• Genomics & Informatics
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• 제10권1호
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• pp.33-39
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• 2012

High-throughput genomic technologies (HGTs), including next-generation DNA sequencing (NGS), microarray, and serial analysis of gene expression (SAGE), have become effective experimental tools for cancer genomics to identify cancer-associated somatic genomic alterations and genes. The main hurdle in cancer genomics is to identify the real causative mutations or genes out of many candidates from an HGT-based cancer genomic analysis. One useful approach is to refer to known cancer genes and associated information. The list of known cancer genes can be used to determine candidates of cancer driver mutations, while cancer gene-related information, including gene expression, protein-protein interaction, and pathways, can be useful for scoring novel candidates. Some cancer gene or mutation databases exist for this purpose, but few specialized tools exist for an automated analysis of a long gene list from an HGT-based cancer genomic analysis. This report presents a new web-accessible bioinformatic tool, called CaGe, a cancer genome annotation system for the assessment of candidates of cancer genes from HGT-based cancer genomics. The tool provides users with information on cancer-related genes, mutations, pathways, and associated annotations through annotation and browsing functions. With this tool, researchers can classify their candidate genes from cancer genome studies into either previously reported or novel categories of cancer genes and gain insight into underlying carcinogenic mechanisms through a pathway analysis. We show the usefulness of CaGe by assessing its performance in annotating somatic mutations from a published small cell lung cancer study.

• Journal of Microbiology and Biotechnology
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• 제24권2호
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• pp.133-143
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• 2014

The development of the gut is controlled and modulated by different interacting mechanisms, such as genetic endowment, intrinsic biological regulatory functions, environment influences and last but no least, the diet influence. In this work, we compared the fecal microbiota of breast-fed (BF), formula-fed (FF), and mixed-fed (MF) infants from Hebei Province, China. By using high-throughput 16S rDNA sequencing analyses, we found some differences in gut microbiota in the three groups. Firmicutes and Proteobacteria were the dominant bacteria at the phylum level in the three groups, where FF infants showed a significant depletion in Bacteroidetes (p < 0.001) and Actinobacteria (p < 0.05). Enterobacteriaceae was the dominant bacteria at the family level in the three groups, but FF infants showed higher Enterobacteriaceae enrichment than BF and MF infants (p < 0.05); the abundance of the Bifidobacteriaceae was only 8.16% in the feces of BF infants, but higher than in MF and FF infants (p < 0.05). The number of genera detected (abundance >0.01%) in BF, MF, and FF infants was only 15, 16, and 13, respectively. This study could provide more accurate and scientific data for the future study of infant intestinal flora.

• Asian-Australasian Journal of Animal Sciences
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• 제30권1호
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• pp.78-84
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• 2017

Objective: The aim of this study was to examine shifts in the composition of the bacterial population in the intestinal tracts (ITs) of weaning piglets by antibiotic treatment using high-throughput sequencing. Methods: Sixty 28-d-old weaning piglets were randomly divided into two treatment groups. The Control group was treated with a basal diet without antibiotics. The Antibiotic group's basal diet contained colistin sulfate at a concentration of 20 g per ton and bacitracin zinc at a concentration of 40 g per ton. All of the pigs were fed for 28 days. Then, three pigs were killed, and the luminal contents of the jejunum, ileum, cecum, and colon were collected for DNA extraction and high-throughput sequencing. Results: The results showed that the average daily weight gain of the antibiotic group was significantly greater (p<0.05), and the incidence of diarrhea lower (p>0.05), than the control group. A total of 812,607 valid reads were generated. Thirty-eight operational taxonomic units (OTUs) that were found in all of the samples were defined as core OTUs. Twenty-one phyla were identified, and approximately 90% of the classifiable sequences belonged to the phylum Firmicutes. Forty-two classes were identified. Of the 232 genera identified, nine genera were identified as the core gut microbiome because they existed in all of the tracts. The proportion of the nine core bacteria varied at the different tract sites. A heat map was used to understand how the numbers of the abundant genera shifted between the two treatment groups. Conclusion: At different tract sites the relative abundance of gut microbiota was different. Antibiotics could cause shifts in the microorganism composition and affect the composition of gut microbiota in the different tracts of weaning piglets.

