• 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.

• 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.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.11a
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• pp.669-671
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• 2014

High Throughput Sequencing (HTS) 기술의 발달로 인해 시퀀싱 비용이 감소함에 따라 다양한 분야에서 이를 활용한 융합 연구가 활발하게 진행되고 있다. HTS 기술에서 가장 중요한 부분은 수백만개의 short read 들을 표준유전체 (reference genome)에 정렬시키는 것인데 RNA 시퀀싱 (RNA-Seq) 의 경우 RNA splicing 으로 인해 일반적인 aligner 로 처리가 불가능하다. 복잡한 RNA-Seq 정렬 문제를 해결하기 위해 그동안 다양한 알고리즘들이 제안되어 왔다. 본 논문에서는 RNA-seq 정렬분야에서 잘 알려진 알고리즘들과 최신 알고리즘들을 살펴봄으로써 RNA-seq 정렬 알고리즘의 동향을 살펴보고자 한다.

• The Korean Journal of Applied Statistics
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• v.33 no.4
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• pp.511-526
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• 2020

Single-cell RNA-sequencing (scRNA-seq) data consists of each cell's RNA expression extracted from large populations of cells. One main purpose of using scRNA-seq data is to identify inter-cellular heterogeneity. However, scRNA-seq data pose statistical challenges when applying traditional clustering methods because they have many missing values and high level of noise due to technical and sampling issues. In this paper, motivated by analyzing scRNA-seq data, we propose a novel spectral-based clustering method by imposing different weights on genes when computing a similarity between cells. Assigning weights on genes and clustering cells are performed simultaneously in the proposed clustering framework. We solve the proposed non-convex optimization using an iterative algorithm. Both real data application and simulation study suggest that the proposed clustering method better identifies underlying clusters compared with existing clustering methods.

• The Journal of Korean Institute of Information Technology
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• v.16 no.12
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• pp.25-30
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• 2018

It is extremely lacking and urgently required that the method of constructing the Gene Regulatory Network (GRN) from RNA-Sequencing data (RNA-Seq) because of Big-Data and GRN in Big-Data has obtained substantial observation as the interactions among relevant featured genes and their regulations. We propose newly the computational comparative feature patterns selection method by implementing a minimum-redundancy maximum-relevancy (MRMR) filter the support vector machine-recursive feature elimination (SVM-RFE) with Intensity-dependent normalization (DEGSEQ) as a preprocessor for emphasizing equal preciseness in RNA-seq in Big-Data. We found out the proposed algorithm might be more scalable and convenient because of all libraries in R package and be more improved in terms of the time consuming in Big-Data and minimum-redundancy maximum-relevancy of a set of feature patterns at the same time.

• Journal of Internet Computing and Services
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• v.20 no.4
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• pp.21-27
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• 2019

Selection of feature pattern gathered from the observation of the RNA sequencing data (RNA-seq) are not all equally informative for identification of differential expressions: some of them may be noisy, correlated or irrelevant because of redundancy in Big-Data sets. Variable selection of feature pattern aims at differential expressed gene set that is significantly relevant for a special task. This issues are complex and important in many domains, for example. In terms of a computational research field of machine learning, selection of feature pattern has been studied such as Random Forest, K-Nearest and Support Vector Machine (SVM). One of most the well-known machine learning algorithms is SVM, which is classical as well as original. The one of a member of SVM-criterion is Support Vector Machine-Recursive Feature Elimination (SVM-RFE), which have been utilized in our research work. We propose a novel algorithm of the SVM-RFE with Q-learning in reinforcement learning for better variable selection of feature pattern. By comparing our proposed algorithm with the well-known SVM-RFE combining Welch' T in published data, our result can show that the criterion from weight vector of SVM-RFE enhanced by Q-learning has been improved by an off-policy by a more exploratory scheme of Q-learning.

