A new AluI satellite DNA in the root-knot nematode Meloidogyne fallax: relationships with satellites from the sympatric species M. hapla and M. chitwoodi

A highly abundant satellite DNA comprising 20% of the Meloidogyne fallax (Nematoda, Tylenchida) genome was cloned and sequenced. The satellite monomer is 173 bp long and has a high A + T content of 72.3%, with frequent runs of A's and T's. The sequence variability of the monomers is 2.7%, mainly due to random distribution of single-point mutations. A search for evidence of internal repeated subunits in the monomer sequence revealed a 6-bp motif (AAATTT) for which five degenerated repeats, differing by just a single base pair, could be identified. Pairwise comparison of the M. fallax satellite with those from the sympatric species Meloidogyne chitwoodi and Meloidogyne hapla revealed a high sequence similarity (68.39%) with one satellite DNA subfamily in M. chitwoodi, which indicated an unexpected close relationship between them. Given the high copy number and the extreme sequence homogeneity among monomeric units, it may be assumed that the satellite DNA of M. fallax could have evolved through some recent and extensive amplification burst in the nematode genome. In this case, its relatively short life would not yet have allowed the accumulation of random mutations in independent amplified repeats. Considering the morphological resemblance between the two species and their ability to produce interspecific fertile hybrids under controlled conditions, these results indicate that M. fallax may share a common ancestor with M. chitwoodi, from which it could have diverged recently. All these data suggest that M. fallax could be the result of a recent speciation process and show that Meloidogyne satellite DNAs may be of interest to resolve phylogenetic relationships among closely related species from this genus.      

Can Zipf's law be adapted to normalize microarrays?

Background  

Normalization is the process of removing non-biological sources of variation between array experiments. Recent investigations of data in gene expression databases for varying organisms and tissues have shown that the majority of expressed genes exhibit a power-law distribution with an exponent close to -1 (i.e. obey Zipf's law). Based on the observation that our single channel and two channel microarray data sets also followed a power-law distribution, we were motivated to develop a normalization method based on this law, and examine how it compares with existing published techniques. A computationally simple and intuitively appealing technique based on this observation is presented.