- Open Access
Transcript copy number estimation using a mouse whole-genome oligonucleotide microarray
© Carter et al.; licensee BioMed Central Ltd. 2005
Received: 31 December 2004
Accepted: 25 May 2005
Published: 30 June 2005
The ability to quantitatively measure the expression of all genes in a given tissue or cell with a single assay is an exciting promise of gene-expression profiling technology. An in situ-synthesized 60-mer oligonucleotide microarray designed to detect transcripts from all mouse genes was validated, as well as a set of exogenous RNA controls derived from the yeast genome (made freely available without restriction), which allow quantitative estimation of absolute endogenous transcript abundance.
One of the most tantalizing promises of gene-expression profiling technology has been to develop assays that measure expression of all genes in a given species . This is especially important for the mouse, which is a standard model for various human diseases. The early and rapid development of murine bioinformatics resources such as the draft genome assembly  and numerous expressed sequence tag (EST) projects have bolstered the feasibility of developing such microarray platforms for the mouse. However, because it has been difficult to identify all murine genes and correctly group genomic and expressed sequences into genes and transcripts, microarray platforms intended to cover all mouse genes are only now being made widely available, long after the draft assembly was released.
Relatively recent microarray technologies, which require sequence information instead of clones as input, allow investigators to design microarray platforms to detect genes without having to obtain clones, including genes which have yet to be cloned or confirmed as an expressed transcript . Platforms that utilize long oligonucleotides give high sensitivity, with the potential for transcript specificity sufficient to distinguish transcripts from the same locus or closely related gene-family members [4, 5].
While microarray-based methods can provide very accurate relative (ratio-based) expression measurements, they usually do not provide absolute expression measurements (that is, transcript copy number). One notable exception described in the literature does provide absolute expression measurements in yeast, but not as copy numbers . That method relies on labeled oligonucleotides complementary to common sequence in each cDNA probe, which are hybridized against each slide as the reference target. In the case of long-oligonucleotide-based microarrays, there is no sequence common to all probes, so such a strategy is not feasible. An appropriate approach for such microarray platforms is to monitor the hybridization behavior of a few spiked-in RNA controls with sequence derived from yeast or other genomes. Control transcript probe intensity data can be used to create a generalized dose-signal model and applied to endogenous transcript intensity data to give transcript abundance estimates. Not only would such absolute expression measurements from microarrays help determine what level of sensitivity is required for downstream validation methods, but they would also allow direct comparison of expression data generated using different methods, as well as a valuable mechanism to compare performance between slides, platforms, or experiments . Most importantly, global absolute expression measurements can be used to more fully describe a given transcriptome, perhaps identifying mRNAs present at less than one copy per cell as candidates for heterogeneous or cell-type-specific expression, or subdividing groups of genes in Gene Ontology (GO) nodes  based on transcript abundance.
The work described here is focused on two goals, aimed at facilitating standardization and comparison among mouse microarray studies: first, to create a long-oligonucleotide-based microarray platform covering all identified mouse genes, which can be made widely available; and second, to develop exogenous RNA controls which will allow quantitative estimation of absolute endogenous transcript abundance. The microarray will be made available to the community through Agilent Technologies and exogenous control plasmid vectors will be available upon request from the authors and the American Type Culture Collection (ATCC) (ATCC MBA-201 to -207) without restriction, to be used with the design presented here or incorporated into any non-yeast microarray platform.
