Enhanced Quantile Normalization of Microarray Data to Reduce Loss of Information in Gene Expression Profiles

The C-SHIFT Algorithm for Normalizing Covariances

Omics technologies are powerful tools for analyzing patterns in gene expression data for thousands of genes. Due to a number of systematic variations in experiments, the raw gene expression data is often obfuscated by undesirable technical noises. Various normalization techniques were designed in an attempt to remove these non-biological errors prior to any statistical analysis. One of the reasons for normalizing data is the need for recovering the covariance matrix used in gene network analysis. In this paper, we introduce a novel normalization technique, called the covariance shift (C-SHIFT) method. This normalization algorithm uses optimization techniques together with the blessing of dimensionality philosophy and energy minimization hypothesis for covariance matrix recovery under additive noise (in biology, known as the bias). Thus, it is perfectly suited for the analysis of logarithmic gene expression data. Numerical experiments on synthetic data demonstrate the method's advantage over the classical normalization techniques. Namely, the comparison is made with Rank, Quantile, cyclic LOESS (locally estimated scatterplot smoothing), and MAD (median absolute deviation) normalization methods. We also evaluate the performance of C-SHIFT algorithm on real biological data.

miREV: An Online Database and Tool to Uncover Potential Reference RNAs and Biomarkers in Small-RNA Sequencing Data Sets from Extracellular Vesicles Enriched Samples

Extracellular vesicles (EVs) are nano-sized, membrane-enclosed vesicles released by cells for intercellular communication. EVs are involved in pathological processes and miRNAs in EVs have gained interest as easily accessible biomolecules in liquid biopsies for diagnostic purposes. To validate potential miRNA biomarker, transcriptome analyses must be carried out to detect suitable reference miRNAs. miREV is a database with over 400 miRNA sequencing data sets and helps the researcher to find suitable reference miRNAs for their individual experimental setup. The researcher can put together a specific sample set in miREV, which is similar to his own experimental concept in order to find the most suitable references. This allows to run validation experiments without having to carry out a complex and costly transcriptome analysis priorly. Additional read count tables of each generated sample set are downloadable for further analysis. miREV is freely available at https://www.physio.wzw.tum.de/mirev/.

NFIB promotes the progression of gastric cancer by upregulating circMAP7D1 to stabilize HER2 mRNA

The present study evaluated the expression levels of nuclear factor I B (NFIB) in gastric cancer (GC) specimens and cells, and its regulatory roles were further elucidated. The expression levels of NFIB were examined in GC and paired normal specimens, and in human GC and normal gastric epithelial cells by reverse transcription‑quantitative PCR. A circular RNA (circRNA) microarray was performed to identify the novel downstream circRNA of NFIB. Cell proliferation was determined by Cell Counting Kit‑8 assay. Furthermore, cell cycle distribution and apoptosis were assessed using flow cytometry. Interactions between RNA were examined by RNA pulldown assay and the stability of target mRNA was evaluated using a mRNA stability assay. The results of the present study revealed that NFIB was upregulated in GC. Furthermore, silencing NFIB suppressed the proliferation of GC cells, whereas cell cycle arrest and apoptosis were enhanced. In addition, significant downregulation of circMAP7D1 (hsa_circ_0004093) was observed in GC cells infected with short hairpin RNA‑NFIB. These findings indicated that circMAP7D1 may be a promising downstream molecule of NFIB in GC, and further functional analyses indicated that circMAP7D1 was involved in NFIB‑modulated GC cell proliferation and apoptosis. Moreover, human epidermal growth factor receptor 2 (HER2) was identified as a novel target of circMAP7D1 in GC, and NFIB was able to increase the stability of HER2 mRNA through regulating circMAP7D1. In conclusion, the present findings indicated that NFIB expression was increased in GC. In addition, NFIB may promote the proliferation of GC cells and function through stabilizing HER2 mRNA by upregulating circMAP7D1. Notably, NFIB and its novel downstream signaling pathway may serve essential roles during the development of GC, and NFIB may be considered a promising candidate for the treatment of patients with GC.

Sex differences in brain gene expression among suicide completers

BACKGROUND

Suicide rates vary substantially by sex. Suicides committed by males significantly outnumber female suicides. Disparities in community and social factors provide a partial explanation for this phenomenon. Thus, the evaluation of sex differences at a biological level might contribute to the elucidation of the factors involved in this imbalance. The aim of the present study was to evaluate sex-specific gene expression patterns in the suicidal brain.

