Evidence map›Paper›PMID 36494363›Full record

ArticleScientific data2022

Mining of novel secondary metabolite biosynthetic gene clusters from acid mine drainage.

Ling Wang, Wan Liu, Jieliang Liang, Linna Zhao, Qiang Li, Chenfen Zhou, Hui Cen, Qingbei Weng, Guoqing Zhang

Open access · goldAbstract read
In one paragraph

Article in Scientific data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
0.9field-weighted citation impact, top 30% 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

8 citing papers in PubMed, 11 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. Biosynthetic Potential ofJournal of fungi (Basel, Switzerland) · 2023
    Article
  8. 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

9 authors at 5 institutions in 1 country.

Ling Wang *School of Life Sciences, Guizhou Normal University, Guiyang, Guizhou, 550025, China.
Wan Liu *National Genomics Data Center, CAS Key Laboratory of Computational Biology, Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China.
Jieliang Liang *School of Life Sciences, South China Normal University, Guangzhou, Guangdong, 510631, China.
Linna ZhaoNational Genomics Data Center, CAS Key Laboratory of Computational Biology, Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China.
Qiang LiNational Genomics Data Center, CAS Key Laboratory of Computational Biology, Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China.
Chenfen ZhouNational Genomics Data Center, CAS Key Laboratory of Computational Biology, Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China.
Hui CenNational Genomics Data Center, CAS Key Laboratory of Computational Biology, Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China.
Qingbei WengSchool of Life Sciences, Guizhou Normal University, Guiyang, Guizhou, 550025, China. wengqb@126.com.
Guoqing ZhangSchool of Life Sciences, Guizhou Normal University, Guiyang, Guizhou, 550025, China. gqzhang@picb.ac.cn.
Chinese Academy of Sciences · CNShanghai Institute of Nutrition and Health · CNGuizhou Normal University · CNQiannan Normal College For Nationalities · CNSouth China Normal University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Acid mine drainage (AMD) is usually acidic (pH < 4) and contains high concentrations of dissolved metals and metalloids, making AMD a typical representative of extreme environments. Recent studies have shown that microbes play a key role in AMD bioremediation, and secondary metabolite biosynthetic gene clusters (smBGCs) from AMD microbes are important resources for the synthesis of antibacterial and anticancer drugs. Here, 179 samples from 13 mineral types were used to analyze the putative novel microorganisms and secondary metabolites in AMD environments. Among 7,007 qualified metagenome-assembled genomes (MAGs) mined from these datasets, 6,340 MAGs could not be assigned to any GTDB species representative. Overall, 11,856 smBGCs in eight categories were obtained from 7,007 qualified MAGs, and 10,899 smBGCs were identified as putative novel smBGCs. We anticipate that these datasets will accelerate research in the field of AMD bioremediation, aid in the discovery of novel secondary metabolites, and facilitate investigation into gene functions, metabolic pathways, and CNPS cycles in AMD.

Indexed as

AcidsMiningBiodegradation, EnvironmentalMetalsMultigene FamilyAcidsMetals

Identifiers

PMID36494363
PMCPMC9734747
OpenAlexW4311662444

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.