Novel algorithm for transcriptome analysis

Physiological Genomics

Volumn 28, Issue 1, 2006, Pages 62-66

  Saama, Peter M ^a   Patel, Osman V ^b   Bettegowda, Anilkumar ^b   Ireland, James J ^b   Smith, George W ^b,c  

^a GENUS PLC   (United States)

^b MICHIGAN STATE UNIVERSITY   (United States)

^c 1230 Anthony Hall ^*  (United States)

Author keywords

Bovine; False discovery rate; Local threshold; Microarray; Mixed model

Indexed keywords

COMPLEMENTARY DNA; TRANSCRIPTOME; RNA;

ANIMAL CELL; CONFERENCE PAPER; CONTROLLED STUDY; COW; DNA MICROARRAY; GENE EXPRESSION; GENETIC ANALYSIS; HOUSEKEEPING GENE; MEIOSIS; NONHUMAN; NUCLEOTIDE SEQUENCE; OOCYTE; PRIORITY JOURNAL; TRANSCRIPTOMICS; ALGORITHM; ANIMAL; ARTICLE; CATTLE; COMPARATIVE STUDY; EVALUATION; FEMALE; GENE EXPRESSION PROFILING; GENETIC TRANSCRIPTION; METABOLISM; METHODOLOGY; PREGNANCY;

BOVINAE;

ALGORITHMS; ANIMALS; CATTLE; FEMALE; GENE EXPRESSION PROFILING; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; OOCYTES; PREGNANCY; RNA; TRANSCRIPTION, GENETIC;

EID: 34247880179     PISSN: 10948341     EISSN: 15312267     Source Type: Journal    
DOI: 10.1152/physiolgenomics.00108.2006     Document Type: Conference Paper

References (22)

1. Allemeersch J, Durinck S, Vanderhaeghen R, Alard P, Maes R, Seeuws K, Bogaert T, Coddens K, Deschouwer K, Van Hummelen P, Vuylsteke M, Moreau Y, Kwekkeboom J, Wijfjes AH, May S, Beynon J, Hilson P, Kuiper MT. Benchmarking the CATMA microarray. A novel tool for arabidopsis transcriptome analysis. Plant Physiol 137: 588-601, 2005.
2. Allison D, Gadbury G, Heo M, Fernandez J, Lee CK, Prolla T, Weindruch RA. mixture model approach for the analysis of microarray gene expression data. Comput Stat Data Anal 39: 1-20, 2002.
3. Asyali MH, Shoukri MM, Demirkaya O, Khabara KS. Assessment of reliability of microarray data and estimation of signal thresholds using mixture modeling. Nucleic Acids Res 32: 2323-2335, 2004.
4. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Roy Stat Soc B Stat Meth 57: 289-300, 1995.
5. Bettegowda A, Patel OV, Ireland JJ, Smith GW. Quantitative analysis of messenger RNA abundance for ribosomal protein L-15, cyclophilin-A, phosphoglycerokinase, beta-glucuronidase, glyceraldehyde 3-phosphate dehydrogenase, beta-actin, and histone H2A during bovine oocyte maturation and early embryogenesis in vitro. Mol Reprod Dev 73: 267-278, 2006.
6. Broët P, Kuznetsov VA, Bergh J, Liu ET, Miller LD. Identifying gene expression changes in breast cancer that distinguish early and late relapse among uncured patients. Bioinformatics 22: 1477-1485, 2006.
7. Dror RO, Murnick JG, Rinaldi NJ, Marinescu VD, Rifkin RM, Young RA. Bayesian estimation of transcript levels using a general model of array measurement noise. J Comput Biol 10: 433-452, 2003.
8. Eppig JJ, Wigglesworth K, Pendola FL. The mammalian oocyte orchestrates the rate of ovarian follicular development. Proc Natl Acad Sci USA 99: 2890-2894, 2002.
9. Febbo PW. Noise and bias in microarray analysis of tumor specimens. J Clin Oncol 24: 3719-3721, 2006.
10. Franz H, Ullmann C, Becker A, Ryan M, Bahn S, Arendt T, Simon M, Pääbo S, Khaitovich P. Systematic analysis of gene expression in human brains before and after death. Genome Biol 6: R112, 2005.
11. Havilio M. Signal deconvolution based expression-detection and background adjustment for microarray data. J Comput Biol 13: 63-80, 2006.
12. Kerr MK, Churchill GA. Statistical design and the analysis of gene expression microarrays. Genet Res 77: 123-128, 2001.
13. Ma J, Morrow DJ, Fernandes J, Walbot V. Comparative profiling of the sense and antisense transcriptome of maize lines. Genome Biol 7: R22, 2006.
14. Patel OV, Suchyta SP, Sipkovsky SS, Yao J, Ireland JJ, Coussens PM, Smith GW. Validation and application of a high fidelity mRNA linear amplification procedure for profiling gene expression. Vet Immunol Immunopathol 105: 331-342, 2005.
15. SAS. STAT9.1 User's Guide. Cary, NC: SAS Institute, 2004.
16. Storey J. A direct approach to false discovery rates. J Roy Stat Soc B Stat Method 64: 479-498, 2002.
17. Suchyta SP, Sipkovsky S, Kruska R, Jeffers A, McNulty A, Coussens MJ, Tempelman RJ, Halgren RG, Saama PM, Bauman DE, Boisclair YR, Burton JL, Collier RJ, DePeters EJ, Ferris TA, Lucy MC, McGuire MA, Medrano JF, Overton TR, Smith TP, Smith GW, Sonstegard TS, Spain JN, Spiers DE, Yao J, Coussens PM. Development and testing of a high-density cDNA microarray resource for cattle. Physiol Genomics 15: 158-164, 2003.
18. Telford NA, Watson AJ, Schultz GA. Transition from maternal to embryonic control in early mammalian development: a comparison of several species. Mol Reprod Dev 26: 90-100, 1990.
19. Yang YH, Buckley MJ, Speed TP. Analysis of cDNA microarray images. Brief Bioinform 2: 341-349, 2001.
20. Williams BA, Gwirtz RM, Wold BJ. Genomic DNA as a cohybridization standard for mammalian microarray measurements. Nucleic Acids Res 32: e81, 2004.
21. Wolfinger RD, Gibson G, Wolfinger E, Bennett L, Hamadeh L, Bushel P, Afshari C, Paules RS. Assessing gene significance from cDNA microarray expression data via mixed model. J Comput Biol 8: 625-637, 2001.
22. Yoon D, Yi SG, Kim JH, Park T. Two-stage normalization using background intensities in cDNA microarray data. BMC Bioinformatics 5: 97, 2004.