Biomedical subjects

Akito Taneda

Publications and source records attributed to Akito Taneda.

4 recordsLinked to original sources

PubMed2005

Twelve novel C. elegans RNA candidates isolated by two-dimensional polyacrylamide gel electrophoresis.

C. elegans small RNAs (<50 nt) were separated by two-dimensional gel electrophoresis (2D-PAGE). cDNAs were prepared from the RNAs extracted from randomly chosen 2D-PAGE spots. Although many cDNA sequences corresponded to parts of known RNAs, twelve novel small RNA candidates were identified: eleven from 2D-PAGE spots of the mixed-stage worm RNA preparation and one from those of the embryonic RNA preparation. These are encoded in the intergenic regions, in the introns of protein-coding genes, in the anti-sense strand of protein-coding sequences and repetitive sequence regions of the genome. None of them showed a characteristic structure of miRNAs, suggesting that they are candidates of other or new classes of RNAs.

Animals↗

PubMed2005

Cofolga: a genetic algorithm for finding the common folding of two RNAs.

In order to predict non-coding RNA genes and functions on the basis of genome sequences, accurate secondary structure prediction is useful. Although single-sequence folding programs such as mfold have been successful, it is of great importance to develop a novel approach for further improvement of the prediction performance. In the present paper, a secondary structure prediction method based on genetic algorithm, Cofolga, is proposed. The program developed performs folding and alignment of two homologous RNAs simultaneously. Cofolga was tested with a dataset composed of 13 tRNAs, seven 5S rRNAs, five RNase P RNAs, and five SRP RNAs; as a result, it turned out that the average prediction accuracies for the tRNAs, 5S rRNAs, RNase P RNAs, and SRP RNAs obtained by Cofolga with an optimal weight factor and default parameters were 83.6, 81.8, 73.5, and 67.7%, respectively. These results were superior to those obtained by a single-sequence folding based on free-energy minimization in which corresponding average prediction accuracies were 52.4, 47.4, 57.7, and 52.3%, respectively. Cofolga has a post-processing in which a single-sequence folding is performed after fixation of a predicted common structure; this post-processing enables Cofolga to predict a structure that is present in one of two RNAs alone. The executable files of Cofolga (for Windows/Unix/Mac) can be obtained by an e-mail request.

Algorithms↗

PubMed2004

Adplot: detection and visualization of repetitive patterns in complete genomes.

MOTIVATION: Repetitive DNA sequences are abundant in genomes and efficient mining of significant repeats is important as the first step of repetitive sequence research. Although many computational tools for the purpose, either automatic or visualization ones, have been developed, detection and analysis of approximate repeats are still non-trivial task. RESULTS: Auto Dot PLOT (Adplot), a dotplot-like repetitive pattern visualization program with a window filtering based on iid Bernoulli trials, is developed and applied to yeast chromosomes and human T cell receptor locus sequence. Typical examples found in yeast chromosomes 1 and 10 and a tandem repeat of periods longer than 10,000 bp in human T cell receptor locus are presented. A complex structure composed of both direct and palindromic repeats found in yeast chromosome 10 is also visualized as specific dot pattern. Computational time measured by a Pentium 3 PC for each yeast auto chromosome with a standard parameter setting is linearly scaled and below 10 s per one chromosome, indicating efficiency of the program. From the examples, it is shown that Adplot can visualize approximate local repeat structures and give us a diagnosis power for inferring a duplicational history of repeats. AVAILABILITY: Adplot can be obtained by an e-mail request.

Algorithms↗

PubMedDate not supplied

Automatic brain tissue extraction method using erosion-dilation treatment (BREED) from three-dimensional magnetic resonance imaging T1-weighted data.

To improve the efficiency of brain image analysis, we propose a full-automatic method for extracting brain tissue from three-dimensional magnetic resonance imaging of T1-weighted data on the human head (brain tissue extraction method using erosion-dilation treatment [BREED]). The extraction processing is realized by combining signal intensity thresholding by means of the discriminant analysis method and an erosion-dilation treatment of the image. The accuracy of BREED is evaluated using both simulated and subject data. BREED can extract brain tissues with high accuracy (approximately 97%) for either simulated or subject data.

Automation↗