Evidence map›Paper›PMID 38260425›Full record

ArticlebioRxiv : the preprint server for biology2024

Novel metagenomics analysis of stony coral tissue loss disease.

Jakob M Heinz, Jennifer Lu, Lindsay K Huebner, Steven L Salzberg, Markus Sommer, Stephanie M Rosales

Open access · greenAbstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. 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, 3 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors at 3 institutions in 1 country.

Jakob M HeinzCenter for Computational Biology, Johns Hopkins University; Baltimore, MD 21211, United States.
Jennifer LuCenter for Computational Biology, Johns Hopkins University; Baltimore, MD 21211, United States.
Lindsay K HuebnerFish and Wildlife Research Institute, Florida Fish and Wildlife Conservation Commission; St. Petersburg, FL 33701, United States.
Steven L SalzbergCenter for Computational Biology, Johns Hopkins University; Baltimore, MD 21211, United States.
Markus SommerCenter for Computational Biology, Johns Hopkins University; Baltimore, MD 21211, United States.
Stephanie M RosalesCooperative Institute for Marine and Atmospheric Studies, University of Miami; Miami, FL 33149, United States.
Johns Hopkins University · USFlorida Fish and Wildlife Conservation Commission · USUniversity of Miami · US

Funding

Computational Methods for Genome Assembly, Transcript Assembly, and Variant DiscoveryR01HG006677 · NHGRI · JOHNS HOPKINS UNIVERSITY · PI SALZBERG, STEVEN L. · 2011 to 2025
$10.7M
Computational Methods for Microbial and Microbiome Sequence AnalysisR35GM130151 · NIGMS · JOHNS HOPKINS UNIVERSITY · PI Steven L. Salzberg · 2019 to 2026
$2.9M
NHGRI NIH HHS R01 HG006677NIGMS NIH HHS R35 GM130151
6 · The paper itself

Abstract

Stony coral tissue loss disease (SCTLD) has devastated coral reefs off the coast of Florida and continues to spread throughout the Caribbean. Although a number of bacterial taxa have consistently been associated with SCTLD, no pathogen has been definitively implicated in the etiology of SCTLD. Previous studies have predominantly focused on the prokaryotic community through 16S rRNA sequencing of healthy and affected tissues. Here, we provide a different analytical approach by applying a bioinformatics pipeline to publicly available metagenomic sequencing samples of SCTLD lesions and healthy tissues from four stony coral species. To compensate for the lack of coral reference genomes, we used data from apparently healthy coral samples to approximate a host genome and healthy microbiome reference. These reads were then used as a reference to which we matched and removed reads from diseased lesion tissue samples, and the remaining reads associated only with disease lesions were taxonomically classified at the DNA and protein levels. For DNA classifications, we used a pathogen identification protocol originally designed to identify pathogens in human tissue samples, and for protein classifications, we used a fast protein sequence aligner. To assess the utility of our pipeline, a species-level analysis of a candidate genus,

Indexed as

BioinformaticsEpidemicFlorida’s Coral ReefMetagenomicsSCTLD

Identifiers

PMID38260425
PMCPMC10802270
OpenAlexW4390656872

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.