold on the relative frequency and fixed or variable cut-offs on the rank) that are relevant for the two case studies (Sections 5 and 6) where the expanded lists intervene in rather complex workflows.…”

Section: Ranking or List Aggregation Let Smentioning

confidence: 99%

“…To overcome these problems, we are now adopting an approach called OneGenE [15] which aims to expand each single gene in an organism. OneGenE's main idea is to calculate the list of gene expansions for each gene in an organism by systematically running single-gene NES 2 RA expansions with fixed parameters, and then combine them afterwards to simulate LGN expansions.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Computing System for Discovering Causal Relationships Among Human Genes to Improve Drug Repositioning

Blanzieri

Tebaldi

Cavecchia

et al. 2021

IEEE Trans. Emerg. Topics Comput.

Self Cite

View full text Add to dashboard Cite

The automatic discovery of causal relationships among human genes can shed light on gene regulatory processes and guide drug repositioning. To this end, a computationally-heavy method for causal discovery is distributed on a volunteer computing grid and, taking advantage of variable subsetting and stratification, proves to be useful for expanding local gene regulatory networks. The input data are purely observational measures of transcripts expression in human tissues and cell lines collected within the FANTOM project. The system relies on the BOINC platform and on optimized client code. The functional relevance of results, measured by analyzing the annotations of the identified interactions, increases significantly over the simple Pearson correlation between the transcripts. Additionally, in 82% of cases networks significantly overlap with known protein-protein interactions annotated in biological databases. In the two case studies presented, this approach has been used to expand the networks of genes associated with two severe human pathologies: prostate cancer and coronary artery disease. The method identified respectively 22 and 36 genes to be evaluated as novel targets for already approved drugs, demonstrating the effective applicability of the approach in pipelines aimed to drug repositioning.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Ranking or List Aggregation Let Smentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Computing System for Discovering Causal Relationships Among Human Genes to Improve Drug Repositioning

Blanzieri

Tebaldi

Cavecchia

et al. 2021

IEEE Trans. Emerg. Topics Comput.

Self Cite

View full text Add to dashboard Cite

show abstract

“…The expansion of each gene of the Vitis vinifera genome originated from the run of the algorithms of the OneGenE system [18,38]. Each expansion procedure consisted of 2000 iterations of our C++ implementation (https://bitbucket.org/francesco-asnicar/pc-boinc/, last accessed on 16 June 2020) of the PC-algorithm skeleton procedure [39] (α = 0.05) to 29 sets of 1000 variables (genes), which included the gene to be expanded, and a random subset of 999 genes sampled without replacement.…”

Section: Computation Of Onegene Expansion Listsmentioning

confidence: 99%

Vitis OneGenE: A Causality-Based Approach to Generate Gene Networks in Vitis vinifera Sheds Light on the Laccase and Dirigent Gene Families

et al. 2021

Self Cite

View full text Add to dashboard Cite

The abundance of transcriptomic data and the development of causal inference methods have paved the way for gene network analyses in grapevine. Vitis OneGenE is a transcriptomic data mining tool that finds direct correlations between genes, thus producing association networks. As a proof of concept, the stilbene synthase gene regulatory network obtained with OneGenE has been compared with published co-expression analysis and experimental data, including cistrome data for MYB stilbenoid regulators. As a case study, the two secondary metabolism pathways of stilbenoids and lignin synthesis were explored. Several isoforms of laccase, peroxidase, and dirigent protein genes, putatively involved in the final oxidative oligomerization steps, were identified as specifically belonging to either one of these pathways. Manual curation of the predicted sequences exploiting the last available genome assembly, and the integration of phylogenetic and OneGenE analyses, identified a group of laccases exclusively present in grapevine and related to stilbenoids. Here we show how network analysis by OneGenE can accelerate knowledge discovery by suggesting new candidates for functional characterization and application in breeding programs.

show abstract

Distributed Computing for Gene Network Expansion in R Environment

Dolgaleva

Blanzieri

Cavecchia

et al. 2022

Lecture Notes in Computer Science

View full text Add to dashboard Cite

OneGenE: Regulatory Gene Network Expansion via Distributed Volunteer Computing on BOINC

Cited by 4 publications

References 25 publications

A Computing System for Discovering Causal Relationships Among Human Genes to Improve Drug Repositioning

A Computing System for Discovering Causal Relationships Among Human Genes to Improve Drug Repositioning

Vitis OneGenE: A Causality-Based Approach to Generate Gene Networks in Vitis vinifera Sheds Light on the Laccase and Dirigent Gene Families

Distributed Computing for Gene Network Expansion in R Environment

Contact Info

Product

Resources

About