01023nas a2200313 4500008004100000022001400041245008100055210006900136260001200205300001400217490000700231653001500238653002600253653002400279653001100303653002300314653002000337653003200357100001500389700002000404700002600424700001700450700002400467700002100491700002600512700002500538710004000563856010600603 2021 eng d a1548-710500aDOME: recommendations for supervised machine learning validation in biology.0 aDOME recommendations for supervised machine learning validation c2021 10 a1122-11270 v1810aAlgorithms10aComputational Biology10aGuidelines as Topic10aHumans10aModels, Biological10aResearch Design10aSupervised Machine Learning1 aWalsh, Ian1 aFishman, Dmytro1 aGarcia-Gasulla, Dario1 aTitma, Tiina1 aPollastri, Gianluca1 aHarrow, Jennifer1 aPsomopoulos, Fotis, E1 aTosatto, Silvio, C E1 aELIXIR Machine Learning Focus Group uhttp://clinbioinfosspa.es/content/dome-recommendations-supervised-machine-learning-validation-biology02115nas a2200409 4500008004100000022001400041245012500055210006900180260001200249300001300261490000700274520080500281653001501086653002101101653002601122653002301148653003001171653001301201653004201214653001101256653002401267653001301291653001201304653002401316653001301340653001801353653002701371653001301398100003101411700002601442700002501468700002401493700002001517700002201537700002001559856012601579 2021 eng d a1553-735800aA versatile workflow to integrate RNA-seq genomic and transcriptomic data into mechanistic models of signaling pathways.0 aversatile workflow to integrate RNAseq genomic and transcriptomi c2021 02 ae10087480 v173 a
MIGNON is a workflow for the analysis of RNA-Seq experiments, which not only efficiently manages the estimation of gene expression levels from raw sequencing reads, but also calls genomic variants present in the transcripts analyzed. Moreover, this is the first workflow that provides a framework for the integration of transcriptomic and genomic data based on a mechanistic model of signaling pathway activities that allows a detailed biological interpretation of the results, including a comprehensive functional profiling of cell activity. MIGNON covers the whole process, from reads to signaling circuit activity estimations, using state-of-the-art tools, it is easy to use and it is deployable in different computational environments, allowing an optimized use of the resources available.
10aAlgorithms10aCell Line, Tumor10aComputational Biology10aDatabases, Factual10aGene Expression Profiling10aGenomics10aHigh-Throughput Nucleotide Sequencing10aHumans10aModels, Theoretical10amutation10aRNA-seq10aSignal Transduction10aSoftware10aTranscriptome10awhole exome sequencing10aWorkflow1 aGarrido-Rodriguez, Martín1 aLópez-López, Daniel1 aOrtuno, Francisco, M1 aPeña-Chilet, Maria1 aMuñoz, Eduardo1 aCalzado, Marco, A1 aDopazo, Joaquin uhttp://clinbioinfosspa.es/content/versatile-workflow-integrate-rna-seq-genomic-and-transcriptomic-data-mechanistic-models01358nas a2200433 4500008004100000022001400041245006500055210006400120260001200184300001200196490000800208653001500216653002800231653003100259100002600290700002800316700001700344700002600361700001800387700001300405700002000418700002100438700002000459700002100479700002200500700002400522700002100546700002200567700002000589700001800609700001900627700002300646700001900669700002500688700002200713700002300735710007100758856009500829 2020 eng d a1476-468700aTransparency and reproducibility in artificial intelligence.0 aTransparency and reproducibility in artificial intelligence c2020 10 aE14-E160 v58610aAlgorithms10aArtificial Intelligence10aReproducibility of Results1 aHaibe-Kains, Benjamin1 aAdam, George, Alexandru1 aHosny, Ahmed1 aKhodakarami, Farnoosh1 aWaldron, Levi1 aWang, Bo1 aMcIntosh, Chris1 aGoldenberg, Anna1 aKundaje, Anshul1 aGreene, Casey, S1 aBroderick, Tamara1 aHoffman, Michael, M1 aLeek, Jeffrey, T1 aKorthauer, Keegan1 aHuber, Wolfgang1 aBrazma, Alvis1 aPineau, Joelle1 aTibshirani, Robert1 aHastie, Trevor1 aIoannidis, John, P