02499nas a2200277   4500008004100000022001400041245005900055210005600114260001300170300001200183490000700195520165100202653001501853653002801868653003001896653003101926653001101957653002001968653004401988100002002032700003002052700002002082700002002102700001602122856008302138       2011                        eng d  a1549-546900aDifferential expression in RNA-seq: a matter of depth.0 aDifferential expression in RNAseq a matter of depth  c2011 Dec  a2213-230 v213 a<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>10aAlgorithms10aExpressed Sequence Tags10aGene Expression Profiling10aGene Expression Regulation10aHumans10aModels, Genetic10aOligonucleotide Array Sequence Analysis1 aTarazona, Sonia1 aGarcía-Alcalde, Fernando1 aDopazo, Joaquin1 aFerrer, Alberto1 aConesa, Ana  uhttp://clinbioinfosspa.es/content/differential-expression-rna-seq-matter-depth02717nas a2200385   4500008004100000022001400041245008300055210006900138260001300207300001200220490000700232520153300239653001201772653002601784653002201810653002301832653002801855653001001883653001301893653002701906653003101933653001301964653002701977100001802004700003302022700001802055700002102073700002902094700002402123700002302147700001802170700002002188700001602208856010702224       2008                        eng d  a1362-496200aHigh-throughput functional annotation and data mining with the Blast2GO suite.0 aHighthroughput functional annotation and data mining with the Bl  c2008 Jun  a3420-350 v363 a<p>Functional genomics technologies have been widely adopted in the biological research of both model and non-model species. An efficient functional annotation of DNA or protein sequences is a major requirement for the successful application of these approaches as functional information on gene products is often the key to the interpretation of experimental results. Therefore, there is an increasing need for bioinformatics resources which are able to cope with large amount of sequence data, produce valuable annotation results and are easily accessible to laboratories where functional genomics projects are being undertaken. We present the Blast2GO suite as an integrated and biologist-oriented solution for the high-throughput and automatic functional annotation of DNA or protein sequences based on the Gene Ontology vocabulary. The most outstanding Blast2GO features are: (i) the combination of various annotation strategies and tools controlling type and intensity of annotation, (ii) the numerous graphical features such as the interactive GO-graph visualization for gene-set function profiling or descriptive charts, (iii) the general sequence management features and (iv) high-throughput capabilities. We used the Blast2GO framework to carry out a detailed analysis of annotation behaviour through homology transfer and its impact in functional genomics research. Our aim is to offer biologists useful information to take into account when addressing the task of functionally characterizing their sequence data.</p>10aAnimals10aComputational Biology10aComputer Graphics10aDatabases, Genetic10aExpressed Sequence Tags10aGenes10aGenomics10aSequence Analysis, DNA10aSequence Analysis, Protein10aSoftware10aVocabulary, Controlled1 aGötz, Stefan1 aGarcía-Gómez, Juan, Miguel1 aTerol, Javier1 aWilliams, Tim, D1 aNagaraj, Shivashankar, H1 aNueda, Maria, José1 aRobles, Montserrat1 aTalon, Manuel1 aDopazo, Joaquin1 aConesa, Ana  uhttp://clinbioinfosspa.es/content/high-throughput-functional-annotation-and-data-mining-blast2go-suite