Results and discussion
The development of a mouse whole-genome microarray in our laboratory has been an ongoing effort, and each new design has been derived in part from its predecessor (see Additional data files 1 and 2 and Materials and methods for details) . Development of the National Institute on Aging (NIA) Mouse Gene Index  facilitated more complete, less redundant microarray design than EST clustering alone for the following reasons. First, clustering was mapped to the genome assembly, improving consolidation of transcriptional units. Second, transcript selection is no longer restricted to library contents, allowing genes absent from NIA cDNA clone collections  to be included from other public sequence collections. Finally, all potential splice variants were solved from EST alignments with genomic sequence, so that probes can be designed to common regions in a transcript family, minimizing the effect of differential splicing. Therefore the index has been the basis of gene/transcript identification and sequence selection for all oligonucleotide array designs subsequent to the NIA Mouse 22K Microarray v1.1. During the preparation of this paper, assembly of a long-oligonucleotide microarray platform with full coverage of the mouse genome was reported by Zhang et al.  using a sequence selection protocol that incorporated all National Center for Biotechnology Information (NCBI) RefSeq entries, including all mRNA transcripts based solely on prediction algorithms, without experimental evidence of expression (XM sequences). In contrast, our protocol included only a minority of the XM sequences (only those annotated as an identified gene).
Yeast controls used in this study with corresponding qPCR primers
Yeast intronic/intergenic control transcript
Vector name ATCC number GenBank Accession
Insert size (bp)
Copies spiked/5 μg total RNA
Forward/reverse qPCR oligo sequence
pNIAysic-1 MBA-201 DQ023287
pNIAysic-2 MBA-202 DQ023288
pNIAysic-3 MBA-203 DQ023289
pNIAysic-4 MBA-204 DQ023290
pNIAysic-5 MBA-205 DQ023291
pNIAysic-6 MBA-206 DQ023292
pNIAysic-7 MBA-207 DQ023293
Yeast controls with corresponding in situ-synthesized 60-mer oligonucleotide probes
NIA probe ID
60-mer oligonucleotide microarray probe sequence
NIA yeast control 1
NIA yeast control 2
NIA yeast control 3
NIA yeast control 4
NIA yeast control 5
NIA yeast control 6
NIA yeast control 7
qPCR primer pairs used to quantitate endogenous transcripts in this study
Forward/reverse qPCR oligo sequence
Microarray expression profiles were generated for three distinct samples each of total RNA from E12.5 whole embryos (EM), E12.5 placenta (PL), R1 embryonic stem cells (ES), and GFP-Exe trophoblast stem cells (TS) . For each microarray, linear regression analysis on mean normalized log10[intensity] values for seven yeast spike-in control probes was used to define a standard curve relating signal intensity to copy number (Figure 2b) for estimation of endogenous transcript abundances. Correlations were very strong between log10[intensity] and log10[input copy number], with r2 ≥ 0.95.
Many factors are likely to affect the accuracy of transcript abundance estimates. Measurements at or near the microarray's detection limit, but still above that of qPCR assays (Figure 4, Lpl and Axl in TS, filled arrows), tend to overestimate transcript abundance, and these data suggest that the lower limit of microarray-based transcript abundance measurement is approximately 0.05 to 0.06 copies per cell in this experiment. Differential transcript splicing can also have an effect: note that for Ank, H19, Hand1, and Igf2bp3 (Figure 4, open arrows), only one tissue out of four shows greater than a tenfold discrepancy, whereas the other measurement pairs are more closely matched. Given the preceding discussion, we present this method as a way to estimate transcript abundances for groups of genes. Accuracy of the estimates for each gene/probe may be further improved in the future by studying the effects of various probe-selection parameters on measured fluorescence intensity.
Expressed genes and transcripts in developing mouse tissues and cultured stem cells
Features ≥ 1 CpC
U-clusters ≥ 1 CpC
Transcripts ≥ 1 CpC
Mean copies per cell
Median copies per cell
Pairwise comparison of expressed transcript sets in developing mouse tissues and cultured cells
Total expressed features
Total expressed U-clusters
Total expressed transcripts
When comparing frequency distributions for complex, in vivo samples and less complex in vitro cultured cells, we might expect to see large differences, particularly in the case of genes expressed at less than one copy per cell. Transcripts present at less than one copy per cell cannot be present in every cell, and therefore must be expressed heterogeneously. As might be expected, whole embryos had the most distinctive frequency distribution of the four samples examined: embryos had significantly fewer transcripts in the range log10[copies per cell] = -1.0 (0.1 copies per cell), but significantly more in the 0-2 (1 to 100 copies per cell) range. This difference, combined with the higher estimate of total transcripts per cell for whole embryos (Table 4), may reflect the activation, within the context of the very high transcriptional activity present in developing embryos, of many developmental pathways that are normally inactive or minimally active.
