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e16032 Background: Response and resistance to AA may involve AR splice variants (ARVs) and other biomarkers. We developed a CTC AR axis panel and explored the association between biomarkers and res...

More than 50% of the lycaenid butterflies have an ant-associated lifestyle (myrmecophily) which may vary from coexistence to specific mutualistic or even parasitic interactions. Ant-related host-plant selection and oviposition has been observed in some myrmecophilous lycaenids. Therefore, it is remarkable that there is no evidence for this behaviour in the highly specialized, obligate myrmecophilous butterflies of the genus Maculinea. In contrast with previous findings, our results provide evidence for ant-related oviposition patterns in Maculinea alcon in relation to the distribution of specific host-ant nests (i.e. Myrmica ruginodis) based on repeated egg counts during the flight period in two populations. We also show that ant-related oviposition can be counterbalanced by intraspecific competition and oviposition deterrency when host plants already carry several eggs. Therefore, the absence of a correlation between egg load and the presence of host-ant nests at the end of the flight period should be interpreted carefully Whether ovipositional cues are obtained either directly (from ants or their nests) or indirectly (from vegetation structure), and whether alternative explanations based on the phenology and growth form of host plants are possible, is discussed.

In recent years, a lot of attention is placed on the selection and evaluation of biomarkers in microarray experiments. Two sets of biomarkers are of importance, namely therapeutic and prognostic. The therapeutic biomarkers would give us information on the response of the genes to treatment in relation to the response of the clinical outcome to the same treatments, whereas the prognostic biomarkers enable us to predict the clinical outcome irrespective of treatments and other confounding factors. In this paper, we use different methods that allow for both linear and non-linear associations to select prognostic markers for depression, the response. ispartof: International Journal of Data Mining and Bioinformatics vol:8 issue:1 pages:24-41 ispartof: location:Switzerland status: published

Microarrays enable the expression levels of thousands of genes to be measured simultaneously. However, only a small fraction of these genes are expected to be expressed under different experimental conditions. Nowadays, filtering has been introduced as a step in the microarray preprocessing pipeline. Gene filtering aims at reducing the dimensionality of data by filtering redundant features prior to the actual statistical analysis. Previous filtering methods focus on the Affymetrix platform and can not be easily ported to the Illumina platform. As such, we developed a filtering method for Illumina bead arrays. We developed an R package, beadarrayFilter, to implement the latter method. In this paper, the main functions in the package are highlighted and using many examples, we illustrate how beadarrayFilter can be used to filter bead arrays. We acknowledge the support from IAP research network grant nr. P6/03 of the Belgian government (Belgian Science Policy), SymBioSys, the Katholieke Universiteit Leuven center of Excellence on Computational Systems Biology, (EF/05/007), and Bioframe of the institute for the Promotion of Innovation by Science and technology in Flanders (IWT: 060045/KUL-BIO-M$S-PLANT).

Earlier identification of potentially efficacious treatments in early development trials requires on-treatment response markers. We hypothesized that mean week 12 or 24 HBsAg decline could be a useful marker for subsequent off-treatment sustained HBsAg clearance at the treatment arm level in HBV trials. We used individual patient data from the studies HBV 9901 (peginterferon [PEG-IFN] versus PEG-IFN+lamivudine for HBeAg-positive CHB), PARC (PEG-IFN±ribavirin for HBeAg-negative CHB) and published data from 0149 (PEG-IFN±tenofovir for HBeAg-positive and HBeAg-negative CHB) and LIRA-B (PEG-IFN for HBeAg-positive CHB) to define the relationship between mean week HBsAg decline and HBsAg loss at 6 months post-treatment. A within-study comparison of HBsAg decline at weeks 12 and 24 between patients with or without HBsAg clearance was used to make projections beyond the observed HBsAg data. Across trials, a more pronounced mean HBsAg decline at week 24 was associated with higher rates of subsequent HBsAg loss. Mean HBsAg decline data at week 24 for patients with or without HBsAg clearance from HBV 9901 (4.3 vs 0.5), PARC (4.8 vs 0.3) and 0149 (PEG-IFN+TDF arm; 4.6 vs 0.6) were used to extrapolate this relationship beyond observed rates of HBsAg. An additional mean 1 log10 decline at week 24 versus a comparator arm is expected to translate into a 20%–30% increase in subsequent HBsAg loss during off-treatment follow-up. Observations were similar for week 12 data, but the relationship was less strong. Mean week 24 HBsAg decline predicts subsequent HBsAg loss and could be a valuable and useful early endpoint in HBV-treatment trials.

