Molecular basis for porcine parvovirus detection in dead fetuses

Reproductive failures are still common grounds for complaint by commercial swine producers. Porcine parvovirus (PPV) is associated with different clinical reproductive signs. The aim of the present study was to investigate PPV fetal infection at swine farms having ongoing reproductive performance problems. The presence of virus in fetal tissues was determined by nested-polymerase chain reaction assay directed to the conserved NS1 gene of PPV in aborted fetuses, mummies and stillborns. Fetuses show a high frequency of PPV infection (96.4%; n = 28). In 60.7% of the fetuses, PPV were detected in all tissue samples (lung, heart, thymus, kidney, and spleen). Viral infection differed among fetal tissues, with a higher frequency in the lung and heart (p < 0.05). Fetuses with up to 99 days of gestational age and from younger sows showed a higher frequency of PPV (p < 0.05). No significant difference in the presence of PPV was detected among the three clinical presentations. The results suggest that PPV remains an important pathogenic agent associated with porcine fetal death.

Pipeline for macro- and microarray analyses

The pipeline for macro- and microarray analyses (PMmA) is a set of scripts with a web interface developed to analyze DNA array data generated by array image quantification software. PMmA is designed for use with single- or double-color array data and to work as a pipeline in five classes (data format, normalization, data analysis, clustering, and array maps). It can also be used as a plugin in the BioArray Software Environment, an open-source database for array analysis, or used in a local version of the web service. All scripts in PMmA were developed in the PERL programming language and statistical analysis functions were implemented in the R statistical language. Consequently, our package is a platform-independent software. Our algorithms can correctly select almost 90 percent of the differentially expressed genes, showing a superior performance compared to other methods of analysis. The pipeline software has been applied to 1536 expressed sequence tags macroarray public data of sugarcane exposed to cold for 3 to 48 h. PMmA identified thirty cold-responsive genes previously unidentified in this public dataset. Fourteen genes were up-regulated, two had a variable expression and the other fourteen were down-regulated in the treatments. These new findings certainly were a consequence of using a superior statistical analysis approach, since the original study did not take into account the dependence of data variability on the average signal intensity of each gene. The web interface, supplementary information, and the package source code are available, free, to non-commercial users at http://ipe.cbmeg.unicamp.br/pub/PMmA.

Recent advances in gene expression data clustering: a case study with comparative results

Several advanced techniques have been proposed for data clustering and many of them have been applied to gene expression data, with partial success. The high dimensionality and the multitude of admissible perspectives for data analysis of gene expression require additional computational resources, such as hierarchical structures and dynamic allocation of resources. We present an immune-inspired hierarchical clustering device, called hierarchical artificial immune network (HaiNet), especially devoted to the analysis of gene expression data. This technique was applied to a newly generated data set, involving maize plants exposed to different aluminum concentrations. The performance of the algorithm was compared with that of a self-organizing map, which is commonly adopted to deal with gene expression data sets. More consistent and informative results were obtained with HaiNet.