• Journal of KIISE:Databases
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• v.41 no.4
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• pp.249-255
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• 2014

Copy number variations(CNVs) are a recently recognized class of human structural variations and are associated with a variety of human diseases, including cancer. To find important cancer genes, researchers identify novel CNVs in patients with a particular cancer and analyze large amounts of genomic and clinical data. We present a tool called CNVDAT which is able to detect CNVs from NGS data and systematically analyze the genomic and clinical data associated with variations. CNVDAT consists of two modules, CNV Detection Engine and Sequence Analyser. CNV Detection Engine extracts CNVs by using the multi-resolution system of scale-space filtering, enabling the detection of the types and the exact locations of CNVs of all sizes even when the coverage level of read data is low. Sequence Analyser is a user-friendly program to view and compare variation regions between tumor and matched normal samples. It also provides a complete analysis function of refGene and OMIM data and makes it possible to discover CNV-gene-phenotype relationships. CNVDAT source code is freely available from http://dblab.hallym.ac.kr/CNVDAT/.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.11a
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• pp.356-359
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• 2008

최근 생물정보학 분야에서 인간 유전체에 존재하는 CNV(copy number variation)에 관한 연구가 주목 받고 있다. CNV 영역은 1kbp-3Mbp 사리의 서열이 반복되거나 결실되는 변이 영역으로 정의된다. 우리는 선행연구에서 기가 시퀀싱(giga sequencing)의 결과 산출되는 DNA 서열조각인 리드(read)를 레퍼런스 시퀀스에 서열 정렬하여 CNV 영역을 찾아내는 새로운 CNV 검색 방식을 제안하였다. 후속 연구로서 본 논문에서는 DNA 서열에 존재하는 repeat 영역 문제를 해결하기 위한 새로운 방안을 제안하고, 리드의 출현 빈도 정보를 분석하여 CNV 영역을 찾아내는 CNV 영역 검색 알고리즘을 보인다. 제안된 알고리즘 Gaussian 분포를 갖는 출현 빈도 정보로부터 통계적 유의성을 갖는 영역을 추출하여 CNV 영역후보로 하고, 다음 경제 과정을 거쳐 최종의 CNV 영역을 추출한다. 성능 평가를 위하여 프로토타임 시스템을 개발하였으며, 시뮬레이션 실험을 수행하였다. 실험 결과에 의하여 제안된 방식은 반복되거나 결실되는 형태의 CNV 영역을 효율적으로 검출하며, 또한 다양한 크기의 CNV 영역을 효율적으로 검출할 수 있음을 입증한다.

• The KIPS Transactions:PartD
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• v.16D no.5
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• pp.661-672
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• 2009

Recently it was found that various genetic structural variations such as CNV(copy number variation) exist in the human genome, and these variations are closely related with disease susceptibility, reaction to treatment, and genetic characteristics. In this paper we propose a new CNV detection algorithm using millions of short DNA sequences generated by giga-sequencing technology. Our method maps the DNA sequences onto the reference sequence, and obtains the occurrence frequency of each read in the reference sequence. And then it detects the statistically significant regions which are longer than 1Kbp as the candidate CNV regions by analyzing the distribution of the occurrence frequency. To select a proper read alignment method, several methods are employed in our algorithm, and the performances are compared. To verify the superiority of our approach, we performed extensive experiments. The result of simulation experiments (using a reference sequence, build 35 of NCBI) revealed that our approach successfully finds all the CNV regions that have various shapes and arbitrary length (small, intermediate, or large size).

