Nothing Special   »   [go: up one dir, main page]

Skip to main content
Log in

Automatic summarisation and annotation of microarray data

  • Focus
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

The study of biological processes within cells is based on the measurement of the activity of different molecules, in particular genes and proteins whose activities are strictly related. The activity of genes is measured through a systematic investigation carried out by microarrays. Such technology enables the investigation of all the genes of an organism in a single experiment, encoding meaningful biological information. Nevertheless, the preprocessing of raw microarray data needs automatic tools that standardise such phase in order to: (a) avoiding errors in analysis phases, and (b) making comparable the results of different laboratories. The preprocessing problem is as much relevant as considering results obtained from analysis platforms of different vendors. Nevertheless, there is currently a lack of tools that allow to manage and preprocess multivendor dataset. This paper presents a software platform (called GSAT, General-purpose Summarisation and Annotation Tool) able to manage and preprocess microarray data. The GSAT allows the summarisation, normalisation and annotation of multivendor microarray data, using web services technology. First experiments and results on Affymetrix data samples are also discussed. GSAT is available online at http://bioingegneria.unicz.it/m-cs as a standalone application or as a plugin of the TMEV microarray data analysis platform.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Notes

  1. http://www.affymetrix.com.

  2. http://www.illumina.com.

  3. http://www.bioconductor.org.

  4. http://www.illumina.com.

  5. http://rmaexpress.bmbolstad.com/.

  6. http://compbio.dfci.harvard.edu/amp/.

  7. http://www.affymetrix.com/support/technical/sample_data/datasets.affx.

References

  • Affymetrix. Affymetrix Power Tools (APT). http://www.affymetrix.com

  • Affymetrix Array design for the GeneChip human genome 133 Set (2001) Affymetrix Technote

  • Arteaga-Salas JM, Zuzan H, Langdon WB, Upton GJG, Harrison AP (2008) An overview of image-processing methods for Affymetrix GeneChips. Briefings Bioinf 9(1):25–33. doi:10.1093/bib/bbm055

    Google Scholar 

  • Brazma A et al (2001) Minimum information about a microarray experiment (miame)-toward standards for microarray data. Nat Genet 29(4):365–371 (December 2001)

    Google Scholar 

  • Cannataro M, Di Martino MT, Guzzi PH, Tagliaferri P, Tassone P, Tradigo G, Veltri P (2008a) An extension of the TIGR M4 suite to preprocess and visualize affymetrix binary files. In: Proceedings of computational intelligence methods for bioinformatics and biostatistics, 5th international meeting, CIBB 2008, Vietri sul Mare, Italy. Springer (3–4 October 2008 )

  • Cannataro M, Di Martino MT, Guzzi PH, Tassone P, Tagliaferri P, Tradigo G, Veltri P (2008b) A tool for managing affymetrix binary files through the tigr TM4 suite. Accepted poster in international meeting of the Microarray and Gene Expression Data Society. Riva del Garda, Italy (1–4 September)

  • Di Martino MT, Guzzi PH, Ventura M, Pietragalla, A, Neri P, Bulotta A, Calimeri T, Barbieri V, Caraglia M, Veltri P, Cannataro M, Tassone P, Tagliaferri P (2008) Whole gene expression profiling shows a differential transcriptional response to cisplatinum in brca-1 defective versus brca1-reconstituted breast cancer cells. Ann Oncol 19:ix103–ix111. doi:10.1093/annonc/mdn618

    Google Scholar 

  • Di Martino MT, Ventura M, Guzzi PH, Pietragalla A, Neri P, Bulotta A, Calimeri T, Barbieri V, Caraglia M, Veltri P, Cannataro M, Tassone P, Tagliaferri P (2009) Differential transcriptional response to cisplatinum in BRCA1-defective versus BRCA1-reconstituted breast cancer cells by microarrays. Cancer Res 69:5062

    Google Scholar 

  • Du P, Kibbe WA, Lin SM (2008) lumi: a pipeline for processing Illumina microarray. Bioinformatics 24(13):1547–1548. doi:10.1093/bioinformatics/btn224

    Google Scholar 

  • Dunning MJ, Smith ML, Ritchie ME, Tavare S (2007) beadarray: R classes and methods for Illumina bead-based data. Bioinformatics 23(16):2183–2184. doi:10.1093/bioinformatics/btm311

    Google Scholar 

  • Dunning MJ, Barbosa-Morais A, Lynch A, Tavare A, Ritchie A (2008) Statistical issues in the analysis of Illumina data. BMC Bioinf 1:85

  • Eggle D, Schultze J (2007) IlluminaGUI: Graphical User Interface for analyzing gene expression data generated on the Illumina platform. Bioinformatics 23(11):1431–1433. doi:10.1093/bioinformatics/btm101

    Google Scholar 

  • Fujita A, Sato JR, Rodrigues LO, Ferreira CE, Sogayar MC (2006) Evaluating different methods of microarray data normalization. BMC Bioinf 7:469 (October 2006)

    Google Scholar 

  • Guide to probe logarithmic intensity error (plier) estimation. http://www.affymetrix.com/support/technical/technotes/plier_technote.pdf