A1 aQuackenbush, John1 aAerts, Hugo, J W L1 aMassive Analysis Quality Control (MAQC) Society Board of Directors uhttp://clinbioinfosspa.es/content/transparency-and-reproducibility-artificial-intelligence01813nas a2200265 4500008004100000022001400041245007700055210006900132260001500201300001400216490000700230520098400237653001501221653001101236653002301247653002401270653002001294653001801314100001901332700002201351700001801373700003101391700002001422856010501442 2019 eng d a1477-405400aA comparison of mechanistic signaling pathway activity analysis methods.0 acomparison of mechanistic signaling pathway activity analysis me c2019 09 27 a1655-16680 v203 aUnderstanding the aspects of cell functionality that account for disease mechanisms or drug modes of action is a main challenge for precision medicine. Classical gene-based approaches ignore the modular nature of most human traits, whereas conventional pathway enrichment approaches produce only illustrative results of limited practical utility. Recently, a family of new methods has emerged that change the focus from the whole pathways to the definition of elementary subpathways within them that have any mechanistic significance and to the study of their activities. Thus, mechanistic pathway activity (MPA) methods constitute a new paradigm that allows recoding poorly informative genomic measurements into cell activity quantitative values and relate them to phenotypes. Here we provide a review on the MPA methods available and explain their contribution to systems medicine approaches for addressing challenges in the diagnostic and treatment of complex diseases.
10aAlgorithms10aHumans10aPostmortem Changes10aSignal Transduction10aSystems biology10aTranscriptome1 aAmadoz, Alicia1 aHidalgo, Marta, R1 aCubuk, Cankut1 aCarbonell-Caballero, José1 aDopazo, Joaquin uhttp://clinbioinfosspa.es/content/comparison-mechanistic-signaling-pathway-activity-analysis-methods02551nas a2200301 4500008004100000022001400041245008500055210006900140260001600209300000700225490000700232520160800239653001501847653001801862653001301880653004201893653001101935653002301946653002701969653001301996100002002009700002002029700002302049700002002072700002002092700002202112856011502134 2015 eng d a1471-210500aFast inexact mapping using advanced tree exploration on backward search methods.0 aFast inexact mapping using advanced tree exploration on backward c2015 Jan 28 a180 v163 aBACKGROUND: Short sequence mapping methods for Next Generation Sequencing consist on a combination of seeding techniques followed by local alignment based on dynamic programming approaches. Most seeding algorithms are based on backward search alignment, using the Burrows Wheeler Transform, the Ferragina and Manzini Index or Suffix Arrays. All these backward search algorithms have excellent performance, but their computational cost highly increases when allowing errors. In this paper, we discuss an inexact mapping algorithm based on pruning strategies for search tree exploration over genomic data.
RESULTS: The proposed algorithm achieves a 13x speed-up over similar algorithms when allowing 6 base errors, including insertions, deletions and mismatches. This algorithm can deal with 400 bps reads with up to 9 errors in a high quality Illumina dataset. In this example, the algorithm works as a preprocessor that reduces by 55% the number of reads to be aligned. Depending on the aligner the overall execution time is reduced between 20-40%.
CONCLUSIONS: Although not intended as a complete sequence mapping tool, the proposed algorithm could be used as a preprocessing step to modern sequence mappers. This step significantly reduces the number reads to be aligned, accelerating overall alignment time. Furthermore, this algorithm could be used for accelerating the seeding step of already available sequence mappers. In addition, an out-of-core index has been implemented for working with large genomes on systems without expensive memory configurations.
