Isolation and characterization of 16 microsatellite loci in the mountain pine beetle, Dendroctonus ponderosae Hopkins (Coleoptera: Curculionidae: Scolytinae).

We isolated 16 polymorphic microsatellite loci in the mountain pine beetle (Dendroctonus ponderosae Hopkins) and developed conditions for amplifying these markers in four multiplex reactions. Three to 14 alleles were detected per locus across two sampled populations. Observed and expected heterozygosities ranged from 0.000 to 0.902 and from 0.100 to 0.830, respectively. Three loci deviated from Hardy-Weinberg equilibrium in one sampled population. One of these loci may be sex linked. These markers will be useful in the study of population structure in this important pest species.

A robust neural networks approach for spatial and intensity-dependent normalization of cDNA microarray data.

MOTIVATION: Microarray experiments are affected by numerous sources of non-biological variation that contribute systematic bias to the resulting data. In a dual-label (two-color) cDNA or long-oligonucleotide microarray, these systematic biases are often manifested as an imbalance of measured fluorescent intensities corresponding to Sample A versus those corresponding to Sample B. Systematic biases also affect between-slide comparisons. Making effective corrections for these systematic biases is a requisite for detecting the underlying biological variation between samples. Effective data normalization is therefore an essential step in the confident identification of biologically relevant differences in gene expression profiles. Several normalization methods for the correction of systemic bias have been described. While many of these methods have addressed intensity-dependent bias, few have addressed both intensity-dependent and spatiality-dependent bias. RESULTS: We present a neural network-based normalization method for correcting the intensity- and spatiality-dependent bias in cDNA microarray datasets. In this normalization method, the dependence of the log-intensity ratio (M) on the average log-intensity (A) as well as on the spatial coordinates (X,Y) of spots is approximated with a feed-forward neural network function. Resistance to outliers is provided by assigning weights to each spot based on how distant their M values is from the median over the spots whose A values are similar, as well as by using pseudospatial coordinates instead of spot row and column indices. A comparison of the robust neural network method with other published methods demonstrates its potential in reducing both intensity-dependent bias and spatial-dependent bias, which translates to more reliable identification of truly regulated genes.