@article {529,
	title = {Differential expression in RNA-seq: a matter of depth.},
	journal = {Genome Res},
	volume = {21},
	year = {2011},
	month = {2011 Dec},
	pages = {2213-23},
	abstract = {<p>Next-generation sequencing (NGS) technologies are revolutionizing genome research, and in particular, their application to transcriptomics (RNA-seq) is increasingly being used for gene expression profiling as a replacement for microarrays. However, the properties of RNA-seq data have not been yet fully established, and additional research is needed for understanding how these data respond to differential expression analysis. In this work, we set out to gain insights into the characteristics of RNA-seq data analysis by studying an important parameter of this technology: the sequencing depth. We have analyzed how sequencing depth affects the detection of transcripts and their identification as differentially expressed, looking at aspects such as transcript biotype, length, expression level, and fold-change. We have evaluated different algorithms available for the analysis of RNA-seq and proposed a novel approach--NOISeq--that differs from existing methods in that it is data-adaptive and nonparametric. Our results reveal that most existing methodologies suffer from a strong dependency on sequencing depth for their differential expression calls and that this results in a considerable number of false positives that increases as the number of reads grows. In contrast, our proposed method models the noise distribution from the actual data, can therefore better adapt to the size of the data set, and is more effective in controlling the rate of false discoveries. This work discusses the true potential of RNA-seq for studying regulation at low expression ranges, the noise within RNA-seq data, and the issue of replication.</p>},
	keywords = {Algorithms, Expressed Sequence Tags, Gene Expression Profiling, Gene Expression Regulation, Humans, Models, Genetic, Oligonucleotide Array Sequence Analysis},
	issn = {1549-5469},
	doi = {10.1101/gr.124321.111},
	author = {Tarazona, Sonia and Garc{\'\i}a-Alcalde, Fernando and Dopazo, Joaquin and Ferrer, Alberto and Conesa, Ana}
}
@article {576,
	title = {Serial Expression Analysis: a web tool for the analysis of serial gene expression data.},
	journal = {Nucleic Acids Res},
	volume = {38},
	year = {2010},
	month = {2010 Jul},
	pages = {W239-45},
	abstract = {<p>Serial transcriptomics experiments investigate the dynamics of gene expression changes associated with a quantitative variable such as time or dosage. The statistical analysis of these data implies the study of global and gene-specific expression trends, the identification of significant serial changes, the comparison of expression profiles and the assessment of transcriptional changes in terms of cellular processes. We have created the SEA (Serial Expression Analysis) suite to provide a complete web-based resource for the analysis of serial transcriptomics data. SEA offers five different algorithms based on univariate, multivariate and functional profiling strategies framed within a user-friendly interface and a project-oriented architecture to facilitate the analysis of serial gene expression data sets from different perspectives. SEA is available at sea.bioinfo.cipf.es.</p>},
	keywords = {Algorithms, Gene Expression Profiling, Internet, Kinetics, Linear Models, Oligonucleotide Array Sequence Analysis, Software},
	issn = {1362-4962},
	doi = {10.1093/nar/gkq488},
	author = {Nueda, Maria Jos{\'e} and Carbonell, Jos{\'e} and Medina, Ignacio and Dopazo, Joaquin and Conesa, Ana}
}
