Assessment of microbial community structure changes by amplified ribosomal DNA restriction analysis (ARDRA)

Amplified ribosomal DNA restriction analysis (ARDRA) is a simple method based on restriction endonuclease digestion of the amplified bacterial 16S rDNA. In this study we have evaluated the suitability of this method to detect differences in activated sludge bacterial communities fed on domestic or industrial wastewater, and subject to different operational conditions. The ability of ARDRA to detect these differences has been tested in modified Ludzack-Ettinger (MLE) configurations. Samples from three activated sludge wastewater treatment plants (WWTPs) with the MLE configuration were collected for both oxic and anoxic reactors, and ARDRA patterns using double enzyme digestions AluI+MspI were obtained. A matrix of Dice similarity coefficients was calculated and used to compare these restriction patterns. Differences in the community structure due to influent characteristics and temperature could be observed, but not between the oxic and anoxic reactors of each of the three MLE configurations. Other possible applications of ARDRA for detecting and monitoring changes in activated sludge systems are also discussed

A polymorphic DNA probe from chromosome 7 (7q22)

Source/Description: p6a-l is a O.9 kb HindIII/BamHl genomic fragment subclone or cosmic cNX.6a in pUC13. cNX.6a was isolated from a non-methylated enriched library from the CMGT cell line Cll (1,2).

An assessment of gene prediction accuracy in large DNA sequences

One of the first useful products from the human genome will be a set of predicted genes. Besides its intrinsic scientific interest, the accuracy and completeness of this data set is of considerable importance for human health and medicine. Though progress has been made on computational gene identification in terms of both methods and accuracy evaluation measures, most of the sequence sets in which the programs are tested are short genomic sequences, and there is concern that these accuracy measures may not extrapolate well to larger, more challenging data sets. Given the absence of experimentally verified large genomic data sets, we constructed a semiartificial test set comprising a number of short single-gene genomic sequences with randomly generated intergenic regions. This test set, which should still present an easier problem than real human genomic sequence, mimics the approximately 200kb long BACs being sequenced. In our experiments with these longer genomic sequences, the accuracy of GENSCAN, one of the most accurate ab initio gene prediction programs, dropped significantly, although its sensitivity remained high. Conversely, the accuracy of similarity-based programs, such as GENEWISE, PROCRUSTES, and BLASTX was not affected significantly by the presence of random intergenic sequence, but depended on the strength of the similarity to the protein homolog. As expected, the accuracy dropped if the models were built using more distant homologs, and we were able to quantitatively estimate this decline. However, the specificities of these techniques are still rather good even when the similarity is weak, which is a desirable characteristic for driving expensive follow-up experiments. Our experiments suggest that though gene prediction will improve with every new protein that is discovered and through improvements in the current set of tools, we still have a long way to go before we can decipher the precise exonic structure of every gene in the human genome using purely computational methodology.