High density oligonucleotide microarrays present a big challenge for statistical data processing methods which aim to separate changes induced by experimental factors from those caused by artifacts and measurement inaccuracies. Despite huge advances in the field of microarray probe design methods, the signal variation between probes that target a single transcript is substantially larger than their between-replicate array variability, suggesting a large influence of various probe-specific effects that introduce bias to the data. In this work we present the influence of probe-related design variations on the expression intensities of individual probes, focusing on five potential sources of high probe signal variance: the GC composition of the probe, the distance between individual probe target sites, G-quadruplex formation in the probe sequence, the occurrence of sequence motifs complementary to the oligo(dT) primer, and the specificity of unrecognized alternative splicing probeset assignment. By focusing on two high quality microarray datasets based on two distinct array designs we show the extent of variance between probes that target a specific transcript providing guidelines for the future design of microarrays and data processing methods.
References
[1]
Kim, K.; Zakharkin, S.O.; Allison, D.B. Expectations, validity, and reality in gene expression profiling. J. Clin. Epidemiol. 2010, 63, 950–959.
[2]
Bemmo, A.; Benovoy, D.; Kwan, T.; Gaffney, D.J.; Jensen, R.V.; Majewski, J. Gene expression and isoform variation analysis using Affymetrix Exon Arrays. BMC Genomics 2008, 9, doi:10.1186/1471-2164-9-529.
Arezi, B.; Xing, W.; Sorge, J.A.; Hogrefe, H.H. Amplification efficiency of thermostable DNA polymerases. Anal. Biochem. 2003, 321, 226–235.
[5]
Degrelle, S.A.; Hennequet-Antier, C.; Chiapello, H.; Piot-Kaminski, K.; Piumi, F.; Robin, S.; Renard, J.P.; Hue, I. Amplification biases: Possible differences among deviating gene expressions. BMC Genomics 2008, 9, doi:10.1186/1471-2164-9-46.
[6]
Sykacek, P.; Kreil, D.P.; Meadows, L.A.; Auburn, R.P.; Fischer, B.; Russell, S.; Micklem, G. The impact of quantitative optimization of hybridization conditions on gene expression analysis. BMC Bioinforma. 2011, 12, doi:10.1186/1471-2105-12-73.
[7]
Fuchs, J.; Fiche, J.B.; Buhot, A.; Calemczuk, R.; Livache, T. Salt concentration effects on equilibrium melting curves from DNA microarrays. Biophys. J. 2010, 99, 1886–1895.
[8]
Li, C.; Wong, W.H. Model-based analysis of oligonucleotide arrays: Expression index computation and outlier detection. Proc. Natl. Acad. Sci. USA 2001, 98, 31–36.
[9]
Dai, M.; Wang, P.; Boyd, A.D.; Kostov, G.; Athey, B.; Jones, E.G.; Bunney, W.E.; Myers, R.M.; Speed, T.P.; Akil, H.; Watson, S.J.; Meng, F. Evolving gene/transcript definitions significantly alter the interpretation of GeneChip data. Nucleic Acids Res. 2005, 33, doi:10.1093/nar/gni179.
[10]
Ferrari, F.; Bortoluzzi, S.; Coppe, A.; Sirota, A.; Safran, M.; Shmoish, M.; Ferrari, S.; Lancet, D.; Danieli, G.A.; Bicciato, S. Novel definition files for human GeneChips based on GeneAnnot. BMC Bioinforma. 2007, 8, doi:10.1186/1471-2105-8-446.
[11]
Marczyk, M.; Jaksik, R.; Polańska, J.; Polański, A. Affymetrix chip definition files construction based on custom probe set annotation database. Stud. Computat. Intell. 2011, 381, 135–144.
[12]
Shi, L.; Reid, L.H.; Jones, W.D.; Shippy, R.; Warrington, J.A.; Baker, S.C.; Collins, P.J.; de Longueville, F.; Kawasaki, E.S.; Lee, K.Y.; et al. The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements. Nat. Biotechnol. 2006, 24, 1151–1161.
[13]
Parkinson, H.; Sarkans, U.; Kolesnikov, N.; Abeygunawardena, N.; Burdett, T.; Dylag, M.; Emam, I.; Farne, A.; Hastings, E.; Holloway, E.; et al. ArrayExpress update-an archive of microarray and high-throughput sequencing-based functional genomics experiments. Nucleic Acids Res. 2011, 39, D1002–D1004.
