• Genomics & Informatics
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• v.15 no.4
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• pp.136-141
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• 2017

Accurate detection of genomic alterations, especially druggable hotspot mutations in tumors, has become an essential part of precision medicine. With targeted sequencing, we can obtain deeper coverage of reads and handle data more easily with a relatively lower cost and less time than whole-exome or whole-genome sequencing. Recently, we designed a customized gene panel for targeted sequencing of major solid cancers. In this study, we aimed to validate its performance. The cancer panel targets 95 cancer-related genes. In terms of the limit of detection, more than 86% of target mutations with a mutant allele frequency (MAF) <1% can be identified, and any mutation with >3% MAF can be detected. When we applied this system for the analysis of Acrometrix Oncology Hotspot Control DNA, which contains more than 500 COSMIC mutations across 53 genes, 99% of the expected mutations were robustly detected. We also confirmed the high reproducibility of the detection of mutations in multiple independent analyses. When we explored copy number alterations (CNAs), the expected CNAs were successfully detected, and this result was confirmed by target-specific genomic quantitative polymerase chain reaction. Taken together, these results support the reliability and accuracy of our cancer panel in detecting mutations. This panel could be useful for key mutation profiling research in solid tumors and clinical translation.

• Genomics & Informatics
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• v.18 no.2
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• pp.17.1-17.4
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• 2020

The amount of content on social media platforms such as Twitter is expanding rapidly. Simultaneously, the lack of patient information seriously hinders the diagnosis and treatment of rare/intractable diseases. However, these patient communities are especially active on social media. Data from social media could serve as a source of patient-centric knowledge for these diseases complementary to the information collected in clinical settings and patient registries, and may also have potential for research use. To explore this question, we attempted to extract patient-centric knowledge from social media as a task for the 3-day Biomedical Linked Annotation Hackathon 6 (BLAH6). We selected amyotrophic lateral sclerosis and multiple sclerosis as use cases of rare and intractable diseases, respectively, and we extracted patient histories related to these health conditions from Twitter. Four diagnosed patients for each disease were selected. From the user timelines of these eight patients, we extracted tweets that might be related to health conditions. Based on our experiment, we show that our approach has considerable potential, although we identified problems that should be addressed in future attempts to mine information about rare/intractable diseases from Twitter.

• Genomics & Informatics
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• v.12 no.1
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• pp.35-41
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• 2014

Single-nucleotide polymorphisms (SNPs) have been emerging out of the efforts to research human diseases and ethnic disparities. A semantic network is needed for in-depth understanding of the impacts of SNPs, because phenotypes are modulated by complex networks, including biochemical and physiological pathways. We identified ethnicity-specific SNPs by eliminating overlapped SNPs from HapMap samples, and the ethnicity-specific SNPs were mapped to the UCSC RefGene lists. Ethnicity-specific genes were identified as follows: 22 genes in the USA (CEU) individuals, 25 genes in the Japanese (JPT) individuals, and 332 genes in the African (YRI) individuals. To analyze the biologically functional implications for ethnicity-specific SNPs, we focused on constructing a semantic network model. Entities for the network represented by "Gene," "Pathway," "Disease," "Chemical," "Drug," "ClinicalTrials," "SNP," and relationships between entity-entity were obtained through curation. Our semantic modeling for ethnicity-specific SNPs showed interesting results in the three categories, including three diseases ("AIDS-associated nephropathy," "Hypertension," and "Pelvic infection"), one drug ("Methylphenidate"), and five pathways ("Hemostasis," "Systemic lupus erythematosus," "Prostate cancer," "Hepatitis C virus," and "Rheumatoid arthritis"). We found ethnicity-specific genes using the semantic modeling, and the majority of our findings was consistent with the previous studies - that an understanding of genetic variability explained ethnicity-specific disparities.

• 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.

• Genomics & Informatics
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• v.14 no.4
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• pp.138-148
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• 2016

The success of genome-wide association studies (GWASs) has enabled us to improve risk assessment and provide novel genetic variants for diagnosis, prevention, and treatment. However, most variants discovered by GWASs have been reported to have very small effect sizes on complex human diseases, which has been a big hurdle in building risk prediction models. Recently, many statistical approaches based on penalized regression have been developed to solve the "large p and small n" problem. In this report, we evaluated the performance of several statistical methods for predicting a binary trait: stepwise logistic regression (SLR), least absolute shrinkage and selection operator (LASSO), and Elastic-Net (EN). We first built a prediction model by combining variable selection and prediction methods for type 2 diabetes using Affymetrix Genome-Wide Human SNP Array 5.0 from the Korean Association Resource project. We assessed the risk prediction performance using area under the receiver operating characteristic curve (AUC) for the internal and external validation datasets. In the internal validation, SLR-LASSO and SLR-EN tended to yield more accurate predictions than other combinations. During the external validation, the SLR-SLR and SLR-EN combinations achieved the highest AUC of 0.726. We propose these combinations as a potentially powerful risk prediction model for type 2 diabetes.

