Evidence map›Paper›PMID 25408504›Full record

ArticlePlanta2015

High-throughput generation of an activation-tagged mutant library for functional genomic analyses in tobacco.

Feng Liu, Daping Gong, Qian Zhang, Dawei Wang, Mengmeng Cui, Zhiguo Zhang, Guanshan Liu, Jinxia Wu, Yuanying Wang

Abstract read
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In one paragraph

Article in Planta, 2015. 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
–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

8 citing papers in PubMed.

  1. Article
  2. Article
  3. Development ofPlant direct · 2021
    Article
  4. Article
  5. Article
  6. Article
  7. 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.

Feng LiuBiotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
Daping Gong
Qian Zhang
Dawei Wang
Mengmeng Cui
Zhiguo Zhang
Guanshan Liu
Jinxia Wu
Yuanying Wang

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tobacco (Nicotiana tabacum L.) is an ideal model system for molecular biological and genetic studies. In this study, activation tagging was used to generate approximately 100,000 transgenic tobacco plants. Southern blot analysis indicated that there were 1.6 T-DNA inserts per line on average in our transformed population. The phenotypes observed include abnormalities in leaf and flower morphology, plant height, flowering time, branching, and fertility. Among 6,000 plants in the T0 generation, 57 displayed obvious phenotypes. Among 4,105 lines in the T1 generation, 311 displayed abnormal phenotypes. Fusion primer and nested integrated PCR was used to identify 963 independent genomic loci of T-DNA insertion sites in 1,257 T1 lines. The distribution of T-DNA insertions was non-uniform and correlated well with the predicted gene density along each chromosome. The insertions were biased toward genic regions and noncoding regions within 5 kb of a gene. Fifteen plants that showed the same phenotype as their parent with a dominant pattern in the T2 generation were chosen randomly to detect the expression levels of genes adjacent to the T-DNA integration sites by semi-quantitative RT-PCR. Fifteen candidate genes were identified. Activation was observed in 7 out of the 15 adjacent genes, including one that was located 13.1 kb away from the enhancer sequence. The activation-tagged population described in this paper will be a highly valuable resource for tobacco functional genomics research using both forward and reverse genetic approaches.

Indexed as

Genome, PlantMutagenesis, InsertionalGene ExpressionGene LibraryGenomicsHigh-Throughput Nucleotide SequencingNicotianaPhenotype

Identifiers

What OpenQuestion holds

Textmetadata
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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.