Evidence map›Paper›PMID 29925824›Full record

ArticleGenes2018

CoreProbe: A Novel Algorithm for Estimating Relative Abundance Based on Metagenomic Reads.

Dongmei Ai, Hongfei Pan, Ruocheng Huang, Li C Xia

Open access · goldAbstract read
In one paragraph

Article in Genes, 2018. 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
0.1field-weighted citation impact, top 53% of its field
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, 2 citations in OpenAlex.

  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

4 authors at 3 institutions in 2 countries.

Dongmei AiSchool of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China. aidongmei@ustb.edu.cn.
Hongfei PanSchool of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China. aidongmei@ustb.edu.cn.
Ruocheng HuangSinotech Genomics, Shanghai 200120, China. ruocheng.huang@sinotechgenomics.com.
Li C XiaDepartment of Medicine, Stanford University School of Medicine, 269 Campus Dr., Stanford, CA 94305, USA. lixia@stanford.edu.
University of Science and Technology Beijing · CNSinotech Genomics (China) · CNStanford University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the rapid development of high-throughput sequencing technology, the analysis of metagenomic sequencing data and the accurate and efficient estimation of relative microbial abundance have become important ways to explore the microbial composition and function of microbes. In addition, the accuracy and efficiency of the relative microbial abundance estimation are closely related to the algorithm and the selection of the reference sequence for sequence alignment. We introduced the microbial core genome as the reference sequence for potential microbes in a metagenomic sample, and we constructed a finite mixture and latent Dirichlet models and used the Gibbs sampling algorithm to estimate the relative abundance of microorganisms. The simulation results showed that our approach can improve the efficiency while maintaining high accuracy and is more suitable for high-throughput metagenomic data. The new approach was implemented in our CoreProbe package which provides a pipeline for an accurate and efficient estimation of the relative abundance of microbes in a community. This tool is available free of charge from the CoreProbe's website: Access the Docker image with the following instruction: sudo docker pull panhongfei/coreprobe:1.0.

Indexed as

core genomeDirichlet modelGibbs samplingmetagenomicsrelative abundance estimation

Identifiers

PMID29925824
PMCPMC6027520
OpenAlexW2809071711

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.