METHODS

postmortem samples from the dorsolateral prefrontal cortex (DLPFC) of 75 Latino individuals were analyzed. We considered the following groups: i) male suicides (n = 38), ii) female suicides (n = 10), iii) male controls (n = 20), and iv) female controls (n = 7). Gene expression profiles were evaluated by microarrays. Differentially expressed genes among the groups were identified with a linear model. Similarities and differences in the gene sets between the sexes were identified.

RESULTS

Differentially expressed genes were identified between suicides and controls of each sex: 1,729 genes in females and 1,997 genes in males. Female-exclusive suicide genes were related to cell proliferation and immune response. Meanwhile, male-exclusive suicide genes were associated to DNA binding and ribonucleic protein complex. Sex-independent suicide genes showed enrichment in mitochondrial and vesicular functions.

LIMITATIONS

Relatively small sample size. Our diagnosis approach was limited to information found on coroner's records. The analysis was limited to a single brain area (DLPFC) and we used microarrays.

CONCLUSION

Previously unexplored sex differences in the brain gene expression of suicide completers were identified, providing valuable foundation for the evaluation of sex-specific factors in suicide.

The prognostic value of a seven-lncRNA signature in patients with esophageal squamous cell carcinoma: a lncRNA expression analysis

Background

Long non-coding RNAs (lncRNAs) have been reported to be prognostic biomarkers in many types of cancer. We aimed to identify a lncRNA signature that can predict the prognosis in patients with esophageal squamous cell carcinoma (ESCC).

Methods

Using a custom microarray, we retrospectively analyzed lncRNA expression profiles in 141 samples of ESCC and 81 paired non-cancer specimens from Sun Yat-Sen University Cancer Center (Guangzhou, China), which were used as a training cohort to identify a signature associated with clinical outcomes. Then we conducted quantitative RT-PCR in another 103 samples of ESCC from the same cancer center as an independent cohort to verify the signature.

Results

Microarray analysis showed that there were 338 lncRNAs significantly differentially expressed between ESCC and non-cancer esophagus tissues in the training cohort. From these differentially expressed lncRNAs, we found 16 lncRNAs associated with overall survival (OS) of ESCC patients using Cox regression analysis. Then a 7-lncRNA signature for predicting survival was identified from the 16 lncRNAs, which classified ESCC patients into high-risk and low-risk groups. Patients with high-risk have shorter OS (HR: 3.555, 95% CI 2.195–5.757, p < 0.001) and disease-free survival (DFS) (HR: 2.537, 95% CI 1.646–3.909, p < 0.001) when compared with patients with low-risk in the training cohort. In the independent cohort, the 7 lncRNAs were detected by qRT-PCR and used to compute risk score for the patients. The result indicates that patients with high risk also have significantly worse OS (HR = 2.662, 95% CI 1.588–4.464, p < 0.001) and DFS (HR 2.389, 95% CI 1.447–3.946, p < 0.001). The univariate and multivariate Cox regression analyses indicate that the signature is an independent factor for predicting survival of patients with ESCC. Combination of the signature and TNM staging was more powerful in predicting OS than TNM staging alone in both the training (AUC: 0.772 vs 0.681, p = 0.002) and independent cohorts (AUC: 0.772 vs 0.660, p = 0.003).

Conclusions

The 7-lncRNA signature is a potential prognostic biomarker in patients with ESCC and may help in treatment decision when combined with the TNM staging system.

Independent prognostic genes and mechanism investigation for colon cancer

PROPOSE

We aimed to explore the potential molecular mechanism and independent prognostic genes for colon cancer (CC).

METHODS

Microarray datasets GSE17536 and GSE39582 were downloaded from Gene Expression Omnibus. Meanwhile, the whole CC-related dataset were downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed mRNA (DEMs) were identified between cancer tissue samples and para-carcinoma tissue samples in TCGA dataset, followed by the KEGG pathway and GO function analyses. Furthermore, the clinical prognostic analysis including overall survival (OS) and disease-free survival (DFS) were performed in all three datasets.