10aAlgorithms10aGenome, Human10aGenomics10aHigh-Throughput Nucleotide Sequencing10aHumans10aSequence Alignment10aSequence Analysis, DNA10aSoftware1 aSalavert, José1 aTomás, Andrés1 aTárraga, Joaquín1 aMedina, Ignacio1 aDopazo, Joaquin1 aBlanquer, Ignacio uhttp://clinbioinfosspa.es/content/fast-inexact-mapping-using-advanced-tree-exploration-backward-search-methods01866nas a2200337 4500008004100000022001400041245010900055210006900164260001600233300001000249490000600259520081400265653001501079653002601094653001501120653002101135653001801156653002001174653001101194653001901205653001401224653002001238653001301258653002401271100001901295700003001314700001901344700002401363700002001387856012101407 2015 eng d a2045-232200aUsing activation status of signaling pathways as mechanism-based biomarkers to predict drug sensitivity.0 aUsing activation status of signaling pathways as mechanismbased c2015 Dec 18 a184940 v53 aMany complex traits, as drug response, are associated with changes in biological pathways rather than being caused by single gene alterations. Here, a predictive framework is presented in which gene expression data are recoded into activity statuses of signal transduction circuits (sub-pathways within signaling pathways that connect receptor proteins to final effector proteins that trigger cell actions). Such activity values are used as features by a prediction algorithm which can efficiently predict a continuous variable such as the IC50 value. The main advantage of this prediction method is that the features selected by the predictor, the signaling circuits, are themselves rich-informative, mechanism-based biomarkers which provide insight into or drug molecular mechanisms of action (MoA).
10aAlgorithms10aAntineoplastic Agents10abiomarkers10aCell Line, Tumor10aCell Survival10agene expression10aHumans10aLethal Dose 5010aNeoplasms10aPhosphorylation10aProteins10aSignal Transduction1 aAmadoz, Alicia1 aSebastián-Leon, Patricia1 aVidal, Enrique1 aSalavert, Francisco1 aDopazo, Joaquin uhttp://clinbioinfosspa.es/content/using-activation-status-signaling-pathways-mechanism-based-biomarkers-predict-drug02206nas a2200349 4500008004100000022001400041245011400055210006900169260001700238300001200255490000600267520098700273653001501260653001201275653002601287653002201313653002101335653002801356653001801384653004001402653002001442653002301462653002701485100002701512700003001539700003201569700003301601700003101634700003301665700003201698856012601730 2012 eng d a1557-996400aUsing GPUs for the exact alignment of short-read genetic sequences by means of the Burrows-Wheeler transform.0 aUsing GPUs for the exact alignment of shortread genetic sequence c2012 Jul-Aug a1245-560 v93 aGeneral Purpose Graphic Processing Units (GPGPUs) constitute an inexpensive resource for computing-intensive applications that could exploit an intrinsic fine-grain parallelism. This paper presents the design and implementation in GPGPUs of an exact alignment tool for nucleotide sequences based on the Burrows-Wheeler Transform. We compare this algorithm with state-of-the-art implementations of the same algorithm over standard CPUs, and considering the same conditions in terms of I/O. Excluding disk transfers, the implementation of the algorithm in GPUs shows a speedup larger than 12, when compared to CPU execution. This implementation exploits the parallelism by concurrently searching different sequences on the same reference search tree, maximizing memory locality and ensuring a symmetric access to the data. The paper describes the behavior of the algorithm in GPU, showing a good scalability in the performance, only limited by the size of the GPU inner memory.
10aAlgorithms10aAnimals10aComputational Biology10aComputer Graphics10aData Compression10aDrosophila melanogaster10aGenes, Insect10aImage Processing, Computer-Assisted10aModels, Genetic10aSequence Alignment10aSequence Analysis, DNA1 aTorres, Jose, Salavert1 aEspert, Ignacio, Blanquer1 aDomínguez, Andrés, Tomás1 aGarcía, Vicente, Hernández1 aCastelló, Ignacio, Medina1 aGiménez, Joaquín, Tárraga1 aBlázquez, Joaquín, Dopazo uhttp://clinbioinfosspa.es/content/using-gpus-exact-alignment-short-read-genetic-sequences-means-burrows-wheeler-transform02499nas 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 aNext-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.
