01781nas a2200277   4500008004100000022001400041245009200055210006900147260001300216300001200229490000700241520089700248653001501145653003001160653001301190653001301203653001801216653004401234653001301278100002401291700002101315700002001336700002001356700001601376856011101392       2010                        eng d  a1362-496200aSerial Expression Analysis: a web tool for the analysis of serial gene expression data.0 aSerial Expression Analysis a web tool for the analysis of serial  c2010 Jul  aW239-450 v383 a<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>10aAlgorithms10aGene Expression Profiling10aInternet10aKinetics10aLinear Models10aOligonucleotide Array Sequence Analysis10aSoftware1 aNueda, Maria, José1 aCarbonell, José1 aMedina, Ignacio1 aDopazo, Joaquin1 aConesa, Ana  uhttp://clinbioinfosspa.es/content/serial-expression-analysis-web-tool-analysis-serial-gene-expression-data02713nas a2200265   4500008004100000022001400041245005800055210005700113260001600170300000700186490001500193520188200208653002402090653003002114653004402144653001702188100002402205700002502229700002002254700002902274700002002303700002002323700001602343856008802359       2009                        eng d  a1471-210500aFunctional assessment of time course microarray data.0 aFunctional assessment of time course microarray data  c2009 Jun 16  aS90 v10 Suppl 63 a<p><b>MOTIVATION: </b>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.</p><p><b>METHODS: </b>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.</p><p><b>RESULTS: </b>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.</p>10aComputer Simulation10aGene Expression Profiling10aOligonucleotide Array Sequence Analysis10aTime Factors1 aNueda, Maria, José1 aSebastián, Patricia1 aTarazona, Sonia1 aGarcia-Garcia, Francisco1 aDopazo, Joaquin1 aFerrer, Alberto1 aConesa, Ana  uhttp://clinbioinfosspa.es/content/functional-assessment-time-course-microarray-data02717nas 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