ArticleGenes2019
Co-Expression Network Analysis and Hub Gene Selection for High-Quality Fiber in Upland Cotton (Gossypium hirsutum) Using RNA Sequencing Analysis.
Article in Genes, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 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
26 citing papers in PubMed, 50 citations in OpenAlex.
- Identification of Key Genes for the Simultaneous Improvement of Fiber Strength and Lint Percentage inInternational journal of molecular sciences · 2026Article
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- Transcriptome analysis of the coexpression network of genes related to antioxidant characteristics after grain filling in purple rice.Scientific reports · 2024Article
- GhWRKY40 Interacts with an Asparaginase GhAPGenes · 2024Article
- Aspartyl proteases identified as candidate genes of a fiber length QTL, qFLTAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
- Leveraging transcriptomics-based approaches to enhance genomic prediction: integrating SNPs and gene networks for cotton fibre quality improvement.Frontiers in plant science · 2024Article
- Article
- Domestication over Speciation in Allopolyploid Cotton Species: A Stronger Transcriptomic Pull.Genes · 2023Article
- Article
- Transcriptome, Ectopic Expression and Genetic Population Analysis Identify Candidate Genes for Fiber Quality Improvement in Cotton.International journal of molecular sciences · 2023Article
- Transcriptome analysis reveals the effect of grafting on gossypol biosynthesis and gland formation in cotton.BMC plant biology · 2023Article
- Comparative transcriptional and co-expression network analysis of two upland cotton accessions with extreme phenotypic differences reveals molecular mechanisms of fiber development.Frontiers in plant science · 2023Article
- Transcriptome and Regional Association Analyses Reveal the Effects of Oleosin Genes on the Accumulation of Oil Content inPlants (Basel, Switzerland) · 2022Article
- A Comprehensive Gene Co-Expression Network Analysis Reveals a Role ofInternational journal of molecular sciences · 2022Article
- Article
- Analysis of transcriptome data and quantitative trait loci enables the identification of candidate genes responsible for fiber strength in Gossypium barbadense.G3 (Bethesda, Md.) · 2022Article
- The Wild Sugarcane and Sorghum Kinomes: Insights Into Expansion, Diversification, and Expression Patterns.Frontiers in plant science · 2021Article
- The Pivotal Role of Major Chromosomes of Sub-Genomes A and D in Fiber Quality Traits of Cotton.Frontiers in genetics · 2021Review
- Quantitative Trait Loci and Transcriptome Analysis Reveal Genetic Basis of Fiber Quality Traits in CCRI70 RIL Population ofFrontiers in plant science · 2021Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
21 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Upland cotton (
Indexed as
Identifiers
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.