Evidence map›Paper›PMID 32993155›Full record

ArticleInternational journal of molecular sciences2020

Characterization of Fecal Microbiota with Clinical Specimen Using Long-Read and Short-Read Sequencing Platform.

Po-Li Wei, Ching-Sheng Hung, Yi-Wei Kao, Ying-Chin Lin, Cheng-Yang Lee, Tzu-Hao Chang, Ben-Chang Shia, Jung-Chun Lin

Abstract read
In one paragraph

Article in International journal of molecular sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing 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

19 citing papers in PubMed.

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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

8 authors.

Po-Li WeiDivision of Colorectal Surgery, Department of Surgery, Taipei Medical University Hospital, Taipei Medical University, Taipei 110, Taiwan.ORCID 0000-0002-7275-2661
Ching-Sheng HungPhD Program in Medical Biotechnology, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan.
Yi-Wei KaoGraduate Institute of Business Administration, College of Management. Fu Jen Catholic University, New Taipei City 242062, Taiwan.
Ying-Chin LinDepartment of Family Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan.
Cheng-Yang LeeOffice of Information Technology, Taipei Medical University, Taipei 106, Taiwan.ORCID 0000-0002-5690-4194
Tzu-Hao ChangGraduate Institute of Biomedical Informatics, Taipei Medical University, Taipei 106, Taiwan.ORCID 0000-0001-7011-3754
Ben-Chang ShiaGraduate Institute of Business Administration, College of Management. Fu Jen Catholic University, New Taipei City 242062, Taiwan.
Jung-Chun LinPhD Program in Medical Biotechnology, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan.ORCID 0000-0003-3781-1504

Funding

Industrial Technology Research Institute A108-088Ministry of Science and Technology MOST108-2320-B-038-034
6 · The paper itself

Abstract

Accurate and rapid identification of microbiotic communities using 16S ribosomal (r)RNA sequencing is a critical task for expanding medical and clinical applications. Next-generation sequencing (NGS) is widely considered a practical approach for direct application to communities without the need for in vitro culturing. In this report, a comparative evaluation of short-read (Illumina) and long-read (Oxford Nanopore Technologies (ONT)) platforms toward 16S rRNA sequencing with the same batch of total genomic DNA extracted from fecal samples is presented. Different 16S gene regions were amplified, bar-coded, and sequenced using the Illumina MiSeq and ONT MinION sequencers and corresponding kits. Mapping of the sequenced amplicon using MinION to the entire 16S rRNA gene was analyzed with the cloud-based EPI2ME algorithm. V3-V4 reads generated using MiSeq were aligned by applying the CLC genomics workbench. More than 90% of sequenced reads generated using distinct sequencers were accurately classified at the genus or species level. The misclassification of sequenced reads at the species level between the two approaches was less substantial as expected. Taken together, the comparative results demonstrate that MinION sequencing platform coupled with the corresponding algorithm could function as a practicable strategy in classifying bacterial community to the species level.

Indexed as

Gastrointestinal MicrobiomeBacteriaFecesGenomicsHigh-Throughput Nucleotide SequencingHumansNanopore SequencingRNA, Ribosomal, 16SSequence Analysis, DNASequence Analysis, RNARNA, Ribosomal, 16S16S rRNAgut microbiotaMinIONMiSeq

Identifiers

PMID32993155
PMCPMC7582668

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Registered trials

None linked

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.