• Korean Journal of Breeding Science
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• v.40 no.1
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• pp.1-7
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• 2008

An attempt was made to link rice embryo proteins to DNA sequences and to understand their functions. One hundred of the 700 spots detected on the embryo 2-DE gels were microsequenced. Of these, 28% of the embryo proteins were matched to DNA sequences with known functions, but 72% of the proteins were unknown in functions as previously reported (Woo et al. 2002). In addition, twenty-four protein spots with 100% of homology and nine with over 80% were matched to ESTs (expressed sequence tags) after expanding the amino acid sequences of the protein spots by Database searches using the available rice EST databases at the NCBI (http://www/ncbi.nlm.nih.gov/) and DDBJ (http://www.ddbj.nig.ac.jp/). The chromosomal location of some proteins were also obtained from the rice genetic map provided by Japanese Rice Genome Research Program (http://rgp.dna.affrc.go.jp). The DNA sequence databases including EST have been reported for rice (Oryza sativa L.) now provides whole or partial gene sequence, and recent advances in protein characterization allow the linking proteins to DNA sequences in the functional analysis. This work shows that proteome analysis could be a useful tool strategy to link sequence information and to functional genomics.

• Journal of KIISE:Databases
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• v.29 no.5
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• pp.381-392
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• 2002

This paper deals with the problem of shape-based retrieval in time-series databases. The shape-based retrieval is defined as the operation that searches for the (sub)sequences whose shapes are similar to that of a given query sequence regardless of their actual element values. In this paper, we propose an effective and efficient approach for shape-based retrieval of subsequences. We first introduce a new similarity model for shape-based retrieval that supports various combinations of transformations such as shifting, scaling, moving average, and time warping. For efficient processing of the shape-based retrieval based on the similarity model, we also propose the indexing and query processing methods. To verify the superiority of our approach, we perform extensive experiments with the real-world S&P 500 stock data. The results reveal that our approach successfully finds all the subsequences that have the shapes similar to that of the query sequence, and also achieves significant speedup up to around 66 times compared with the sequential scan method.

• Genomics & Informatics
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• v.4 no.4
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• pp.173-181
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• 2006

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.

• ETRI Journal
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• v.32 no.5
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• pp.676-686
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• 2010

Mining sequential patterns is an important research issue in data mining and knowledge discovery with broad applications. However, the existing sequential pattern mining approaches consider only binary frequency values of items in sequences and equal importance/significance values of distinct items. Therefore, they are not applicable to actually represent many real-world scenarios. In this paper, we propose a novel framework for mining high-utility sequential patterns for more real-life applicable information extraction from sequence databases with non-binary frequency values of items in sequences and different importance/significance values for distinct items. Moreover, for mining high-utility sequential patterns, we propose two new algorithms: UtilityLevel is a high-utility sequential pattern mining with a level-wise candidate generation approach, and UtilitySpan is a high-utility sequential pattern mining with a pattern growth approach. Extensive performance analyses show that our algorithms are very efficient and scalable for mining high-utility sequential patterns.

• The KIPS Transactions:PartD
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• v.10D no.3
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• pp.501-512
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• 2003

This paper proposes an indexing technique for fast retrieval of similar image subsequences using the multi-dimensional time warping distance. The time warping distance is a more suitable similarity measure than Lp distance in many applications where sequences may be of different lengths and/or different sampling rates. Our indexing scheme employs a disk-based suffix tree as an index structure and uses a lower-bound distance function to filter out dissimilar subsequences without false dismissals. It applies the normaliration for an easier control of relative weighting of feature dimensions and the discretization to compress the index tree. Experiments on medical and synthetic image sequences verify that the proposed method significantly outperforms the naive method and scales well in a large volume of image sequence databases.

• Journal of KIISE:Databases
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• v.28 no.3
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• pp.344-356
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• 2001

This paper deals with the subsequence searching problem under time-warping in sequence databases. Our work is motivated by the observation that subsequence searches slow down quadratically as the average length of data sequences increases. To resolve this problem, the Segment-Based Approach for Subsequence Searches (SBSS) is proposed. The SBASS divides data and query sequences into a series of segments, and retrieves all data subsequences that satisfy the two conditions: (1) the number of segments is the same as the number of segments in a query sequence, and (2) the distance of every segment pair is less than or equal to a tolerance. Our segmentation scheme allows segments to have different lengths; thus we employ the time warping distance as a similarity measure for each segment pair. For efficient retrieval of similar subsequences, we extract feature vectors from all data segments exploiting their monotonically changing properties, and build a spatial index using feature vectors. Using this index, queries are processed with the four steps: (1) R-tree filtering, (2) feature filtering, (3) successor filtering, and (4) post-processing. The effectiveness of our approach is verified through extensive experiments.

