Salvaging Affymetrix probes after probe-level re-annotation

Background: Affymetrix GeneChips can be re-annotated at the probe-level by breaking up the original probe-sets and recomposing new probe-sets based on up-to-date genomic knowledge, such as available in Entrez Gene. This results in custom Chip Description Files (CDF). Using these custom CDFs improves the quality of the data and thus the results of related gene expression studies. However, 44-71% of the probes on a GeneChip are lost in this re-annotation process. Although generally aimed at less known genes, losing these probes obviously means a substantial loss of expensive experiment data. Biologists are therefore very reluctant to adopt this approach. Findings: We aimed to re-introduce the non-affected Affymetrix probe-sets after these re-annotation procedures. For this, we developed an algorithm (CDF-Merger) and applied it to standard Affymetrix CDFs and custom Brainarray CDFs to obtain Hybrid CDFs. Thus, salvaging lost Affymetrix probes with our CDF-Merger restored probe content up to 94%. Because the salvaged probes (up to 54% of the probe content on the arrays) represent less-reliable probe-sets, we made the origin of all probe-set definitions traceable, so biologists can choose at any time in their analyses, which subset of probe-sets they want to use. Conclusion: The availability of up-to-date Hybrid CDFs plus R environment allows for easy implementation of our approach

SigWinR; the SigWin-detector updated and ported to R

<p>Abstract</p> <p>Background</p> <p>Our SigWin-detector discovers significantly enriched windows of (genomic) elements in any sequence of values (genes or other genomic elements in a DNA sequence) in a fast and reproducible way. However, since it is grid based, only (life) scientists with access to the grid can use this tool. Therefore and on request, we have developed the SigWinR package which makes the SigWin-detector available to a much wider audience. At the same time, we have introduced several improvements to its algorithm as well as its functionality, based on the feedback of SigWin-detector end users.</p> <p>Findings</p> <p>To allow usage of the SigWin-detector on a desktop computer, we have rewritten it as a package for R: SigWinR. R is a free and widely used multi platform software environment for statistical computing and graphics. The package can be installed and used on all platforms for which R is available. The improvements involve: a visualization of the input-sequence values supporting the interpretation of Ridgeograms; a visualization allowing for an easy interpretation of enriched or depleted regions in the sequence using windows of pre-defined size; an option that allows the analysis of circular sequences, which results in rectangular Ridgeograms; an application to identify regions of co-altered gene expression (ROCAGEs) with a real-life biological use-case; adaptation of the algorithm to allow analysis of non-regularly sampled data using a constant window size in physical space without resampling the data. To achieve this, support for analysis of windows with an even number of elements was added.</p> <p>Conclusion</p> <p>By porting the SigWin-detector as an R package, SigWinR, improving its algorithm and functionality combined with adequate performance, we have made SigWin-detector more useful as well as more easily accessible to scientists without a grid infrastructure.</p

Single cell transcriptomics of neighboring hyphae of Aspergillus niger

Single cell profiling was performed to assess differences in RNA accumulation in neighboring hyphae of the fungus Aspergillus niger. A protocol was developed to isolate and amplify RNA from single hyphae or parts thereof. Microarray analysis resulted in a present call for 4 to 7% of the A. niger genes, of which 12% showed heterogeneous RNA levels. These genes belonged to a wide range of gene categories

RNA isolation method for single embryo transcriptome analysis in zebrafish

Background: Transcriptome analysis during embryogenesis usually requires pooling of embryos to obtain sufficient RNA. Hence, the measured levels of gene-expression represent the average mRNA levels of pooled samples and the biological variation among individuals is confounded. This can irreversibly reduce the robustness, resolution, or expressiveness of the experiment. Therefore, we developed a robust method to isolate abundant high-quality RNA from individual embryos to perform single embryo transcriptome analyses using zebrafish as a model organism. Available methods for embryonic zebrafish RNA isolation minimally utilize ten embryos. Further downscaling of these methods to one embryo is practically not feasible. Findings: We developed a single embryo RNA extraction method based on sample homogenization in liquid nitrogen, RNA extraction with phenol and column purification. Evaluation of this method showed that: the quality of the RNA was very good with an average RIN value of 8.3-8.9; the yield was always ≥ 200 ng RNA per embryo; the method was applicable to all stages of zebrafish embryogenesis; the success rate was almost 100%; and the extracted RNA performed excellent in microarray experiments in that the technical variation was much lower than the biological variation. Conclusions: Presented is a high-quality, robust RNA isolation method. Obtaining sufficient RNA from single embryos eliminates the necessity of sample pooling and its associated drawbacks. Although our RNA isolation method has been setup for transcriptome analysis in zebrafish, it can also be used for other model systems and other applications like (q)PCR and transcriptome sequencing