RESULTS

A total of 633 up- and 321 down-regulated mRNAs were revealed in TCGA dataset. The up-regulated mRNAs were mainly assembled in functions including extracellular matrix and pathways including Wnt signaling. The down-regulated mRNAs were mainly assembled in functions like Digestion and pathways like Drug metabolism. Furthermore, up-regulation of UL16-binding protein 2 (ULBP2) was associated with OS in CC patients. A total of 12 DEMs including Surfactant Associated 2 (SFTA2) were potential DFS prognostic genes in CC patients. Meanwhile, the GRP and Transmembrane Protein 37 (TMEM37) were two outstanding independent DFS prognostic genes in CC.

CONCLUSIONS

ULBP2 might be a potential novel OS prognostic biomarker in CC, while GRP and TMEM37 could be served as the independent DFS prognostic genes in CC. Furthermore, functions including extracellular matrix and digestion, as well as pathways including Wnt signaling and drug metabolism might play important roles in the process of CC.

Statistical analysis of non-coding RNA data

With rapid progress in high-throughput genome technology, the study of noncoding RNA has arisen as a highly popular topic in biomedical research. Noncoding RNA plays fundamental roles in cell proliferation, cell differentiation and epigenetic regulation, and the study of noncoding RNA will yield novel insights into gene regulation and provide new clues for disease treatment. However, due to the large volume and diverse functions of noncoding RNAs, the analysis of these RNAs has proved to be a challenging task. In this review, we review the commonly used computational tools for the identification of noncoding RNAs, and discuss popular statistical tools for their analysis. Due to the large body of noncoding RNA classes, we focus on the analysis of microRNA and long noncoding RNA, two of the most widely studied classes of noncoding RNAs. Specific examples are provided to show the context of the analysis. This review aims to provide up-to-date information on existing tools and methods for identifying and analyzing noncoding RNA.

Effective Removal of Noisy Data Via Batch Effect Processing

In order to have faith in the analysis of data, a key factor is to have confidence that the data is reliable. In the case of microRNA, reliability includes understanding the collection methods, ensuring that the analysis is appropriate, and ensuring that the data itself is accurate. A key element in ensuring data accuracy is the removal of noise. While there can be several sources of noise, a common source of noise is the batch effect, which can be defined as systematic variability in the data caused by non-biological factors. This chapter will present various techniques designed to remove variability caused by batch effects and the potential effectiveness.

Chondrocytes Cocultured with Stromal Vascular Fraction of Adipose Tissue Present More Intense Chondrogenic Characteristics Than with Adipose Stem Cells

Partial replacement of chondrocytes by stem cells has been proposed to improve the performance of autologous chondrocyte implantation (ACI). Our previous studies showed that the increased cartilage production in pellet cocultures of chondrocytes and mesenchymal stem cells (MSCs) is due to a trophic role of the MSCs by stimulating chondrocyte proliferation and matrix production rather than MSCs actively undergoing chondrogenic differentiation. The aim of this study is to compare the trophic effects of stromal vascular fraction cells (SVF) and in vitro expanded adipose stem cells (ASC). SVF and culture-expanded ASC (n = 9) were cocultured with primary human chondrocytes in pellets. By glycosaminoglycan (GAG) and DNA assays, we showed that coculture pellets of SVF and chondrocytes have more GAG deposition than that of ASC and chondrocytes. Results of the short tandem repeats analysis indicated that the increase in the chondrocyte proportion in the coculture pellets is more pronounced in the SVF coculture group than in the ASC coculture group. Using flow cytometry and microarray, we demonstrated that SVF and ASC have different characteristics in cell surface markers and gene expression profiles. SVF is more heterogeneous than ASC, whereas ASC is more enriched in cells from the mesenchymal lineage than SVF. By subcutaneous implantation into nude mice, we showed that constructs of SVF and chondrocytes are better in depositing cartilage matrix than the mixture of ASC and chondrocytes. Taken together, SVF is better than ASC in terms of forming cartilage matrix in pellet coculture and in coimplantation models omitting the need for prior cell expansion. Our study suggests that the SVF in combination with primary human chondrocytes may be a good cell combination for one-stage cartilage repair.

