ReviewFrontiers in genetics2014
The analytical landscape of static and temporal dynamics in transcriptome data.
Review in Frontiers in genetics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
17 citing papers in PubMed.
- Confounder-Adjusted Differentiation of Colorectal Cancer via Dynamic Propagation of Pathway Influence.International journal of molecular sciences · 2025Article
- Dynamics in zebrafish development define transcriptomic specificity after angiogenesis inhibitor exposure.Archives of toxicology · 2025Article
- 3D RNA-seq: a powerful and flexible tool for rapid and accurate differential expression and alternative splicing analysis of RNA-seq data for biologists.RNA biology · 2021Article
- Review
- RNA-Seq Transcriptome Analysis of Peripheral Blood From Cattle Infected WithFrontiers in veterinary science · 2021Article
- Improvement of Free Fatty Acid Secretory Productivity inFrontiers in microbiology · 2021Article
- Understanding the Molecular Mechanisms of Asthma through Transcriptomics.Allergy, asthma & immunology research · 2020Review
- Temporal dynamics in meta longitudinal RNA-Seq data.Scientific reports · 2019Article
- Article
- Functional proteomic analysis of corticosteroid pharmacodynamics in rat liver: Relationship to hepatic stress, signaling, energy regulation, and drug metabolism.Journal of proteomics · 2017Article
- MicroRNA roles in signalling during lactation: an insight from differential expression, time course and pathway analyses of deep sequence data.Scientific reports · 2017Article
- Systematic identification of an integrative network module during senescence from time-series gene expression.BMC systems biology · 2017Article
- Multi-time series RNA-seq analysis of Enterobacter lignolyticus SCF1 during growth in lignin-amended medium.PloS one · 2017Article
- Article
- It is time to take timing seriously in clinical genetics.European journal of human genetics : EJHG · 2015Article
- Dynamics in Transcriptomics: Advancements in RNA-seq Time Course and Downstream Analysis.Computational and structural biotechnology journal · 2015Review
- FunPat: function-based pattern analysis on RNA-seq time series data.BMC genomics · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Interpreting gene expression profiles often involves statistical analysis of large numbers of differentially expressed genes, isoforms, and alternative splicing events at either static or dynamic spectrums. Reduced sequencing costs have made feasible dense time-series analysis of gene expression using RNA-seq; however, statistical methods in the context of temporal RNA-seq data are poorly developed. Here we will review current methods for identifying temporal changes in gene expression using RNA-seq, which are limited to static pairwise comparisons of time points and which fail to account for temporal dependencies in gene expression patterns. We also review recently developed very few number of temporal dynamic RNA-seq specific methods. Application and development of RNA-specific temporal dynamic methods have been continuously under the development, yet, it is still in infancy. We fully cover microarray specific temporal methods and transcriptome studies in initial digital technology (e.g., SAGE) between traditional microarray and new RNA-seq.
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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.