• Journal of Digital Contents Society
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• v.10 no.2
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• pp.259-268
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• 2009

Although Sequencing & Navigation(S&N) of ADL SCORM 2004 specifies relatively simple ones of possible sequencing behaviors based on IMS SS(Simple Sequencing) in learning context, it is practically not easy to implement the sequencing properly within SCORM content packages. Actually, many Korean content developers construct SCORM content packages by choosing one out of well-known LSAL sequencing templates and just inserting their contents into it. In this paper, we survey a number of widely used SCORM sequencing templates provided by LSAL, ADL and Xerceo, and also domestic conventional SCORM sequencing models, and then present a new SCORM sequencing models well conformant to Korean e-learning environments. We finally develop 3 types of SCORM sequencing templates and their contents samples by applying our sequencing models to a number of available domestic SCOs. We expect that our products can be widely used and referenced as a guidance by content developers troubled in implementing a variety of SCORM sequencings.

• The KIPS Transactions:PartD
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• v.13D no.3 s.106
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• pp.425-436
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• 2006

The e-learning system in which the learning is carried out by predefined procedures cannot offer proper learning suitable to the capability of individual learner. To solve this problem, SCORM sequencing can be used to define various learning procedures according to the capabilities of learners. Currently the sequencing is designed by teachers or learning contents producers to regularize the learning program. However, the predefined sequencing may not reflect the characteristics of the learning group. If inappropriate sequencing is designed it may cause the unnecessary repetition of learning. In this paper, we propose an automated evaluation system in which dynamic sequencing is applied. The dynamic sequencing reflects the evaluation results to the standard scores used by sequencing. By changing the standard scores, the sequencing changes dynamically according to the evaluation results of a learning group. Through several experiments, we verified that the proposed learning system that uses the dynamic sequencing is effective for providing the proper learning procedures suitable to the capabilities of learners.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.1260-1263
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• 2011

최근 차세대 시퀀싱 (Next Generation Sequencing, NGS) 기술이 발전하면서 DNA, RNA 등의 시퀀싱 데이터를 이용한 유전체 분석 방식에 관한 연구가 활발히 이루어지고 있다. 차세대 시퀀싱 데이터를 이용한 유전체 분석 방식은 마이크로어레이 혹은 EST/cDNA 데이터를 이용한 기존의 분석 방식에 비하여 비용이 적게 들고 정확한 결과를 얻을 수 있다는 장점이 있다. 그러나 이 들 DNA, RNA 시퀀싱 데이터는 각 시퀀스의 길이가 짧고 전체 용량은 매우 커서 이 들 데이터로부터 정확한 분석 결과를 추출하는 데에 많은 어려움이 있다. 본 연구에서는 클라우드 컴퓨팅 기술을 기반으로 하여 대용량의 RNA 시퀀싱 데이터를 고속으로 처리하는 병렬 SNP 추출 알고리즘을 제안한다. 전체 게놈 데이터 중 유전자 영역만을 high coverage로 시퀀싱하여 얻어지는 RNA 시퀀싱 데이터는 유전자 변이 추출을 목적으로 분석되며, SNP(Single Nucleotide Polymorphism)와 같은 유전자 변이는 질병의 원인 규명 및 치료법 개발에 직접 이용된다. 제안된 알고리즘은 동시에 실행되는 다수의 Map/Reduce 함수에 의해서 대규모 RNA 시퀀스를 병렬로 처리하며, 레퍼런스 시퀀스에 매핑된 각 염기의 출현 빈도와 품질점수를 이용하여 SNP를 추출한다. 또한 이 들 SNP 추출 결과에 대한 시각적 분석 도구를 제공하여 SNP 추출 과정 및 근거를 시각적으로 확인/검증할 수 있도록 지원한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.1203-1206
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• 2013

차세대 유전자 서열 시퀀싱 기법이 등장함에 따라 참조 유전자 서열로부터 리드를 생성하는 시퀀서의 기술이 다양화 되었다. 이전 시퀀싱 방식에 비해 비용 및 시간 측면에서 효율성이 증대 되었으나, 매핑도구의 검증을 위해서 다양한 생물학적 특이성을 반영하거나 비용이 소요되지 않는 방법을 연구하는 과정에서 리드 시퀀싱 시뮬레이터가 개발되었다. 본 논문에서는 현재 사용되고 있는 리드 시퀀싱 시뮬레이터에서 반영된 시퀀싱 기법을 분석하고 시뮬레이터의 기능적 특성을 분석하고자 한다. 이는 시뮬레이터 개발에 필요한 기능 설계 및 생물학적 특성을 반영하는데 활용하고자 한다.

• Journal of Digital Convergence
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• v.10 no.6
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• pp.189-196
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• 2012

In this paper, we propose a Scorm-based Sequencing & Navigation Model for Collaborative Learning. It is an e-Learning process control model that is used to efficiently and graphically defining Scorm's content aggregation model and its sequencing prerequistites through a formal approach. To define a process based model uses the expanded ICN(Information Control Net) model. which is called SCOSNCN(SCO Sequencing & Navigation Control Net). We strongly believe that the process-driven model delivers a way of much more convenient content aggregating work and system, in terms of not only defining the intended sequence and ordering of learning activities, but also building the runtime environment for sequencing and navigation of learning activities and experiences.

