Time Series Expression Analyses Using RNA-seq: A Statistical Approach.
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 bee...
| Publicado en: | BioMed Research International Vol. 2013; pp. 203681 - 203682 |
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| Autores principales: | , , , , |
| Formato: | research Journal Article |
| Publicado: |
Wiley-Blackwell
2013
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104287244&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104287244 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 2013 vid: 2013 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 104287244 104287244 2012116837 NLM23586021 PMC3622290 104287244 ppf: 203681 ppct: 1 formats: fmt: @attributes: type: P tig: atl: Time Series Expression Analyses Using RNA-seq: A Statistical Approach. aug: au: Oh, Sunghee Song, Seongho Grabowski, Gregory Zhao, Hongyu Noonan, James P affil: Department of Pediatrics, Children's Hospital Medical Center, Cincinnati, OH 45229-3039, USA. sug: subj: Gene Expression Gene Expression Profiling Methods RNA Sequence Analysis Statistics and Numerical Data Nucleotides Human Probability Models, Statistical Sequence Analysis Methods ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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