Evidence map›Paper›PMID 37773075›Full record

ArticleGenome biology2023

happi: a hierarchical approach to pangenomics inference.

Pauline Trinh, David S Clausen, Amy D Willis

Open access · goldAbstract read
In one paragraph

Article in Genome biology, 2023. 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
0.6field-weighted citation impact, top 29% 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

4 citing papers in PubMed, 4 citations in OpenAlex.

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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

3 authors at 1 institution in 1 country.

Pauline TrinhDepartment of Environmental & Occupational Health Sciences, University of Washington, Seattle, WA, USA.
David S ClausenDepartment of Biostatistics, University of Washington, Seattle, WA, USA.
Amy D WillisDepartment of Biostatistics, University of Washington, Seattle, WA, USA. adwillis@uw.edu.ORCID http://orcid.org/0000-0002-2802-4317
University of Washington · US

Funding

Biostatistics, Epidemiologic & Bioinformatic Training in Environmental HealthT32ES015459 · NIEHS · UNIVERSITY OF WASHINGTON · PI June T. Spector · 2009 to 2026
$10.6M
Statistical methods to enhance reproducible microbiome discoveryR35GM133420 · NIGMS · UNIVERSITY OF WASHINGTON · PI Amy D Willis · 2019 to 2026
$2.8M
Statistical pangenomics to study the effects of zoonotic exposure on the gut microbiomeR21AI168679 · NIAID · UNIVERSITY OF WASHINGTON · PI WILLIS, AMY D · 2022 to 2023
$428k
NIAID NIH HHS R21 AI168679NIEHS NIH HHS T32 ES015459NIGMS NIH HHS R35 GM133420
6 · The paper itself

Abstract

Recovering metagenome-assembled genomes (MAGs) from shotgun sequencing data is an increasingly common task in microbiome studies, as MAGs provide deeper insight into the functional potential of both culturable and non-culturable microorganisms. However, metagenome-assembled genomes vary in quality and may contain omissions and contamination. These errors present challenges for detecting genes and comparing gene enrichment across sample types. To address this, we propose happi, an approach to testing hypotheses about gene enrichment that accounts for genome quality. We illustrate the advantages of happi over existing approaches using published Saccharibacteria MAGs, Streptococcus thermophilus MAGs, and via simulation.

Indexed as

MetagenomicsMicrobiotaComputer SimulationMetagenomeSequence Analysis, DNAHypothesis testingMetagenome-assembled genomesMicrobiomeShotgun metagenomicsStatistical models

Identifiers

PMID37773075
PMCPMC10540326
OpenAlexW4387157875

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.