ReviewPlanta2016
Bioinformatic landscapes for plant transcription factor system research.
Review in Planta, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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.
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.
Who cites it
5 citing papers in PubMed, 16 citations in OpenAlex.
- Article
- Transcriptome analyses reveal the expression profile of genes related to lignan biosynthesis inPhysiology and molecular biology of plants : an international journal of functional plant biology · 2022Article
- Transcriptome profiling reveals key genes in regulation of the tepal trichome development in Lilium pumilum D.C.Plant cell reports · 2021Article
- Article
- Transcriptome Analysis of theFrontiers in plant science · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
MAIN
conclusionDiverse bioinformatic resources have been developed for plant transcription factor (TF) research. This review presents the bioinformatic resources and methodologies for the elucidation of plant TF-mediated biological events. Such information is helpful to dissect the transcriptional regulatory systems in the three reference plants Arabidopsis , rice, and maize and translation to other plants. Transcription factors (TFs) orchestrate diverse biological programs by the modulation of spatiotemporal patterns of gene expression via binding cis-regulatory elements. Advanced sequencing platforms accompanied by emerging bioinformatic tools revolutionize the scope and extent of TF research. The system-level integration of bioinformatic resources is beneficial to the decoding of TF-involved networks. Herein, we first briefly introduce general and specialized databases for TF research in three reference plants Arabidopsis, rice, and maize. Then, as proof of concept, we identified and characterized heat shock transcription factor (HSF) members through the TF databases. Finally, we present how the integration of bioinformatic resources at -omics layers can aid the dissection of TF-mediated pathways. We also suggest ways forward to improve the bioinformatic resources of plant TFs. Leveraging these bioinformatic resources and methodologies opens new avenues for the elucidation of transcriptional regulatory systems in the three model systems and translation to other plants.
Indexed as
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What OpenQuestion holds
Registered trials
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.