Parallel algorithm for the unsupervised binning of metagenomic sequences
ACM International Conference Proceeding Series, Page: 48-53
2021
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
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Conference Paper Description
The binning of metagenomic sequences is one of crucial steps in metagenomic projects which allow the study of uncultured organisms. Although the projects need to analyze a huge amount of data, most available binning methods run in single mode, and thus require much processing time. This paper proposes a parallel binning algorithm for metagenomic sequences without reference databases. The method is able to utilize the strength of computing clusters and shared-memory multiprocessing systems by using MPI and OpenMP techniques. Experimental results show that the proposed algorithm outperforms a single-mode binning algorithm in the aspect of computational performance while still achieving similar classification quality. The source codes and datasets used in this work can be downloaded from https://bioinfolab.fit.hcmute.edu.vn/BiMetaPL.
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