Evidence map›Paper›PMID 34098872›Full record

ArticleBMC bioinformatics2021

Exploring the functional composition of the human microbiome using a hand-curated microbial trait database.

J L Weissman, Sonia Dogra, Keyan Javadi, Samantha Bolten, Rachel Flint, Cyrus Davati, Jess Beattie, Keshav Dixit, Tejasvi Peesay, Shehar Awan and 6 more

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In one paragraph

Article in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. 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

16 authors.

J L WeissmanDepartment of Biology, University of Maryland - College Park, College Park, MD, USA.
Sonia DograDepartment of Biology, University of Maryland - College Park, College Park, MD, USA.
Keyan JavadiDepartment of Biology, University of Maryland - College Park, College Park, MD, USA.
Samantha BoltenDepartment of Biology, University of Maryland - College Park, College Park, MD, USA.
Rachel FlintDepartment of Biology, University of Maryland - College Park, College Park, MD, USA.
Cyrus DavatiDepartment of Biology, University of Maryland - College Park, College Park, MD, USA.
Jess BeattieDepartment of Biology, University of Maryland - College Park, College Park, MD, USA.
Keshav DixitDepartment of Biology, University of Maryland - College Park, College Park, MD, USA.
Tejasvi PeesayDepartment of Biology, University of Maryland - College Park, College Park, MD, USA.
Shehar AwanDepartment of Biology, University of Maryland - College Park, College Park, MD, USA.
Peter ThielenResearch and Exploratory Development Department, Johns Hopkins Applied Physics Laboratory, Laurel, MD, USA.
Florian BreitwieserResearch and Exploratory Development Department, Johns Hopkins Applied Physics Laboratory, Laurel, MD, USA.
Philip L F JohnsonDepartment of Biology, University of Maryland - College Park, College Park, MD, USA.
David KarigBioengineering Department, Clemson University, Clemson, SC, USA.
William F FaganDepartment of Biology, University of Maryland - College Park, College Park, MD, USA.
Sharon BewickBiological Sciences Department, Clemson University, Clemson, SC, USA. sbewick@clemson.edu.

Funding

Army Research Laboratory #W911NF-14-1-0490
6 · The paper itself

Abstract

backgroundEven when microbial communities vary wildly in their taxonomic composition, their functional composition is often surprisingly stable. This suggests that a functional perspective could provide much deeper insight into the principles governing microbiome assembly. Much work to date analyzing the functional composition of microbial communities, however, relies heavily on inference from genomic features. Unfortunately, output from these methods can be hard to interpret and often suffers from relatively high error rates.

resultsWe built and analyzed a domain-specific microbial trait database from known microbe-trait pairs recorded in the literature to better understand the functional composition of the human microbiome. Using a combination of phylogentically conscious machine learning tools and a network science approach, we were able to link particular traits to areas of the human body, discover traits that determine the range of body areas a microbe can inhabit, and uncover drivers of metabolic breadth.

conclusionsDomain-specific trait databases are an effective compromise between noisy methods to infer complex traits from genomic data and exhaustive, expensive attempts at database curation from the literature that do not focus on any one subset of taxa. They provide an accurate account of microbial traits and, by limiting the number of taxa considered, are feasible to build within a reasonable time-frame. We present a database specific for the human microbiome, in the hopes that this will prove useful for research into the functional composition of human-associated microbial communities.

Indexed as

BacteriaMicrobiotaHumansPhenotypeFunctional communityPhylogenetic correctionRandom forestTrait database

Identifiers

PMID34098872
PMCPMC8186035

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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.