  • Harbron C, Chang KM, South MC (2007) Refplus: an r package extending the rma algorithm. Bioinformatics 23(18):2493–2494. doi:10.1093/bioinformatics/btm357

    Google Scholar 

  • Harris MA, Clark J, Ireland A, Lomax J, Ashburner M, Foulger R, Eilbeck K, Lewis S, Marshall B, Mungall C, Richter J, Rubin GM, Blake JA, Bult C, Dolan M, Drabkin H, Eppig JT, Hill DP, Ni L, Ringwald M, Balakrishnan R, Cherry JM, Christie KR, Costanzo MC, Dwight SS, Engel S, Fisk DG, Hirschman JE, Hong EL, Nash RS, Sethuraman A, Theesfeld CL, Botstein D, Dolinski K, Feierbach B, Berardini T, Mundodi S, Rhee SY, Apweiler R, Barrell D, Camon E, Dimmer E, Lee V, Chisholm R, Gaudet P, Kibbe W, Kishore R, Schwarz EM, Sternberg P, Gwinn M, Hannick L, Wortman J, Berriman M, Wood V, Tonellato P, Jaiswal P, Seigfried T, White R (2004) The gene ontology (go) database and informatics resource. Nucleic Acids Res Nucleic Acids Res 32(Database issue):258–261 (January 2004)

  • Hibbs MA, Dirksen NC, Li K, Troyanskaya OG (2005) Visualization methods for statistical analysis of microarray clusters. BMC Bioinf 6

  • Irizarry RA, Bolstad BM, Collin F, Cope LM, Hobbs B, Speed TP (2003a) Summaries of affymetrix genechip probe level data. Nucleic Acids Res 31(4) (February 2003)

  • Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, Terence P (2003b) Speed. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4(2):249–264

  • Kapur K, Jiang H, Xing Y, Wong WH (2008) Cross-hybridization modeling on affymetrix exon arrays. Bioinformatics 24(24):2887–2893. doi:10.1093/bioinformatics/btn571

    Google Scholar 

  • Kuhn K, Baker SC, Chudin E, Lieu MH, Oeser S, Bennett H, Rigault P, Barker D, McDaniel TK, Chee MS (2004) A novel, high-performance random array platform for quantitative gene expression profiling. Genome Res 14(11):2347–2356

    Article  Google Scholar 

  • Li C, Hung Wong W (2001) Model-based analysis of oligonucleotide arrays: model validation, design issues and standard error application. Genome Biol 2(8)

  • Michael B, Freudenberg J, Thompson S, Aronow B, Pavlidis P (2005) Experimental comparison and cross-validation of the Affymetrix and Illumina gene expression analysis platforms Nucl Acids Res 33:18

    Google Scholar 

  • Quackenbush J (2001) Computational genetics: computational analysis of microarray data. Nat Rev Genet 2:418–427. doi:10.1038/35076576

    Google Scholar 

  • Reimers M, Carey VJ (2006) Bioconductor: an open source framework for bioinformatics and computational biology. Methods Enzymol 411:119–134

    Google Scholar 

  • Rocke D, Durbin B (2001) A model for measurement error for gene expression arrays. J Comput Biol 8(6):557–569

    Article  Google Scholar 

  • Rubinstein BIP, McAuliffe J, Cawley S, Palaniswami M, Ramamohanarao K, Speed TP (2003) Machine learning in low-level microarray analysis. SIGKDD Explor Newsl 5(2):130–139

    Article  Google Scholar 

  • Saeed AI, Sharov V, White J, Li J, Liang W, Bhagabati N, Braisted J, Klapa M, Currier T, Thiagarajan M, Sturn A, Snuffin M, Rezantsev A, Popov D, Ryltsov A, Kostukovich E, Borisovsky I, Liu Z, Vinsavich A, Trush V, Quackenbush J (2003) Tm4: a free, open-source system for microarray data management and analysis. Biotechniques 34(2):374–378

    Google Scholar 

  • Sanges R, Cordero F, Calogero RA (2007) onechannelgui: a graphical interface to bioconductor tools, designed for life scientists who are not familiar with r language. Bioinformatics 23(24):3406–3408. doi:10.1093/bioinformatics/btm469

    Google Scholar 

  • Tu Y, Stolovitzky G, Klein U (2002) Quantitative noise analysis for gene expression microarray experiments. Proc Natl Acad Sci 99(22):14031–14036

    Article  MATH  MathSciNet  Google Scholar 

  • Welle S, Brooks AI, Thornton CA (2002) Computational method for reducing variance with affymetrix microarrays. BMC Bioinf 3 (August 2002)

Download references

Acknowledgments

Authors are grateful to Andrea Greco for his work on prototype implementation.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Pietro H. Guzzi.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Guzzi, P.H., Di Martino, M.T., Tradigo, G. et al. Automatic summarisation and annotation of microarray data. Soft Comput 15, 1505–1512 (2011). https://doi.org/10.1007/s00500-010-0600-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-010-0600-4

Keywords

Navigation