ArticleBioMed research international2013
Time series expression analyses using RNA-seq: a statistical approach.
Article in BioMed research international, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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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
15 citing papers in PubMed.
- Identification and clustering analysis of drug-responsive temporally varying genes through high-frequency longitudinal RNA sequencing.Scientific reports · 2026Article
- Longitudinal omics data analysis: approaches and applications.Computational and structural biotechnology journal · 2026Review
- GeTeSEPdb: A comprehensive database and online tool for the identification and analysis of gene profiles with temporal-specific expression patterns.Computational and structural biotechnology journal · 2024Article
- Carotenoids in Health as Studied by Omics-Related Endpoints.Advances in nutrition (Bethesda, Md.) · 2023Review
- GECO: gene expression clustering optimization app for non-linear data visualization of patterns.BMC bioinformatics · 2021Article
- Spatiotemporal Changes in Transcriptome of Odontogenic and Non-odontogenic Regions in the Dental Arch ofFrontiers in cell and developmental biology · 2021Article
- Temporal dynamics in meta longitudinal RNA-Seq data.Scientific reports · 2019Article
- Measuring mRNA Levels Over Time During the Yeast S. cerevisiae Hypoxic Response.Journal of visualized experiments : JoVE · 2017Article
- Article
- Statistical inference for time course RNA-Seq data using a negative binomial mixed-effect model.BMC bioinformatics · 2016Article
- Efficient extraction of small and large RNAs in bacteria for excellent total RNA sequencing and comprehensive transcriptome analysis.BMC research notes · 2015Article
- Dynamics in Transcriptomics: Advancements in RNA-seq Time Course and Downstream Analysis.Computational and structural biotechnology journal · 2015Review
- Stratification of gene coexpression patterns and GO function mining for a RNA-Seq data series.BioMed research international · 2014Article
- A review of subsequence time series clustering.TheScientificWorldJournal · 2014Review
- The analytical landscape of static and temporal dynamics in transcriptome data.Frontiers in genetics · 2014Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
RNA-seq is becoming the de facto standard approach for transcriptome analysis with ever-reducing cost. It has considerable advantages over conventional technologies (microarrays) because it allows for direct identification and quantification of transcripts. Many time series RNA-seq datasets have been collected to study the dynamic regulations of transcripts. However, statistically rigorous and computationally efficient methods are needed to explore the time-dependent changes of gene expression in biological systems. These methods should explicitly account for the dependencies of expression patterns across time points. Here, we discuss several methods that can be applied to model timecourse RNA-seq data, including statistical evolutionary trajectory index (SETI), autoregressive time-lagged regression (AR(1)), and hidden Markov model (HMM) approaches. We use three real datasets and simulation studies to demonstrate the utility of these dynamic methods in temporal analysis.
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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.