• Genomics & Informatics
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• v.17 no.2
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• pp.17.1-17.3
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• 2019

Text mining has become an important research method in biology, with its original purpose to extract biological entities, such as genes, proteins and phenotypic traits, to extend knowledge from scientific papers. However, few thorough studies on text mining and application development, for plant molecular biology data, have been performed, especially for rice, resulting in a lack of datasets available to solve named-entity recognition tasks for this species. Since there are rare benchmarks available for rice, we faced various difficulties in exploiting advanced machine learning methods for accurate analysis of the rice literature. To evaluate several approaches to automatically extract information from gene/protein entities, we built a new dataset for rice as a benchmark. This dataset is composed of a set of titles and abstracts, extracted from scientific papers focusing on the rice species, and is downloaded from PubMed. During the 5th Biomedical Linked Annotation Hackathon, a portion of the dataset was uploaded to PubAnnotation for sharing. Our ultimate goal is to offer a shared task of rice gene/protein name recognition through the BioNLP Open Shared Tasks framework using the dataset, to facilitate an open comparison and evaluation of different approaches to the task.

• Korean Journal of Plant Resources
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• v.23 no.6
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• pp.526-534
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• 2010

Genomes of clusters of related eukaryotes are now being sequenced at an increasing rate. In this paper, we developed an accurate, low-cost method for annotation of gene prediction and exon-intron structure. The gene prediction was adapted for delta 1-pyrroline-5-carboxylate-synthetase (p5cs) gene from China wild-type of the halophytic Leymus chinensis (Trin.), naturally adapted to highly-alkali soils. Due to complex adaptive mechanisms in halophytes, more attentions are being paid on the regulatory elements of stress adaptation in halophytes. P5CS encodes delta 1-pyrroline-5-carboxylate-synthetase, a key regulatory enzyme involved in the biosynthesis of proline, that has direct correlation with proline accumulation in vivo and positive relationship with stress tolerance. Using analysis of reverse transcription-polymerase chain reaction (RT-PCR) and PCR, and direct sequencing, 1076 base pairs (bp) of cDNA in length and 2396 bp of genomic DNA in length were obtained from direct sequencing results. Through gene prediction and exon-intron structure verification, the full-length of cDNA sequence was divided into eight parts, with seven parts of intron insertion. The average lengths of determinated coding regions and non-coding regions were 154.17 bp and 188.57 bp, respectively. Nearly all splice sites displayed GT as the donor sites at the 5' end of intron region, and 71.43% displayed AG as the acceptor sites at the 3' end of intron region. We conclude that this method is a cost-effective way for obtaining an experimentally verified genome annotation.

• Journal of the Korean Data and Information Science Society
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• v.18 no.2
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• pp.489-506
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• 2007

Gene structure prediction, which is to predict protein coding regions in a given nucleotide sequence, is the most important process in annotating genes and greatly affects gene analysis and genome annotation. As eukaryotic genes have more complicated structures in DNA sequences than those of prokaryotic genes, analysis programs for eukaryotic gene structure prediction have more diverse and more complicated computational models. There are Ab Initio method, Similarity-based method, and Ensemble method for gene prediction method for eukaryotic genes. Each Method use various algorithms. This paper introduce how to predict genes using HMM(Hidden Markov Model) algorithm and present the process of gene prediction with well-known gene prediction programs.

• Molecular & Cellular Toxicology
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• v.1 no.4
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• pp.281-283
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• 2005

Extraction of biologically meaningful data and their validation are very important for toxicogenomics study because it deals with huge amount of heterogeneous data. BINGO is an annotation mining tool for biological interpretation of gene groups. Several statistical modeling approaches using Gene Ontology (GO) have been employed in many programs for that purpose. The statistical methodologies are useful in investigating the most significant GO attributes in a gene group, but the coherence of the resultant GO attributes over the entire group is rarely assessed. BINGO complements the statistical methods with graph-theoretic measures using the GO directed acyclic graph (DAG) structure. In addition, BINGO visualizes the consistency of a gene group more intuitively with a group-based GO subgraph. The input group can be any interesting list of genes or gene products regardless of its generation process if the group is built under a functional congruency hypothesis such as gene clusters from DNA microarray analysis.

