• Animal Bioscience
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• v.37 no.3
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• pp.461-470
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• 2024

Objective: The objective of this study was to investigate the genetic diversity, population structure and whole-genome selection signatures of Luxi cattle to reveal its genomic characteristics in terms of meat and carcass traits, skeletal muscle development, body size, and other traits. Methods: To further analyze the genomic characteristics of Luxi cattle, this study sequenced the whole-genome of 16 individuals from the core conservation farm in Shandong region, and collected 174 published genomes of cattle for conjoint analysis. Furthermore, three different statistics (pi, F_st, and XP-EHH) were used to detect potential positive selection signatures related to selection in Luxi cattle. Moreover, gene ontology and Kyoto encyclopedia of genes and genomes pathway enrichment analyses were performed to reveal the potential biological function of candidate genes harbored in selected regions. Results: The results showed that Luxi cattle had high genomic diversity and low inbreeding levels. Using three complementary methods (pi, F_st, and XP-EHH) to detect the signatures of selection in the Luxi cattle genome, there were 2,941, 2,221 and 1,304 potentially selected genes identified, respectively. Furthermore, there were 45 genes annotated in common overlapping genomic regions covered 0.723 Mb, including PLAG1 zinc finger (PLAG1), dedicator of cytokinesis 3 (DOCK3), ephrin A2 (EFNA2), DAZ associated protein 1 (DAZAP1), Ral GTPase activating protein catalytic subunit alpha 1 (RALGAPA1), mediator complex subunit 13 (MED13), and decaprenyl diphosphate synthase subunit 2 (PDSS2), most of which were enriched in pathways related to muscle growth and differentiation and immunity. Conclusion: In this study, we provided a series of genes associated with important economic traits were found in positive selection regions, and a scientific basis for the scientific conservation and genetic improvement of Luxi cattle.

• Journal of Information Processing Systems
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• v.13 no.2
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• pp.204-214
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• 2017

Artificial intelligence, especially deep learning technology, is penetrating the majority of research areas, including the field of bioinformatics. However, deep learning has some limitations, such as the complexity of parameter tuning, architecture design, and so forth. In this study, we analyze these issues and challenges in regards to its applications in bioinformatics, particularly genomic analysis and medical image analytics, and give the corresponding approaches and solutions. Although these solutions are mostly rule of thumb, they can effectively handle the issues connected to training learning machines. As such, we explore the tendency of deep learning technology by examining several directions, such as automation, scalability, individuality, mobility, integration, and intelligence warehousing.

• International Journal of Oral Biology
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• v.45 no.3
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• pp.77-83
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• 2020

In the oral cavity, complex microbial community is shaped by various host and environmental factors. Extensive literature describing the oral microbiome in the context of oral health and disease is available. Advances in DNA sequencing technologies and data analysis have drastically improved the analysis of the oral microbiome. For microbiome study, bacterial 16S ribosomal RNA gene amplification and sequencing is often employed owing to the cost-effective and fast nature of the method. In this review, practical considerations for performing a microbiome study, including experimental design, molecular analysis technology, and general data analysis, will be discussed.

• Genomics & Informatics
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• v.4 no.3
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• pp.129-132
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• 2006

Toxicogenomics has recently emerged in the field of toxicology and the DNA microarray technique has become common strategy for predictive toxicology which studies molecular mechanism caused by exposure of chemical or environmental stress. Although microarray experiment offers extensive genomic information to the researchers, yet high dimensional characteristic of the data often makes it hard to extract meaningful result. Therefore we developed toxicant enrichment analysis similar to the common enrichment approach. We also developed web-based system graPT to enable considerable prediction of toxic endpoints of experimental chemical.

• Genomics & Informatics
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• v.21 no.2
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• pp.27.1-27.7
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• 2023

Recombination events complicate the evolutionary history of populations and species and have a significant impact on the inference of isolation-with-migration (IM) models. However, several existing methods have been developed, assuming no recombination within a locus and free recombination between loci. In this study, we investigated the effect of recombination on the estimation of IM models using genomic data. We conducted a simulation study to evaluate the consistency of the parameter estimators with up to 1,000 loci and analyze true gene trees to examine the sources of errors in estimating the IM model parameters. The results showed that the presence of recombination led to biased estimates of the IM model parameters, with population sizes being more overestimated and migration rates being more underestimated as the number of loci increased. The magnitude of the biases tended to increase with the recombination rates when using 100 or more loci. On the other hand, the estimation of splitting times remained consistent as the number of loci increased. In the absence of recombination, the estimators of the IM model parameters remained consistent.

