Genomic DNA-based probe selection by using high density oligonucleotide arrays has recently been applied to heterologous species (Xspecies). With the advent of this new approach, researchers are able to study the genome and transcriptome of a non-model or an underutilised crop species through current state-of-the-art microarray platforms. However, a software package with a graphical user interface (GUI) to analyse and parse the oligonucleotide probe pair level data is still lacking when an experiment is designed on the basis of this cross species approach. A novel computer program called Pigeons has been developed for customised array data analysis to allow the user to import and analyse Affymetrix GeneChip ？ probe level data through XSpecies. One can determine empirical boundaries for removing poor probes based on genomic hybridisation of the test species to the Xspecies array, followed by making a species-specific Chip Description File (CDF) file for transcriptomics in the heterologous species, or Pigeons can be used to examine an experimental design to identify potential Single-Feature Polymorphisms (SFPs) at the DNA or RNA level. Pigeons is also focused around visualization and interactive analysis of the datasets. The software with its manual (the current release number version 1.2.1) is freely available at the website of the Nottingham Arabidopsis Stock Centre (NASC).
References
[1]
Wang, J. Computational biology of genome expression and regulation—A review of microarray bioinformatics. J. Environ. Pathol. Toxicol. Oncol. 2008, 27, 157–179, doi:10.1615/JEnvironPatholToxicolOncol.v27.i3.10.
[2]
Kumar, R.M. The widely used diagnostics “DNA microarrays”—A review. Am. J. Infect. Dis. 2009, 5, 214–225, doi:10.3844/ajidsp.2009.214.225.
[3]
Hammond, J.P.; Broadley, M.R.; Craigon, D.J.; Higgins, J.; Emmerson, Z.F.; Townsend, H.J.; White, P.J.; May, S.T. Using genomic DNA-based probe-selection to improve the sensitivity of high-density oligonucleotide arrays when applied to heterologous species. Plant Methods 2005, 1, 10, doi:10.1186/1746-4811-1-10.
[4]
Hammond, J.P.; Bowen, H.C.; White, P.J.; Mills, V.; Pyke, K.A.; Baker, A.J.; Whiting, S.N.; May, S.T.; Broadley, M.R. A comparison of the Thlaspi caerulescens and Thlaspi arvense shoot transcriptomes. New Phytol. 2006, 170, 239–260, doi:10.1111/j.1469-8137.2006.01662.x.
[5]
Graham, N.S.; Broadley, M.R.; Hammond, J.P.; White, P.J.; May, S.T. Optimising the analysis of transcript data using high density oligonucleotide arrays and genomic DNA-based probe selection. BMC Genomics 2007, 8, 344, doi:10.1186/1471-2164-8-344.
[6]
Broadley, M.R.; White, P.J.; Hammond, J.P.; Graham, N.S.; Bowen, H.C.; Emmerson, Z.F.; Fray, R.G.; Iannetta, P.P.M.; McNicol, J.W.; May, S.T. Evidence of neutral transcriptome evolution in plants. New Phytol. 2008, 180, 587–593, doi:10.1111/j.1469-8137.2008.02640.x.
[7]
Davey, M.W.; Graham, N.S.; Vanholme, B.; Swennen, R.; May, S.T.; Keulemans, J. Heterologous oligonucleotide microarrays for transcriptomics in a non-model species; A proof-of-concept study of drought stress in Musa. BMC Genomics 2009, 10, 436, doi:10.1186/1471-2164-10-436.
[8]
Kreyszig, E. Advanced Engineering Mathematics, 10th ed. ed.; John Wiley & Sons: Hoboken, NJ, USA, 2011; pp. 790–842.
[9]
Xu, R.; Wunsch, D., II. Survey of clustering algorithms. IEEE Trans. Neural Netw. 2005, 16, 645–678, doi:10.1109/TNN.2005.845141.
[10]
Schena, M.; Shalon, D.; Heller, R.; Chai, A.; Brown, P.O.; Davis, R.W. Parallel human genome analysis: Microarray-based expression monitoring of 1,000 genes. Proc. Natl Acad. Sci. USA 1996, 93, 10614–10619, doi:10.1073/pnas.93.20.10614.
