Biomedical subjects

Yuh-Jyh Hu

Publications and source records attributed to Yuh-Jyh Hu.

4 recordsLinked to original sources

PubMed2003

GPRM: A genetic programming approach to finding common RNA secondary structure elements.

RNA molecules play an important role in many biological activities. Knowing its secondary structure can help us better understand the molecule's ability to function. The methods for RNA structure determination have traditionally been implemented through biochemical, biophysical and phylogenetic analyses. As the advance of computer technology, an increasing number of computational approaches have recently been developed. They have different goals and apply various algorithms. For example, some focus on secondary structure prediction for a single sequence; some aim at finding a global alignment of multiple sequences. Some predict the structure based on free energy minimization; some make comparative sequence analyses to determine the structure. In this paper, we describe how to correctly use GPRM, a genetic programming approach to finding common secondary structure elements in a set of unaligned coregulated or homologous RNA sequences. GPRM can be accessed at http://bioinfo.cis.nctu.edu.tw/service/gprm/.

Internet↗

PubMed2003

Finding subtle motifs with variable gaps in unaligned DNA sequences.

Biologists have determined that the control and regulation of gene expression is primarily determined by relatively short sequences in the region surrounding a gene. These sequences vary in length, position, redundancy, orientation, and bases. Finding these short sequences is a fundamental problem in molecular biology with important applications. Though there exist many different approaches to signal (i.e. short sequence) finding, some new study shows that this problem still leaves plenty of room for improvement. In 2000, Pevzner and Sze proposed the Challenge Problem of motif detection. They reported that most current motif finding algorithms are incapable of detecting the target motifs in their Challenge Problem. In this paper, we show that using an iterative-restart design, our new algorithm can correctly find the target motifs. Furthermore, taking into account the fact that some transcription factors form a dimer or even more complex structures, and transcription process can sometimes involve multiple factors with variable spacers in between, we extend the original problem to an even more challenging one by addressing the issue of combinatorial signals with gaps of variable lengths. To demonstrate the effectiveness of our algorithm, we tested it on a series of the new challenge problem as well as real regulons, and compared it with some current representative motif-finding algorithms.

Algorithms↗

PubMed2002

Prediction of consensus structural motifs in a family of coregulated RNA sequences.

Given a set of homologous or functionally related RNA sequences, the consensus motifs may represent the binding sites of RNA regulatory proteins. Unlike DNA motifs, RNA motifs are more conserved in structures than in sequences. Knowing the structural motifs can help us gain a deeper insight of the regulation activities. There have been various studies of RNA secondary structure prediction, but most of them are not focused on finding motifs from sets of functionally related sequences. Although recent research shows some new approaches to RNA motif finding, they are limited to finding relatively simple structures, e.g. stem-loops. In this paper, we propose a novel genetic programming approach to RNA secondary structure prediction. It is capable of finding more complex structures than stem-loops. To demonstrate the performance of our new approach as well as to keep the consistency of our comparative study, we first tested it on the same data sets previously used to verify the current prediction systems. To show the flexibility of our new approach, we also tested it on a data set that contains pseudoknot motifs which most current systems cannot identify. A web-based user interface of the prediction system is set up at http://bioinfo. cis.nctu.edu.tw/service/gprm/.

Algorithms↗

PubMed2002

The NCTU BioInfo Archive of biological data sets for bioinformatics research and experimentation.

UNLABELLED: Most of the current bioinformatics literature lacks an explicit and clear description of the data used in experiments when introducing or evaluating computational tools. Without the exact data set that was fed into computational tools in experiments, any mistaken preparation of the data for later experiments may lead to discrepancy in conclusion. The NCTU BioInfo Archive is a new web-based bioinformatic data archive. It serves as a test bed for evaluating computational tools, as a bridge to link other research communities with bioinformatics, and also as an environment full of motivations and possibilities to encourage more exploratory research in bioinformatics. AVAILABILITY: http://bioinfo.cis.nctu.edu.tw

Computational Biology↗