GEPAS, an experiment-oriented pipeline for the analysis of microarray gene expression data

被引:70
作者
Vaquerizas, JM
Conde, L
Yankilevich, P
Cabezón, A
Minguez, P
Díaz-Uriarte, R
Al-Shahrour, F
Herrero, J
Dopazo, J
机构
[1] CNIO, Bioinformat Unit, Madrid 28029, Spain
[2] EMBL, EBI, Ensembl Team, Cambridge, England
[3] Ctr Invest Principe Felipe, INB, Valencia 46013, Spain
关键词
D O I
10.1093/nar/gki500
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
The Gene Expression Profile Analysis Suite, GEPAS, has been running for more than three years. With > 76000 experiments analysed during the last year and a daily average of almost 300 analyses, GEPAS can be considered a well-established and widely used platform for gene expression microarray data analysis. GEPAS is oriented to the analysis of whole series of experiments. Its design and development have been driven by the demands of the biomedical community, probably the most active collective in the field of microarray users. Although clustering methods have obviously been implemented in GEPAS, our interest has focused more on methods for finding genes differentially expressed among distinct classes of experiments or correlated to diverse clinical outcomes, as well as on building predictors. There is also a great interest in CGH-arrays which fostered the development of the corresponding tool in GEPAS: InSilicoCGH. Much effort has been invested in GEPAS for developing and implementing efficient methods for functional annotation of experiments in the proper statistical framework. Thus, the popular FatiGO has expanded to a suite of programs for functional annotation of experiments, including information on transcription factor binding sites, chromosomal location and tissues. The web-based pipeline for microarray gene expression data, GEPAS, is available at http://www.gepas.org.
引用
收藏
页码:W616 / W620
页数:5
相关论文
共 31 条
[21]   MATCH™:: a tool for searching transcription factor binding sites in DNA sequences [J].
Kel, AE ;
Gössling, E ;
Reuter, I ;
Cheremushkin, E ;
Kel-Margoulis, OV ;
Wingender, E .
NUCLEIC ACIDS RESEARCH, 2003, 31 (13) :3576-3579
[22]  
Kohonen T., 1997, Self-organizing Maps, V2nd ed.
[23]   The InterPro Database, 2003 brings increased coverage and new features [J].
Mulder, NJ ;
Apweiler, R ;
Attwood, TK ;
Bairoch, A ;
Barrell, D ;
Bateman, A ;
Binns, D ;
Biswas, M ;
Bradley, P ;
Bork, P ;
Bucher, P ;
Copley, RR ;
Courcelle, E ;
Das, U ;
Durbin, R ;
Falquet, L ;
Fleischmann, W ;
Griffiths-Jones, S ;
Haft, D ;
Harte, N ;
Hulo, N ;
Kahn, D ;
Kanapin, A ;
Krestyaninova, M ;
Lopez, R ;
Letunic, I ;
Lonsdale, D ;
Silventoinen, V ;
Orchard, SE ;
Pagni, M ;
Peyruc, D ;
Ponting, CP ;
Selengut, JD ;
Servant, F ;
Sigrist, CJA ;
Vaughan, R ;
Zdobnov, EM .
NUCLEIC ACIDS RESEARCH, 2003, 31 (01) :315-318
[24]  
Saal LH, 2002, GENOME BIOL, V3
[25]   Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification [J].
Simon, R ;
Radmacher, MD ;
Dobbin, K ;
McShane, LM .
JOURNAL OF THE NATIONAL CANCER INSTITUTE, 2003, 95 (01) :14-18
[26]  
Smyth Gordon K, 2003, Methods Mol Biol, V224, P111
[27]  
Sneath P. H. A., NUMERICAL TAXONOMY
[28]   Assembly of microarrays for genome-wide measurement of DNA copy number. [J].
Snijders, AM ;
Nowak, N ;
Segraves, R ;
Blackwood, S ;
Brown, N ;
Conroy, J ;
Hamilton, G ;
Hindle, AK ;
Huey, B ;
Kimura, K ;
Law, S ;
Myambo, K ;
Palmer, J ;
Ylstra, B ;
Yue, JP ;
Gray, JW ;
Jain, AN ;
Pinkel, D ;
Albertson, DG .
NATURE GENETICS, 2001, 29 (03) :263-264
[29]   Gene expression profiling predicts clinical outcome of breast cancer [J].
van't Veer, LJ ;
Dai, HY ;
van de Vijver, MJ ;
He, YDD ;
Hart, AAM ;
Mao, M ;
Peterse, HL ;
van der Kooy, K ;
Marton, MJ ;
Witteveen, AT ;
Schreiber, GJ ;
Kerkhoven, RM ;
Roberts, C ;
Linsley, PS ;
Bernards, R ;
Friend, SH .
NATURE, 2002, 415 (6871) :530-536
[30]   DNMAD:: web-based diagnosis and normalization for microarray data [J].
Vaquerizas, JM ;
Dopazo, J ;
Díaz-Uriarte, R .
BIOINFORMATICS, 2004, 20 (18) :3656-3658