• Genomics & Informatics
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• v.15 no.4
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• pp.178-182
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• 2017

Next-generation sequencing (NGS) technology has become a trend in the genomics research area. There are many software programs and automated pipelines to analyze NGS data, which can ease the pain for traditional scientists who are not familiar with computer programming. However, downstream analyses, such as finding differentially expressed genes or visualizing linkage disequilibrium maps and genome-wide association study (GWAS) data, still remain a challenge. Here, we introduce a dockerized web application written in R using the Shiny platform to visualize pre-analyzed RNA sequencing and GWAS data. In addition, we have integrated a genome browser based on the JBrowse platform and an automated intermediate parsing process required for custom track construction, so that users can easily build and navigate their personal genome tracks with in-house datasets. This application will help scientists perform series of downstream analyses and obtain a more integrative understanding about various types of genomic data by interactively visualizing them with customizable options.

• Genomics & Informatics
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• v.16 no.4
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• pp.29.1-29.5
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• 2018

Hybrid capture-based targeted sequencing is being used increasingly for genomic variant profiling in tumor patients. Unique molecular index (UMI) technology has recently been developed and helps to increase the accuracy of variant calling by minimizing polymerase chain reaction biases and sequencing errors. However, UMI-adopted targeted sequencing data analysis is slightly different from the methods for other types of omics data, and its pipeline for variant calling is still being optimized in various study groups for their own purposes. Due to this provincial usage of tools, our group built an analysis pipeline for global application to many studies of targeted sequencing generated with different methods. First, we generated hybrid capture-based data using genomic DNA extracted from tumor tissues of colorectal cancer patients. Sequencing libraries were prepared and pooled together, and an 8-plexed capture library was processed to the enrichment step before 150-bp paired-end sequencing with Illumina HiSeq series. For the analysis, we evaluated several published tools. We focused mainly on the compatibility of the input and output of each tool. Finally, our laboratory built an analysis pipeline specialized for UMI-adopted data. Through this pipeline, we were able to estimate even on-target rates and filtered consensus reads for more accurate variant calling. These results suggest the potential of our analysis pipeline in the precise examination of the quality and efficiency of conducted experiments.

• Journal of Animal Science and Technology
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• v.55 no.3
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• pp.173-178
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• 2013

The characteristics of lactation persistency was investigated for new evaluation trait using 4,366,900 milk yield records from 436,690 heads of Korean Holstein dairy cattle. The average lactation persistencies of first parity, second parity and over third parity were 97.5%, 95.1% and 94.6%, respectively and there was a trend that after the peak yield, lactation persistency decreased collectively. The average days of peak milk yields after calving was about 50 days, but only 33.2% of cows reached peak yields at 36~66 days (second test day). Also, there was a difference between the milk yield of cows which reached peak yields at first test day by lactation days and that of cows which reached peak yields at second to fourth test day. The estimates of heritabilty and repeatability for mean lactation persistency were 0.16 and 0.35, respectively. The genetic correlation between cumulative lactation persistency from third to tenth test day and that from third to seventh test day was 0.91 and while it increased in later test day, it decreased sharply in earlier test day. The breeding value correlations of Data II and III for Data I were 0.80 and 0.72, respectively, while the rank correlations were 0.78 and 0.71, respectively. Based on the results, the breeding value and rank correlations decreased when more data were added.

• The Korea Genome Conference
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• 2003.09a
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• pp.53.2-53.2
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• 2003

• Genomics & Informatics
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• v.12 no.1
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• pp.21-34
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• 2014

A visual analysis approach and the developed supporting technology provide a comprehensive solution for analyzing large and complex integrated genomic and biomedical data. This paper presents a methodology that is implemented as an interactive visual analysis technology for extracting knowledge from complex genetic and clinical data and then visualizing it in a meaningful and interpretable way. By synergizing the domain knowledge into development and analysis processes, we have developed a comprehensive tool that supports a seamless patient-to-patient analysis, from an overview of the patient population in the similarity space to the detailed views of genes. The system consists of multiple components enabling the complete analysis process, including data mining, interactive visualization, analytical views, and gene comparison. We demonstrate our approach with medical scientists on a case study of childhood cancer patients on how they use the tool to confirm existing hypotheses and to discover new scientific insights.