In contrast, the high degree of similarity between the frequency distributions for placenta, ES, and TS cells (Figure 5) suggests that levels of expression heterogeneity can be similar for complex tissues and cultured cells. In fact, there is evidence in ES cells that gene expression within a culture is not as uniform as previously supposed, and even key differentiation markers such as Oct4 and cKit are expressed in cellular subpopulations within cultures . Taken together, these observations suggest that cultured ES and TS cells, although clonally isolated, are quite heterogeneous in terms of their gene-expression patterns, with a transcriptional complexity similar to that of E12.5 placenta. Further study, perhaps using in situ hybridization or single-cell RT-PCR methods, will be required to address this issue, but it does beg the question of whether or not this heterogeneity is common to all cultured cells, or a feature specific to pluripotent stem cells.
Here we present an oligonucleotide microarray for gene-expression profiling with representation of the entire mouse genome, according to the NIA Mouse Gene Index version 2.0 . An integral feature of this new whole-genome microarray design is a set of probes detecting yeast spike-in control transcripts, which will be available to the community without restriction. Using qPCR, we have shown that this control system allows the reproducible estimation of absolute transcript levels. A valuable tool for the mammalian functional genomics community, this system is a step towards standardization of microarray results by using exogenous RNA control systems that are compatible with multiple microarray platforms and model organisms.
Materials and methods
Microarray design: target sequence selection
The NIA Mouse 44K Microarray v2.0 (Whole Genome 60-mer Oligo) design was based on the NIA Mouse Gene Index v2.0 . Like the first version of the NIA Mouse Gene Index , it combines data from multiple transcript databases (RefSeq, Ensembl, Riken, GenBank, and NIA) to construct gene/transcript models which represent all possible transcripts. Briefly, 249,200 ESTs developed at NIA were clustered using clustering tools from The Institute for Genome Reserach (TIGR) , generating 58,713 consensus and singleton sequences which were then combined with the other datasets. The major difference in version 2 from version 1 is the use of a clustering method based on genome alignments rather than sequence homology between NIA EST clusters and public sequences. Individual sequences were aligned to the mouse genome  using BLAT , then clustered by an algorithm similar to the one described by Eyras et al. , to be published elsewhere. Our assembly included 30,796 primary genes and 1,318 gene copies or pseudogenes, as well as 28,928 clusters that did not match our criteria for high-confidence genes (open reading frame (ORF) of more than 100 amino acids or multiple exons). There were 65,477 transcripts associated with primary genes. Because transcripts were built from sequence alignments to the mouse genome, they match published genomic sequences  (February 2003 edition) exactly.
Microarray design: oligonucleotide probe design and selection
In designing a mouse whole-genome microarray, we began by examining existing designs - the NIA Mouse 22K Microarray v1.1 (Development 60-mer Oligo) , which became commercially available from Agilent as the Agilent Mouse (Development) Oligonucleotide Microarray (see Additional data files 1 and 2), and the National Institute of Environmental Health Sciences (NIEHS) Toxicogenomics Consortium mouse array (Agilent Mouse Microarray). Criteria for selecting previously designed probes included a good match to the target gene's major transcript with the longest ORF, minimum predicted cross-reactivity with other expressed sequences, and nonredundancy. Although a perfect match of all 60 base-pairs (bp) of the oligonucleotide was preferred, we also accepted up to two mismatches to the genome if the oligonucleotide matched perfectly to the RefSeq sequence, and oligonucleotide sequences that did not match 100% to the RefSeq entry were corrected. An oligonucleotide was considered cross-reactive if its last 43 bp (solution end) matched to a non-target gene with less than five mismatches. Deletion placement studies using in-situ synthesized 60-mer oligonucleotide probes suggest that the 17 bp at the support surface have a negligible effect on hybridization intensity ; thus only the external 43 bp were considered important. While the cross-reactivity criterion is easily satisfied for unique genes with low similarity to other genes, many gene families had high sequence similarity between member transcripts, and it was impossible to find regions with low predicted cross-reactivity. In this case we considered the whole gene family as a target; then the oligonucleotide was considered cross-reactive only if it matched to genes outside the family. Gene families were assembled using a 30% transcript length alignment as a threshold of similarity; alignments for each pair of transcripts were generated using BLAT . According to the nonredundancy criterion, we left only one oligonucleotide that matched to each gene or gene family, and when probes from both the NIA Mouse 22K v1.1 and NIEHS Toxicogenomics arrays matched well to the same gene, preference was given to the NIA oligonucleotide.
