MOTIVATION: How to find motifs from genome-scale functional sequences, such as all the promoters in a genome, is a challenging problem. Word-based methods count the occurrences of oligomers to detect excessively represented ones. This approach is known to be fast and accurate compared with other methods. However, two problems have hampered the application of such methods to large-scale data. One is the computational cost necessary for clustering similar oligomers, and the other is the bias in the frequency of fixed-length oligomers, which complicates the detection of significant words. RESULTS: We introduce a method that uses a DNA Gray code and equiprobable oligomers, which solve the clustering problem and the oligomer bias, respectively. Our method can analyze 18 000 sequences of ~1 kbp long in 30 s. We also show that the accuracy of our method is superior to that of a leading method, especially for large-scale data and small fractions of motif-containing sequences. AVAILABILITY: The online and stand-alone versions of the application, named Hegma, are available at our website: http://www.genome.ist.i.kyoto-u.ac.jp/~ichinose/hegma/ CONTACT: [email protected]; [email protected]

MOTIVATION: The hidden Markov model (HMM) is a valuable technique for gene-finding, especially because its flexibility enables the inclusion of various sequence features. Recent programs for bacterial gene-finding include the information of ribosomal binding site (RBS) to improve the recognition accuracy of the start codon, using this feature. We report here our attempt to extend the model into the total transcriptional unit, enabling the prediction of operon structures. RESULTS: First, we improved the prediction accuracy of coding sequences (CDSs) by employing the models of 'typical', 'atypical' and 'negative (false-positive)' classes as well as the models of RBS and its downstream spacer. The sensitivity of exactly predicting the 204 experimentally confirmed CDSs reached 90.2% in an objective test. Based on the prediction result of CDSs, the positions of the promoters and terminators were predicted. Our model could exactly recognize 60% of 390 known transcriptional units. Thus, the accuracy and significance of this prediction problem is far from trivial. We would like to propose this problem as an open theme in bioinformatics because the ongoing or planned post-sequencing projects will produce much data for future improvements.

MOTIVATION: Automatic extraction of motifs that occur frequently on a set of unaligned DNA sequences is useful for predicting the binding sites of unknown transcription factors. Several programs for this purpose have been released. However, in our opinion, they are not practical enough to be applied to a large number of upstream sequences. RESULTS: We propose a new program called YEBIS (Yet another Environment for the analysis of BIopolymer Sequences) which is capable of extracting a set of motifs, without any a priori knowledge, from a number of functionally related DNA sequences. Using the hidden Markov model, these motifs are represented in a more general form than other conventional methods, such as the weight matrix method. When applied to several sets of benchmark data, it was found that YEBIS had comparable capability to the existing methods, but was much faster. Moreover, it could extract all known motifs from the LTR sequences (long terminal repeat sequences) in a single run. Finally, it could be successfully applied to approximately 400 human promoter sequences and some of the extracted motifs turned out to be known cis-elements. Therefore, YEBIS could be a practical tool for exploring the upstream sequences of genomic ORFs, some of which are regulated in a similar fashion. AVAILABILITY: YEBIS will be distributed to academic users free of charge. All requests should be sent to the address below. CONTACT: E-MAIL: [email protected]