Evidence map›Paper›PMID 26954507›Full record

ArticlePloS one2016

CLUSTOM-CLOUD: In-Memory Data Grid-Based Software for Clustering 16S rRNA Sequence Data in the Cloud Environment.

Jeongsu Oh, Chi-Hwan Choi, Min-Kyu Park, Byung Kwon Kim, Kyuin Hwang, Sang-Heon Lee, Soon Gyu Hong, Arshan Nasir, Wan-Sup Cho, Kyung Mo Kim

Abstract read
In one paragraph

Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Jeongsu OhMicrobial Resource Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon, Republic of Korea.
Chi-Hwan ChoiDepartment of Bio-Information Technology, Chungbuk National University, CheongJu, Republic of Korea.
Min-Kyu ParkDepartment of Business Data Convergence, Chungbuk National University, CheongJu, Republic of Korea.
Byung Kwon KimBioNano Health Guard Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon, Republic of Korea.
Kyuin HwangDivision of Polar Life Sciences, Korea Polar Research Institute, Incheon, Republic of Korea.
Sang-Heon LeeMicrobial Resource Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon, Republic of Korea.
Soon Gyu HongDivision of Polar Life Sciences, Korea Polar Research Institute, Incheon, Republic of Korea.
Arshan NasirDepartment of Biosciences, COMSATS Institute of Information Technology, Islamabad, Pakistan.
Wan-Sup ChoDepartment of Management Information Systems/BK Plus Team, Chungbuk National University, CheongJu, Republic of Korea.
Kyung Mo KimMicrobial Resource Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon, Republic of Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

High-throughput sequencing can produce hundreds of thousands of 16S rRNA sequence reads corresponding to different organisms present in the environmental samples. Typically, analysis of microbial diversity in bioinformatics starts from pre-processing followed by clustering 16S rRNA reads into relatively fewer operational taxonomic units (OTUs). The OTUs are reliable indicators of microbial diversity and greatly accelerate the downstream analysis time. However, existing hierarchical clustering algorithms that are generally more accurate than greedy heuristic algorithms struggle with large sequence datasets. To keep pace with the rapid rise in sequencing data, we present CLUSTOM-CLOUD, which is the first distributed sequence clustering program based on In-Memory Data Grid (IMDG) technology-a distributed data structure to store all data in the main memory of multiple computing nodes. The IMDG technology helps CLUSTOM-CLOUD to enhance both its capability of handling larger datasets and its computational scalability better than its ancestor, CLUSTOM, while maintaining high accuracy. Clustering speed of CLUSTOM-CLOUD was evaluated on published 16S rRNA human microbiome sequence datasets using the small laboratory cluster (10 nodes) and under the Amazon EC2 cloud-computing environments. Under the laboratory environment, it required only ~3 hours to process dataset of size 200 K reads regardless of the complexity of the human microbiome data. In turn, one million reads were processed in approximately 20, 14, and 11 hours when utilizing 20, 30, and 40 nodes on the Amazon EC2 cloud-computing environment. The running time evaluation indicates that CLUSTOM-CLOUD can handle much larger sequence datasets than CLUSTOM and is also a scalable distributed processing system. The comparative accuracy test using 16S rRNA pyrosequences of a mock community shows that CLUSTOM-CLOUD achieves higher accuracy than DOTUR, mothur, ESPRIT-Tree, UCLUST and Swarm. CLUSTOM-CLOUD is written in JAVA and is freely available at http://clustomcloud.kopri.re.kr.

Indexed as

Cluster AnalysisEnvironmental MicrobiologySoftwareComputational BiologyHumansReproducibility of ResultsRNA, Ribosomal, 16SWorkflowRNA, Ribosomal, 16S

Identifiers

PMID26954507
PMCPMC4783016

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.