[14]
Wei, H.R.; Kuan, P.F.; Tian, S.L.; Yang, C.H.; Nie, J.; Sengupta, S.; Ruotti, V.; Jonsdottir, G.A.; Keles, S.; Thomson, J.A.; Stewart, R. A study of the relationships between oligonucleotide properties and hybridization signal intensities from NimbleGen microarray datasets. Nucleic Acids Res. 2008, 36, 2926–2938.
[15]
Boedigheimer, M.J.; Wolfinger, R.D.; Bass, M.B.; Bushel, P.R.; Chou, J.W.; Cooper, M.; Corton, J.C.; Fostel, J.; Hester, S.; Lee, J.S.; et al. Sources of variation in baseline gene expression levels from toxicogenomics study control animals across multiple laboratories. BMC Genomics 2008, 9, doi:10.1186/1471-2164-9-285.
[16]
Lazar, C.; Meganck, S.; Taminau, J.; Steenhoff, D.; Coletta, A.; Molter, C.; Weiss-Solis, D.Y.; Duque, R.; Bersini, H.; Nowe, A. Batch effect removal methods for microarray gene expression data integration: A survey. Brief. Bioinforma. 2013, 14, 469–490.
[17]
Wu, Z.J.; Irizarry, R.A.; Gentleman, R.; Martinez-Murillo, F.; Spencer, F. A model-based background adjustment for oligonucleotide expression arrays. J. Am. Stat. Assoc. 2004, 99, 909–917.
[18]
Memon, F.N.; Upton, G.J.; Harrison, A.P. A comparative study of the impact of g-stack probes on various Affymetrix Genechips of mammalia. J. Nucleic Acids 2010, 2010, doi:10.4061/2010/489736.
[19]
Upton, G.J.; Langdon, W.B.; Harrison, A.P. G-spots cause incorrect expression measurement in Affymetrix microarrays. BMC Genomics 2008, 9, 613, doi:10.1186/1471-2164-9-613.
[20]
Fasold, M.; Stadler, P.F.; Binder, H. G-stack modulated probe intensities on expression arrays—Sequence corrections and signal calibration. BMC Bioinforma. 2010, 11, doi:10.1186/1471-2105-11-207.
[21]
Shanahan, H.P.; Memon, F.N.; Upton, G.J.; Harrison, A.P. Normalized Affymetrix expression data are biased by G-quadruplex formation. Nucleic Acids Res. 2012, 40, 3307–3315.
[22]
Langdon, W.B.; Upton, G.J.; Harrison, A.P. Probes containing runs of guanines provide insights into the biophysics and bioinformatics of Affymetrix GeneChips. Brief. Bioinforma. 2009, 10, 259–277.
[23]
Kerkhoven, R.M.; Sie, D.; Nieuwland, M.; Heimerikx, M.; de Ronde, J.; Brugman, W.; Velds, A. The T7-primer is a source of experimental bias and introduces variability between microarray platforms. PLoS One 2008, 3, e1980, doi:10.1371/journal.pone.0001980.
[24]
Yu, H.; Wang, F.; Tu, K.; Xie, L.; Li, Y.Y.; Li, Y.X. Transcript-level annotation of Affymetrix probesets improves the interpretation of gene expression data. BMC Bioinforma. 2007, 8, doi:10.1186/1471-2105-8-194.
[25]
Jaksik, R.; Polanska, J.; Herok, R.; Rzeszowska-Wolny, J. Calculation of reliable transcript levels of annotated genes on the basis of multiple probe-sets in Affymetrix microarrays. Acta Biochim. Pol. 2009, 56, 271–277.
[26]
Robinson, T.J.; Dinan, M.A.; Dewhirst, M.; Garcia-Blanco, M.A.; Pearson, J.L. SplicerAV: A tool for mining microarray expression data for changes in RNA processing. BMC Bioinforma. 2010, 11, doi:10.1186/1471-2105-11-108.
[27]
Jaksik, R.; Rzeszowska-Wolny, J. The distribution of GC nucleotides and regulatory sequence motifs in genes and their adjacent sequences. Gene 2012, 492, 375–381.
