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<div style="background-color: #D4E2FC; border-top: 1px solid #5F92F2; font-size: bigger; padding-left: 15px; margin: 12px -5px -5px -5px;">'''BOINC project page template'''</div> [[File:{{#setmainimage:Tn-grid.jpg}}|alt=logo image|center|frameless]] BOINC based [https://gene.disi.unitn.it/test/ '''''TN-Grid'''''] is a '''''[[wikipedia:Volunteer computing|volunteer computing]]''''' project that needs your help to do research in various scientific projects. == Why TN-Grid? == * why this topic/object of study? == Goal == * summarize the objectives and challenges which the project addresses, before jumping into details == Sub-projects == ==== gene@home ==== Every living being has a genetic code and a set of genes, which are needed to produce proteins starting from coded pieces of information. Genes are necessary for life and maintenance of organisms and are expressed inside cells: the contained information is transcribed and translated into proteins. This gene expression phenomenon is based on a complex chain of events in which some particular proteins act on genes regions and can be simplified through a causal relationship between two genes. Causality is a kind of cause-and-effect binding between two variables: it means that the occurrence of the one is the cause of the appearance of the other. Gene expression information is usually represented in Gene Regulatory Networks (GRN), which use edges to indicate the causal relationship between two genes. This representation is very useful to predict and manipulate the behavior of a system. Every GRN can be expanded in order to add or suggest new genes related to the ones already known; this allows for amplification of the research and the analysis of a network. However, there are just a few methods available to perform the expansion, which is still an open challenge in the Bioinformatics world. The project gene@home is meant to perform the GRN expansion and exploits an algorithm called PC-IM. It is an iterative implementation of the PC algorithm, which finds a gene network and studies its causal relationships, aimed to estimate if a list of new genes can have a causal relationship with an already known GRN. In particular, the new genes are partitioned in blocks and merged with the GRN; afterwards the PC is applied on each block to look for new possible relationships. At the end of the process the algorithm self-evaluates its performance, and based on this decides the final network to return as an output.[https://gene.disi.unitn.it/test/genehome/en/description/basic-description.php] In collaboration with Fondazione Edmund Mach (FEM) and the Department of Information Engineering and Computer Science (DISI) of UNITN. == Project team / Sponsors == National Research Council of Italy (CNR) & University of Trento, Italy (UniTN). BOINC.Italy. Tn-Grid got also a supporting grant from AMD, via the Covid-19 AMD HPC Fund == Scientific results == ==== gene@home ==== The PC-IM algorithm has been evaluated on expression data of the plant Arabidopsis Thaliana, using its flower organ specification gene regulatory network (FOS-GRN). There have been three kind of evaluation: '''1. PRELIMINARY EVALUATION''' Expression data in silico (generated from mathematical equations) and in vivo (real data available in public databases) have been studied in order to find the most reliable ones. The in silico method showed greater precision and sensitivity, but its results were influenced by the algorithm used; whereas the in vivo method overcame this problem, and for this reason it has been chosen. Also, the PC and ARACNE algorithm have been compared, to find the most effective one in the GRN expansion. The winner was the PC, since it has better performance when applied to real gene expression data and a better PPV. '''2. PC-IM EVALUATION''' Four sub-experiments has been performed to analyze the PC-IM algorithm: '''a.''' Size of blocks: the algorithm has been run with blocks of different sizes. The optimal values were found by using 1000 genes '''b.''' Number of iterations: nine different iteration values have been analyzed; the best performance