Evidence map›Paper›PMID 42108292›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2026

Absolute Quantification of Bacteria in the Microbiome and Its Application.

Jia-Xin Guo, Yi-Zhou Gao

Abstract read
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In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

Jia-Xin GuoThe Center for Microbes, Development and Health, Shanghai Institute of Immunity and Infection, Chinese Academy of Sciences, Shanghai, China.
Yi-Zhou GaoThe Center for Microbes, Development and Health, Shanghai Institute of Immunity and Infection, Chinese Academy of Sciences, Shanghai, China. gaoyizhou@hrbmu.edu.cn.ORCID https://orcid.org/0000-0003-3539-4647

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The advent of genomics and deep sequencing technologies has facilitated the development of absolute quantification techniques, which offer researchers more objective and precise sequencing outcomes. Unlike traditional relative quantification methods, which provide comparative data, absolute quantification delivers definitive measurements of genes or taxa. This analytical approach mitigates the potential for extraneous influences when comparing disparate samples, thereby reducing analytical errors. The implementation of absolute quantification techniques enhances our comprehension of microbial community structures, ecological dynamics, and their associations with host health or disease conditions. This chapter emphasizes a straightforward and broadly applicable method for genomic quantification, which necessitates the incorporation of a specified amount of internal standard DNA into the samples, eliminating the need for subsequent adjustments during library construction and sequencing. By assessing the proportion of internal standard DNA across various samples, sequencing data can be transformed into absolute quantification metrics. The internal standard method for absolute quantification is versatile and can be effectively utilized across multiple domains, including disease diagnosis, microbial ecology research, the fermentation industry, and environmental monitoring. Overall, absolute quantification methods furnish a more accurate and holistic perspective for microbiome research.

Indexed as

BacteriaDNA, BacterialHigh-Throughput Nucleotide SequencingMetagenomicsMicrobiotaGenomicsHumansReference StandardsDNA, BacterialInternal standard methodMultiple internal standardsNext-generation sequencing analysisQuantitative microbiomeSpike DNA

Identifiers

What OpenQuestion holds

Textmetadata
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.