Sequencing technology does not eliminate biological variability.
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Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and CufflinksA survey of best practices for RNA-seq data analysisParameterizing cell-to-cell regulatory heterogeneities via stochastic transcriptional profiles.RNA-sequencing from single nuclei.Informatics for RNA Sequencing: A Web Resource for Analysis on the Cloud.Gene set enrichment analysis of RNA-Seq data: integrating differential expression and splicingInference of alternative splicing from RNA-Seq data with probabilistic splice graphs.Normalizing RNA-sequencing data by modeling hidden covariates with prior knowledge.GLiMMPS: robust statistical model for regulatory variation of alternative splicing using RNA-seq data.A flexible count data model to fit the wide diversity of expression profiles arising from extensively replicated RNA-seq experiments.Differential expression analysis of RNA-seq data at single-base resolutionMeasuring differential gene expression with RNA-seq: challenges and strategies for data analysis.svaseq: removing batch effects and other unwanted noise from sequencing datarMATS: robust and flexible detection of differential alternative splicing from replicate RNA-Seq dataFunPat: function-based pattern analysis on RNA-seq time series data.Statistical models for RNA-seq data derived from a two-condition 48-replicate experiment.Multiple sources of bias confound functional enrichment analysis of global -omics dataTTCA: an R package for the identification of differentially expressed genes in time course microarray dataRNA-seq mixology: designing realistic control experiments to compare protocols and analysis methodsA comparison of RNA-seq and exon arrays for whole genome transcription profiling of the L5 spinal nerve transection model of neuropathic pain in the rat.Transfer and functional consequences of dietary microRNAs in vertebrates: concepts in search of corroboration: negative results challenge the hypothesis that dietary xenomiRs cross the gut and regulate genes in ingesting vertebrates, but important qReCount: a multi-experiment resource of analysis-ready RNA-seq gene count datasets.MATS: a Bayesian framework for flexible detection of differential alternative splicing from RNA-Seq data.Removing technical variability in RNA-seq data using conditional quantile normalization.Detecting differential usage of exons from RNA-seq dataGenome-wide localization of protein-DNA binding and histone modification by a Bayesian change-point method with ChIP-seq data.DEXUS: identifying differential expression in RNA-Seq studies with unknown conditionsA new shrinkage estimator for dispersion improves differential expression detection in RNA-seq data.Combining next-generation sequencing and microarray technology into a transcriptomics approach for the non-model organism Chironomus riparius.RNA-Seq analysis implicates dysregulation of the immune system in schizophreniaPROPER: comprehensive power evaluation for differential expression using RNA-seqLathyrus sativus transcriptome resistance response to Ascochyta lathyri investigated by deepSuperSAGE analysis.Fighting a losing battle: vigorous immune response countered by pathogen suppression of host defenses in the chytridiomycosis-susceptible frog Atelopus zeteki.Evaluating whole transcriptome amplification for gene profiling experiments using RNA-Seq.Transcriptome Analysis for Abnormal Spike Development of the Wheat Mutant dms.AuPairWise: A Method to Estimate RNA-Seq Replicability through Co-expression.Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation.A Survey of MicroRNA Length Variants Contributing to miRNome Complexity in Peach (Prunus Persica L.).Minireview: applications of next-generation sequencing on studies of nuclear receptor regulation and functionSurgery increases cell death and induces changes in gene expression compared with anesthesia alone in the developing piglet brain.
P2860
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P2860
Sequencing technology does not eliminate biological variability.
description
2011 nî lūn-bûn
@nan
2011年の論文
@ja
2011年論文
@yue
2011年論文
@zh-hant
2011年論文
@zh-hk
2011年論文
@zh-mo
2011年論文
@zh-tw
2011年论文
@wuu
2011年论文
@zh
2011年论文
@zh-cn
name
Sequencing technology does not eliminate biological variability.
@en
type
label
Sequencing technology does not eliminate biological variability.
@en
prefLabel
Sequencing technology does not eliminate biological variability.
@en
P2860
P50
P356
P1433
P1476
Sequencing technology does not eliminate biological variability.
@en
P2093
Rafael A Irizarry
P2860
P2888
P304
P356
10.1038/NBT.1910
P577
2011-07-11T00:00:00Z