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.1264-1267
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• 2011

시퀀싱 기술의 발달로 최근에는 비교적 저렴한 비용으로 개인의 유전체 시퀀싱 데이터를 산출할 수 있게 되었다. 하지만 이를 기반으로 하는 기존의 분석 방법은 매우 고가의 컴퓨팅 환경을 요구하기 때문에 분석을 위한 비용이 매우 높은 문제가 있다. 본 논문에서 클라우드 컴퓨팅 환경의 병렬 CNV 검출알고리즘을 제안한다. 제안하는 방법은 모양 기반의 CNV 검출 알고리즘인 CNV_shape을 MapReduce 기법으로 개발한 것으로 시퀀싱 데이터를 레퍼런스 서열에 매핑한 결과로부터 리드 커버리지 (read coverage)를 계산하여 커버리지가 감소하거나 증가하는 일정 길이 이상의 영역을 검출하는 방법이다. 클라우드 컴퓨팅 환경에 적용하고 노드의 밸런싱 유지를 위한 방법으로 파티셔닝 기법을 사용하였다. 또한 실 데이터를 이용한 실험을 통해 제안하는 방법의 효율적 데이터 처리를 보인다.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06c
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• pp.19-21
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• 2012

모든 암 세포는 체세포 변이를 동반한다. 따라서 암 유전체 변이 분석에 의하여 암을 발생시키는 유전자 및 진단/치료법을 찾아낼 수 있다. 본 연구에서는 차세대 시퀀싱 데이터를 이용하여 암 특이적 단이 반복 변이(copy number variation, CNV) 유형을 밝히는 새로운 알고리즘을 제안한다. 제안하는 방식은 암 환자의 정상 세포와 암세포로부터 얻어진 정상 유전체와 암 유전체를 동시 분석하여 각각 CNV 후보 영역을 추출하며, 통계적 유의성 분석을 통하여 암 특이적 CNV 후보 영역을 선별하고, 다음 후처리 과정에서 참조 표준 서열(reference sequence)에 존재하는 오류 영역 보정 작업을 수행하여 정확한 암 특이적 CNV 영역을 추출해 낸다. 또한 다수의 대용량 유전체 데이터 동시 분석을 위하여 맵리듀스(MapReduce) 기법을 기반으로 하는 병렬 수행 알고리즘을 제안한다.

• Genomics & Informatics
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• v.6 no.4
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• pp.231-234
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• 2008

Precise and reliable identification of CNV is still important to fully understand the effect of CNV on genetic diversity and background of complex diseases. SNP marker has been used frequently to detect CNVs, but the analysis of SNP chip data for identifying CNV has not been well established. We compared various normalization methods for CNV analysis and suggest optimal normalization procedure for reliable CNV call. Four normal Koreans and NA10851 HapMap male samples were genotyped using Affymetrix Genome-Wide Human SNP array 5.0. We evaluated the effect of median and quantile normalization to find the optimal normalization for CNV detection based on SNP array data. We also explored the effect of Robust Multichip Average (RMA) background correction for each normalization process. In total, the following 4 combinations of normalization were tried: 1) Median normalization without RMA background correction, 2) Quantile normalization without RMA background correction, 3) Median normalization with RMA background correction, and 4) Quantile normalization with RMA background correction. CNV was called using SW-ARRAY algorithm. We applied 4 different combinations of normalization and compared the effect using intensity ratio profile, box plot, and MA plot. When we applied median and quantile normalizations without RMA background correction, both methods showed similar normalization effect and the final CNV calls were also similar in terms of number and size. In both median and quantile normalizations, RMA backgroundcorrection resulted in widening the range of intensity ratio distribution, which may suggest that RMA background correction may help to detect more CNVs compared to no correction.

• Asian-Australasian Journal of Animal Sciences
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• v.33 no.9
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• pp.1378-1386
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• 2020

Objective: Chinese indigenous sheep breeds can be classified into the following three categories by their tail morphology: fat-tailed, fat-rumped and thin-tailed sheep. The typical sheep breeds corresponding to fat-tailed, fat-rumped, and thin-tailed sheep are large-tailed Han, Altay, and Tibetan sheep, respectively. Detection of copy number variation (CNV) and selection signatures provides information on the genetic mechanisms underlying the phenotypic differences of the different sheep types. Methods: In this study, PennCNV software and F-statistics (F_ST) were implemented to detect CNV and selection signatures, respectively, on the X chromosome in three Chinese indigenous sheep breeds using ovine high-density 600K single nucleotide polymorphism arrays. Results: In large-tailed Han, Altay, and Tibetan sheep, respectively, a total of six, four and 22 CNV regions (CNVRs) with lengths of 1.23, 0.93, and 7.02 Mb were identified on the X chromosome. In addition, 49, 34, and 55 candidate selection regions with respective lengths of 27.49, 16.47, and 25.42 Mb were identified in large-tailed Han, Altay, and Tibetan sheep, respectively. The bioinformatics analysis results indicated several genes in these regions were associated with fat, including dehydrogenase/reductase X-linked, calcium voltage-gated channel subunit alpha1 F, and patatin like phospholipase domain containing 4. In addition, three other genes were identified from this analysis: the family with sequence similarity 58 member A gene was associated with energy metabolism, the serine/arginine-rich protein specific kinase 3 gene was associated with skeletal muscle development, and the interleukin 2 receptor subunit gamma gene was associated with the immune system. Conclusion: The results of this study indicated CNVRs and selection regions on the X chromosome of Chinese indigenous sheep contained several genes associated with various heritable traits.

