Evidence map›Paper›PMID 36330044›Full record

ArticleNAR genomics and bioinformatics2022

AsgeneDB: a curated orthology arsenic metabolism gene database and computational tool for metagenome annotation.

Xinwei Song, Yiqun Li, Erinne Stirling, Kankan Zhao, Binhao Wang, Yongguan Zhu, Yongming Luo, Jianming Xu, Bin Ma

Erratum issuedOpen access · goldAbstract read
In one paragraph

Article in NAR genomics and bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
2.2field-weighted citation impact, top 13% 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

7 citing papers in PubMed, 21 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors at 3 institutions in 1 country.

Xinwei SongInstitute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310000, China.
Yiqun LiInstitute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310000, China.
Erinne StirlingInstitute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310000, China.
Kankan ZhaoInstitute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310000, China.
Binhao WangInstitute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310000, China.
Yongguan ZhuState Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100000, China.
Yongming LuoKey Laboratory of Soil Environment and Pollution Remediation, Institute of Soil Science, Chinese Academy of Science, Nanjing 210000, China.
Jianming XuInstitute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310000, China.
Bin MaInstitute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310000, China.ORCID https://orcid.org/0000-0002-0188-1319
Zhejiang University · CNChinese Academy of Sciences · CNInstitute of Soil Science · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Arsenic (As) is the most ubiquitous toxic metalloid in nature. Microbe-mediated As metabolism plays an important role in global As biogeochemical processes, greatly changing its toxicity and bioavailability. While metagenomic sequencing may advance our understanding of the As metabolism capacity of microbial communities in different environments, accurate metagenomic profiling of As metabolism remains challenging due to low coverage and inaccurate definitions of As metabolism gene families in public orthology databases. Here we developed a manually curated As metabolism gene database (AsgeneDB) comprising 400 242 representative sequences from 59 As metabolism gene families, which are affiliated with 1653 microbial genera from 46 phyla. AsgeneDB achieved 100% annotation sensitivity and 99.96% annotation accuracy for an artificial gene dataset. We then applied AsgeneDB for functional and taxonomic profiling of As metabolism in metagenomes from various habitats (freshwater, hot spring, marine sediment and soil). The results showed that AsgeneDB substantially improved the mapping ratio of short reads in metagenomes from various environments. Compared with other databases, AsgeneDB provides more accurate, more comprehensive and faster analysis of As metabolic genes. In addition, we developed an R package, Asgene, to facilitate the analysis of metagenome sequencing data. Therefore, AsgeneDB and the associated Asgene package will greatly promote the study of As metabolism in microbial communities in various environments.

Identifiers

PMID36330044
PMCPMC9623898
OpenAlexW4308023752

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Registered trials

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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.