• Molecules and Cells
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• 제40권11호
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• pp.814-822
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• 2017

Meiotic homologous recombination generates new combinations of preexisting genetic variation and is a crucial process in plant breeding. Within the last decade, our understanding of plant meiotic recombination and genome diversity has advanced considerably. Innovation in DNA sequencing technology has led to the exploration of high-resolution genetic and epigenetic information in plant genomes, which has helped to accelerate plant breeding practices via high-throughput genotyping, and linkage and association mapping. In addition, great advances toward understanding the genetic and epigenetic control mechanisms of meiotic recombination have enabled the expansion of breeding programs and the unlocking of genetic diversity that can be used for crop improvement. This review highlights the recent literature on plant meiotic recombination and discusses the translation of this knowledge to the manipulation of meiotic recombination frequency and location with regards to crop plant breeding.

• Genomics & Informatics
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• 제17권1호
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• pp.6.1-6.9
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• 2019

Understanding the role of the microbiome in human health and how it can be modulated is becoming increasingly relevant for preventive medicine and for the medical management of chronic diseases. The development of high-throughput sequencing technologies has boosted microbiome research through the study of microbial genomes and allowing a more precise quantification of microbiome abundances and function. Microbiome data analysis is challenging because it involves high-dimensional structured multivariate sparse data and because of its compositional nature. In this review we outline some of the procedures that are most commonly used for microbiome analysis and that are implemented in R packages. We place particular emphasis on the compositional structure of microbiome data. We describe the principles of compositional data analysis and distinguish between standard methods and those that fit into compositional data analysis.

• Journal of Microbiology and Biotechnology
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• 제22권2호
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• pp.159-165
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• 2012

The rhizobacterial composition varies according to the soil properties. To test if the effect of herbicides on the rhizobacterial communities of genetically modified NK603 glyphosate-tolerant maize varies according to different soil locations, a comparison was made between the effects of glyphosate (Roundup Plus), a post-emergence applied herbicide, and a pre-emergence applied herbicide (GTZ) versus untreated soil. The potential effect was monitored by direct amplification, cloning, and sequencing of the soil DNA encoding 16S rRNA, and high-throughput DNA pyrosequencing of the bacterial DNA coding for the 16S rRNA hypervariable V6 region. The results obtained using three different methods to analyze the herbicide effect on the rhizobacterial communities of genetically modified NK603 maize were comparable to those previously obtained when glyphosate-tolerant maize was grown in soil with different characteristics. Both herbicides decreased the bacterial diversity in the rhizosphere, with Actinobacteria being the taxonomic group most affected. The results suggest that both herbicides affected the structure of the maize rhizobacterial community, but glyphosate was environmentally less aggressive.

• 한국작물학회:학술대회논문집
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• 한국작물학회 2017년도 9th Asian Crop Science Association conference
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• pp.14-14
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• 2017

The discipline of plant breeding is experiencing a renaissance impacting crop improvement as a result of new technologies, however fundamental questions remain for predicting the phenotype and how the environment and genetics shape it. Inexpensive DNA sequencing, genotyping, new statistical methods, high throughput phenotyping and gene-editing are revolutionizing breeding methods and strategies for improving both quantitative and qualitative traits. Genomic selection (GS) models use genome-wide markers to predict performance for both phenotyped and non-phenotyped individuals. Aerial and ground imaging systems generate data on correlated traits such as canopy temperature and normalized difference vegetative index that can be combined with genotypes in multivariate models to further increase prediction accuracy and reduce the cost of advanced trials with limited replication in time and space. Design of a GS training population is crucial to the accuracy of prediction models and can be affected by many factors including population structure and composition. Prediction models can incorporate performance over multiple environments and assess GxE effects to identify a highly predictive subset of environments. We have developed a methodology for analyzing unbalanced datasets using genome-wide marker effects to group environments and identify outlier environments. Environmental covariates can be identified using a crop model and used in a GS model to predict GxE in unobserved environments and to predict performance in climate change scenarios. These new tools and knowledge challenge the plant breeder to ask the right questions and choose the tools that are appropriate for their crop and target traits. Contemporary plant breeding requires teams of people with expertise in genetics, phenotyping and statistics to improve efficiency and increase prediction accuracy in terms of genotypes, experimental design and environment sampling.