After filtering with the above criteria, we obtained 6,563 probes from the NIA Mouse 22K Microarray v1.1 and 9,551 probes from the NIEHS Toxicogenomics array. Among these oligonucleotides, 3,327 did not match the target gene's major transcript with the longest ORF, so we generated an additional 3,327 probes for major transcripts of the same genes. Then we generated 22,850 probes for the best transcripts of primary genes in the gene index that were not represented in the NIA Mouse 22K Microarray v1.1 (Development 60-mer Oligo) and NIEHS Toxicogenomics arrays, for a total of 42,291 non-control oligonucleotide probes (see Additional data file 2). For each transcript we generated ten probes using ArrayOligoSelector , then selected the best oligonucleotide on the basis of minimum predicted cross-reactivity, proximity to the 3' end, and degree of matching to RefSeq or GenBank sequences. The latter criterion was important only in cases of mismatches between genomic sequence and RefSeq or GenBank.
All microarray data described in this report were generated using the NIA Mouse 44K Microarray v2.1 (Whole Genome 60-mer Oligo) and NIA Mouse 22K Microarray v2.0 (Development 60-mer Oligo). We have slightly modified the probe content of the NIA Mouse 44K v2.0 array by including Agilent's standard QC probe set, removing candidate spike-in control probes which were not used, and including additional probes for known genes that have existing probes with poor performance or ambiguous targeting. The updated version (NIA Mouse 44K Microarray v2.1 (Whole Genome 60-mer Oligo) will be made available to the community (see Additional data file 1).
Yeast spike-in controls
Yeast (S. cerevisiae) sequences were selected from public repositories [14, 15] to produce exogenous RNA control transcripts, commonly referred to as 'spike-in' controls. Fourteen candidates (ten intergenic and four intronic) were selected on the basis of sequence length and the absence of restriction endonuclease cleavage sites important for our cloning strategy. Sequences with significant matches to transcripts in the NIA mouse Gene Index v2.0  were discarded, and ten of the 14 remaining candidates were successfully cloned from genomic DNA, with one sequence divided into two clones for a total of 11 potential controls. Yeast sequences were amplified with added 5' SalI and 3' XbaI sites from S. cerevisiae genomic DNA (ATCC 2601D) using Sigma RedTaq, and cloned directly into pCR4-TOPO (Invitrogen). TA-TOPO clones were verified by sequencing on an Applied Biosystems 3100 capillary DNA sequencer, and inserts were directionally subcloned into pSP64 Poly(A) (Promega Catalog number P1241) using the introduced SalI and XbaI sites. A total of 63 60-mer oligonucleotide 'sense-strand' probes were selected for the 14 candidate sequences using both ArrayOligoSelector software  and arbitrary manual selection. Oligonucleotide probes were compared to NIA Gene Index transcripts, and no significant matches were found. Control probes were spotted ten times each in various locations throughout the slides.
