A Run-Time Library for Parallel Processing on a Multi-core DSP

Portable run-time support for dynamic object-oriented parallel processing

ACM Transactions on Computer Systems ◽

10.1145/227695.227696 ◽

1996 ◽

Vol 14 (2) ◽

pp. 139-170 ◽

Cited By ~ 29

Author(s):

Andrew S. Grimshaw ◽

Jon B. Weissman ◽

W. Timothy Strayer

Keyword(s):

Parallel Processing ◽

Object Oriented ◽

Dynamic Object ◽

Run Time ◽

Oriented Parallel

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miR-Island: an ultrafast and memory-efficient tool for plant miRNA annotation and expression analysis

10.21203/rs.2.19370/v1 ◽

2019 ◽

Author(s):

Tiantian Gao ◽

Xin Meng ◽

Wei Zhang ◽

Weibo Jin

Keyword(s):

Parallel Processing ◽

Secondary Structure ◽

Personal Computer ◽

Structure Prediction ◽

Secondary Structure Prediction ◽

Sequencing Data ◽

Efficient Tool ◽

Plant Mirna ◽

Run Time ◽

Memory Efficient

Abstract Background Next-generation sequencing of small RNAs has yielded an abundance of microRNA (miRNA) profiling data for diverse plant species. Many programs have been developed for plant miRNA annotation based on sequencing data, but these programs typically require computers with powerful hardware configurations. At present, few ultrafast computational tools are available for this type of data analysis on standard personal computers. Results We present miR-Island, an ultrafast tool for plant miRNA identification from deep sequencing data. Two important strategies contribute to the speed of the miR-Island program: (1) extracting precursor candidates using a pseudogenome and (2) using parallel processing for RNA secondary structure prediction. In our analysis, the pseudogenomic strategy reduced the time required for miRNA precursor extraction from 85 seconds to 19 seconds in Salvia miltiorrhiza , and parallel processing significantly reduced the time for the secondary structure prediction of 3957 S. miltiorrhiza miRNA precursors from 90 seconds to 32 seconds. miR-Island completed miRNA annotation for Arabidopsis in 18 minutes on a standard personal computer, which was less than 50% of the time for ShortStack and 9% of that for miRDeep-P. In terms of accuracy, miR-Island identified 128 miRNAs, which included 68 known miRNAs in miRBase. ShortStack predicted 55 total miRNAs, including 38 known miRNAs in miRBase. Of the 175 total miRNAs predicted by miRDeep-P, only 57 miRNAs were registered in miRBase. Agreement between miR-Island and ShortStack was moderate (kappa = 0.47). For the prediction of miRNAs from three other plant datasets, miR-Island spent approximately <50% of the ShortStack run time and <2.5% of the miRDeep-P run time. When the three programs were run on contig-level S. miltiorrhiza data, miR-Island, miRDeep-P and ShortStack finished the prediction in 17 minutes, 11 hours and 40 minutes, respectively. Conclusion Unlike other approaches, miR-Island is an ultrafast and memory-efficient tool for plant miRNA annotation and quantification on standard personal computer using the strategies of pseudo genome and multiple threads. In addition, miR-Island is a single command Perl script that is convenient to use for miRNA annotation and quantification. miR-Island was implemented in Linux and is available under the GPL GPU license from https://github.com/janeyurigao/miR-Island.

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