ReviewComputational and structural biotechnology journal2015
Dynamics in Transcriptomics: Advancements in RNA-seq Time Course and Downstream Analysis.
Review in Computational and structural biotechnology journal, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 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
44 citing papers in PubMed, 87 citations in OpenAlex.
- ChEA-KG and ChEA-KG-TS: a network-based transcription factor enrichment analysis tool with an accompanying time-series workflow.Nucleic acids research · 2026Article
- Article
- Mechanistic Studies of Antibacterial PNAs by RNA-Seq Analysis.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Cluefish: mining the dark matter of transcriptional data series with over-representation analysis enhanced by aggregated biological prior knowledge.NAR genomics and bioinformatics · 2025Article
- Drought Response in the Transcriptome and Ionome of Wild and DomesticatedPlant-environment interactions (Hoboken, N.J.) · 2025Article
- Carotenoids in Health as Studied by Omics-Related Endpoints.Advances in nutrition (Bethesda, Md.) · 2023Review
- RNA-Seq-Based Transcriptome Analysis of Nitric Oxide Scavenging Response inJournal of fungi (Basel, Switzerland) · 2023Article
- Next-generation sequencing reveals altered gene expression and enriched pathways in triple-negative breast cancer cells treated with oleuropein and oleocanthal.Functional & integrative genomics · 2023Article
- Gene Functional Networks from Time Expression Profiles: A Constructive Approach Demonstrated in Chili Pepper (Plants (Basel, Switzerland) · 2023Article
- Harnessing introns to monitor gene expression.Nature cell biology · 2022Article
- TIMEOR: a web-based tool to uncover temporal regulatory mechanisms from multi-omics data.Nucleic acids research · 2021Article
- Introducing the novel Cytoscape app TimeNexus to analyze time-series data using temporal MultiLayer Networks (tMLNs).Scientific reports · 2021Article
- Dense time-course gene expression profiling of the Drosophila melanogaster innate immune response.BMC genomics · 2021Article
- Transcriptome Analyses Throughout Chili Pepper Fruit Development Reveal Novel Insights into the Domestication Process.Plants (Basel, Switzerland) · 2021Article
- Identify differential genes and cell subclusters from time-series scRNA-seq data using scTITANS.Computational and structural biotechnology journal · 2021Article
- Improved 93-11 Genome and Time-Course Transcriptome Expand Resources for Rice Genomics.Frontiers in plant science · 2021Article
- Article
- rmRNAseq: differential expression analysis for repeated-measures RNA-seq data.Bioinformatics (Oxford, England) · 2020Article
- Time-course RNA-seq analysis provides an improved understanding of gene regulation during the formation of nodule-like structures in rice.Plant molecular biology · 2020Article
- Development of genetic quality tests for good manufacturing practice-compliant induced pluripotent stem cells and their derivatives.Scientific reports · 2020Article
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 at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Analysis of gene expression has contributed to a plethora of biological and medical research studies. Microarrays have been intensively used for the profiling of gene expression during diverse developmental processes, treatments and diseases. New massively parallel sequencing methods, often named as RNA-sequencing (RNA-seq) are extensively improving our understanding of gene regulation and signaling networks. Computational methods developed originally for microarrays analysis can now be optimized and applied to genome-wide studies in order to have access to a better comprehension of the whole transcriptome. This review addresses current challenges on RNA-seq analysis and specifically focuses on new bioinformatics tools developed for time series experiments. Furthermore, possible improvements in analysis, data integration as well as future applications of differential expression analysis are discussed.
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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.