Spike-in RNA was transcribed, polyadenylated, and purified using Ambion mMessage mMachine, poly(A) tailing, and MegaClear kits, then sized and quantitated by RNA 6000 Nano assay on an Agilent Bioanalyzer 2100. Spike-in RNAs were pooled to create tenfold concentration differences, from 104 to 1010 copies per microliter (Table 1). Before preparation of microarray targets, 1 μl of this control transcript mixture was added to 5-μg aliquots of each total RNA sample, including the reference RNA. A separate pool with all yeast control transcripts present at the same copy number was added to reference RNA and converted to cDNA for use as a standard in qPCR assays.
Total RNA was prepared using TriZol reagent (Invitrogen) from E12.5 C57BL/6J embryos, pooled by litter, and corresponding E12.5 C57BL/6J placenta pools . Total RNA was also prepared from R1 ES cells passaged briefly on gelatin to remove feeder cells, and GFP-Exe TS cells grown on plastic in conditioned medium as previously described . Total RNA quantity and quality were assessed by RNA 6000 Nano assay. For oligonucleotide signal linearity testing, E12.5 embryo and placenta total RNA were pooled, based on this quantitation, to produce duplicate samples with 0, 25, 50, 75, and 100% placental RNA content.
cRNA target labeling
Fluorescently labeled microarray targets were prepared from 2.5 μg aliquots of total RNA samples with yeast sequence control mixtures added as described above, using a Low RNA Input Fluorescent Linear Amplification Kit (Agilent). A reference target (Cy5-CTP-labeled) was produced from Stratagene Universal Mouse Reference RNA, and all other targets were labeled with Cy3-CTP. Targets were purified using an RNeasy Mini Kit (Qiagen) as directed by Agilent's clean-up protocol, and quantitated on a NanoDrop scanning spectrophotometer (NanoDrop Technologies).
All hybridizations compared one Cy3-CTP-labeled experimental target to the single Cy5-CTP-labeled reference target. Microarrays were hybridized and washed according to Agilent protocol G4140-90030 (Agilent 60-mer oligo microarray processing protocol - SSC Wash, v1.0). Slides were scanned on an Agilent DNA Microarray Scanner, using standard settings, including automatic PMT adjustment.
Real-time quantitative RT-PCR
Primer sets were designed and tested for SYBR Green chemistry using an established in-house protocol . Total RNA was used to prepare cDNA as described previously . Because the microarray targets were oligo(dT) primed, all cDNA synthesis reactions were oligo(dT) primed as well, and qPCR primer sets were designed so that amplicons were upstream of 60-mer oligonucleotide probes when possible, or less than 650 bp downstream. These steps were taken to minimize the effects of 3' end-labeling bias from microarray target synthesis. Yeast spike-in standard curve cDNA was prepared by mixing equal copy numbers of each synthetic yeast RNA with Mouse Universal Reference total RNA, followed by cDNA synthesis. A standard for copy-number measurement of endogenous mouse genes was prepared by transcribing cDNA clones and adding these transcripts in equal numbers to yeast total RNA, followed by cDNA synthesis. A BioMek 2000 liquid-handling system (Beckman) was used to aliquot cDNA into 96- and 384-well plates, then assemble and aliquot PCR master mix into 20-25 μl reactions. Plates were run on ABI 7700 or ABI 7900 HT Sequence Detection Systems using the default cycling program, and data was processed using SDS 1.9 or SDS 2.2 software (Applied Biosystems) and Microsoft Excel.
Microarray images were processed with Agilent Feature Extractor A.7.5.1 software to generate normalized, background-subtracted feature intensities. Dye normalization was performed by applying a LOWESS algorithm to all significant, non-control and non-outlier features. Analysis of variance (ANOVA) and replicate averaging was performed as previously described  using NIA Array Analysis Tool software , which normalizes each probe according to reference RNA signals.
