Discovering Candidates for Gene Network Expansion by Distributed Volunteer Computing

Abstract

Our group has recently developed gene@home, a BOINC project that permits to search for candidate genes for the expansion of a gene regulatory network using gene expression data. The gene@home project adopts intensive variablesubsetting strategies enabled by the computational power provided by the volunteers who have joined the project by means of the BOINC client, and exploits the PC algorithm for discovering putative causal relationships within each subset of variables. This paper presents our TN-Grid infrastructure that is hosting the gene@home project. Gene@home implements a novel method for Network Expansion by Subsetting and Ranking Aggregation (NESRA), producing a list of genes that are candidates for the gene network expansion task. NESRA is an algorithm that has: 1) a ranking procedure that systematically subsets the variables; the subsetting is iterated several times and a ranked list of candidates is produced by counting the number of times a relationship is found; 2) several ranking steps are executed with different values of the dimension of the subsets and with different number of iterations producing several ranked lists; 3) the ranked lists are aggregated by using a state-of-the-art ranking aggregator. In our experimental results, we show that a single ranking step is enough to outperform both PC and PC*. Evaluations and experiments are done by means of the gene@home project on a real gene regulatory network of the model plant Arabidopsis thaliana.

NES2RA

“…Depending on the probabilities, the genes will be included in the data for the run of the PC-algorithm. If all the probabilities are zero NES 2 RA coincides with its previous version NESRA (Asnicar et al, 2015a). The high-level structure of NES 2 RA is described in Figure 1 and Algorithm 1.…”

“…When we found a gene belonging to Class 1 or Class 2 we considered it to be a true positive, while a gene falling in Class 3 or Class 4 was considered a false positive. The precision of the genes in the candidate output list is (Chen et al, 2012) 3 265441_at AT2G20870 Cell wall protein precursor Class 1 (Cai et al, 2007) 4 255644_at AT4G00870 Basic helix-loop-helix (bHLH) family protein Class 2 (Hu et al, 2003) 5 261375_at AT1G53160 SPL4 (SQUAMOSA PROMOTER BINDING PROTEIN-LIKE 4) Class 1 (Lal et al, 2011) 6 249939_at AT5G22430 Similar to unknown protein Class 1 (Zik and Irish, 2003) 7 255448_at AT4G02810 FAF1 (FANTASTIC FOUR 1) Class 1 (Wahl et al, 2010) 8 245842_at AT1G58430 RXF26 Class 1 (Shi et al, 2011) 9 256259_at AT3G12460 DEDDy 3'-5' exonuclease domain-containing protein Class 4 10 260355_at AT1G69180 CRC (CRABS CLAW) Class 1 (Lee et al, 2005) (Asnicar et al, 2015a) 0:9060:098 0:6560:049 0:6360:038 0:4360:016 ARACNE (Asnicar et al, 2015a) 0.20 0.30 0.35 0.45 the ratio between the number of true positives and the sum of true positives and false positives. Other measures, like F1 and Recall, can not be computed on real organisms' data sets because no complete ground truth is available.…”

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

“…However, the worst-case complexity of the algorithm makes this approach not directly suitable for data of complex organisms with tens of thousands of genes. These problems can be mitigated by subsetting the variables and restricting the inference to the expansion of known gene regulatory networks of interest [10], [11].…”

“…The TN-GRID platform, which runs on a virtual server in the Data Center of the University of Trento, hosted the NES 2 RA algorithm [10], [11] which has been already used to calculate gene network expansions in plants and microor- ganisms, such as A. thaliana, E.coli and V. vinifera [10], [13]. NES 2 RA [11] outperformed the state of the art algorithm ARACNE [14] in the task of network expansion.…”

Using Volunteer Computing to Study Some Features of Diagonal Latin Squares

“…The de ning characteristic of this type of computing is that it uses resources of volunteer's computers. Volunteer computing projects have been successfully used over the past two decades to solve problems from various areas (e.g., [2,13,35]). In the present study, two volunteer computing projects, Gerasim@home and SAT@home, were used to solve two mentioned combinatorial problems.…”