OligoRAP – an Oligo Re-Annotation Pipeline to improve annotation and estimate target specificity

Background - High throughput gene expression studies using oligonucleotide microarrays depend on the specificity of each oligonucleotide (oligo or probe) for its target gene. However, target specific probes can only be designed when a reference genome of the species at hand were completely sequenced, when this genome were completely annotated and when the genetic variation of the sampled individuals were completely known. Unfortunately there is not a single species for which such a complete data set is available. Therefore, it is important that probe annotation can be updated frequently for optimal interpretation of microarray experiments. Results - In this paper we present OligoRAP, a pipeline to automatically update the annotation of oligo libraries and estimate oligo target specificity. OligoRAP uses a reference genome assembly with Ensembl and Entrez Gene annotation supplemented with a set of unmapped transcripts derived from RefSeq and UniGene to handle assembly gaps. OligoRAP produces alignments of each oligo with the reference assembly as well as with unmapped transcripts. These alignments are re-mapped to the annotation sources, which results in a concise, as complete as possible and up-to-date annotation of the oligo library. The building blocks of this pipeline are BioMoby web services creating a highly modular and distributed system with a robust, remote programmatic interface. OligoRAP was used to update the annotation for a subset of 791 oligos from the ARK-Genomics 20 K chicken array, which were selected as starting material for the oligo annotation session of the EADGENE/SABRE Post-analysis workshop. Based on the updated annotation about one third of these oligos is problematic with regard to target specificity. In addition, the accession numbers or ids the oligos were originally designed for no longer exist in the updated annotation for almost half of the oligos. Conclusion - As microarrays are designed on incomplete data, it is important to update probe annotation and check target specificity regularly. OligoRAP provides both and due to its design based on BioMoby web services it can easily be embedded as an oligo annotation engine in customised applications for microarray data analysis. The dramatic difference in updated annotation and target specificity for the ARK-Genomics 20 K chicken array as compared to the original data emphasises the need for regular updates

Using R in Taverna: RShell v1.2

Background: R is the statistical language commonly used by many life scientists in (omics) data by the open source workflow management system Taverna. However, Taverna had limited support for R, because it supported just a few data types and only a single output. Also, there was no support for graphical output and persistent sessions. Altogether this made using R in Taverna impractical.\ud \ud Findings: We have developed an R plugin for Taverna: RShell, which provides R functionality within workflows designed in Taverna. In order to fully support the R language, our RShell plugin directly uses the R interpreter. The RShell plugin consists of a Taverna processor for R scripts and an RShell Session Manager that communicates with the R server. We made the RShell processor highly configurable allowing the user to define multiple inputs and outputs. Also, various data types are supported, such as strings, numeric data and images. To limit data transport between multiple RShell processors, the RShell plugin also supports persistent sessions. Here, we will describe the architecture of RShell and the new features that are introduced in version 1.2, i.e.: i) Support for R up to and including R version 2.9; ii) Support for persistent sessions to limit data transfer; iii) Support for vector graphics output through PDF; iv) Syntax highlighting of the R code; v) Improved usability through fewer port types. Our new RShell processor is backwards compatible with workflows that use older versions of the RShell processor. We demonstrate the value of the RShell processor by a use-case workflow that maps oligonucleotide probes designed with DNA sequence information from Vega onto the Ensembl genome assembly.\ud \ud Conclusion: Our RShell plugin enables Taverna users to employ R scripts within their workflows in a highly configurable way

The core genome of the anaerobic oral pathogenic bacterium Porphyromonas gingivalis

