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Y Totoki

Publications and source records attributed to Y Totoki.

8 recordsLinked to original sources
PubMed2003

DIGIT: a novel gene finding program by combining gene-finders.

We have developed a general purpose algorithm which finds genes by combining plural existing gene-finders. The algorithm has been implemented into a novel gene-finder named DIGIT. An outline of the algorithm is as follows. First, existing gene-finders are applied to an uncharacterized genomic sequence (input sequence). Next, DIGIT produces all possible exons from the results of gene-finders, and assigns them their exon types, reading frames and exon scores. Finally, DIGIT searches a set of exons whose additive score is maximized under their reading frame constraints. Bayesian procedure and a hidden Markov model are used to infer exon scores and search the exon set, respectively. We have designed DIGIT so as to combine the results of FGENESH, GENSCAN and HMMgene, and have assessed its prediction accuracy by using recently compiled benchmark data sets. For all data sets, DIGIT successfully discarded many false-positive exons predicted by individual gene-finders and yielded remarkable improvements in sensitivity and specificity at the gene level compared with the best gene level accuracies achieved by any single gene-finder.

Algorithms↗
PubMed2001

A novel bacterial gene-finding system with improved accuracy in locating start codons.

Although a number of bacterial gene-finding programs have been developed, there is still room for improvement especially in the area of correctly detecting translation start sites. We developed a novel bacterial gene-finding program named GeneHacker Plus. Like many others, it is based on a hidden Markov model (HMM) with duration. However, it is a 'local' model in the sense that the model starts from the translation control region and ends at the stop codon of a coding region. Multiple coding regions are identified as partial paths, like local alignments in the Smith-Waterman algorithm, regardless of how they overlap. Moreover, our semiautomatic procedure for constructing the model of the translation control region allows the inclusion of an additional conserved element as well as the ribosome-binding site. We confirmed that GeneHacker Plus is one of the most accurate programs in terms of both finding potential coding regions and precisely locating translation start sites. GeneHacker Plus is also equipped with an option where the results from database homology searches are directly embedded in the HMM. Although this option does not raise the overall predictability, labeled similarity information can be of practical use. GeneHacker Plus can be accessed freely at http://elmo.ims.u-tokyo.ac.jp/GH/.

Algorithms↗
PubMed2000

The DNA sequence of human chromosome 21.

Chromosome 21 is the smallest human autosome. An extra copy of chromosome 21 causes Down syndrome, the most frequent genetic cause of significant mental retardation, which affects up to 1 in 700 live births. Several anonymous loci for monogenic disorders and predispositions for common complex disorders have also been mapped to this chromosome, and loss of heterozygosity has been observed in regions associated with solid tumours. Here we report the sequence and gene catalogue of the long arm of chromosome 21. We have sequenced 33,546,361 base pairs (bp) of DNA with very high accuracy, the largest contig being 25,491,867 bp. Only three small clone gaps and seven sequencing gaps remain, comprising about 100 kilobases. Thus, we achieved 99.7% coverage of 21q. We also sequenced 281,116 bp from the short arm. The structural features identified include duplications that are probably involved in chromosomal abnormalities and repeat structures in the telomeric and pericentromeric regions. Analysis of the chromosome revealed 127 known genes, 98 predicted genes and 59 pseudogenes.

Base Sequence↗
PubMed1999

Modeling and predicting transcriptional units of Escherichia coli genes using hidden Markov models.

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.

Algorithms↗
PubMed1998

Automatic extraction of motifs represented in the hidden Markov model from a number of DNA sequences.

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: yada@tokyo.jst.go.jp

Algorithms↗
PubMed1997

Functional prediction of B. subtilis genes from their regulatory sequences.

In bacterial cells, gene expression is regulated by multiple sigma factors, each of which has its promoter specificity, according to their conditions. Thus, if we can discriminate which sigma factor binds to the upstream region of a given coding sequence, we can predict in what condition it will be expressed. In this paper, we show this approach is feasible for the analysis of Bacillus subtilis genome. Based on our collection of known promoter sequences, we prepared 8 predictors to characterize known sigma factors using the hidden Markov model and their prediction accuracies were estimated with a cross-validation test. Furthermore, we predicted the sigma-dependencies for each of 1415 candidate genes in the genome. Our prediction results are experimentally testable and seem useful for the post-sequencing project.

Bacillus subtilis↗
PubMed1995

Comprehensive study on iterative algorithms of multiple sequence alignment.

Multiple sequence alignment is an important problem in the biosciences. To date, most multiple alignment systems have employed a tree-based algorithm, which combines the results of two-way dynamic programming in a tree-like order of sequence similarity. The alignment quality is not, however, high enough when the sequence similarity is low. Once an error occurs in the alignment process, that error can never be corrected. Recently, an effective new class of algorithms has been developed. These algorithms iteratively apply dynamic programming to partially aligned sequences to improve their alignment quality. The iteration corrects any errors that may have occurred in the alignment process. Such an iterative strategy requires heuristic search methods to solve practical alignment problems. Incorporating such methods yields various iterative algorithms. This paper reports our comprehensive comparison of iterative algorithms. We proved that performance improves remarkably when using a tree-based iterative method, which iteratively refines an alignment whenever two subalignments are merged in a tree-based way. We propose a tree-dependent, restricted partitioning technique to efficiently reduce the execution time of iterative algorithms.

Algorithms↗