Background Deep-sequencing allows for an in-depth characterization of sequence variation in complex populations. However, technology associated errors may impede a powerful assessment of low-frequency mutations. Fortunately, base calls are complemented with quality scores which are derived from a quadruplet of intensities, one channel for each nucleotide type for Illumina sequencing. The highest intensity of the four channels determines the base that is called. Mismatch bases can often be corrected by the second best base, i.e. the base with the second highest intensity in the quadruplet. A virus variant model-based clustering method, ViVaMBC, is presented that explores quality scores and second best base calls for identifying and quantifying viral variants. ViVaMBC is optimized to call variants at the codon level (nucleotide triplets) which enables immediate biological interpretation of the variants with respect to their antiviral drug responses. Results Using mixtures of HCV plasmids we show that our method accurately estimates frequencies down to 0.5%. The estimates are unbiased when average coverages of 25,000 are reached. A comparison with the SNP-callers V-Phaser2, ShoRAH, and LoFreq shows that ViVaMBC has a superb sensitivity and specificity for variants with frequencies above 0.4%. Unlike the competitors, ViVaMBC reports a higher number of false-positive findings with frequencies below 0.4% which might partially originate from picking up artificial variants introduced by errors in the sample and library preparation step. Conclusions ViVaMBC is the first method to call viral variants directly at the codon level. The strength of the approach lies in modeling the error probabilities based on the quality scores. Although the use of second best base calls appeared very promising in our data exploration phase, their utility was limited. They provided a slight increase in sensitivity, which however does not warrant the additional computational cost of running the offline base caller. Apparently a lot of information is already contained in the quality scores enabling the model based clustering procedure to adjust the majority of the sequencing errors. Overall the sensitivity of ViVaMBC is such that technical constraints like PCR errors start to form the bottleneck for low frequency variant detection. Electronic supplementary material The online version of this article (doi:10.1186/s12859-015-0458-7) contains supplementary material, which is available to authorized users.

The objective was to perform a dioxin body burden estimate based on a probabilistic intake assessment of PCDDs, PCDFs and dioxin-like PCBs because of the so-called 1999 'Belgian dioxin incident'. Monte Carlo simulation techniques were used to combine detailed 7-day food intake data on the individual level from a sample of 14-18-year-old adolescents with 'background' and 'incident-related' food contamination data. In background conditions, 3% of the adolescents had an intake

Background: Integrating transcriptomic experiments within drug development is increasingly advocated for the early detection of toxicity. This is partly to reduce costs related to drug failures in the late, and expensive phases of clinical trials. Such an approach has proven useful both in the study of toxicology and carcinogenicity. However, general lack of translation of in vitro findings to in vivo systems remains one of the bottle necks in drug development. This paper proposes a method for identifying disconnected genes between in vitro and in vivo toxicogenomic rat experiments. The analytical framework is based on the joint modeling of dose-dependent in vitro and in vivo data using a fractional polynomial framework and biclustering algorithm. Results: Most disconnected genes identified belonged to known pathways, such as drug metabolism and oxidative stress due to reactive metabolites, bilirubin increase, glutathion depletion and phospholipidosis. We also identified compounds that were likely to induce disconnect in gene expression between in vitro and in vivo toxicogenomic rat experiments. These compounds include: sulindac and diclofenac (both linked to liver damage), naphtyl isothiocyanate (linked to hepatoxocity), indomethacin and naproxen (linked to gastrointestinal problem and damage of intestines). Conclusion: The results confirmed that there are important discrepancies between in vitro and in vivo toxicogenomic experiments. However, the contribution of this paper is to provide a tool to identify genes that are disconnected between the two systems. Pathway analysis of disconnected genes may improve our understanding of uncertainties in the mechanism of actions of drug candidates in humans, especially concerning the early detection of toxicity. Martin Otava and Ziv Shkedy gratefully acknowledge the support from the IAP Research Network P7/06 of the Belgian State (Belgian Science Policy). Martin Otava gratefully acknowledge the financial support of the Research Project BOF11DOC09 of Hasselt University. Authors are grateful to Vladimir Nerandzic for helpful advice about biological background and to Suzanne Boyd (WRIHW, Durham University, UK) and Prof James Mason (DCTU, Durham University, UK) for helpful language corrections and editing. The computational resources and services used for the simulation studies were provided by the VSC (Flemish Supercomputer Center), funded by the Hercules Foundation and the Flemish Government - department EWI.