Results from the simulated data for matched DTW.

<p>Both panels show the results of the different levels of noise (low noise at top, high noise at bottom) and different amounts of time shift between the sequences (0 shift on left, 9 units shift on right). All time series were 20 units long. Panel A shows the percentage of elements which were (correctly) recognised as matches using an FDR of 5% and the matched DTW function. Panel B shows the percentage of matches where the matched pattern exactly matched the intended differences in the time courses.</p

Comparing DTW4Omics selected genes with correlation analysis.

<p>G2 is not shown as it gave no correlated genes under any conditions and with H₂O₂ no genes were found with correlation so these rows are omitted.</p

Output plots.

<p>A – showing gene and endpoint before and after warping. B – Histogram of real and permuted distances obtained through DTW. C – Histogram of the permuted distances obtained for the most significant gene, with the real distance marked by an X.</p

DTW4Omics: Comparing Patterns in Biological Time Series

<div><p>When studying time courses of biological measurements and comparing these to other measurements eg. gene expression and phenotypic endpoints, the analysis is complicated by the fact that although the associated elements may show the same patterns of behaviour, the changes do not occur simultaneously. In these cases standard correlation-based measures of similarity will fail to find significant associations. Dynamic time warping (DTW) is a technique which can be used in these situations to find the optimal match between two time courses, which may then be assessed for its significance. We implement DTW4Omics, a tool for performing DTW in R. This tool extends existing R scripts for DTW making them applicable for “omics” datasets where thousands entities may need to be compared with a range of markers and endpoints. It includes facilities to estimate the significance of the matches between the supplied data, and provides a set of plots to enable the user to easily visualise the output. We illustrate the utility of this approach using a dataset linking the exposure of the colon carcinoma Caco-2 cell line to oxidative stress by hydrogen peroxide (H₂O₂) and menadione across 9 timepoints and show that on average 85% of the genes found are not obtained from a standard correlation analysis between the genes and the measured phenotypic endpoints. We then show that when we analyse the genes identified by DTW4Omics as significantly associated with a marker for oxidative DNA damage (8-oxodG), through over-representation, an Oxidative Stress pathway is identified as the most over-represented pathway demonstrating that the genes found by DTW4Omics are biologically relevant. In contrast, when the positively correlated genes were similarly analysed, no pathways were found. The tool is implemented as an R Package and is available, along with a user guide from <a href="http://web.tgx.unimaas.nl/svn/public/dtw/" target="_blank">http://web.tgx.unimaas.nl/svn/public/dtw/</a>.</p></div

Oxidative Stress Mechanisms Do Not Discriminate between Genotoxic and Nongenotoxic Liver Carcinogens

It is widely accepted that in chemical carcinogenesis different modes-of-action exist, e.g., genotoxic (GTX) versus nongenotoxic (NGTX) carcinogenesis. In this context, it has been suggested that oxidative stress response pathways are typical for NGTX carcinogenesis. To evaluate this, we examined oxidative stress-related changes in gene expression, cell cycle distribution, and (oxidative) DNA damage in human hepatoma cells (HepG2) exposed to GTX-, NGTX-, and noncarcinogens, at multiple time points (4–8–24–48–72 h). Two GTX (azathriopine (AZA) and furan) and two NGTX (tetradecanoyl-phorbol-acetate, (TPA) and tetrachloroethylene (TCE)) carcinogens as well as two noncarcinogens (diazinon (DZN, d-mannitol (Dman)) were selected, while per class one compound was deemed to induce oxidative stress and the other not. Oxidative stressors AZA, TPA, and DZN induced a 10-fold higher number of gene expression changes over time compared to those of furan, TCE, or Dman treatment. Genes commonly expressed among AZA, TPA, and DZN were specifically involved in oxidative stress, DNA damage, and immune responses. However, differences in gene expression between GTX and NGTX carcinogens did not correlate to oxidative stress or DNA damage but could instead be assigned to compound-specific characteristics. This conclusion was underlined by results from functional readouts on ROS formation and (oxidative) DNA damage. Therefore, oxidative stress may represent the underlying cause for increased risk of liver toxicity and even carcinogenesis; however, it does not discriminate between GTX and NGTX carcinogens

Automatic vs. manual curation of a multi-source chemical dictionary: the impact on text mining

<p>Previously, we developed a combined dictionary dubbed Chemlist for the identification of small molecules and drugs in text based on a number of publicly available databases and tested it on an annotated corpus. To achieve an acceptable recall and precision we used a number of automatic and semi-automatic processing steps together with disambiguation rules. However, it remained to be investigated which impact an extensive manual curation of a multi-source chemical dictionary would have on chemical term identification in text. ChemSpider is a chemical database that has undergone extensive manual curation aimed at establishing valid chemical name-to-structure relationships.</p

Additional file 1: Table S1. of Newborn sex-specific transcriptome signatures and gestational exposure to fine particles: findings from the ENVIRONAGE birth cohort

Descriptive characteristics of the ENVIRONAGE birth cohort participants compared to all births in Flanders (Northern part of Belgium) from 2002 to 2011. Table S2. Significant differentially expressed genes by long-term PM2.5 exposure in cord blood of girls and boys. Table S3. Top ten significant genes in cord blood of newborn boys and girls associated with long-term PM2.5 exposure. Table S4. Significant differentially expressed genes by short-term PM2.5 exposure in cord blood of girls and boys. Table S5. Top ten significant genes in cord blood of newborn boys and girls associated with short-term PM2.5 exposure. Figure S1. Histogram representing the percentage of genes with p-value <0.05 for each variable included in the model. Figure S2. Principal component analysis plot showing the transcriptomic response to long- and short-term PM2.5 exposure in (A, C) girls and (B, D) boys. Figure S3. Pathways modulated by long-term PM2.5exposure for girls (A) and boys (B) resulting from GSEA. Figure S4. Pathways modulated by short-term PM2.5 exposure for girls (A) and boys (B) resulting from GSEA. (DOCX 1749 kb

Additional file 1: Figure S1. of Transcriptome-wide analyses indicate mitochondrial responses to particulate air pollution exposure

Volcano plots for short-term exposure (A: women, B: men) and for medium-term PM10 exposure (C: women, D: men). (DOCX 492 kb