Revealing gene clusters associated with the development of cholangiocarcinoma, based on a time series analysis

Cholangiocarcinoma (CC) is a rapidly lethal malignancy and currently is considered to be incurable. Biomarkers related to the development of CC remain unclear. The present study aimed to identify differentially expressed genes (DEGs) between normal tissue and intrahepatic CC, as well as specific gene expression patterns that changed together with the development of CC. By using a two‑way analysis of variance test, the biomarkers that could distinguish between normal tissue and intrahepatic CC dissected from different days were identified. A k‑means cluster method was used to identify gene clusters associated with the development of CC according to their changing expression pattern. Functional enrichment analysis was used to infer the function of each of the gene sets. A time series analysis was constructed to reveal gene signatures that were associated with the development of CC based on gene expression profile changes. Genes related to CC were shown to be involved in 'mitochondrion' and 'focal adhesion'. Three interesting gene groups were identified by the k‑means cluster method. Gene clusters with a unique expression pattern are related with the development of CC. The data of this study will facilitate novel discoveries regarding the genetic study of CC by further work.

Transcriptional profiling of hilar nodes from pigs after experimental infection with Actinobacillus pleuropneumoniae

The gram-negative bacterium Actinobacillus pleuropneumoniae (APP) is an inhabitant of the porcine upper respiratory tract and the causative agent of porcine pleuropneumonia (PP). In recent years, knowledge about the proinflammatory cytokine and chemokine gene expression that occurs in lung and lymph node of the APP-infected swine has been advanced. However, systematic gene expression profiles on hilar nodes from pigs after infection with Actinobacillus pleuropneumoniae have not yet been reported. The transcriptional responses were studied in hilar nodes (HN) from swine experimentally infected with APP and the control groupusing Agilent Porcine Genechip, including 43,603 probe sets. 9,517 transcripts were identified as differentially expressed (DE) at the p ≤ 0.01 level by comparing the log2 (normalized signal) of the two groups named treatment group (TG) and controls (CG). Eight hundred and fifteen of these DE transcripts were annotated as pig genes in the GenBank database (DB). Two hundred and seventy-two biological process categories (BP), 75 cellular components and 171 molecular functions were substantially altered in the TG compared to CG. Many BP were involved in host immune responses (i.e., signaling, signal transmission, signal transduction, response to stimulus, oxidation reduction, response to stress, immune system process, signaling pathway, immune response, cell surface receptor linked signaling pathway). Seven DE gene pathways (VEGF signaling pathway, Long-term potentiation, Ribosome, Asthma, Allograft rejection, Type I diabetes mellitus and Cardiac muscle contraction) and statistically significant associations with host responses were affected. Many cytokines (including NRAS, PI3K, MAPK14, CaM, HSP27, protein phosphatase 3, catalytic subunit and alpha isoform), mediating the proliferation and migration of endothelial cells and promoting survival and vascular permeability, were activated in TG, whilst many immunomodulatory cytokines were suppressed. The significant changes in the expression patterns of the genes, GO terms, and pathways, led to a decrease of antigenic peptides with antigen presenting cells presented to T lymphocytes via the major histocompatibility complex, and alleviated immune response induced APP of HN. The immune response ability of HN in the APP-infected pigs was weakened; however, cell proliferation and migration ability was enhanced.

Transcriptional profiling of swine lung tissue after experimental infection with Actinobacillus pleuropneumoniae

Porcine pleuropneumonia is a highly contagious respiratory disease that causes great economic losses worldwide. In this study, we aimed to explore the underlying relationship between infection and injury by investigation of the whole porcine genome expression profiles of swine lung tissues post-inoculated with experimentally Actinobacillus pleuropneumoniae. Expression profiling experiments of the control group and the treatment group were conducted using a commercially available Agilent Porcine Genechip including 43,603 probe sets. Microarray analysis was conducted on profiles of lung from challenged versus non-challenged swine. We found 11,929 transcripts, identified as differentially expressed at the p ≤0.01 level. There were 1188 genes annotated as swine genes in the GenBank Data Base. GO term analysis identified a total of 89 biological process categories, 82 cellular components and 182 molecular functions that were significantly affected, and at least 27 biological process categories that were related to the host immune response. Gene set enrichment analysis identified 13 pathways that were significantly associated with host response. Many proinflammatory-inflammatory cytokines were activated and involved in the regulation of the host defense response at the site of inflammation; while the cytokines involved in regulation of the host immune response were suppressed. All changes of genes and pathways of induced or repressed expression not only led to a decrease in antigenic peptides presented to T lymphocytes by APCs via the MHC and alleviated immune response injury induced by infection, but also stimulated stem cells to produce granulocytes (neutrophils, eosinophils, and basophils) and monocyte, and promote neutrophils and macrophages to phagocytose bacterial and foreign antigen at the site of inflammation. The defense function of swine infection with Actinobacillus pleuropneumoniae was improved, while its immune function was decreased.

