Evidence map›Paper›PMID 35793793›Full record

ArticleJournal of proteome research2022

Uncovering Hidden Members and Functions of the Soil Microbiome Using

Joon-Yong Lee, Hugh D Mitchell, Meagan C Burnet, Ruonan Wu, Sarah C Jenson, Eric D Merkley, Ernesto S Nakayasu, Carrie D Nicora, Janet K Jansson, Kristin E Burnum-Johnson and 1 more

Open access · greenAbstract read
In one paragraph

Article in Journal of proteome research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
2.6field-weighted citation impact, top 9% 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

15 citing papers in PubMed, 24 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

11 authors at 2 institutions in 1 country.

Joon-Yong LeeBiological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States.ORCID 0000-0002-5864-8518
Hugh D MitchellBiological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States.
Meagan C BurnetBiological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States.
Ruonan WuBiological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States.ORCID 0000-0001-9466-4462
Sarah C JensonSignature Sciences and Technology Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States.ORCID 0000-0002-0807-5651
Eric D MerkleySignature Sciences and Technology Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States.ORCID 0000-0002-5486-4723
Ernesto S NakayasuBiological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States.ORCID 0000-0002-4056-2695
Carrie D NicoraBiological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States.
Janet K JanssonBiological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States.
Kristin E Burnum-JohnsonEnvironmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99354, United States.ORCID 0000-0002-2722-4149
Samuel H PayneBiology Department, Brigham Young University, Provo, Utah 84602, United States.ORCID 0000-0002-8351-1994
Pacific Northwest National Laboratory · USBrigham Young University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Metaproteomics has been increasingly utilized for high-throughput characterization of proteins in complex environments and has been demonstrated to provide insights into microbial composition and functional roles. However, significant challenges remain in metaproteomic data analysis, including creation of a sample-specific protein sequence database. A well-matched database is a requirement for successful metaproteomics analysis, and the accuracy and sensitivity of PSM identification algorithms suffer when the database is incomplete or contains extraneous sequences. When matched DNA sequencing data of the sample is unavailable or incomplete, creating the proteome database that accurately represents the organisms in the sample is a challenge. Here, we leverage a

Indexed as

MicrobiotaProteomicsPeptidesProteomeRNA, Ribosomal, 16SSoilPeptidesProteomeRNA, Ribosomal, 16SSoildeep learning modelde novo sequencingmetaproteomicssoil microbiome

Identifiers

PMID35793793
PMCPMC9361346
OpenAlexW4284680271

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.