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-depth03015nas a2200541 4500008004100000022001400041245013100055210006900186260001300255300001100268490000700279520146000286653001501746653002301761653002701784653003001811653001301841653001101854653003001865653004401895653001401939653003001953653001301983653002001996100001102016700001902027700001802046700001302064700001802077700001802095700002102113700001402134700001602148700001802164700001202182700001402194700001202208700001202220700001102232700001402243700001802257700001702275700001702292700001202309700001102321700001602332856012502348 2010 eng d a1473-115000aFunctional analysis of multiple genomic signatures demonstrates that classification algorithms choose phenotype-related genes.0 aFunctional analysis of multiple genomic signatures demonstrates c2010 Aug a310-230 v103 aGene expression signatures of toxicity and clinical response benefit both safety assessment and clinical practice; however, difficulties in connecting signature genes with the predicted end points have limited their application. The Microarray Quality Control Consortium II (MAQCII) project generated 262 signatures for ten clinical and three toxicological end points from six gene expression data sets, an unprecedented collection of diverse signatures that has permitted a wide-ranging analysis on the nature of such predictive models. A comprehensive analysis of the genes of these signatures and their nonredundant unions using ontology enrichment, biological network building and interactome connectivity analyses demonstrated the link between gene signatures and the biological basis of their predictive power. Different signatures for a given end point were more similar at the level of biological properties and transcriptional control than at the gene level. Signatures tended to be enriched in function and pathway in an end point and model-specific manner, and showed a topological bias for incoming interactions. Importantly, the level of biological similarity between different signatures for a given end point correlated positively with the accuracy of the signature predictions. These findings will aid the understanding, and application of predictive genomic signatures, and support their broader application in predictive medicine.
10aAlgorithms10aDatabases, Genetic10aEndpoint Determination10aGene Expression Profiling10aGenomics10aHumans10aNeural Networks, Computer10aOligonucleotide Array Sequence Analysis10aPhenotype10aPredictive Value of Tests10aProteins10aQuality Control1 aShi, W1 aBessarabova, M1 aDosymbekov, D1 aDezso, Z1 aNikolskaya, T1 aDudoladova, M1 aSerebryiskaya, T1 aBugrim, A1 aGuryanov, A1 aBrennan, R, J1 aShah, R1 aDopazo, J1 aChen, M1 aDeng, Y1 aShi, T1 aJurman, G1 aFurlanello, C1 aThomas, R, S1 aCorton, J, C1 aTong, W1 aShi, L1 aNikolsky, Y uhttp://clinbioinfosspa.es/content/functional-analysis-multiple-genomic-signatures-demonstrates-classification-algorithms01781nas 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 aSerial 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.
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-data03382nas a2200325 4500008004100000022001400041245007200055210006900127260001600196300000800212490000700220520234600227653001502573653002102588653004402609653002602653653002802679653001102707653003002718653001302748653001102761653004402772653003002816653003102846100002002877700001902897700002502916700002002941856009502961 2009 eng d a1471-216400aGene set internal coherence in the context of functional profiling.0 aGene set internal coherence in the context of functional profili c2009 Apr 27 a1970 v103 aBACKGROUND: Functional profiling methods have been extensively used in the context of high-throughput experiments and, in particular, in microarray data analysis. Such methods use available biological information to define different types of functional gene modules (e.g. gene ontology -GO-, KEGG pathways, etc.) whose representation in a pre-defined list of genes is further studied. In the most popular type of microarray experimental designs (e.g. up- or down-regulated genes, clusters of co-expressing genes, etc.) or in other genomic experiments (e.g. Chip-on-chip, epigenomics, etc.) these lists are composed by genes with a high degree of co-expression. Therefore, an implicit assumption in the application of functional profiling methods within this context is that the genes corresponding to the modules tested are effectively defining sets of co-expressing genes. Nevertheless not all the functional modules are biologically coherent entities in terms of co-expression, which will eventually hinder its detection with conventional methods of functional enrichment.
