Effects of pooling mRNA in microarray class comparisons

Shih, Joanna H.; Michalowska, Aleksandra M.; Dobbin, Kevin K.; Ye, Yumei; Qiu, Ting; Green, Jeffrey E.

doi:10.1093/bioinformatics/bth391

Cited by 69 publications

(62 citation statements)

References 10 publications

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“…Appropriate pooling has been shown to improve efficiency for microarray experiments (Peng et al 2003). One study, however (Shih et al 2004), found discordances between the average expression of individual samples and that of a corresponding pool sample, the magnitude of the discordance in each probe set increasing monotonically with its signal in the microarray. In any case, this effect was not associated with our results.…”

Section: Discussionmentioning

confidence: 99%

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Gene-Expression Changes Caused by Inbreeding Protect Against Inbreeding Depression in Drosophila

et al. 2012

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We present a transcriptomic analysis aimed at investigating whether the changes in gene expression that occur under inbreeding generally reduce or enhance inbreeding depression. Discerning between these two alternatives can be addressed only when both changes in expression due to inbreeding and to inbreeding depression are estimated simultaneously. We used Affymetrix 2.0 arrays to study the changes in gene expression associated with both inbreeding and inbreeding depression for fitness in four sets of inbred sublines of Drosophila melanogaster. We found that for most genes showing changes in expression associated with inbreeding, the least depressed sublines were those showing the largest departures in expression from that of the outbred control. This suggests a pattern consistent with a protective role of expression changes against inbreeding effects, and would reveal a new dimension of the transcriptomics of inbreeding. The variation in depression observed could then be due not only to the genetic damages primarily originating that depression, but also possibly to differences in the ability to carry out the appropriate adjustments in gene expression to cope with the inbreeding. We also found that these expression changes with a putative protective role against inbreeding effects show a clear specificity on RNA synthesis and splicing and energy derivation functions. INBREEDING depression, the reduction in fitness observed in the progeny of genetically related individuals, plays a key role in population biology, affecting processes as diverse as the management of livestock and endangered wild species (Keller and Waller 2002;Koenig and Simianer 2006), the evolution of mating systems (Kelly 2005), and the dispersal strategies (Motro 1991;Gandon 1999). The population genetics of this depression is well understood (Lynch 1991;Charlesworth and Charlesworth 1999;Charlesworth and Willis 2009), but the genomic details about the mechanisms causing it are just starting to be unveiled (Paige 2010). In particular, it would be important to determine the relationship between gene regulation and inbreeding, as regulatory variation underlies much of phenotypic diversity (Wilson et al. 1974;Carroll 2005;Ranz and Machado 2006). Evidence has been provided for significant intraspecific variation in transcript abundance for a large fraction of the genome (Primig et al. 2000;Sandberg et al. 2000) and to show that much of such variation is heritable (Cavalieri et al. 2000;Karp et al. 2000;Jin et al. 2001), suggesting that regulatory variation is likely the main mediator of phenotypic divergence in evolution (King and Wilson 1975;Wray et al. 2003;Hoekstra and Coyne 2007). Therefore, genetic correlations between expression phenotypes and organismal phenotypes point to the molecular pathways that underlie the organismal phenotypes (Rockman and Kruglyak 2006). Thus, an understanding of the molecular basis of inbreeding depression requires knowledge of variation at the whole-genome level (Ayroles et al. 2009).Using whole-genome cD...

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Section: Discussionmentioning

confidence: 99%

“…probe sets was divided in 20 intervals according to their mean microarray signal, the correlation between the intervals' median of the averages and their proportion of protective expression patterns (the relationship studied in Shih et al 2004) was 0.188 (P = 0.427).…”

Section: Discussionmentioning

confidence: 99%

Gene-Expression Changes Caused by Inbreeding Protect Against Inbreeding Depression in Drosophila

et al. 2012

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“…be increased (31,32). HCC SAGE library from single patient may not be as representative as microarray data obtained from hundreds of HCC samples.…”

Section: Discussionmentioning

confidence: 99%

Transcriptome analysis of hepatitis B virus-associated small hepatocellular carcinoma by serial analysis of gene expression

Kim

Lee²,

Park³

et al. 2009

Int J Oncol

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Abstract. Serial analysis of gene expression (SAGE) is a sequence-based tool that enables quantitative study of comprehensive gene expression profiles. In this study, we generated a SAGE library of hepatitis B virus (HBV)-associated small hepatocellular carcinoma (HCC) and compared it with libraries of normal liver and other organs in order to identify genes which are specifically expressed in HBV-associated small HCC. A total of 18,107 SAGE tags were obtained from the HCC SAGE library. Among the 7,405 unique tags, 240 were significantly overexpressed in HCC compared to normal liver. Seventeen genes were unequivocally matched to SAGE tags which were up-regulated 15-fold or higher in HCC compared to normal liver: four genes (fatty acid desaturase 2, ·-L-2-fucosidase, testis-specific protein Y-encoded-like 2 and Gon-4-like) were significantly overexpressed in the HCC SAGE library compared to any other SAGE libraries of human normal tissues. The significance of these genes with respect to carcinogenesis and early diagnosis of HCC needs to be elucidated in further studies.

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“…If a large number of samples are to be assayed, the phenotype classes should be intermixed in the group assayed at each time. Pooling samples is generally not advantageous [3]. When dual-label arrays are used, there are additional design issues to be addressed concerning whether to use a common reference RNA or to pair the samples from different classes for co-hybridization on each array.…”

Section: Methodsmentioning

confidence: 99%

Microarray-based expression profiling and informatics

Simon

2008

Current Opinion in Biotechnology

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SummaryMicroarray based expression profiling is a powerful technology for studying biological mechanisms and for developing clinically valuable predictive classifiers. The high dimensional readout for each sample assayed makes it possible to do new kinds of studies but also increases the risks of misleading conclusions. We review here the current state-of-the-art for design and analysis of microarray based investigations.

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Effects of pooling mRNA in microarray class comparisons

Abstract: Supplementary material referenced in the text is available at http://linus.nci.nih.gov/brb/TechReport.htm.

Cited by 69 publications

References 10 publications

Gene-Expression Changes Caused by Inbreeding Protect Against Inbreeding Depression in Drosophila

Gene-Expression Changes Caused by Inbreeding Protect Against Inbreeding Depression in Drosophila

Transcriptome analysis of hepatitis B virus-associated small hepatocellular carcinoma by serial analysis of gene expression

Microarray-based expression profiling and informatics

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