• Korean Journal of Clinical Laboratory Science
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• v.55 no.4
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• pp.235-243
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• 2023

As next-generation sequencing has been developed and used widely, RNA-sequencing (RNA-seq) has rapidly emerged as the first choice of tools to validate global transcriptome profiling. With the significant advances in RNA-seq, various types of RNA-seq have evolved in conjunction with the progress in bioinformatic tools. On the other hand, it is difficult to interpret the complex data underlying the biological meaning without a general understanding of the types of RNA-seq and bioinformatic approaches. In this regard, this paper discusses the two main sections of RNA-seq. First, two major variants of RNA-seq are described and compared with the standard RNA-seq. This provides insights into which RNA-seq method is most appropriate for their research. Second, the most widely used RNA-seq data analyses are discussed: (1) exploratory data analysis and (2) pathway enrichment analysis. This paper introduces the most widely used exploratory data analysis for RNA-seq, such as principal component analysis, heatmap, and volcano plot, which can provide the overall trends in the dataset. The pathway enrichment analysis section introduces three generations of pathway enrichment analysis and how they generate enriched pathways with the RNA-seq dataset.

• Journal of Applied Biological Chemistry
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• v.59 no.4
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• pp.299-304
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• 2016

When used for amplicon sequencing, Illumina platforms produce more than hundreds of sequence artefacts, which affects operational taxonomic units based analyses such as differential abundance and network analyses. Nevertheless it has become a major tool for fecal microbial community analysis. In addition, results from sequence-based fecal microbial community analysis vary depending on conditions of samples (i.e., freshness, time of storage and quantity). We investigated if freeze-drying samples could improve quality of sequence data. Our results showed reduced number of possible artefacts while maintaining overall microbial community structure. Therefore, freeze-drying feces prior to DNA extraction is recommended for Illumina-based microbial community analysis.

• Journal of Internet Computing and Services
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• v.18 no.5
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• pp.47-53
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• 2017

In past few years, high-throughput sequencing, big-data generation, cloud computing, and computational biology are revolutionary. RNA sequencing is emerging as an attractive alternative to DNA microarrays. And the methods for constructing Gene Regulatory Network (GRN) from RNA-Seq are extremely lacking and urgently required. Because GRN has obtained substantial observation from genomics and bioinformatics, an elementary requirement of the GRN has been to maximize distinguishable genes. Despite of RNA sequencing techniques to generate a big amount of data, there are few computational methods to exploit the huge amount of the big data. Therefore, we have suggested a novel gene selection algorithm combining Support Vector Machines and Intensity-dependent normalization, which uses log differential expression ratio in RNAseq. It is an extended variation of support vector machine recursive feature elimination (SVM-RFE) algorithm. This algorithm accomplishes minimum relevancy with subsets of Big-Data, such as NCBI-GEO. The proposed algorithm was compared to the existing one which uses gene expression profiling DNA microarrays. It finds that the proposed algorithm have provided as convenient and quick method than previous because it uses all functions in R package and have more improvement with regard to the classification accuracy based on gene ontology and time consuming in terms of Big-Data. The comparison was performed based on the number of genes selected in RNAseq Big-Data.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06a
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• pp.37-40
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• 2011

선택 스플라이싱 (alternative splicing)은 mRNA (messenger RNA)의 전구체인 pre-mRNA가 mRNA로 전사될 때 pre-mRNA의 엑손 영역들 (exons)이 여러 가지 유형 (pattern)으로 다시 연결되는 과정을 말한다. 선택 스플라이싱에 의해 하나의 유전자로부터 서로 다른 mRNA가 만들어 지고 서로 다른 이소형의 단백질 (protein isoforms)이 생성된다. 현재까지 알려진 선택 스플라이싱의 유형은 약 7가지 종류가 있으며, 유전자의 돌연변이 및 질병과 밀접한 연관성을 가지고 있는 것으로 알려져 있다. 본 연구에서는 차세대 시퀀싱 (Next Generation Sequencing : NGS) 기술로 생성된 RNA-Seq 데이터로부터 각 유전자 영역에 대한 선택 스플라이싱 유형을 분류/추출하는 새로운 알고리즘을 제안한다. 제안된 알고리즘에서는 RNA-Seq 데이터를 DNA 시퀀스와 mRNA 트랜스크립트 시퀀스에 동시 매핑하고, 각 엑손 영역에 정렬된 RNA-Seq 데이터의 커버리지 정보 및 엑손의 접합 (junction) 정보를 이용하여 발현된 트랜스크립트 (transcript)의 종류와 양을 측정한다. 알고리즘의 유효성을 보이기 위하여 시뮬레이션 데이터를 이용한 인간 유전자 영역에서의 선택 스플라이싱 유형 추출 실험을 수행하였으며, 검증된 선택 스플라이싱 DB와 비교, 검증하였다.