MILANO – custom annotation of microarray results using automatic literature searches

BACKGROUND: High-throughput genomic research tools are becoming standard in the biologist's toolbox. After processing the genomic data with one of the many available statistical algorithms to identify statistically significant genes, these genes need to be further analyzed for biological significance in light of all the existing knowledge. Literature mining – the process of representing literature data in a fashion that is easy to relate to genomic data – is one solution to this problem. RESULTS: We present a web-based tool, MILANO (Microarray Literature-based Annotation), that allows annotation of lists of genes derived from microarray results by user defined terms. Our annotation strategy is based on counting the number of literature co-occurrences of each gene on the list with a user defined term. This strategy allows the customization of the annotation procedure and thus overcomes one of the major limitations of the functional annotations usually provided with microarray results. MILANO expands the gene names to include all their informative synonyms while filtering out gene symbols that are likely to be less informative as literature searching terms. MILANO supports searching two literature databases: GeneRIF and Medline (through PubMed), allowing retrieval of both quick and comprehensive results. We demonstrate MILANO's ability to improve microarray analysis by analyzing a list of 150 genes that were affected by p53 overproduction. This analysis reveals that MILANO enables immediate identification of known p53 target genes on this list and assists in sorting the list into genes known to be involved in p53 related pathways, apoptosis and cell cycle arrest. CONCLUSIONS: MILANO provides a useful tool for the automatic custom annotation of microarray results which is based on all the available literature. MILANO has two major advances over similar tools: the ability to expand gene names to include all their informative synonyms while removing synonyms that are not informative and access to the GeneRIF database which provides short summaries of curated articles relevant to known genes. MILANO is available at

An analysis of intra array repeats: the good, the bad and the non informative

BACKGROUND: On most common microarray platforms many genes are represented by multiple probes. Although this is quite common no one has systematically explored the concordance between probes mapped to the same gene. RESULTS: Here we present an analysis of all the cases of multiple probe sets measuring the same gene on the Affymetrix U133a GeneChip and found that although in the majority of cases both measurements tend to agree there are a significant number of cases in which the two measurements differ from each other. In these cases the measurements can not be simply averaged but rather should be handled individually. CONCLUSION: Our analysis allows us to provide a comprehensive list of the correlation between all pairs of probe sets that are mapped to the same gene and thus allows microarray users to sort out the cases that deserve further analysis. Comparison between the set of highly correlated pairs and the set of pairs that tend to differ from each other reveals potential factors that may affect it

Backup in gene regulatory networks explains differences between binding and knockout results

The complementarity of gene expression and protein–DNA interaction data led to several successful models of biological systems. However, recent studies in multiple species raise doubts about the relationship between these two datasets. These studies show that the overwhelming majority of genes bound by a particular transcription factor (TF) are not affected when that factor is knocked out. Here, we show that this surprising result can be partially explained by considering the broader cellular context in which TFs operate. Factors whose functions are not backed up by redundant paralogs show a fourfold increase in the agreement between their bound targets and the expression levels of those targets. In addition, we show that incorporating protein interaction networks provides physical explanations for knockout effects. New double knockout experiments support our conclusions. Our results highlight the robustness provided by redundant TFs and indicate that in the context of diverse cellular systems, binding is still largely functional

Reconstructing dynamic regulatory maps

Even simple organisms have the ability to respond to internal and external stimuli. This response is carried out by a dynamic network of protein–DNA interactions that allows the specific regulation of genes needed for the response. We have developed a novel computational method that uses an input–output hidden Markov model to model these regulatory networks while taking into account their dynamic nature. Our method works by identifying bifurcation points, places in the time series where the expression of a subset of genes diverges from the rest of the genes. These points are annotated with the transcription factors regulating these transitions resulting in a unified temporal map. Applying our method to study yeast response to stress, we derive dynamic models that are able to recover many of the known aspects of these responses. Predictions made by our method have been experimentally validated leading to new roles for Ino4 and Gcn4 in controlling yeast response to stress. The temporal cascade of factors reveals common pathways and highlights differences between master and secondary factors in the utilization of network motifs and in condition-specific regulation

The 2007 IEEE CEC simulated car racing competition

This paper describes the simulated car racing competition that was arranged as part of the 2007 IEEE Congress on Evolutionary Computation. Both the game that was used as the domain for the competition, the controllers submitted as entries to the competition and its results are presented. With this paper, we hope to provide some insight into the efficacy of various computational intelligence methods on a well-defined game task, as well as an example of one way of running a competition. In the process, we provide a set of reference results for those who wish to use the simplerace game to benchmark their own algorithms. The paper is co-authored by the organizers and participants of the competitio