The impact of quantile and rank normalization procedures on the testing power of gene differential expression analysis

Background

Quantile and rank normalizations are two widely used pre-processing techniques designed to remove technological noise presented in genomic data. Subsequent statistical analysis such as gene differential expression analysis is usually based on normalized expressions. In this study, we find that these normalization procedures can have a profound impact on differential expression analysis, especially in terms of testing power.

Results

We conduct theoretical derivations to show that the testing power of differential expression analysis based on quantile or rank normalized gene expressions can never reach 100% with fixed sample size no matter how strong the gene differentiation effects are. We perform extensive simulation analyses and find the results corroborate theoretical predictions.

Conclusions

Our finding may explain why genes with well documented strong differentiation are not always detected in microarray analysis. It provides new insights in microarray experimental design and will help practitioners in selecting proper normalization procedures.

Identification of a gene-expression signature for predicting lymph node metastasis in patients with early stage cervical carcinoma

BACKGROUND

Pelvic lymph node metastasis (PLNM) is an important prognostic factor for patients with cervical carcinoma. The objective of this study was to identify a gene-expression signature that could predict PLNM in cervical carcinoma.

METHODS

Eighty-eight women with cervical carcinoma with PLNM (n = 23) and without PLNM (n = 65) were divided randomly into a training group and a test group. An oligonucleotide microarray that contained probes for 1440 human cancer-related genes was fabricated in-house and was used to detect the gene expression profile of cervical carcinoma. The gene expression levels detected in the microarray were verified by quantitative reverse transcriptase-polymerase chain reaction (qRT-PCR).

RESULTS

A gene-expression signature for predicting PLNM was developed in patients from the training group, including 11 genes: ribosomal protein L35 (RPL35); thymosin β 10 (TMSB10); tyrosine 3-mono-oxytenase/tryptophan 5-mono-oxygenase activation protein, ζ polypeptide (YWHAZ); biotinidase (BTD); lactate dehydrogenase A (LDHA); glucuronidase β (GUSB); superoxide dismutase 2 (SOD2); nuclear receptor subfamily 3, group C, member 2 (NR3C2); fructosamine 3 kinase (FN3K); x-ray repair cross-complementing 4 (XRCC4); and wingless-type mouse mammary tumor virus integration site family member 2 (WNT2). In the test group, the signature's accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 91%, 90.9%, 93.9%, 83.3%, and 96.9%, respectively, for predicting PLNM. The expression levels of 5 genes in the signature were confirmed by qRT-PCR. A multivariate analysis demonstrated that patients with 11-gene high-risk scores were had a 33-fold increased risk for PLNM compared with patients who had low-risk scores. The 5-year overall and disease-free survival rates for patients who had 11-gene high-risk scores were marginally significantly lower than the rates for patients who had 11-gene low-risk scores (P = .087 and P = .174, respectively).

CONCLUSIONS

In this study, 11-gene signature for predicting PLNM in cervical carcinoma was identified that may help clinicians in planning therapy for patients with cervical carcinoma.

Current Clinical and Pharmaceutical Applications of Microarrays: From Disease Biomarkers Discovery to Automated Diagnostics

Microarrays used for measuring chromosomal aberrations in genomic DNA and for defining gene expression patterns have become almost routine. A microarray consists of an arrayed series of microscopic spots each containing either DNA or protein molecules known as feature reporters. Advances in microarray fabrication and in feature detection systems, such as high-resolution scanners and their associated software, lead to high-throughput screening of the genome or the transcriptome of a cell or a group of cells in only few days. Despite the potential of high-density microarrays, several problems about data interpretation are still to be solved. In addition, targeted microarrays are shown to be useful tools for rapid and accurate diagnosis of diseases. The aim of this review was to discuss the impact of microarrays on different application levels from the definition of disease biomarkers to pharmaceutical and clinical diagnostics.