<p>Abstract</p> <p>Background</p> <p>The Gram negative anaerobic bacterium <it>Porphyromonas gingivalis </it>has long been recognized as a causative agent of periodontitis. Periodontitis is a chronic infectious disease of the tooth supporting tissues eventually leading to tooth-loss. Capsular polysaccharide (CPS) of <it>P. gingivalis </it>has been shown to be an important virulence determinant. Seven capsular serotypes have been described. Here, we used micro-array based comparative genomic hybridization analysis (CGH) to analyze a representative of each of the capsular serotypes and a non-encapsulated strain against the highly virulent and sequenced W83 strain. We defined absent calls using <it>Arabidopsis thaliana </it>negative control probes, with the aim to distinguish between aberrations due to mutations and gene gain/loss.</p> <p>Results</p> <p>Our analyses allowed us to call aberrant genes, absent genes and divergent regions in each of the test strains. A conserved core <it>P. gingivalis </it>genome was described, which consists of 80% of the analyzed genes from the sequenced W83 strain. The percentage of aberrant genes between the test strains and control strain W83 was 8.2% to 13.7%. Among the aberrant genes many CPS biosynthesis genes were found. Most other virulence related genes could be found in the conserved core genome. Comparing highly virulent strains with less virulent strains indicates that <it>hmuS, </it>a putative CobN/Mg chelatase involved in heme uptake, may be a more relevant virulence determinant than previously expected. Furthermore, the description of the 39 W83-specific genes could give more insight in why this strain is more virulent than others.</p> <p>Conclusion</p> <p>Analyses of the genetic content of the <it>P. gingivalis </it>capsular serotypes allowed the description of a <it>P. gingivalis </it>core genome. The high resolution data from three types of analysis of triplicate hybridization experiments may explain the higher divergence between <it>P. gingivalis </it>strains than previously recognized.</p

Gene Expression Profiling in a Mouse Model Identifies Fetal Liver- and Placenta-Derived Potential Biomarkers for Down Syndrome Screening

BACKGROUND: As a first step to identify novel potential biomarkers for prenatal Down Syndrome screening, we analyzed gene expression in embryos of wild type mice and the Down Syndrome model Ts1Cje. Since current Down Syndrome screening markers are derived from placenta and fetal liver, these tissues were chosen as target. METHODOLOGY/PRINCIPAL FINDINGS: Placenta and fetal liver at 15.5 days gestation were analyzed by microarray profiling. We confirmed increased expression of genes located at the trisomic chromosomal region. Overall, between the two genotypes more differentially expressed genes were found in fetal liver than in placenta. Furthermore, the fetal liver data are in line with the hematological aberrations found in humans with Down Syndrome as well as Ts1Cje mice. Together, we found 25 targets that are predicted (by Gene Ontology, UniProt, or the Human Plasma Proteome project) to be detectable in human serum. CONCLUSIONS/SIGNIFICANCE: Fetal liver might harbor more promising targets for Down Syndrome screening studies. We expect these new targets will help focus further experimental studies on identifying and validating human maternal serum biomarkers for Down Syndrome screening

RNA isolation for transcriptomics of human and mouse small skin biopsies

<p>Abstract</p> <p>Background</p> <p>Isolation of RNA from skin biopsies presents a challenge, due to the tough nature of skin tissue and a high presence of RNases. As we lacked the dedicated equipment, i.e. homogenizer or bead-beater, needed for the available RNA from skin isolation methods, we adapted and tested our zebrafish single-embryo RNA-isolation protocol for RNA isolation from skin punch biopsies.</p> <p>Findings</p> <p>We tested our new RNA-isolation protocol in two experiments: a large-scale study with 97 human skin samples, and a small study with 16 mouse skin samples. Human skin was sampled with 4.0 mm biopsy punches and for the mouse skin different punch diameter sizes were tested; 1.0, 1.5, 2.0, and 2.5 mm. The average RNA yield in human samples was 1.5 μg with an average RNA quality RIN value of 8.1. For the mouse biopsies, the average RNA yield was 2.4 μg with an average RIN value of 7.5. For 96% of the human biopsies and 100% of the mouse biopsies we obtained enough high-quality RNA. The RNA samples were successfully tested in a transcriptomics analysis using the Affymetrix and Roche NimbleGen platforms.</p> <p>Conclusions</p> <p>Using our new RNA-isolation protocol, we were able to consistently isolate high-quality RNA, which is apt for further transcriptomics analysis. Furthermore, this method is already useable on biopsy material obtained with a punch diameter as small as 1.5 mm.</p