• Genomics & Informatics
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• 제2권4호
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• pp.174-179
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• 2004

We developed various plasmid cloning vectors that are useful in the construction of genomic and shotgun libraries. Two medium copy vectors, pCUGlblu21 (pCb21) and pAGlblu21 (pAb21), which are resistant to kanamycin ($Km^R$) and chloramphenicol ($Cam^R$), respectively, are useful for cloning DNA inserts ranging from 5kb to 15kb. Two high copy vectors, pCUGlblu31 (pCb31) and pAGlblu31 (pAb31), containing $Km^R$ and $Cam^R$, respectively, are useful for DNA inserts less than 5kb. These vectors are well adapted for large-scale genome sequencing projects by providing choice of copy number and selectable marker. The small vector size is another advantage of these vectors. All vectors contain lacZa including multicloning sites that originated from pBluscriptllsk- for easy cloning and sequencing. Two medium copy vectors contain unique and rare cutting Swal (ATTTAAAT) restriction enzyme sites for easy determination of insert size. We developed two combined vectors, pC21A31 and pC31A21, which are combinations of (pCb21 + pAb31) and (pCb31 + pAb21), respectively. These two vectors provide four choices of vectors such as $Km^R$ and medium, $Cam^R$ and high, $Cam^R$ and medium, and $Km^R$ and high copy vectors by restriction enzyme cutting, dephosphorylation, and gel purification. These vectors were successfully applied to high throughput shotgun sequencing of rice, tomato, and brassica BAC clones. With an example of extremely biased hydro sheared 3 kb shotgun library of a tomato BAC clone, which is originated from cytogenetically defined peri-centromeric region, we suggest the utility of an additional 10 kb library for sequence assembly of the difficult-to-assemble BAC clone.

• Genomics & Informatics
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• 제11권4호
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• pp.200-210
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• 2013

Studying biological networks, such as protein-protein interactions, is key to understanding complex biological activities. Various types of large-scale biological datasets have been collected and analyzed with high-throughput technologies, including DNA microarray, next-generation sequencing, and the two-hybrid screening system, for this purpose. In this review, we focus on network-based approaches that help in understanding biological systems and identifying biological functions. Accordingly, this paper covers two major topics in network biology: reconstruction of gene regulatory networks and network-based applications, including protein function prediction, disease gene prioritization, and network-based genome-wide association study.

• Molecules and Cells
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• 제45권5호
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• pp.273-283
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• 2022

During meiosis, homologous chromosomes (homologs) pair and undergo genetic recombination via assembly and disassembly of the synaptonemal complex. Meiotic recombination is initiated by excess formation of DNA double-strand breaks (DSBs), among which a subset are repaired by reciprocal genetic exchange, called crossovers (COs). COs generate genetic variations across generations, profoundly affecting genetic diversity and breeding. At least one CO between homologs is essential for the first meiotic chromosome segregation, but generally only one and fewer than three inter-homolog COs occur in plants. CO frequency and distribution are biased along chromosomes, suppressed in centromeres, and controlled by pro-CO, anti-CO, and epigenetic factors. Accurate and high-throughput detection of COs is important for our understanding of CO formation and chromosome behavior. Here, we review advanced approaches that enable precise measurement of the location, frequency, and genomic landscapes of COs in plants, with a focus on Arabidopsis thaliana.