• Genomics & Informatics
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• v.5 no.3
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• pp.133-136
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• 2007

The Gene Set network viewer (GSnet) visualizes the functional enrichment of a given gene set with a protein interaction network and is implemented as a plug-in for the Cytoscape platform. The functional enrichment of a given gene set is calculated using a hypergeometric test based on the Gene Ontology annotation. The protein interaction network is estimated using public data. Set operations allow a complex protein interaction network to be decomposed into a functionally-enriched module of interest. GSnet provides a new framework for gene set analysis by integrating a priori knowledge of a biological network with functional enrichment analysis.

• The korean journal of orthodontics
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• v.33 no.3 s.98
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• pp.185-197
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• 2003

This study was performed to identify the characteristics of the OFC1 gene (locus: chromosome 6p24.3) in Korean patients, which is assumed to be the major gene behind the nonsyndromic cleft lip and palate. The sample consisted of 80 subjects: 40 nonsyndromic cleft lip and palate patients (proband, 20 males and females, mean age 14.2 years); and 40 normal adults (20 males and 20 females, mean age 25.6 years). Using PCR-based assay, the OFC1 gene was amplified, sequenced, and then searched for similar protein structures. Results were as follows: 1. The OFC1 gene contains the microsatellite marker 'CA' repeats. The number of the reference 'CA' repeats was 21 times, and formed as TA(CA)11TA(CA)10. But, in Koreans, the number of tandem 'CA' repeats was varied from 17 to 26 except 18, and 'CA' repeats consisted of TA(CA)n. 2. Nine allelic variants were found. Distribution of the OFC1 allele was similar between the patients and control group. 3. There was a replacement of the base 'T' to 'C' after 11 tandem 'CA' repeats in Koreans compared with Weissenbach's report. However, the difference did not seem to be the ORF prediction results between Koreans and Weissenbach's report. 4. The BLAST search results showed the Telomerase reverse transcriptase (TERT) and the Nucleotide binding protein 2 (NBP2) as similar proteins. The TERT was a protein product by the hTERT gene in the locus 5p15.33 (NCBI Genome Annotation; NT023089) The NBP2 was a protein product by the ABCC3 (ATP-binding cassette, sub-family C) gene in the locus 17q22 (NCBI Genome Annotation; NT010783). 5. In the Pedant-Pro database analysis, the predictable protein structure of the OFC1 gene had at least one transmembrane region and one non-globular region.

• Genomics & Informatics
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• v.21 no.2
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• pp.21.1-21.14
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• 2023

Fusobacterium nucleatum is a gram-negative bacteria associated with diverse infections like appendicitis and colorectal cancer. It mainly attacks the epithelial cells in the oral cavity and throat of the infected individual. It has a single circular genome of 2.7 Mb. Many proteins in F. nucleatum genome are listed as "Uncharacterized." Annotation of these proteins is crucial for obtaining new facts about the pathogen and deciphering the gene regulation, functions, and pathways along with discovery of novel target proteins. In the light of new genomic information, an armoury of bioinformatic tools were used for predicting the physicochemical parameters, domain and motif search, pattern search, and localization of the uncharacterized proteins. The programs such as receiver operating characteristics determine the efficacy of the databases that have been employed for prediction of different parameters at 83.6%. Functions were successfully assigned to 46 uncharacterized proteins which included enzymes, transporter proteins, membrane proteins, binding proteins, etc. Apart from the function prediction, the proteins were also subjected to string analysis to reveal the interacting partners. The annotated proteins were also put through homology-based structure prediction and modeling using Swiss PDB and Phyre2 servers. Two probable virulent factors were also identified which could be investigated further for potential drug-related studies. The assigning of functions to uncharacterized proteins has shown that some of these proteins are important for cell survival inside the host and can act as effective drug targets.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.210-210
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• 2022