For each probe identified as differentially expressed in mixing experiments (false discovery rate < 0.05) , linear regressions of ratios against pure placental RNA across the five levels of placental RNA content were calculated, and observed ratios were back-calculated for population analysis as
where P oi is the observed fraction placental RNA content calculated from a given probe i, I pi and I100iare the normalized log10[intensity] values for the probe i at placental RNA percentages p and 100, respectively, and a i and b i are the intercept and slope of the ratios versus the input placental RNA fraction for probe i. For the population of observed fractions at each input placental RNA fraction, the mean and median were calculated, along with the 2.5, 25, 75, and 97.5 percentile boundaries (Figure 1).
For endogenous transcript abundance estimation experiments, linear regression analysis was performed on seven yeast spike-in probe mean normalized log10[intensity] values for each microarray and the results were used to back-calculate estimated copy numbers for endogenous transcripts as
where C hmi is the microarray-estimated number of copies per hybridization for probe i, I i is the normalized log10[intensity] for probe i, and a and b are the intercept and slope of spike-in control probe microarray signal intensities versus. input spike-in transcript copy numbers. Dividing these values by the estimated number of cells represented in each hybridization,
converts them to estimates of transcript copies per cell. Amounts of total RNA extracted per cell for the four tissue types (EM 3.0 pg/cell, PL 2.0 pg/cell, ES 2.3 pg/cell, TS 3.0 pg/cell) were estimated from cell counts, RNA yields, and in the case of E12.5 embryo and placenta, our estimate that the average cell volume in these tissues is approximately 1.5 × 10-9 cm3 per cell (data not shown).
For measurement of abundances of mouse endogenous gene and spiked-in yeast transcripts in total RNA and labeled/amplified target mixtures by qPCR, linear regression of threshold cycle (C t ) values versus input spike-in transcript copy numbers in a standard was used to back-calculate copy numbers per well of the transcripts in the total RNA samples and labeled/amplified target mixtures. These results were converted to copies per cell as follows:
In the case of endogenous mouse transcript measurements, results from both the microarray and qPCR were normalized to Gapd expression.
Additional data files
The following additional data are available with the online version of this paper. Additional data file 1 is a table containing a standardized naming scheme for NIA oligonucleotide microarray platforms. Additional data file 2 is a table containing additional information on previous NIA microarray platforms and how they relate to that presented in this work. Additional data file 3 contains annotation of all probes in the NIA 44K Mouse Microarray v2.1.
The authors thank Peter Webb at Agilent Technologies for his assistance in preparing the microarray design for production, and his colleague Paul Wolber for advice in constructing the yeast spike-in control constructs. Yong Qian of the NIA provided invaluable bioinformatics and computational support for many aspects of this work. We also thank Janet Rossant and Tilo Kunath for providing ES and TS cell RNA. DNA microarrays produced according to NIA designs are available commercially from Agilent Technologies. However, The National Institutes on Health and The National Institute on Aging do not endorse these products or make any claims or guarantees as to their quality or performance.