• Interdisciplinary Bio Central
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• v.4 no.4
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• pp.11.1-11.8
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• 2012

Genes that are indispensable for survival are referred to as essential gene. Due to the momentous significance of these genes for cellular activity they can be selected potentially as drug targets. Here in this study, an essential gene for Streptococcus suis was predicted using coherent statistical analysis and powerful genome comparison computational method. At first the whole genome protein scatter plot was generated and subsequently, on the basis of statistical significance, a reference genome was chosen. The parameters set forth for selecting the reference genome was that the genome of the query (Streptococcus suis) and subject must fall in the same genus and yet they must vary to a good degree. Streptococcus pneumoniae was found to be suitable as the reference genome. A whole genome comparison was performed for the reference (Streptococcus pneumoniae) and the query genome (Streptococcus suis) and 14 conserved proteins from them were subjected to a screen for potential essential gene property. Among those 14 only one essential gene was found to be with impressive similarity score between reference and query. The essential gene encodes for a type of 'Clp protease'. Clp proteases play major roles in degrading misfolded proteins. Results found here should help formulating a drug against Strptococcus suis which is responsible for mild to severe clinical conditions in human. However, like many other computational studies, the study has to be validated furthermore through in vitro assays for concrete proof.

• Asian Oncology Nursing
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• v.12 no.4
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• pp.267-273
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• 2012

Purpose: The purpose of this review article is to introduce how the Korean Society of Genetic Nursing (KSGN) has evolved and tried to translate genomic knowledge to nursing practice, and then to suggest the future role of genetic nurses in Korea. Methods: A literature review was performed and the current status of genetic counselling in Korea was explored. Then the educational and clinical experiences of the authors were incorporated. Finally, the main activities of Korean nursing for genetics were identified. Results: Two types of genetic counsellor certification have been issued in Korea: one is issued by the Korean Society of Genetic Medicine, another by the Korean Society of Breast Cancer since June 2011. A few Korean nursing researchers have continuously performed research related to genetic nursing and undertook several research projects funded by the government since 2003. In February 2011, KSGN was established and is now trying to establish further international networks. Conclusion: Nursing genetic experts should be trained to integrate all specialties for genetic counselling, so they can provide holistic genetic services including ethical, legal, and social issues (ELSI).

• Clinical and Experimental Reproductive Medicine
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• v.42 no.3
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• pp.77-85
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• 2015

One of the key factors of early development is the specification of competence between the oocyte and the sperm, which occurs during gametogenesis. However, the starting point, growth, and maturation for acquiring competence during spermatogenesis and oogenesis in mammals are very different. Spermatogenesis includes spermiogenesis, but such a metamorphosis is not observed during oogenesis. Glycosylation, a ubiquitous modification, is a preliminary requisite for distribution of the structural and functional components of spermatids for metamorphosis. In addition, glycosylation using epididymal or female genital secretory glycans is an important process for the sperm maturation, the acquisition of the potential for fertilization, and the acceleration of early embryo development. However, nonemzymatic unexpected covalent bonding of a carbohydrate and malglycosylation can result in falling fertility rates as shown in the diabetic male. So far, glycosylation during spermatogenesis and the dynamics of the plasma membrane in the process of capacitation and fertilization have been evaluated, and a powerful role of glycosylation in spermatogenesis and early development is also suggested by structural bioinformatics, functional genomics, and functional proteomics. Further understanding of glycosylation is needed to provide a better understanding of fertilization and embryo development and for the development of new diagnostic and therapeutic tools for infertility.

• Genomics & Informatics
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• v.5 no.2
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• pp.83-87
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• 2007

The analysis of DNA microarray data is a rapidly evolving area of bioinformatics, and various types of microarray are emerging as some of the most exciting technologies for use in biological and clinical research. In recent years, microarray technology has been utilized in various applications such as the profiling of mRNAs, assessment of DNA copy number, genotyping, and detection of methylated sequences. However, the analysis of these heterogeneous microarray platform experiments does not need to be performed separately. Rather, these platforms can be co-analyzed in combination, for cross-validation. There are a number of separate laboratory information management systems (LIMS) that individually address some of the needs for each platform. However, to our knowledge there are no unified LIMS systems capable of organizing all of the information regarding multi-platform microarray experiments, while additionally integrating this information with tools to perform the analysis. In order to address these requirements, we developed a web-based LIMS system that provides an integrated framework for storing and analyzing microarray information generated by the various platforms. This system enables an easy integration of modules that transform, analyze and/or visualize multi-platform microarray data.

• Genomics & Informatics
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• v.16 no.2
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• pp.30-35
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• 2018

Patient-derived xenograft (PDX) models are useful tools for tumor biology research and testing the efficacy of candidate anticancer drugs targeting the druggable mutations identified in tumor tissue. However, it is still unknown how much of the genetic alterations identified in primary tumors are consistently detected in tumor tissues in the PDX model. In this study, we analyzed the genetic alterations of three primary colorectal cancers (CRCs) and matched xenograft tissues in PDX models using a next-generation sequencing cancer panel. Of the 17 somatic mutations identified from the three CRCs, 14 (82.4%) were consistently identified in both primary and xenograft tumors. The other three mutations identified in the primary tumor were not detected in the xenograft tumor tissue. There was no newly identified mutation in the xenograft tumor tissues. In addition to the somatic mutations, the copy number alteration profiles were also largely consistent between the primary tumor and xenograft tissue. All of these data suggest that the PDX tumor model preserves the majority of the key mutations detected in the primary tumor site. This study provides evidence that the PDX model is useful for testing targeted therapies in the clinical field and research on precision medicine.