• Journal of Genetic Medicine
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• v.20 no.2
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• pp.46-51
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• 2023

Exonic copy number variation (CNV), involving deletions and duplications at the gene's exon level, presents challenges in detection due to their variable impact on gene function. The study delves into the complexities of identifying large CNVs and investigates less familiar but recurrent exonic CNVs, notably enriched in East Asian populations. Examining specific cases like DRC1, STX16, LAMA2, and CFTR highlights the clinical implications and prevalence of exonic CNVs in diverse populations. The review addresses diagnostic challenges, particularly for single exon alterations, advocating for a strategic, multi-method approach. Diagnostic methods, including multiplex ligation-dependent probe amplification, droplet digital PCR, and CNV screening using next-generation sequencing data, are discussed, with whole genome sequencing emerging as a powerful tool. The study underscores the crucial role of ethnic considerations in understanding specific CNV prevalence and ongoing efforts to unravel subtle variations. The ultimate goal is to advance rare disease diagnosis and treatment through ethnically-specific therapeutic interventions.

• Journal of Genetic Medicine
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• v.12 no.2
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• pp.61-65
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• 2015

Whole genome sequencing (WGS)-based noninvasive prenatal test (NIPT) is the first method applied in the clinical setting out of various NIPT techniques. Several companies, such as Sequenom, BGI, and Illumina offer WGS-based NIPT, each with different technical and bioinformatic approaches. Sequenom, BGI, and Illumina utilize z-, t-, and L-scores, as well as normalized chromosome values, respectively, for trisomy detection. Their outstanding performance has been demonstrated in clinical studies of more than 100,000 pregnancies. The sensitivity and specificity for detection of trisomies 13, 18, and 21 were above 98%, as reported by all three companies. Unlike other techniques, WGS-based NIPT can detect other trisomies as well as clinically significant segmental duplications/deletions within a chromosome, which could expand the scope of NIPT. Incorrect results could be due to low fetal fraction, fetoplacental mosaicism, confined placental mosaicism or maternal copy number variation (CNV). Among those, maternal CNV is a significant contributor of false positive results and therefore genome wide scanning plays an important role in preventing the occurrence of false positives. In this article, the bioinformatic techniques and clinical performance of three major companies are comprehensively reviewed.

• Genomics & Informatics
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• v.6 no.3
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• pp.126-129
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• 2008

The robust identification and comprehensive profiling of copy number alterations (CNAs) is highly challenging. The amount of data obtained from high-throughput technologies such as array-based comparative genomic hybridization is often too large and it is required to develop a comprehensive and versatile tool for the detection and visualization of CNAs in a genome-wide scale. With this respective, we introduce a software framework, CGHscape that was originally developed to explore the CNAs for the study of copy number variation (CNV) or tumor biology. As a standalone program, CGHscape can be easily installed and run in Microsoft Windows platform. With a user-friendly interface, CGHscape provides a method for data smoothing to cope with the intrinsic noise of array data and CNA detection based on SW-ARRAY algorithm. The analysis results can be demonstrated as log2 plots for individual chromosomes or genomic distribution of identified CNAs. With extended applicability, CGHscape can be used for the initial screening and visualization of CNAs facilitating the cataloguing and characterizing chromosomal alterations of a cohort of samples.