RESULTS: Using a large collection of microarray data we have carried out a detailed survey of internal correlation in GO terms and KEGG pathways, providing a coherence index to be used for measuring functional module co-regulation. An unexpected low level of internal correlation was found among the modules studied. Only around 30% of the modules defined by GO terms and 57% of the modules defined by KEGG pathways display an internal correlation higher than the expected by chance.This information on the internal correlation of the genes within the functional modules can be used in the context of a logistic regression model in a simple way to improve their detection in gene expression experiments.
CONCLUSION: For the first time, an exhaustive study on the internal co-expression of the most popular functional categories has been carried out. Interestingly, the real level of coexpression within many of them is lower than expected (or even inexistent), which will preclude its detection by means of most conventional functional profiling methods. If the gene-to-function correlation information is used in functional profiling methods, the results obtained improve the ones obtained by conventional enrichment methods.
10aAlgorithms10aBreast Neoplasms10aCarcinoma, Intraductal, Noninfiltrating10aComputational Biology10aDatabases, Nucleic Acid10aFemale10aGene Expression Profiling10aGenomics10aHumans10aOligonucleotide Array Sequence Analysis10aPapillomavirus Infections10aReproducibility of Results1 aMontaner, David1 aMinguez, Pablo1 aAl-Shahrour, Fátima1 aDopazo, Joaquin uhttp://clinbioinfosspa.es/content/gene-set-internal-coherence-context-functional-profiling03089nas a2200385 4500008004100000022001400041245013400055210006900189260001300258300001200271490000700283520182800290653001502118653001002133653002602143653002302169653002802192653002502220653003802245653002202283653001802305653001102323653001802334653001902352653003602371653001302407653003302420100002202453700002102475700001802496700002002514700002002534700002502554856012402579 2008 eng d a1098-100400aUse of estimated evolutionary strength at the codon level improves the prediction of disease-related protein mutations in humans.0 aUse of estimated evolutionary strength at the codon level improv c2008 Jan a198-2040 v293 aPredicting the functional impact of protein variation is one of the most challenging problems in bioinformatics. A rapidly growing number of genome-scale studies provide large amounts of experimental data, allowing the application of rigorous statistical approaches for predicting whether a given single point mutation has an impact on human health. Up until now, existing methods have limited their source data to either protein or gene information. Novel in this work, we take advantage of both and focus on protein evolutionary information by using estimated selective pressures at the codon level. Here we introduce a new method (SeqProfCod) to predict the likelihood that a given protein variant is associated with human disease or not. Our method relies on a support vector machine (SVM) classifier trained using three sources of information: protein sequence, multiple protein sequence alignments, and the estimation of selective pressure at the codon level. SeqProfCod has been benchmarked with a large dataset of 8,987 single point mutations from 1,434 human proteins from SWISS-PROT. It achieves 82% overall accuracy and a correlation coefficient of 0.59, indicating that the estimation of the selective pressure helps in predicting the functional impact of single-point mutations. Moreover, this study demonstrates the synergic effect of combining two sources of information for predicting the functional effects of protein variants: protein sequence/profile-based information and the evolutionary estimation of the selective pressures at the codon level. The results of large-scale application of SeqProfCod over all annotated point mutations in SWISS-PROT (available for download at http://sgu.bioinfo.cipf.es/services/Omidios/; last accessed: 24 August 2007), could be used to support clinical studies.