• Journal of KIISE:Databases
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• v.34 no.2
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• pp.119-132
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• 2007

In molecular biology, approximate subsequence search is one of the most important operations. In this paper, we propose an accurate and efficient method for approximate subsequence search in large DNA databases. The proposed method basically adopts a binary trie as its primary structure and stores all the window subsequences extracted from a DNA sequence. For approximate subsequence search, it traverses the binary trie in a breadth-first fashion and retrieves all the matched subsequences from the traversed path within the trie by a dynamic programming technique. However, the proposed method stores only window subsequences of the pre-determined length, and thus suffers from large post-processing time in case of long query sequences. To overcome this problem, we divide a query sequence into shorter pieces, perform searching for those subsequences, and then merge their results. To verify the superiority of the proposed method, we conducted performance evaluation via a series of experiments. The results reveal that the proposed method, which requires smaller storage space, achieves 4 to 17 times improvement in performance over the suffix tree based method. Even when the length of a query sequence is large, our method is more than an order of magnitude faster than the suffix tree based method and the Smith-Waterman algorithm.

• Genomics & Informatics
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• v.2 no.3
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• pp.142-146
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• 2004

Biological data and analysis tools are accumulated in distributed databases and web servers. For this reason, biologists who want to find information from the web should be aware of the various kinds of resources where it is located and how it is retrieved. Integrating the data from heterogeneous biological resources will enable biologists to discover new knowledge across the specific domain boundaries from sequences to expression, structure, and pathway. And inevitably biological databases contain noisy data. Therefore, consensus among databases will confirm the reliability of its contents. We have developed WeSAT that integrates distributed and heterogeneous biological databases and analysis tools, providing through Web Services protocols. In WeSAT, biologists are retrieved specific entries in SWISS-PROT/EMBL, PDB, and KEGG, which have annotated information about sequence, structure, and pathway. And further analysis is carried by integrated services for example homology search and multiple alignments. WeSAT makes it possible to retrieve real time updated data and analysis from the scattered databases in a single platform through Web Services.

• Korean Journal of Poultry Science
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• v.28 no.2
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• pp.143-153
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• 2001

Using bioinformatic tools for searching the massive genome databases, it is possible to Identify new genes in few minutes for initial discoveries based on evolutionary conservation, domain homology, and tissue expression patterns, followed by further verification and characterization using the bench-top works. The development of high-density two-dimensional arrays has allowed the analysis of the expression of thousands of genes simultaneously in the humans, mice, rats, yeast, and bacteria to elucidate the genes and pathways involved in physiological processes. In addition, rapid and automated protein identification is being achieved by searching protein and nucleotide sequence databases directly with data generated from mass spectrometry. Recently, analysis at the bio-chemical level such as biochemical screening and metabolic profiling (Biochemical genomics) has been introduced as an additional approach for categorical assignment of gene function. To make advantage of recent achievements in computational approaches for facilitated gene discoveries in the avian model, chicken expression sequence tags (ESTs) have been reported and deposited in the international databases. By searching EST databases, a chicken heparanase gene was identified and functionally confirmed by subsequent experiments. Using combination of sub-tractive hybridization assay and Genbank database searches, a chicken heme -binding protein family (cSOUL/HBP) was isolated in the retina and pineal gland of domestic chicken and verified by Northern blot analysis. Microarrays have identified several host genes whose expression levels are elevated following infection of chicken embryo fibroblasts (CEF) with Marek's disease virus (MDV). The ongoing process of chicken genome projects and new discoveries and breakthroughs in genomics and proteomics will no doubt reveal new and exciting information and advances in the avian research.

• ALGAE
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• v.35 no.3
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• pp.293-301
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• 2020

The applications of DNA barcoding have a wide range of uses, such as in taxonomic studies to help elucidate cryptic species and phylogenetic relationships and analyzing environmental samples for biodiversity monitoring and conservation assessments of species. After obtaining the DNA barcode sequences, sequence similarity-based homology analysis is commonly used. This means that the obtained barcode sequences are compared to the DNA barcode reference databases. This bioinformatic analysis necessarily implies that the overall quantity and quality of the reference databases must be stringently monitored to not have an adverse impact on the accuracy of species identification. With the development of next-generation sequencing techniques, a noticeably large number of DNA barcode sequences have been produced and are stored in online databases, but their degree of validity, accuracy, and reliability have not been extensively investigated. In this study, we investigated the extent to which the amount and types of erroneous barcode sequences were deposited in publicly accessible databases. Over 4.1 million sequences were investigated in three largescale DNA barcode databases (NCBI GenBank, Barcode of Life Data System [BOLD], and Protist Ribosomal Reference database [PR2]) for four major DNA barcodes (cytochrome c oxidase subunit 1 [COI], internal transcribed spacer [ITS], ribulose bisphosphate carboxylase large chain [rbcL], and 18S ribosomal RNA [18S rRNA]); approximately 2% of erroneous barcode sequences were found and their taxonomic distributions were uneven. Consequently, our present findings provide compelling evidence of data quality problems along with insufficient and unreliable annotation of taxonomic data in DNA barcode databases. Therefore, we suggest that if ambiguous taxa are presented during barcoding analysis, further validation with other DNA barcode loci or morphological characters should be mandated.