MOTIVATION: The automatic alignment of rRNA sequences can reproduce manual expert alignments with high, but not perfect, fidelity. We examine the use of empirical methods for the identification of regions of an alignment of a new sequence with an existing large alignment which can confidently be predicted to be correctly aligned. RESULTS: We show how to use a simple jack-knife procedure to derive an estimate of the reliability that is to be expected at each position of a large alignment of eukaryotic rRNA sequences. These reliabilities are then improved using measures that are specific to the input sequence. Regions where the sequence-specific reliability method performs particularly well are identified and seen to correspond with elements in the structure of the rRNA molecules that vary between species in the alignment. We also compare these reliability measures to an algorithmic alignment stability measure. AVAILABILITY: The software is available free of charge by sending an e-mail message to [email protected]. CONTACT: [email protected]

MOTIVATION: Large alignments of ribosomal RNA sequences are maintained at various sites. New sequences are added to these alignments using a combination of manual and automatic methods. We examine the use of profile alignment methods for rRNA alignment and try to optimize the choice of parameters and sequence weights. RESULTS: Using a large alignment of eukaryotic SSU rRNA sequences as a test case, we empirically compared the performance of various sequence weighting schemes over a range of gap penalties. We developed a new weighting scheme which gives most weight to the sequences in the profile that are most similar to the new sequence. We show that it gives the most accurate alignments when combined with a more traditional sequence weighting scheme. AVAILABILITY: The source code of all software is freely available by anonymous ftp from chah.ucc.ie in the directory /home/ftp/pub/emmet,in the compressed file PRNAA.tar: CONTACT: [email protected], [email protected]