Silybum marianum is an annual or biennial plant from the Asteraceae family. It can grow in low-nutrient soil and drought conditions, making it easy to cultivate. From the seed, a specialized plant metabolite called silymarin (flavonolignan complex) is produced and is known to alleviate the liver from hepatitis and toxins damages. To infer the phylogenetic placement of a Korean milk thistle, we conducted a chloroplast assembly and annotation following by a comparison with existing Chinese reference genome (NC_028027). The chloroplast genome structure was highly similar with an assembly size of 152,642 bp, an 153,202 bp for Korean and Chinese milk thistle respectively. Moreover, there were similarities at the gene level, coding sequence (n = 82), transfer RNA (n = 31) and ribosomal RNA (n = 4). From all coding sequences gene set, the phylogenetic tree inference placed the Korean cultivar into the milk thistle clade; corroborating the expected tree. Moreover, an investigation the tree based only on the ycf1 gene confirmed the same tree; suggesting that ycf1 gene is a potential marker for DNA barcoding and population diversity study in milk thistle genus. Overall, the provided data represents a valuable resource for population genomics and species-centered determination since several species have been reported in the Silybum genus.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.1
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• pp.537-545
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• 2018

In this study, transcriptome analysis was conducted to collect various information from Fagopyrum esculentum and Fagopyrum tataricum during the early germination stage. Total RNA was extracted from the seeds and at 12, 24, and 36 hrs after germination of Jeju native Fagopyrum esculentum and Fagopyrum tataricum and sequenced using the Illumina Hiseq 2000 platform. Raw data analysis was conducted using the Dynamic Trim and Lengths ORT programs in the SolexaQA package, and assembly and annotation were performed. Based on RNA-seq raw data, we obtained 16.5 Gb and 16.2 Gb of transcriptome data corresponding to about 84.2% and 81.5% of raw data, respectively. De novo assembly and annotation revealed 43,494 representative transcripts corresponding to 47.5Mb. Among them, 23,165 sequences were shown to have similar sequences with annotation DB. Moreover, Gene Ontology (GO) analysis of buckwheat representative transcripts confirmed that the gene is involved in metabolic processes (49.49%) of biological processes, as well as cell function (46.12%) in metabolic process, and catalytic activity (80.43%) in molecular function In the case of gibberellin receptor GID1C, which is related to germination of seeds, the expression levels increased with time after germination in both F. esculentum and F. tataricum. The expression levels of gibberellin 20-oxidase 1 were increased within 12 hrs of gemination in F. esculentum but continuously until 36 hrs in F. tataricum. This buckwheat transcriptome profile analysis of the early germination stage will help to identify the mechanism causing functional and morphological differences between species.

• Korean Journal of Microbiology
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• v.39 no.4
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• pp.207-215
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• 2003

Gene structure prediction, which is to predict protein coding regions in a given nucleotide sequence, is the most important process in annotating genes and greatly affects gene analysis and genome annotation. As eukaryotic genes have more complicated stuructures in DNA sequences than those of prokaryotic genes, analysis programs for eukaryotic gene structure prediction have more diverse and more complicated computational models. We have developed EGSP, a eukaryotic gene structure program, using duration hidden markov model. The program consists of two major processes, one of which is a training process to produce parameter values from training data sets and the other of which is to predict protein coding regions based on the parameter values. The program predicts multiple genes rather than a single gene from a DNA sequence. A few computational models were implemented to detect signal pattern and their scanning efficiency was tested. Prediction performance was calculated and was compared with those of a few commonly used programs, GenScan, GeneID and Morgan based on a few criteria. The results show that the program can be practically used as a stand-alone program and a module in a system. For gene prediction of eukaryotic microbial genomes, training and prediction analysis was done with Saccharomyces chromosomes and the result shows the program is currently practically applicable to real eukaryotic microbial genomes.