[11]
Cui, X.; Churchill, G.A. Statistical tests for differential expression in cDNA microarray experiments. Genome Biol. 2003, 4, 210, doi:10.1186/gb-2003-4-4-210.
[12]
Kooperberg, C.; Aragaki, A.; Strand, A.D.; Olson, J.M. Significance testing for small microarray experiments. Stat. Med. 2005, 24, 2281–2298, doi:10.1002/sim.2109.
[13]
Mayes, S.; Stadler, S.; Basu, S.; Murchie, E.; Massawe, F.; Kilian, A.; Roberts, J.A.; Mohler, V.; Wenzel, G.; Beena, R.; et al. BAMLINK—A cross disciplinary programme to enhance the role of bambara groundnut (Vigna subterranea L. Verdc.) for food security in Africa and India. Acta Hortic. 2009, 806, 137–150.
[14]
Basu, S.; Mayes, S.; Davey, M.; Roberts, J.A.; Azam-Ali, S.N.; Mithren, R.; Pasquet, R.S. Inheritance of “domestication” traits in bambara groundnut (Vigna subterranea L. Verdc.). Euphytica 2007, 157, 59–68, doi:10.1007/s10681-007-9396-4.
[15]
Bezdek, J. Pattern Recognition with Fuzzy Objective Function Algorithms, 1st ed. ed.; Plenum Press: New York, NY, USA, 1981; pp. 95–154.
[16]
Jeffery, I.B.; Higgins, D.G.; Culhane, A.C. Comparison and evaluation of methods for generating differentially expressed gene lists from microarray data. BMC Bioinform. 2006, 7, 359, doi:10.1186/1471-2105-7-359.
[17]
Dudoit, S.; Yang, Y.H.; Callow, M.J.; Speed, T.P. Statistical methods for identifying genes with differential expression in replicated cDNA microarray experiments. Stat. Sin. 2002, 12, 111–139.
[18]
Irizarry, R.A.; Hobbs, B.; Collin, F.; Beazer-Barclay, Y.D.; Antonellis, K.J.; Scherf, U.; Speed, T.P. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 2003, 4, 249–264, doi:10.1093/biostatistics/4.2.249.
[19]
Bolstad, B.M.; Irizarry, R.A.; Astrand, M.; Speed, T.P. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 2003, 19, 185–193, doi:10.1093/bioinformatics/19.2.185.
[20]
Tukey, J.W.; McLaughlin, D.H. Less vulnerable confidence and significance procedures for location based on a single sample: Trimming/Winsorization 1. Sankhya A 1963, 25, 331–352.
[21]
Patel, K.R.; Mudholkar, G.S.; Fernando, J.L.I. Student’s t approximations for three simple robust estimators. J. Am. Stat. Assoc. 1988, 83, 1203–1210.
[22]
Graham, N.S.; Clutterbuck, A.L.; James, N.; Lea, R.G.; Mobasheri, A.; Broadley, M.R.; May, S.T. Equine transcriptome quantification using human GeneChip arrays can be improved using genomic DNA hybridisation and probe selection. Vet. J. 2010, 186, 323–327, doi:10.1016/j.tvjl.2009.08.030.
[23]
Graham, N.S.; May, S.T.; Daniel, Z.C.T.R.; Emmerson, Z.F.; Brameld, J.M.; Parr, T. Use of the Affymetrix Human GeneChip array and genomic DNA hybridisation probe selection to study ovine transcriptomes. Animal 2011, 5, 861–866, doi:10.1017/S1751731110002533.
[24]
Fukuyama, Y.; Sugeno, M. A New Method of Choosing the Number of Clusters for the Fuzzy C-Mean Method. Available online: http://citeseer.uark.edu:8080/citeseerx/showciting;jsessionid=1AF0955F44EC87078947AADEDE29D50C?cid=664813 (accessed on 10 December 2013).
[25]
Benjamini, Y.; Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. B 1995, 57, 289–300.
