Multiple sequence alignment (MSA) is the most important step for many of biological sequence analyses, homology search, and protein structural assignments. However, large amount of data make biologists difficult to perform MSA analyses and it requires much computational time to align many sequences. Here, we have developed a simple and fast web alignment tool for aligning, editing, and visualizing large amount of sequence data. We used a cluster server installed ClustalW-MPI using web services and message passing interface (MPI). It also enables users to edit multiple sequence alignments for manual editing and to download the input data and results such as alignments and phylogenetic tree.
Sequence Alignment ; Sequence Analysis

No abstract available.
Nucleopolyhedrovirus* ; Sequence Analysis*

No abstract available.
Base Sequence* ; DNA* ; Internet* ; Sequence Analysis, DNA*

Two species of cultivated Phellinus sp. were identified as P. baumii by internal transcribed spacer (ITS) sequence analysis. The fruit bodies of the examined strains were similar to those of naturally occurring strains, having a bracket-like form, yellow-to-orange color, and poroid hymenial surfaces. The DNA sequences of ITS region of both strains showed a homology of 99% with ITS1 to ITS2 sequences of P. (Inonotus) baumii strain PB0806.
Base Sequence ; Fruit ; Sequence Analysis ; Sprains and Strains

Pattern finding is one of the important tasks in a protein or DNA sequence analysis. Alignment is the widely used technique for finding patterns in sequence analysis. BLAST (Basic Local Alignment Search Tool) is one of the most popularly used tools in bio-informatics to explore available DNA or protein sequence databases. BLAST may generate a huge output for a large sequence data that contains various sequence patterns. However, BLAST does not provide a tool to summarize and analyze the patterns or matched alignments in the BLAST output file. BLAST lacks of general and robust parsing tools to extract the essential information out from its output. This paper presents a pattern summary system which is a powerful and comprehensive tool for discovering pattern structures in huge amount of sequence data in the BLAST. The pattern summary system can identify clusters of patterns, extract the cluster pattern sequences from the subject database of BLAST, and display the clusters graphically to show the distribution of clusters in the subject database.
Computational Biology ; Databases, Protein ; DNA ; Sequence Analysis* ; Sequence Analysis, DNA

With the rapid development of molecular biotechnology, transcriptomics has been widely used in the study of gene expression. In recent years, the techniques for microbial transcriptomics research have also been rapidly developing. At the gene level, the way for obtaining sequence information has been developed from complementary validation of RNA fragment through DNA microarray to direct sequencing of full-length RNA. Spatially, the traditional population transcriptomics technique has been developed into spatial, single cell and epigenetic transcriptomics studies. With the application of transcriptomics techniques in the field of microbial research, the corresponding defects were gradually revealed and constantly improved. In this paper, the traditional and new transcriptomics techniques in the field of microbial research are summarized to provide reference for microbial transcriptomics research.
Transcriptome ; Oligonucleotide Array Sequence Analysis ; Sequence Analysis ; RNA ; Biotechnology

Formamide has been widely used in urea/polyacrylamide gel to solve the compression problems that are occasionally found during the DNA sequencing of G/C rich regions. In this study, however, 10% formamide was added in annealing solution in stead of adding to the gel. The compressions were unfolded efficiently with a more rapid annealing reaction on ice in the presence of 10% formamide.
DNA* ; Ice ; Sequence Analysis, DNA*

No Abstract Available.
DNA* ; Oligonucleotide Array Sequence Analysis*

DNA chips are becoming increasingly popular as a convenient way to perform vast amounts of experiments related to genes on a single chip. And the importance of analyzing the data that is provided by such DNA chips is becoming significant. A very important analysis on DNA chip data would be clustering genes to identify gene groups which have similar properties such as cancer. Clustering data for DNA chips usually deal with a large search space and has a very fuzzy characteristic. The Particle Swarm Optimization algorithm which was recently proposed is a very good candidate to solve such problems. In this paper, we propose a clustering mechanism that is based on the Particle Swarm Optimization algorithm. Our experiments show that the PSO-based clustering algorithm developed is efficient in terms of execution time for clustering DNA chip data, and thus be used to extract valuable information such as cancer related genes from DNA chip data with high cluster accuracy and in a timely manner.
Cluster Analysis ; DNA ; Oligonucleotide Array Sequence Analysis

OBJECTIVETo identify a novel human leukocyte antigen (HLA) A allele.

METHODSA new HLA-A allele was found during routine HLA genotyping by polymerase chain reaction-sequence specific oligonucleotide probes (PCR-SSOP) and sequencing-based typing (SBT).

RESULTSThe novel HLA-A*30 allele was identical to A*300101 except that a nucleotide C at position 294 of exon 2 is substituted by A, resulting in codon 98 changed from GAC (D) to GAA (E).

CONCLUSIONA new HLA allele, HLA-A*3020, was identified, and was named officially by the WHO Nomenclature Committee.