- Ko MS: An 'equalized cDNA library' by the reassociation of short double-stranded cDNAs. Nucleic Acids Res. 1990, 18: 5705-5711.PubMedPubMed CentralView ArticleGoogle Scholar
- Waterston RH, Lindblad-Toh K, Birney E, Rogers J, Abril JF, Agarwal P, Agarwala R, Ainscough R, Alexandersson M, An P, et al: Initial sequencing and comparative analysis of the mouse genome. Nature. 2002, 420: 520-562. 10.1038/nature01262.PubMedView ArticleGoogle Scholar
- Schadt EE, Edwards SW, GuhaThakurta D, Holder D, Ying L, Svetnik V, Leonardson A, Hart KW, Russell A, Li G, et al: A comprehensive transcript index of the human genome generated using microarrays and computational approaches. Genome Biol. 2004, 5: R73-10.1186/gb-2004-5-10-r73.PubMedPubMed CentralView ArticleGoogle Scholar
- Singh-Gasson S, Green RD, Yue Y, Nelson C, Blattner F, Sussman MR, Cerrina F: Maskless fabrication of light-directed oligonucleotide microarrays using a digital micromirror array. Nat Biotechnol. 1999, 17: 974-978. 10.1038/13664.PubMedView ArticleGoogle Scholar
- Hughes TR, Mao M, Jones AR, Burchard J, Marton MJ, Shannon KW, Lefkowitz SM, Ziman M, Schelter JM, Meyer MR, et al: Expression profiling using microarrays fabricated by an ink-jet oligonucleotide synthesizer. Nat Biotechnol. 2001, 19: 342-347. 10.1038/86730.PubMedView ArticleGoogle Scholar
- Dudley AM, Aach J, Steffen MA, Church GM: Measuring absolute expression with microarrays with a calibrated reference sample and an extended signal intensity range. Proc Natl Acad Sci USA. 2002, 99: 7554-7559. 10.1073/pnas.112683499.PubMedPubMed CentralView ArticleGoogle Scholar
- van Bakel H, Holstege FC: In control: systematic assessment of microarray performance. EMBO Rep. 2004, 5: 964-969. 10.1038/sj.embor.7400253.PubMedPubMed CentralView ArticleGoogle Scholar
- Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, et al: Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000, 25: 25-29. 10.1038/75556.PubMedPubMed CentralView ArticleGoogle Scholar
- Carter MG, Hamatani T, Sharov AA, Carmack CE, Qian Y, Aiba K, Ko NT, Dudekula DB, Brzoska PM, Hwang SS, Ko MS: In situ-synthesized novel microarray optimized for mouse stem cell and early developmental expression profiling. Genome Res. 2003, 13: 1011-1021. 10.1101/gr.878903.PubMedPubMed CentralView ArticleGoogle Scholar
- Sharov AA, Piao Y, Matoba R, Dudekula DB, Qian Y, VanBuren V, Falco G, Martin PR, Stagg CA, Bassey UC, et al: Transcriptome analysis of mouse stem cells and early embryos. PLoS Biol. 2003, 1: E74-10.1371/journal.pbio.0000074.PubMedPubMed CentralView ArticleGoogle Scholar
- Carter MG, Piao Y, Dudekula DB, Qian Y, VanBuren V, Sharov AA, Tanaka TS, Martin PR, Bassey UC, Stagg CA, et al: The NIA cDNA project in mouse stem cells and early embryos. C R Biol. 2003, 326: 931-940.PubMedView ArticleGoogle Scholar
- Zhang W, Morris QD, Chang R, Shai O, Bakowski MA, Mitsakakis N, Mohammad N, Robinson MD, Zirngibl R, Somogyi E, et al: The functional landscape of mouse gene expression. J Biol. 2004, 3: 21-10.1186/jbiol16.PubMedPubMed CentralView ArticleGoogle Scholar
- Parrish ML, Wei N, Duenwald S, Tokiwa GY, Wang Y, Holder D, Dai H, Zhang X, Wright C, Hodor P, et al: A microarray platform comparison for neuroscience applications. J Neurosci Methods. 2004, 132: 57-68. 10.1016/j.jneumeth.2003.09.013.PubMedView ArticleGoogle Scholar
- Grate L, Ares M: Searching yeast intron data at Ares lab web site. Methods Enzymol. 2002, 350: 380-392.PubMedView ArticleGoogle Scholar
- Ares Lab Yeast Intron Database. [http://www.cse.ucsc.edu/compbio/yeast_introns/currentDB/stuff.html]