• Proceedings of the Korea Multimedia Society Conference
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• 2003.11b
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• pp.897-900
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• 2003

본 논문에서는 SCORM 기반 시퀀싱 모델을 기반으로 학습객체의 구조에 대한 정보, 학습자에게 학습 객체를 어떻게 전달할 지를 결정하는 규칙 등을 포함하고 있는 컨텐츠 구조를 제시하고 학습 컨텐츠의 재사용과 공유가 가능하고 동일한 학습 컨텐츠에 서로 다른 교수법을 적용하여 교육의 효과를 달리할 수 있도록 시퀀싱을 위한 컨텐츠 패키지 메타데이터 생성기를 개발한다. 또한 학습자 정보 트래킹을 위한 SCO(Sharable Content Object)함수를 부착하여 학습 객체가 SCORM RTE(Run-Time Environment)와 통신 할 수 있도록 PIF(Package Interchange File)로 자동 패키징 시킨다.

• Proceedings of the Korea Information Processing Society Conference
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• 2006.05a
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• pp.477-480
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• 2006

본 논문에서는 기존 학습 관리 시스템(Learning Management System : LMS)의 JAVA Applet으로 구현된 SCORM 2004 시퀀싱 엔진 및 데이터 모델에서 나타난 문제점을 해결할 수 있는 Ajax(Asynchronous JavaScript and Xml) 기반의 SCORM 2004 시퀀싱 엔진 및 데이터 모델을 제안한다. 기존 JAVA Applet 으로 구현된 시퀀싱 엔진 및 데이터 모델에서의 VM(Virtual Machine) 구동상에 발생하는 보안 및 인증 문제, VM 구동에 따른 제한점 및 시스템 처리속도의 문제점을 해결하였다.

• Journal of the KSME
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• v.53 no.5
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• pp.51-55
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• 2013

이 글에서는 단분자해석방법을 이용하는 3세대 DNA 시퀀싱에 나타나는 다중물리-다중스케일 유동에 대한 해석방법들을 소개하고, 관련된 예제로 탄소나노튜브을 통한 DNA를 포함한 전해질 유동의 최신결과들을 소개하고자 한다.

• Korean Journal of Clinical Laboratory Science
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• v.56 no.1
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• pp.10-20
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• 2024

RNA-sequencing (RNA-seq) is a technique used for providing global patterns of transcriptomes in samples. However, it can only provide the average gene expression across cells and does not address the heterogeneity within the samples. The advances in single-cell RNA sequencing (scRNA-seq) technology have revolutionized our understanding of heterogeneity and the dynamics of gene expression at the single-cell level. For example, scRNA-seq allows us to identify the cell types in complex tissues, which can provide information regarding the alteration of the cell population by perturbations, such as genetic modification. Since its initial introduction, scRNA-seq has rapidly become popular, leading to the development of a huge number of bioinformatic tools. However, the analysis of the big dataset generated from scRNA-seq requires a general understanding of the preprocessing of the dataset and a variety of analytical techniques. Here, we present an overview of the workflow involved in analyzing the scRNA-seq dataset. First, we describe the preprocessing of the dataset, including quality control, normalization, and dimensionality reduction. Then, we introduce the downstream analysis provided with the most commonly used computational packages. This review aims to provide a workflow guideline for new researchers interested in this field.

• Journal of Life Science
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• v.29 no.1
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• pp.1-8
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• 2019

In Hanwoo cattle (Korean native cattle), there is a scarcity of comparative analysis papers using highdepth sequencing and haplotype phasing, particularly a comparative analysis of the Truseq Synthetic Long-Read Haplotyping sequencing platform serviced by Illumina (TSLRH) versus the Chromium Genome Sequencing platform serviced by 10X Genomics (10XG). DNA was extracted from the sperm of a Hanwoo breeding bull (ID: TN1505D2184/27214) provided by Hanwoo research canter and used for the generation of sequence data from both the sequencing platforms. We then identified SNVs using an appropriate analysis pipeline tailored for each platform. The TSLRH and 10XG platforms generated a total of 355,208,304 and 1,632,772,004 reads, respectively, corresponding to a Q30 (%) of 89.04% and 88.60%, respectively, of which 351,992,768(99.09%) and 1,526,641,824(93.50%) were successfully mapped. For the TSLRH and 10XG platforms, the mean depth of the sequencing was 13.04X and 74.3X, the longest phase block was 1,982,706 bp and 1,480,081 bp, the N50 phase block was 57,637 bp and 114,394 bp, the total number of SNVs identified was 4,534,989 and 8,496,813, and the total phased rate was 72.29% and 87.67%, respectively. Moreover, for each chromosome, we identified unique and common SNVs using both sequencing platforms. The number of SNVs was directly proportional to the length of the chromosome. Based on our results, we recommend the use of the 10XG platform for haplotype phasing and SNV identification, as it generated a longer N50 phase block, in addition to a higher mean depth, total number of reads, total number of SNVs, and phase rate, than the TSLRH platform.