@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 = {

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.

}, 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 {580, title = {Functional assessment of time course microarray data.}, journal = {BMC Bioinformatics}, volume = {10 Suppl 6}, year = {2009}, month = {2009 Jun 16}, pages = {S9}, abstract = {

MOTIVATION: Time-course microarray experiments study the progress of gene expression along time across one or several experimental conditions. Most developed analysis methods focus on the clustering or the differential expression analysis of genes and do not integrate functional information. The assessment of the functional aspects of time-course transcriptomics data requires the use of approaches that exploit the activation dynamics of the functional categories to where genes are annotated.

METHODS: We present three novel methodologies for the functional assessment of time-course microarray data. i) maSigFun derives from the maSigPro method, a regression-based strategy to model time-dependent expression patterns and identify genes with differences across series. maSigFun fits a regression model for groups of genes labeled by a functional class and selects those categories which have a significant model. ii) PCA-maSigFun fits a PCA model of each functional class-defined expression matrix to extract orthogonal patterns of expression change, which are then assessed for their fit to a time-dependent regression model. iii) ASCA-functional uses the ASCA model to rank genes according to their correlation to principal time expression patterns and assess functional enrichment on a GSA fashion. We used simulated and experimental datasets to study these novel approaches. Results were compared to alternative methodologies.

RESULTS: Synthetic and experimental data showed that the different methods are able to capture different aspects of the relationship between genes, functions and co-expression that are biologically meaningful. The methods should not be considered as competitive but they provide different insights into the molecular and functional dynamic events taking place within the biological system under study.

}, keywords = {Computer Simulation, Gene Expression Profiling, Oligonucleotide Array Sequence Analysis, Time Factors}, issn = {1471-2105}, doi = {10.1186/1471-2105-10-S6-S9}, author = {Nueda, Maria Jos{\'e} and Sebasti{\'a}n, Patricia and Tarazona, Sonia and Garcia-Garcia, Francisco and Dopazo, Joaquin and Ferrer, Alberto and Conesa, Ana} }