Evidence map›Paper›PMID 39071384›Full record

ArticlebioRxiv : the preprint server for biology2024

Comprehensive analysis of microbial content in whole-genome sequencing samples from The Cancer Genome Atlas project.

Yuchen Ge, Jennifer Lu, Daniela Puiu, Mahler Revsine, Steven L Salzberg

Abstract 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.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Yuchen GeCenter for Computational Biology, Johns Hopkins University, Baltimore, Maryland, United States.ORCID 0000-0001-6955-590X
Jennifer LuCenter for Computational Biology, Johns Hopkins University, Baltimore, Maryland, United States.ORCID 0000-0001-9167-2002
Daniela PuiuCenter for Computational Biology, Johns Hopkins University, Baltimore, Maryland, United States.ORCID 0000-0002-2386-9265
Mahler RevsineCenter for Computational Biology, Johns Hopkins University, Baltimore, Maryland, United States.ORCID 0000-0002-3638-9762
Steven L SalzbergCenter for Computational Biology, Johns Hopkins University, Baltimore, Maryland, United States.ORCID 0000-0002-8859-7432

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

In recent years, a growing number of publications have reported the presence of microbial species in human tumors and of mixtures of microbes that appear to be highly specific to different cancer types. Our recent re-analysis of data from three cancer types revealed that technical errors have caused erroneous reports of numerous microbial species found in sequencing data from The Cancer Genome Atlas (TCGA) project. Here we have expanded our analysis to cover all 5,734 whole-genome sequencing (WGS) data sets currently available from TCGA, covering 25 distinct types of cancer. We analyzed the microbial content using updated computational methods and databases, and compared our results to those from two major recent studies that focused on bacteria, viruses, and fungi in cancer. Our results expand upon and reinforce our recent findings, which showed that the presence of microbes is far smaller than had been previously reported, and that many species identified in TCGA data are either not present at all, or are known contaminants rather than microbes residing within tumors. As part of this expanded analysis, and to help others avoid being misled by flawed data, we have released a dataset that contains detailed read counts for bacteria, viruses, archaea, and fungi detected in all 5,734 TCGA samples, which can serve as a public reference for future investigations.

Identifiers

PMID39071384
PMCPMC11275966

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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.