PMRD: plant microRNA database

MicroRNAs (miRNA) are approximately 21 nucleotide-long non-coding small RNAs, which function as post-transcriptional regulators in eukaryotes. miRNAs play essential roles in regulating plant growth and development. In recent years, research into the mechanism and consequences of miRNA action has made great progress. With whole genome sequence available in such plants as Arabidopsis thaliana, Oryza sativa, Populus trichocarpa, Glycine max, etc., it is desirable to develop a plant miRNA database through the integration of large amounts of information about publicly deposited miRNA data. The plant miRNA database (PMRD) integrates available plant miRNA data deposited in public databases, gleaned from the recent literature, and data generated in-house. This database contains sequence information, secondary structure, target genes, expression profiles and a genome browser. In total, there are 8433 miRNAs collected from 121 plant species in PMRD, including model plants and major crops such as Arabidopsis, rice, wheat, soybean, maize, sorghum, barley, etc. For Arabidopsis, rice, poplar, soybean, cotton, medicago and maize, we included the possible target genes for each miRNA with a predicted interaction site in the database. Furthermore, we provided miRNA expression profiles in the PMRD, including our local rice oxidative stress related microarray data (LC Sciences miRPlants_10.1) and the recently published microarray data for poplar, Arabidopsis, tomato, maize and rice. The PMRD database was constructed by open source technology utilizing a user-friendly web interface, and multiple search tools. The PMRD is freely available at http://bioinformatics.cau.edu.cn/PMRD. We expect PMRD to be a useful tool for scientists in the miRNA field in order to study the function of miRNAs and their target genes, especially in model plants and major crops.

Singular Value Decomposition-based Alternative Splicing Detection

Altered alternative splicing has been identified as an important factor in tumorigenesis. The Affymetrix exon tiling array is designed for detecting alternative splicing events in a transcriptome-wide fashion; however, there are currently few analysis tools that are well studied for effective detection of alternative splicing events. We propose a new screening procedure based on singular value decomposition (SVD) of the residual matrix from a robust additive model fit to probe selection region (PSR) data. With this approach, we analyze the exon tiling array data from a brain cancer study conducted at the M. D. Anderson Cancer Center, and show that the proposed SVD-based approach is able to better accommodate outlying measures and capitalize on the multidimensional group-by-PSR gene expression profiles for more effective detection of group-specific alternative splicing events as well as the PSRs that are most likely associated with the alternative splicing. Lab validation confirmed some of our findings, but the list of candidates detected with our proposed method may provide a better signpost to guide further investigations.

A comparison on effects of normalisations in the detection of differentially expressed genes

BACKGROUND

Various normalisation techniques have been developed in the context of microarray analysis to try to correct expression measurements for experimental bias and random fluctuations. Major techniques include: total intensity normalisation; intensity dependent normalisation; and variance stabilising normalisation. The aim of this paper is to discuss the impact of normalisation techniques for two-channel array technology on the process of identification of differentially expressed genes.

RESULTS

Through three precise simulation plans, we quantify the impact of normalisations: (a) on the sensitivity and specificity of a specified test statistic for the identification of deregulated genes, (b) on the gene ranking induced by the statistic.

CONCLUSION

Although we found a limited difference of sensitivities and specificities for the test after each normalisation, the study highlights a strong impact in terms of gene ranking agreement, resulting in different levels of agreement between competing normalisations. However, we show that the combination of two normalisations, such as glog and lowess, that handle different aspects of microarray data, is able to outperform other individual techniques.

Microarrays and the relationship of mRNA variation to protein variation during the cell cycle

Microarray analyses have led to the postulated existence and identification of numerous genes that are believed to be expressed and presumably to act in a cell-cycle-specific manner because their expression varies during the cell cycle. It is important to see how protein variation can be produced from mRNA variation. We have calculated the protein content throughout the cell cycle resulting from cell-cycle-specific mRNA expression, and compared the result to protein content resulting from constant, cell-cycle independent, mRNA expression. For stable proteins, cell-cycle-specific mRNA expression leads to a maximum 2-fold change in protein content compared to proteins synthesized from constantly expressed mRNA. More realistic sinusoidal patterns of mRNA expression exhibit much smaller ratios of 1.25 or lower, even for extremely large amplitudes in mRNA expression. For unstable proteins that have a cycle-independent half-life, only at extremely short protein half-lives does mRNA variation have a significant impact on variation of protein content during the division cycle. We also apply these findings to proteins with a cycle-specific decay pattern. mRNA variations during the eukaryotic division cycle variation of mRNA during the cell cycle can have only a minimal affect on the variation of protein content during the cell cycle. We conclude that mRNA variations during the division cycle, as measured by microarrays, cannot by themselves, identify cycle-specific functions related to protein variations.