ArticleNucleic acids research2018
PhytoNet: comparative co-expression network analyses across phytoplankton and land plants.
Article in Nucleic acids research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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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.
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Who cites it
7 citing papers in PubMed, 25 citations in OpenAlex.
- Review
- Multi-Omics Approaches and Resources for Systems-Level Gene Function Prediction in the Plant Kingdom.Plants (Basel, Switzerland) · 2022Review
- LSTrAP-Crowd: prediction of novel components of bacterial ribosomes with crowd-sourced analysis of RNA sequencing data.BMC biology · 2020Article
- Expression Atlas ofThe Plant cell · 2020Article
- Fungi.guru: Comparative genomic and transcriptomic resource for the fungi kingdom.Computational and structural biotechnology journal · 2020Article
- Article
- Recent advances in gene function prediction using context-specific coexpression networks in plants.F1000Research · 2019Review
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Phytoplankton consists of autotrophic, photosynthesizing microorganisms that are a crucial component of freshwater and ocean ecosystems. However, despite being the major primary producers of organic compounds, accounting for half of the photosynthetic activity worldwide and serving as the entry point to the food chain, functions of most of the genes of the model phytoplankton organisms remain unknown. To remedy this, we have gathered publicly available expression data for one chlorophyte, one rhodophyte, one haptophyte, two heterokonts and four cyanobacteria and integrated it into our PlaNet (Plant Networks) database, which now allows mining gene expression profiles and identification of co-expressed genes of 19 species. We exemplify how the co-expressed gene networks can be used to reveal functionally related genes and how the comparative features of PhytoNet allow detection of conserved transcriptional programs between cyanobacteria, green algae, and land plants. Additionally, we illustrate how the database allows detection of duplicated transcriptional programs within an organism, as exemplified by two putative DNA repair programs within Chlamydomonas reinhardtii. PhytoNet is available from www.gene2function.de.
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