Single-Species Microarrays and Comparative Transcriptomics

BACKGROUND: Prefabricated expression microarrays are currently available for only a few species but methods have been proposed to extend their application to comparisons between divergent genomes. METHODOLOGY/PRINCIPAL FINDINGS: Here we demonstrate that the hybridization intensity of genomic DNA is a poor basis on which to select unbiased probes on Affymetrix expression arrays for studies of comparative transcriptomics, and that doing so produces spurious results. We used the Affymetrix Xenopus laevis microarray to evaluate expression divergence between X. laevis, X. borealis, and their F1 hybrids. When data are analyzed with probes that interrogate only sequences with confirmed identity in both species, we recover results that differ substantially analyses that use genomic DNA hybridizations to select probes. CONCLUSIONS/SIGNIFICANCE: Our findings have implications for the experimental design of comparative expression studies that use single-species microarrays, and for our understanding of divergent expression in hybrid clawed frogs. These findings also highlight important limitations of single-species microarrays for studies of comparative transcriptomics of polyploid species

Relative impact of key sources of systematic noise in Affymetrix and Illumina gene-expression microarray experiments

<p>Abstract</p> <p>Background</p> <p>Systematic processing noise, which includes batch effects, is very common in microarray experiments but is often ignored despite its potential to confound or compromise experimental results. Compromised results are most likely when re-analysing or integrating datasets from public repositories due to the different conditions under which each dataset is generated. To better understand the relative noise-contributions of various factors in experimental-design, we assessed several Illumina and Affymetrix datasets for technical variation between replicate hybridisations of Universal Human Reference (UHRR) and individual or pooled breast-tumour RNA.</p> <p>Results</p> <p>A varying degree of systematic noise was observed in each of the datasets, however in all cases the relative amount of variation between standard control RNA replicates was found to be greatest at earlier points in the sample-preparation workflow. For example, 40.6% of the total variation in reported expressions were attributed to replicate extractions, compared to 13.9% due to amplification/labelling and 10.8% between replicate hybridisations. Deliberate probe-wise batch-correction methods were effective in reducing the magnitude of this variation, although the level of improvement was dependent on the sources of noise included in the model. Systematic noise introduced at the chip, run, and experiment levels of a combined Illumina dataset were found to be highly dependant upon the experimental design. Both UHRR and pools of RNA, which were derived from the samples of interest, modelled technical variation well although the pools were significantly better correlated (4% average improvement) and better emulated the effects of systematic noise, over all probes, than the UHRRs. The effect of this noise was not uniform over all probes, with low GC-content probes found to be more vulnerable to batch variation than probes with a higher GC-content.</p> <p>Conclusions</p> <p>The magnitude of systematic processing noise in a microarray experiment is variable across probes and experiments, however it is generally the case that procedures earlier in the sample-preparation workflow are liable to introduce the most noise. Careful experimental design is important to protect against noise, detailed meta-data should always be provided, and diagnostic procedures should be routinely performed prior to downstream analyses for the detection of bias in microarray studies.</p

A new method for class prediction based on signed-rank algorithms applied to Affymetrix® microarray experiments

<p>Abstract</p> <p>Background</p> <p>The huge amount of data generated by DNA chips is a powerful basis to classify various pathologies. However, constant evolution of microarray technology makes it difficult to mix data from different chip types for class prediction of limited sample populations. Affymetrix^®technology provides both a quantitative fluorescence signal and a decision (<it>detection call</it>: absent or present) based on signed-rank algorithms applied to several hybridization repeats of each gene, with a per-chip normalization. We developed a new prediction method for class belonging based on the detection call only from recent Affymetrix chip type. Biological data were obtained by hybridization on U133A, U133B and U133Plus 2.0 microarrays of purified normal B cells and cells from three independent groups of multiple myeloma (MM) patients.</p> <p>Results</p> <p>After a call-based data reduction step to filter out non class-discriminative probe sets, the gene list obtained was reduced to a predictor with correction for multiple testing by iterative deletion of probe sets that sequentially improve inter-class comparisons and their significance. The error rate of the method was determined using leave-one-out and 5-fold cross-validation. It was successfully applied to (i) determine a sex predictor with the normal donor group classifying gender with no error in all patient groups except for male MM samples with a Y chromosome deletion, (ii) predict the immunoglobulin light and heavy chains expressed by the malignant myeloma clones of the validation group and (iii) predict sex, light and heavy chain nature for every new patient. Finally, this method was shown powerful when compared to the popular classification method Prediction Analysis of Microarray (PAM).</p> <p>Conclusion</p> <p>This normalization-free method is routinely used for quality control and correction of collection errors in patient reports to clinicians. It can be easily extended to multiple class prediction suitable with clinical groups, and looks particularly promising through international cooperative projects like the "Microarray Quality Control project of US FDA" MAQC as a predictive classifier for diagnostic, prognostic and response to treatment. Finally, it can be used as a powerful tool to mine published data generated on Affymetrix systems and more generally classify samples with binary feature values.</p

