ReviewFEMS yeast research2020
Extracting novel hypotheses and findings from RNA-seq data.
Review in FEMS yeast research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Oxidative stress promotes the progression of age-related macular degeneration by up-regulating Spp1 to activate the JAK/STAT signaling pathway.Scientific reports · 2026Article
- Molecular mechanisms associated with rootstock-scion interactions in rubber trees.The plant genome · 2026Article
- Pichia pastoris RNA-Sequencing and Data Analysis.Methods in molecular biology (Clifton, N.J.) · 2026Article
- An Interplay between Transcription Factors and Recombinant Protein Synthesis inInternational journal of molecular sciences · 2024Article
- Selective inference forJournal of machine learning research : JMLR · 2023Article
- PlantExp: a platform for exploration of gene expression and alternative splicing based on public plant RNA-seq samples.Nucleic acids research · 2023Article
- RNA Sequencing Data from Human Intracranial Aneurysm Tissue Reveals a Complex Inflammatory Environment Associated with Rupture.Molecular diagnosis & therapy · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Over the past decade, improvements in technology and methods have enabled rapid and relatively inexpensive generation of high-quality RNA-seq datasets. These datasets have been used to characterize gene expression for several yeast species and have provided systems-level insights for basic biology, biotechnology and medicine. Herein, we discuss new techniques that have emerged and existing techniques that enable analysts to extract information from multifactorial yeast RNA-seq datasets. Ultimately, this minireview seeks to inspire readers to query datasets, whether previously published or freshly obtained, with creative and diverse methods to discover and support novel hypotheses.
Indexed as
Identifiers
What Socratic 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 Socratic graph.