Glyphosate in post-emergence at Roundup Ready® soybean

Com o surgimento de cultivares de soja RR® muitos agricultores adotaram o uso do glyphosate no manejo de plantas daninhas em pós-emergência nos cerrados. No entanto, as informações sobre o uso desta tecnologia no cultivo de soja na região dos cerrados é muito importante quando se deseja alcançar altos rendimentos. Portanto, o objetivo do trabalho foi avaliar os efeitos da aplicação do glyphosate em pós-emergência nas características agronômicas e na nodulação da soja RR. O ensaio foi realizado em delineamento experimental de blocos casualizados em esquema fatorial (3x4)+1 com três repetições, correspondendo a três doses de glyphosate (960; 1.920 e 2.880 g e.a. ha-1) associada a quatro estádios fenológicos de aplicação (V1; V2; V3 e V1+V4) acrescido de um tratamento adicional sem aplicação de herbicida (testemunha). Avaliou-se o rendimento de grãos, a massa de mil grãos, altura de plantas, fitointoxicação, fechamento foliar, comprimento e massa seca radicular, além da massa seca e viabilidade dos nódulos. Os resultados demonstraram que a dose e o estádio de aplicação do glyphosate não influenciaram a nodulação, as características agronômicas e o rendimento de grãos. O maior percentual de fitointoxicação e menor fechamento foliar foram constatados com a dose de 2.880 g e.a. ha-1 de glyphosate aplicada nos estádios fenológicos V1+V4, sem, contudo comprometer a nodulação e o rendimento de grãos.Generation of RR® soybean genotypes allowed many farmers adopted the use of glyphosate in weed management in post-emergence in cerrado. However, information about the use of this technology on soybean crop growth in “cerrado” region is important in order to obtain desired yields. Thus, the objective of this study was to evaluate the effects of glyphosate application in post-emergence condition over agronomic characteristics and over RR soybean nodulation. The trial was conducted in randomized blocks experimental design in factorial scheme (3x4)+1 with three replications corresponding to three glyphosate doses (960; 1,920 and  2,880 g a.e ha-1) associated to four application phenological stages of application (V1, V2, V3 and V1+V4) plus a treatment without herbicide application (check). The following characteristics were evaluated: grain yield, mass of a thousand grain, plant height, phytointoxication, leaf closing, root dry matter and length, besides dry mass and nodules viability. Results revealed that the dose and stage of glyphosate application did not affect the nodulation, agronomic characteristics and grain yield. The highest percentage of phytointoxication and lowest leaf closing were observed at 2,880 g a.e. ha-1 glyphosate dose sprayed on phenological stages V1+V4, without affecting nodulation and grain yield

Metal-Organic Frameworks for Sensing Applications in the Gas Phase

Several metal-organic framework (MOF) materials were under investigated to test their applicability as sensor materials for impedimetric gas sensors. The materials were tested in a temperature range of 120 °C - 240 °C with varying concentrations of O2, CO2, C3H8, NO, H2, ethanol and methanol in the gas atmosphere and under different test gas humidity conditions. Different sensor configurations were studied in a frequency range of 1 Hz -1 MHz and time-continuous measurements were performed at 1 Hz. The materials did not show any impedance response to O2, CO2, C3H8, NO, or H2 in the gas atmospheres, although for some materials a significant impedance decrease was induced by a change of the ethanol or methanol concentration in the gas phase. Moreover, pronounced promising and reversible changes in the electric properties of a special MOF material were monitored under varying humidity, with a linear response curve at 120 °C. Further investigations were carried out with differently doped MOF materials of this class, to evaluate the influence of special dopants on the sensor effect

A probabilistic generative model for GO enrichment analysis

The Gene Ontology (GO) is extensively used to analyze all types of high-throughput experiments. However, researchers still face several challenges when using GO and other functional annotation databases. One problem is the large number of multiple hypotheses that are being tested for each study. In addition, categories often overlap with both direct parents/descendents and other distant categories in the hierarchical structure. This makes it hard to determine if the identified significant categories represent different functional outcomes or rather a redundant view of the same biological processes. To overcome these problems we developed a generative probabilistic model which identifies a (small) subset of categories that, together, explain the selected gene set. Our model accommodates noise and errors in the selected gene set and GO. Using controlled GO data our method correctly recovered most of the selected categories, leading to dramatic improvements over current methods for GO analysis. When used with microarray expression data and ChIP-chip data from yeast and human our method was able to correctly identify both general and specific enriched categories which were overlooked by other methods