- Tanaka S, Kunath T, Hadjantonakis AK, Nagy A, Rossant J: Promotion of trophoblast stem cell proliferation by FGF4. Science. 1998, 282: 2072-2075. 10.1126/science.282.5396.2072.PubMedView ArticleGoogle Scholar
- Van Ness J, Hahn WE: Sequence complexity of cDNA transcribed from a diverse mRNA population. Nucleic Acids Res. 1980, 8: 4259-4270.PubMedPubMed CentralView ArticleGoogle Scholar
- Axel R, Feigelson P, Schutz G: Analysis of the complexity and diversity of mRNA from chicken liver and oviduct. Cell. 1976, 7: 247-254. 10.1016/0092-8674(76)90024-6.PubMedView ArticleGoogle Scholar
- Holland MJ: Transcript abundance in yeast varies over six orders of magnitude. J Biol Chem. 2002, 277: 14363-14366. 10.1074/jbc.C200101200.PubMedView ArticleGoogle Scholar
- Hastie ND, Bishop JO: The expression of three abundance classes of messenger RNA in mouse tissues. Cell. 1976, 9: 761-774. 10.1016/0092-8674(76)90139-2.PubMedView ArticleGoogle Scholar
- Bishop JO, Morton JG, Rosbash M, Richardson M: Three abundance classes in HeLa cell messenger RNA. Nature. 1974, 250: 199-204.PubMedView ArticleGoogle Scholar
- Jongeneel CV, Iseli C, Stevenson BJ, Riggins GJ, Lal A, Mackay A, Harris RA, O'Hare MJ, Neville AM, Simpson AJ, Strausberg RL: Comprehensive sampling of gene expression in human cell lines with massively parallel signature sequencing. Proc Natl Acad Sci USA. 2003, 100: 4702-4705. 10.1073/pnas.0831040100.PubMedPubMed CentralView ArticleGoogle Scholar
- Hubner K, Fuhrmann G, Christenson LK, Kehler J, Reinbold R, De La Fuente R, Wood J, Strauss JF, Boiani M, Scholer HR: Derivation of oocytes from mouse embryonic stem cells. Science. 2003, 300: 1251-1256. 10.1126/science.1083452.PubMedView ArticleGoogle Scholar
- NIA Mouse Gene Index 2.0. [http://lgsun.grc.nia.nih.gov/geneindex1/index.html]
- Pertea G, Huang X, Liang F, Antonescu V, Sultana R, Karamycheva S, Lee Y, White J, Cheung F, Parvizi B, et al: TIGR Gene Indices clustering tools (TGICL): a software system for fast clustering of large EST datasets. Bioinformatics. 2003, 19: 651-652. 10.1093/bioinformatics/btg034.PubMedView ArticleGoogle Scholar
- Kent WJ: BLAT - the BLAST-like alignment tool. Genome Res. 2002, 12: 656-664. 10.1101/gr.229202. Article published online before March 2002.PubMedPubMed CentralView ArticleGoogle Scholar
- Eyras E, Caccamo M, Curwen V, Clamp M: ESTGenes: alternative splicing from ESTs in Ensembl. Genome Res. 2004, 14: 976-987. 10.1101/gr.1862204.PubMedPubMed CentralView ArticleGoogle Scholar
- Bozdech Z, Zhu J, Joachimiak MP, Cohen FE, Pulliam B, DeRisi JL: Expression profiling of the schizont and trophozoite stages of Plasmodium falciparum with a long-oligonucleotide microarray. Genome Biol. 2003, 4: R9-10.1186/gb-2003-4-2-r9.PubMedPubMed CentralView ArticleGoogle Scholar
- NIA Array Analysis Tool. [http://lgsun.grc.nia.nih.gov/ANOVA/index.html]
- Gene Expression Omnibus. [http://www.ncbi.nlm.nih.gov/geo]
- Edgar R, Domrachev M, Lash AE: Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 2002, 30: 207-210. 10.1093/nar/30.1.207.PubMedPubMed CentralView ArticleGoogle Scholar
- ArrayExpress. [http://www.ebi.ac.uk/arrayexpress]
- Brazma A, Parkinson H, Sarkans U, Shojatalab M, Vilo J, Abeygunawardena N, Holloway E, Kapushesky M, Kemmeren P, Lara GG, et al: ArrayExpress - a public repository for microarray gene expression data at the EBI. Nucleic Acids Res. 2003, 31: 68-71. 10.1093/nar/gkg091.PubMedPubMed CentralView ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permitsunrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.