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P-Hint-Hunt: a deep parallelized whole genome DNA methylation detection tool

Overview of attention for article published in BMC Genomics, March 2017
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3 tweeters

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Title
P-Hint-Hunt: a deep parallelized whole genome DNA methylation detection tool
Published in
BMC Genomics, March 2017
DOI 10.1186/s12864-017-3497-9
Pubmed ID
Authors

Shaoliang Peng, Shunyun Yang, Ming Gao, Xiangke Liao, Jie Liu, Canqun Yang, Chengkun Wu, Wenqiang Yu

Abstract

The increasing studies have been conducted using whole genome DNA methylation detection as one of the most important part of epigenetics research to find the significant relationships among DNA methylation and several typical diseases, such as cancers and diabetes. In many of those studies, mapping the bisulfite treated sequence to the whole genome has been the main method to study DNA cytosine methylation. However, today's relative tools almost suffer from inaccuracies and time-consuming problems. In our study, we designed a new DNA methylation prediction tool ("Hint-Hunt") to solve the problem. By having an optimal complex alignment computation and Smith-Waterman matrix dynamic programming, Hint-Hunt could analyze and predict the DNA methylation status. But when Hint-Hunt tried to predict DNA methylation status with large-scale dataset, there are still slow speed and low temporal-spatial efficiency problems. In order to solve the problems of Smith-Waterman dynamic programming and low temporal-spatial efficiency, we further design a deep parallelized whole genome DNA methylation detection tool ("P-Hint-Hunt") on Tianhe-2 (TH-2) supercomputer. To the best of our knowledge, P-Hint-Hunt is the first parallel DNA methylation detection tool with a high speed-up to process large-scale dataset, and could run both on CPU and Intel Xeon Phi coprocessors. Moreover, we deploy and evaluate Hint-Hunt and P-Hint-Hunt on TH-2 supercomputer in different scales. The experimental results illuminate our tools eliminate the deviation caused by bisulfite treatment in mapping procedure and the multi-level parallel program yields a 48 times speed-up with 64 threads. P-Hint-Hunt gain a deep acceleration on CPU and Intel Xeon Phi heterogeneous platform, which gives full play of the advantages of multi-cores (CPU) and many-cores (Phi).

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 7 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 2 29%
Student > Postgraduate 1 14%
Student > Bachelor 1 14%
Student > Doctoral Student 1 14%
Researcher 1 14%
Other 0 0%
Unknown 1 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 2 29%
Nursing and Health Professions 1 14%
Biochemistry, Genetics and Molecular Biology 1 14%
Mathematics 1 14%
Medicine and Dentistry 1 14%
Other 0 0%
Unknown 1 14%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 11 May 2017.
All research outputs
#9,309,912
of 12,122,587 outputs
Outputs from BMC Genomics
#5,174
of 7,135 outputs
Outputs of similar age
#176,435
of 266,219 outputs
Outputs of similar age from BMC Genomics
#73
of 94 outputs
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