The Annotation, Mapping, Expression and Network (AMEN) suite of tools for molecular systems biology

<p>Abstract</p> <p>Background</p> <p>High-throughput genome biological experiments yield large and multifaceted datasets that require flexible and user-friendly analysis tools to facilitate their interpretation by life scientists. Many solutions currently exist, but they are often limited to specific steps in the complex process of data management and analysis and some require extensive informatics skills to be installed and run efficiently.</p> <p>Results</p> <p>We developed the Annotation, Mapping, Expression and Network (AMEN) software as a stand-alone, unified suite of tools that enables biological and medical researchers with basic bioinformatics training to manage and explore genome annotation, chromosomal mapping, protein-protein interaction, expression profiling and proteomics data. The current version provides modules for (i) uploading and pre-processing data from microarray expression profiling experiments, (ii) detecting groups of significantly co-expressed genes, and (iii) searching for enrichment of functional annotations within those groups. Moreover, the user interface is designed to simultaneously visualize several types of data such as protein-protein interaction networks in conjunction with expression profiles and cellular co-localization patterns. We have successfully applied the program to interpret expression profiling data from budding yeast, rodents and human.</p> <p>Conclusion</p> <p>AMEN is an innovative solution for molecular systems biological data analysis freely available under the GNU license. The program is available via a website at the Sourceforge portal which includes a user guide with concrete examples, links to external databases and helpful comments to implement additional functionalities. We emphasize that AMEN will continue to be developed and maintained by our laboratory because it has proven to be extremely useful for our genome biological research program.</p

A Honey Bee Hexamerin, HEX 70a, Is Likely to Play an Intranuclear Role in Developing and Mature Ovarioles and Testioles

Insect hexamerins have long been known as storage proteins that are massively synthesized by the larval fat body and secreted into hemolymph. Following the larval-to-pupal molt, hexamerins are sequestered by the fat body via receptor-mediated endocytosis, broken up, and used as amino acid resources for metamorphosis. In the honey bee, the transcript and protein subunit of a hexamerin, HEX 70a, were also detected in ovaries and testes. Aiming to identify the subcellular localization of HEX 70a in the female and male gonads, we used a specific antibody in whole mount preparations of ovaries and testes for analysis by confocal laser-scanning microscopy. Intranuclear HEX 70a foci were evidenced in germ and somatic cells of ovarioles and testioles of pharate-adult workers and drones, suggesting a regulatory or structural role. Following injection of the thymidine analog EdU we observed co-labeling with HEX 70a in ovariole cell nuclei, inferring possible HEX 70a involvement in cell proliferation. Further support to this hypothesis came from an injection of anti-HEX 70a into newly ecdysed queen pupae where it had a negative effect on ovariole thickening. HEX 70a foci were also detected in ovarioles of egg laying queens, particularly in the nuclei of the highly polyploid nurse cells and in proliferating follicle cells. Additional roles for this storage protein are indicated by the detection of nuclear HEX 70a foci in post-meiotic spermatids and spermatozoa. Taken together, these results imply undescribed roles for HEX 70a in the developing gonads of the honey bee and raise the possibility that other hexamerins may also have tissue specific functions

A novel simple satellite DNA is colocalized with the Stalker retrotransposon in Drosophila melanogaster heterochromatin

In the T(1;2)dor( var7) multibreak rearrangement the distal 1A-2B segment of the X chromosome of; Drosophila melanogaster is juxtaposed to an inverted portion of the heterochromatin of chromosome 2. Analysis of mitotic chromosomes by a series of handing techniques has permitted us precisely to locate the heterochromatic breakpoint of this translocation in the h42 region of 2R. Cloning and sequencing of the eu-heterochromatic junction revealed that the translocated 1A-2B fragment is joined to (AACAC)(n) repeats, which represent a previously undescribed satellite DNA in D. melanogaster. These repeated sequences have been estimated to account for about 1 Mb of the D. melanogaster genome. The repeats are located mainly in the Y chromosome and in the heterochromatin of the right arm of chromosome 2 (2Rh), where they are colocalized with the Stanier retrotransposon