scholarly journals Pangenome analysis of the soil-borne fungal phytopathogen Rhizoctonia solani and development of a comprehensive web resource: RsolaniDB

2020 ◽  
Author(s):  
A. Kaushik ◽  
D.P. Roberts ◽  
A. Ramaprasad ◽  
S. Mfarrej ◽  
Mridul Nair ◽  
...  
Keyword(s):  
Rhizoctonia Solani ◽  
Large Scale ◽  
Functional Enrichment ◽  
Web Resource ◽  
Basidiomycetous Fungus ◽  
Large Scale Data ◽  
Collective Group ◽  
Fungal Phytopathogen ◽  
First Time ◽  
Scale Data

AbstractRhizoctonia solani is a collective group of genetically and pathologically diverse basidiomycetous fungus that damages economically important crops. Its isolates are classified into 13 Anastomosis Groups (AGs) and subgroups having distinctive morphology and host range. The genetic factors driving the unique features of R. solani pathology are not well characterized due to the limited availability of its annotated genomes. Therefore, we performed genome sequencing, assembly, annotation and functional analysis of 12 R. solani isolates covering 7 AGs and selected subgroups (AG1-IA, AG1-IB, AG1-IC, AG2-2IIIB, AG3-PT (isolates Rhs 1AP and the hypovirulent Rhs1A1), AG3-TB, AG4-HG-I (isolates Rs23 and R118-11), AG5, AG6, and AG8), in which six genomes are reported for the first time, wherein we discovered unique and shared secretomes, CAZymes, and effectors across the AGs. Using a pangenome comparative analysis of 12 R. solani isolates and 15 other basidiomycetes, we also elucidated the molecular factors potentially involved in determining the AG-specific host preference, and the attributes distinguishing them from other Basidiomycetes. Finally, we present the largest repertoire of R. solani genomes and their annotated components as a comprehensive database, viz. RsolaniDB, with tools for large-scale data mining, functional enrichment and sequence analysis not available with other state-of-the-art platforms, to assist mycologists in formulating new hypotheses.

ProGen:Provenance database generator for large-scale data set

2009 ◽  
Vol 28 (11) ◽  
pp. 2737-2740
Author(s):  
Xiao ZHANG ◽  
Shan WANG ◽  
Na LIAN
Keyword(s):  
Large Scale ◽  
Data Set ◽  
Large Scale Data ◽  
Scale Data

COMMUNITY-CURATED DATA RESOURCES AND LARGE-SCALE DATA-MODEL SYNTHESES: THE CHILDREN OF COHMAP

2016 ◽  
Author(s):  
John W. Williams ◽  
◽  
Simon Goring ◽  
Eric Grimm ◽  
Jason McLachlan
Keyword(s):  
Data Model ◽  
Large Scale ◽  
Large Scale Data ◽  
Scale Data

scholarly journals Local and global approaches of affinity propagation clustering for large scale data

2008 ◽  
Vol 9 (10) ◽  
pp. 1373-1381 ◽  
Author(s):  
Ding-yin Xia ◽  
Fei Wu ◽  
Xu-qing Zhang ◽  
Yue-ting Zhuang
Keyword(s):  
Large Scale ◽  
Affinity Propagation ◽  
Large Scale Data ◽  
Affinity Propagation Clustering ◽  
Scale Data

scholarly journals A Framework for International Collaboration on ITER Using Large-Scale Data Transfer to Enable Near-Real-Time Analysis

2021 ◽  
Vol 77 (2) ◽  
pp. 98-108
Author(s):  
R. M. Churchill ◽  
C. S. Chang ◽  
J. Choi ◽  
J. Wong ◽  
S. Klasky ◽  
...  
Keyword(s):  
Real Time ◽  
International Collaboration ◽  
Large Scale ◽  
Data Transfer ◽  
Time Analysis ◽  
Real Time Analysis ◽  
Large Scale Data ◽  
Scale Data

scholarly journals Multi-GPU approach to global induction of classification trees for large-scale data mining

2021 ◽  
Author(s):  
Krzysztof Jurczuk ◽  
Marcin Czajkowski ◽  
Marek Kretowski
Keyword(s):  
Data Mining ◽  
Large Scale ◽  
Real Life ◽  
Population Based ◽  
Tree Structure ◽  
Global Approach ◽  
Data Parallel ◽  
Large Scale Data ◽  
The Impact ◽  
Scale Data

AbstractThis paper concerns the evolutionary induction of decision trees (DT) for large-scale data. Such a global approach is one of the alternatives to the top-down inducers. It searches for the tree structure and tests simultaneously and thus gives improvements in the prediction and size of resulting classifiers in many situations. However, it is the population-based and iterative approach that can be too computationally demanding to apply for big data mining directly. The paper demonstrates that this barrier can be overcome by smart distributed/parallel processing. Moreover, we ask the question whether the global approach can truly compete with the greedy systems for large-scale data. For this purpose, we propose a novel multi-GPU approach. It incorporates the knowledge of global DT induction and evolutionary algorithm parallelization together with efficient utilization of memory and computing GPU’s resources. The searches for the tree structure and tests are performed simultaneously on a CPU, while the fitness calculations are delegated to GPUs. Data-parallel decomposition strategy and CUDA framework are applied. Experimental validation is performed on both artificial and real-life datasets. In both cases, the obtained acceleration is very satisfactory. The solution is able to process even billions of instances in a few hours on a single workstation equipped with 4 GPUs. The impact of data characteristics (size and dimension) on convergence and speedup of the evolutionary search is also shown. When the number of GPUs grows, nearly linear scalability is observed what suggests that data size boundaries for evolutionary DT mining are fading.

On Workload-Aware DRAM Failure Prediction in Large-Scale Data Centers

2021 ◽  
Author(s):  
Xingyi Wang ◽  
Yu Li ◽  
Yiquan Chen ◽  
Shiwen Wang ◽  
Yin Du ◽  
...  
Keyword(s):  
Large Scale ◽  
Data Centers ◽  
Failure Prediction ◽  
Large Scale Data ◽  
Scale Data
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