10aAlgorithms10aCodon10aComputational Biology10aDatabases, Protein10aDNA Mutational Analysis10aEvolution, Molecular10aGenetic Predisposition to Disease10aGenetic Variation10aGenome, Human10aHumans10aIduronic Acid10aPoint Mutation10aPolymorphism, Single Nucleotide10aProteins10aTumor Suppressor Protein p531 aCapriotti, Emidio1 aArbiza, Leonardo1 aCasadio, Rita1 aDopazo, Joaquin1 aDopazo, Hernán1 aMarti-Renom, Marc, A uhttp://clinbioinfosspa.es/content/use-estimated-evolutionary-strength-codon-level-improves-prediction-disease-related-002296nas a2200421 4500008004100000022001400041245005300055210005100108260001300159300001100172490000700183520104700190653001501237653002401252653002601276653003701302653002301339653001301362653002801375653002501403653001301428653002701441653002301468653003101491653003401522653001301556653003601569100002501605700001901630700002201649700001801671700002101689700001901710700002501729700002001754700001701774856008301791 2007 eng d a1362-496200aDBAli tools: mining the protein structure space.0 aDBAli tools mining the protein structure space c2007 Jul aW393-70 v353 aThe DBAli tools use a comprehensive set of structural alignments in the DBAli database to leverage the structural information deposited in the Protein Data Bank (PDB). These tools include (i) the DBAlit program that allows users to input the 3D coordinates of a protein structure for comparison by MAMMOTH against all chains in the PDB; (ii) the AnnoLite and AnnoLyze programs that annotate a target structure based on its stored relationships to other structures; (iii) the ModClus program that clusters structures by sequence and structure similarities; (iv) the ModDom program that identifies domains as recurrent structural fragments and (v) an implementation of the COMPARER method in the SALIGN command in MODELLER that creates a multiple structure alignment for a set of related protein structures. Thus, the DBAli tools, which are freely accessible via the World Wide Web at http://salilab.org/DBAli/, allow users to mine the protein structure space by establishing relationships between protein structures and their functions.
10aAlgorithms10aAmino Acid Sequence10aComputational Biology10aData Interpretation, Statistical10aDatabases, Protein10aInternet10aMolecular Sequence Data10aProtein Conformation10aProteins10aPseudomonas aeruginosa10aSequence Alignment10aSequence Analysis, Protein10aSequence Homology, Amino Acid10aSoftware10aStructure-Activity Relationship1 aMarti-Renom, Marc, A1 aPieper, Ursula1 aMadhusudhan, M, S1 aRossi, Andrea1 aEswar, Narayanan1 aDavis, Fred, P1 aAl-Shahrour, Fátima1 aDopazo, Joaquin1 aSali, Andrej uhttp://clinbioinfosspa.es/content/dbali-tools-mining-protein-structure-space-002514nas a2200337 4500008004100000022001400041245007300055210006900128260001600197300000800213490000600221520148900227653001501716653002301731653002401754653003001778653002301808653002101831653002401852653001301876653002001889653002801909100002501937700002101962700002001983700002402003700001902027700002002046700002002066856009002086 2007 eng d a1471-210500aFrom genes to functional classes in the study of biological systems.0 aFrom genes to functional classes in the study of biological syst c2007 Apr 03 a1140 v83 aBACKGROUND: With the popularization of high-throughput techniques, the need for procedures that help in the biological interpretation of results has increased enormously. Recently, new procedures inspired in systems biology criteria have started to be developed.
RESULTS: Here we present FatiScan, a web-based program which implements a threshold-independent test for the functional interpretation of large-scale experiments that does not depend on the pre-selection of genes based on the multiple application of independent tests to each gene. The test implemented aims to directly test the behaviour of blocks of functionally related genes, instead of focusing on single genes. In addition, the test does not depend on the type of the data used for obtaining significance values, and consequently different types of biologically informative terms (gene ontology, pathways, functional motifs, transcription factor binding sites or regulatory sites from CisRed) can be applied to different classes of genome-scale studies. We exemplify its application in microarray gene expression, evolution and interactomics.
CONCLUSION: Methods for gene set enrichment which, in addition, are independent from the original data and experimental design constitute a promising alternative for the functional profiling of genome-scale experiments. A web server that performs the test described and other similar ones can be found at: http://www.babelomics.org.
10aAlgorithms10aChromosome Mapping10aComputer Simulation10aGene Expression Profiling10aModels, Biological10aMultigene Family10aSignal Transduction10aSoftware10aSystems biology10aUser-Computer Interface1 aAl-Shahrour, Fátima1 aArbiza, Leonardo1 aDopazo, Hernán1 aHuerta-Cepas, Jaime1 aMinguez, Pablo1 aMontaner, David1 aDopazo, Joaquin uhttp://clinbioinfosspa.es/content/genes-functional-classes-study-biological-systems-0