Biomedical subjects

Feng-Mao Lin

Publications and source records attributed to Feng-Mao Lin.

5 recordsLinked to original sources

PubMed2006

Database to dynamically aid probe design for virus identification.

Viral infection poses a major problem for public health, horticulture, and animal husbandry, possibly causing severe health crises and economic losses. Viral infections can be identified by the specific detection of viral sequences in many ways. The microarray approach not only tolerates sequence variations of newly evolved virus strains, but can also simultaneously diagnose many viral sequences. Many chips have so far been designed for clinical use. Most are designed for special purposes, such as typing enterovirus infection, and compare fewer than 30 different viral sequences. None considers primer design, increasing the likelihood of cross hybridization to similar sequences from other viruses. To prevent this possibility, this work establishes a platform and database that provides users with specific probes of all known viral genome sequences to facilitate the design of diagnostic chips. This work develops a system for designing probes online. A user can select any number of different viruses and set the experimental conditions such as melting temperature and length of probe. The system then returns the optimal sequences from the database. We have also developed a heuristic algorithm to calculate the probe correctness and show the correctness of the algorithm. (The system that supports probe design for identifying viruses has been published on our web page http://bioinfo.csie.ncu.edu.tw/.)

Algorithms↗

PubMed2005

SpliceInfo: an information repository for mRNA alternative splicing in human genome.

We have developed an information repository named SpliceInfo to collect the occurrences of the four major alternative-splicing (AS) modes in human genome; these include exon skipping, 5'-alternative splicing, 3'-alternative splicing and intron retention. The dataset is derived by comparing the nucleotide and protein sequences available for a given gene for evidence of AS. Additional features such as the tissue specificity of the mRNA, the protein domain contained by exons, the GC-ratio of exons, the repeats contained within the exons, and the Gene Ontology are annotated computationally for each exonic region that is alternatively spliced. Motivated by a previous investigation of AS-related motifs such as exonic splicing enhancer and exonic splicing silencer, this resource also provides a means of identifying motifs candidates and this should help to identify potential regulatory mechanisms within a particular exonic sequence set and its two flanking intronic sequence sets. This is carried out using motif discovery tools to identify motif candidates related to alternative splicing regulation and together with a secondary structure prediction tool, will help in the identification of the structural properties of such regulatory motifs. The integrated resource is now available on http://SpliceInfo.mbc.NCTU.edu.tw/.

Alternative Splicing↗

PubMed2004

i-Genome: a database to summarize oligonucleotide data in genomes.

BACKGROUND: Information on the occurrence of sequence features in genomes is crucial to comparative genomics, evolutionary analysis, the analyses of regulatory sequences and the quantitative evaluation of sequences. Computing the frequencies and the occurrences of a pattern in complete genomes is time-consuming. RESULTS: The proposed database provides information about sequence features generated by exhaustively computing the sequences of the complete genome. The repetitive elements in the eukaryotic genomes, such as LINEs, SINEs, Alu and LTR, are obtained from Repbase. The database supports various complete genomes including human, yeast, worm, and 128 microbial genomes. CONCLUSIONS: This investigation presents and implements an efficiently computational approach to accumulate the occurrences of the oligonucleotides or patterns in complete genomes. A database is established to maintain the information of the sequence features, including the distributions of oligonucleotide, the gene distribution, the distribution of repetitive elements in genomes and the occurrences of the oligonucleotides. The database can provide more effective and efficient way to access the repetitive features in genomes.

Alu Elements↗

PubMed2004

Identifying the combination of genetic factors that determine susceptibility to cervical cancer.

Cervical cancer is common among women all over the world. Although infection with high-risk types of human papillomavirus (HPV) has been identified as the primary cause of cervical cancer, only some of those infected go on to develop cervical cancer. Obviously, the progression from HPV infection to cancer involves other environmental and host factors. Recent population-based twin and family studies have demonstrated the importance of the hereditary component of cervical cancer, associated with genetic susceptibility. Consequently, single-nucleotide polymorphism (SNP) markers and microsatellites should be considered genetic factors for determining what combinations of genetic factors are involved in precancerous changes to cervical cancer. This study employs a Bayesian network and four different decision tree algorithms, and compares the performance of these learning algorithms. The results of this study raise the possibility of investigations that could identify combinations of genetic factors, such as SNPs and microsatellites, that influence the risk associated with common complex multifactorial diseases, such as cervical cancer.

Algorithms↗

PubMedDate not supplied

MultiProtIdent: identifying proteins using database search and protein-protein interactions.

Protein identification is important in proteomics. Proteomic analyses based on mass spectra (MS) constitute innovative ways to identify the components of protein complexes. Instruments can obtain the mass spectrum to an accuracy of 0.01 Da or better, but identification errors are inevitable. This study shows a novel tool, MultiProtIdent, which can identify proteins using additional information about protein-protein interactions and protein functional associations. Both single and multiple Peptide Mass Fingerprints (PMFs) are input to MultiProtIdent, which matches the PMFs to a theoretical peptide mass database. The relationships or interactions among proteins are considered to reduce false positives in PMF matching. Experiments to identify protein complexes reveal that MultiProtIdent is highly promising. The website associated with this study is http://dbms104.csie.ncu.edu.tw/.

Algorithms↗