have been obtained with 100 iterations '''c.''' Robustness: the PC-IM has been run both with a FOS-GRN and with a non-real GRN as inputs. In the first case it has reached better PPV and sensitivity, showing that it is robust '''d.''' Comparison with GENIES: the algorithm has been compared with GENIES, a competitor method recently developed for the LGN expansion. This one showed better expansion performance, but it did not find any extra gene. The result of the PC-IM was a larger number of genes in the final expansion list. Therefore, the PC-IM can be considered more efficient in the LGN expansion task '''3. BIOLOGICAL VALIDATION''' The final results of the PC-IM has been validated through a bibliographic search. This process found certain correlations or non-correlations for almost the 50% of genes; whereas for the remaining ones there were not useful references describing their functions. In conclusion, even though it is not possible to validate all the genes because some of them are not addressed in studies, those at the top of the expansion list are strongly related to the LGN. Also, results have been evaluated compared to the ones obtained by using random genes. The output of the PC-IM was significant, contrary to that of the random ones (the LR+ value was very low). This means that the genes found by the PC-IM have good probabilities to be related with those of the LGN, and that have low probability to be randomly obtained.[https://gene.disi.unitn.it/test/genehome/en/description/results.php] == Scientific publications == ==== gene@home ==== # Pilati, Stefania, Giulia Malacarne, David Navarro-PayΓ‘ ''et al''. '''''[https://www.mdpi.com/2218-273X/11/12/1744 Vitis OneGenE: A Causality-Based Approach to Generate Gene Networks in Vitis vinifera Sheds Light on the Laccase and Dirigent Gene Families]'''''. Biomolecules (2021). DOI: 10.3390/biom11121744. # Blanzieri, Enrico, Toma Tebaldi, Valter Cavecchia ''et al''. '''''[https://ieeexplore.ieee.org/document/9224179/ A Computing System for Discovering Causal Relationships Among Human Genes to Improve Drug Repositioning]'''''. IEEE Transactions on Emerging Topics in Computing (2021). DOI: 10.1109/TETC.2020.3031024. # Asnicar, Francesco, Luca Masera, Davide Pistore, Samuel Valentini, Valter Cavecchia and Enrico Blanzieri. OneGenE: '''''[https://ieeexplore.ieee.org/document/8671629/ Regulatory Gene Network Expansion via Distributed Volunteer Computing on BOINC]'''''. 2019 27th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP) (2019). DOI: 10.1109/EMPDP.2019.8671629. # Malacarne, Giulia, Stefania Pilati, Samuel Valentini ''et al''. '''''[https://www.frontiersin.org/article/10.3389/fpls.2018.01385/full Discovering Causal Relationships in Grapevine Expression Data to Expand Gene Networks. A Case Study: Four Networks Related to Climate Change]'''''. Frontiers in Plant Science (2018). DOI: 10.3389/fpls.2018.01385. # Asnicar, Francesco, Luca Masera, Emanuela Coller ''et al''. [http://journals.sagepub.com/doi/10.1177/1094342016662508 '''''NES 2 RA: Network expansion by stratified variable subsetting and ranking aggregation''''']. The International Journal of High Performance Computing Applications (2018). DOI: 10.1177/1094342016662508. # Asnicar, Francesco, Luca Erculiani, Francesca Galante ''et al''. '''''[http://ieeexplore.ieee.org/document/7345656/ Discovering Candidates for Gene Network Expansion by Distributed Volunteer Computing]'''''. 2015 IEEE Trustcom/BigDataSE/ISPA (2015). DOI: 10.1109/Trustcom.2015.640. # Erculiani, Luca, Francesca Galante, Caterina Gallo ''et al''. '''''[http://f1000research.com/posters/4-562 Discovering candidates for gene network expansion by variable subsetting and ranking aggregation]'''''. (2015). DOI: 10.7490/F1000RESEARCH.1110311.1. # Asnicar, F., Nadir Sella, L. Masera ''et al''. '''''[https://www.semanticscholar.org/paper/TN-Grid-and-gene%40home-project%3A-volunteer-computing-Asnicar-Sella/e9fa0174faa804a3d9673a73530e0e8f66633916 TN-Grid and gene@home project: volunteer computing for bioinformatics]'''''. International Conference on High Performance Computing (2015).
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