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Abstract Background A complete gene-expression microarray should preferably detect all genomic sequences that can be expressed as RNA in an organism, i.e. the transcriptome. However, our knowledge of a transcriptome of any organism still is incomplete and transcriptome information is continuously being updated. Here, we present a strategy to integrate heterogeneous sequence information that can be used as input for an up-to-date microarray design. Findings Our algorithm consists of four steps. In the first step transcripts from different resources are grouped into Transcription Clusters (TCs) by looking at the similarity of all transcripts. TCs are groups of transcripts with a similar length. If a transcript is much smaller than a TC to which it is highly similar, it will be annotated as a subsequence of that TC and is used for probe design only if the probe designed for the TC does not query the subsequence. Secondly, all TCs are mapped to a genome assembly and gene information is added to the design. Thirdly TC members are ranked according to their trustworthiness and the most reliable sequence is used for the probe design. The last step is the actual array design. We have used this strategy to build an up-to-date zebrafish microarray. Conclusions With our strategy and the software developed, it is possible to use a set of heterogeneous transcript resources for microarray design, reduce the number of candidate target sequences on which the design is based and reduce redundancy. By changing the parameters in the procedure it is possible to control the similarity within the TCs and thus the amount of candidate sequences for the design. The annotation of the microarray is carried out simultaneously with the design.

Abstract Background 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. Findings 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. Conclusion 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.

Abstract 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.

Background: A complete gene-expression microarray should preferably detect all genomic sequences that can be expressed as RNA in an organism, i.e. the transcriptome. However, our knowledge of a transcriptome of any organism still is incomplete and transcriptome information is continuously being updated. Here, we present a strategy to integrate heterogeneous sequence information that can be used as input for an up-to-date microarray design. Findings: Our algorithm consists of four steps. In the first step transcripts from different resources are grouped into Transcription Clusters (TCs) by looking at the similarity of all transcripts. TCs are groups of transcripts with a similar length. If a transcript is much smaller than a TC to which it is highly similar, it will be annotated as a subsequence of that TC and is used for probe design only if the probe designed for the TC does not query the subsequence. Secondly, all TCs are mapped to a genome assembly and gene information is added to the design. Thirdly TC members are ranked according to their trustworthiness and the most reliable sequence is used for the probe design. The last step is the actual array design. We have used this strategy to build an up-to-date zebrafish microarray. Conclusions: With our strategy and the software developed, it is possible to use a set of heterogeneous transcript resources for microarray design, reduce the number of candidate target sequences on which the design is based and reduce redundancy. By changing the parameters in the procedure it is possible to control the similarity within the TCs and thus the amount of candidate sequences for the design. The annotation of the microarray is carried out simultaneously with the design. [ABSTRACT FROM AUTHOR]

Beta-lactam antibiotics are the most applied antimicrobials in human and veterinarian health care. Hence, beta-lactam resistance is a major health problem. Gene amplification of AmpC beta-lactamase is a main contributor to de novo β-lactam resistance in Escherichia coli . However, the time course of amplification and the accompanying DNA mutations are unclear. Here, we study the progression of ampC amplification and ampC promoter mutations during the evolution of resistance induced by stepwise increasing amoxicillin concentrations. AmpC promoter mutations occurred by day 2, while the approximately eight-fold amplification occurred after more than 6 days of amoxicillin exposure. The combination of the amplification and the promoter mutations increased the ampC mRNA level by an average factor of 200 after 22 days. An IS 1 insertion is identified in the amplification junction after resistance induction in the wild type (WT) and the ampC gene complementation strain (CompA), but not in ∆ ampC , suggesting that the amplification depends on mobile genetic element transposition. In order to elucidate the correlation between gene mutations and ampC amplification, the DNA mutations acquired during resistance evolution by the WT, ∆ ampC , and CompA were analyzed. Compared to evolved ∆ ampC , several resistance-causing mutations are absent in evolved WT, while more mutations accumulated in stress response. The amoxicillin-resistant ∆ ampC did not show amplification of the fragment around the original ampC position but exhibited a large duplication or triplication at another position, suggesting the essential role of the duplicated genes in resistance development.IMPORTANCEAmoxicillin is the most used antimicrobial against bacterial infections. DNA fragments containing ampC are amplified upon prolonged and stepwise increasing exposure to amoxicillin, causing resistance. These ampC -containing fragments have been identified in extended-spectrum beta-lactamase plasmids, which are considered the main cause of beta-lactam resistance. In this study, we document the time course of two important factors for ampC transcription enhancement, ampC amplification and ampC promoter mutations, during de novo amoxicillin resistance evolution. We propose that the transposon IS 1 contributes to the amplification ampC region, that the sigma factor 70 regulates ampC overexpression, and that these combined form the backbone of a putative mechanism for ampC amplification.