Maturation of the tRNA 3 terminus is a complicated process in bacteria. Usually, it is initiated by an endonucleolytic cleavage carried out by RNase E and Z in different bacteria. In Escherichia coli, RNase E cleaves AU-rich sequences downstream of tRNA, producing processing intermediates with a few extra residues at the 3 end; these are then removed by exoribonuclease trimming to generate the mature 3 end. Here we show that essentially all E. coli tRNA precursors contain a potential RNase E cleavage site, the AU-rich sequence element (AUE), in the 3 trailer. This suggests that RNase E cleavage and exonucleolytic trimming is a general pathway for tRNA maturation in this organism. Remarkably, the AUE immediately downstream of each tRNA is selectively conserved in bacteria having RNase E and tRNA-specific exoribonucleases, suggesting that this pathway for tRNA processing is also commonly used in these bacteria. Two types of RNase E-like proteins are identified in actinobacteria and the ␣-subdivision of proteobacteria. The tRNA 3 proximal AUE is conserved in bacteria with only one type of E-like protein.Selective conservation of the AUE is usually not observed in bacteria without RNase E. These results demonstrate a novel example of co-evolution of RNA sequences with processing activities.

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The performance of a classification model is invariably affected by the characteristics of the measurement data it is built upon. If the quality of the data is generally poor, then the classification model will demonstrate poor performance. The detection and removal of noisy instances will improve quality of the data, and consequently, the performance of the classification model. We investigate a noise handling technique that attempts to improve the quality of datasets for classification purposes by eliminating instances that are likely to be noise. Our approach uses twenty five different classification techniques to create an ensemble filter for eliminating likely noise. The basic assumption is that if a given majority of classifiers in the ensemble misclassify an instance, then it is likely to be a noisy instance. Using a relatively large number of base-level classifiers in the ensemble filter facilitates in achieving the desired level of noise removal conservativeness with several possible levels of filtering. It also provides a higher degree of confidence in the noise elimination procedure as the results are less likely to get influenced by (possibly) inappropriate learning bias of a few algorithms with twenty five base-level classifiers than with relatively smaller number of base-level classifiers. Empirical case studies of two high assurance software projects demonstrates the effectiveness of our noise elimination approach by the significant improvement achieved in classification accuracies at various levels of noise filtering.