Evidence map›Paper›PMID 33775256›Full record

ArticleMicrobiome2021

animalcules: interactive microbiome analytics and visualization in R.

Yue Zhao, Anthony Federico, Tyler Faits, Solaiappan Manimaran, Daniel Segrè, Stefano Monti, W Evan Johnson

Open access · goldAbstract readVideo-Audio Media
In one paragraph

Article in Microbiome, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed
4.0field-weighted citation impact, top 5% 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

27 citing papers in PubMed, 46 citations in OpenAlex.

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  11. Marked genetic diversity withinInternational journal for parasitology. Parasites and wildlife · 2024
    Article
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  13. Tools and methodology toFrontiers in microbiology · 2024
    Review
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  20. Innovation (Cambridge (Mass.)) · 2023
    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

7 authors at 1 institution in 1 country.

Yue Zhao *Section of Computational Biomedicine, Boston University School of Medicine, Boston, MA, USA.
Anthony Federico *Section of Computational Biomedicine, Boston University School of Medicine, Boston, MA, USA.
Tyler FaitsSection of Computational Biomedicine, Boston University School of Medicine, Boston, MA, USA.
Solaiappan ManimaranSection of Computational Biomedicine, Boston University School of Medicine, Boston, MA, USA.
Daniel SegrèBioinformatics Program, College of Engineering, Boston University, Boston, MA, USA.
Stefano MontiSection of Computational Biomedicine, Boston University School of Medicine, Boston, MA, USA.
W Evan JohnsonSection of Computational Biomedicine, Boston University School of Medicine, Boston, MA, USA. wej@bu.edu.ORCID 0000-0002-6247-6595
Boston University · US

Funding

Removing batch effects in high-throughput biomedical studiesR01GM127430 · NIGMS · RUTGERS BIOMEDICAL AND HEALTH SCIENCES · PI William Evan Johnson · 2018 to 2026
$2.4M
An interactive analysis toolkit for single cell RNA-seq in cancer researchU01CA220413 · NCI · BOSTON UNIVERSITY MEDICAL CAMPUS · PI JOHNSON, WILLIAM EVAN · 2017 to 2019
$1.2M
Microbiome-based biomarkers and models of lung cancer development and treatmentR21CA260382 · NCI · RUTGERS BIOMEDICAL AND HEALTH SCIENCES · PI JOHNSON, WILLIAM EVAN, SEGRE, DANIEL · 2022 to 2023
$377k
Intercepting head and neck cancer using functional genomics approachesF31DE029701 · NIDCR · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · PI FEDERICO, ANTHONY · 2020 to 2021
$80k
NCI NIH HHS R21 CA260382NCI NIH HHS U01 CA220413NIDCR NIH HHS F31 DE029701NIGMS NIH HHS R01 GM127430
6 · The paper itself

Abstract

backgroundMicrobial communities that live in and on the human body play a vital role in health and disease. Recent advances in sequencing technologies have enabled the study of microbial communities at unprecedented resolution. However, these advances in data generation have presented novel challenges to researchers attempting to analyze and visualize these data.

resultsTo address some of these challenges, we have developed animalcules, an easy-to-use interactive microbiome analysis toolkit for 16S rRNA sequencing data, shotgun DNA metagenomics data, and RNA-based metatranscriptomics profiling data. This toolkit combines novel and existing analytics, visualization methods, and machine learning models. For example, the toolkit features traditional microbiome analyses such as alpha/beta diversity and differential abundance analysis, combined with new methods for biomarker identification are. In addition, animalcules provides interactive and dynamic figures that enable users to understand their data and discover new insights. animalcules can be used as a standalone command-line R package or users can explore their data with the accompanying interactive R Shiny interface.

conclusionsWe present animalcules, an R package for interactive microbiome analysis through either an interactive interface facilitated by R Shiny or various command-line functions. It is the first microbiome analysis toolkit that supports the analysis of all 16S rRNA, DNA-based shotgun metagenomics, and RNA-sequencing based metatranscriptomics datasets. animalcules can be freely downloaded from GitHub at https://github.com/compbiomed/animalcules or installed through Bioconductor at https://www.bioconductor.org/packages/release/bioc/html/animalcules.html . Video abstract.

Indexed as

MicrobiotaSoftwareData Interpretation, StatisticalHumansMetagenomicsRNA, Ribosomal, 16SRNA, Ribosomal, 16SBiomarker identificationInteractive toolkitMicrobiome analysisVisualization

Identifiers

PMID33775256
PMCPMC8006385
OpenAlexW3030932204

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.