매우 빠른 속도로 발전하고 있는 차세대 염기서열 분석 플랫폼과 최신 생물정보학적 분석도구들로 말미암아, 1,000달러 이하의 가격으로 인간 유전체염기서열을 해독하고자 하는 궁극적인 목표가 조만간 곧 실현될 수 있을 것 같다. 차세대 염기서열 분석 분야의 급속한 기술적 진전은 NGS데이터의 분석과 관리를 위한 통계적 방법과 생물정보학적 분석도구들에 대한 수요를 꾸준히 증대시키고 있다. NGS 플랫폼이 상용화되어 쓰이기 시작한 초창기부터, NGS 데이터를 분석하고 해석하거나, 가시화 해주는 다수의 응용프로그램이나 도구들이 개발되어 활용되어 왔다. 그러나, NGS 데이터의 엄청난 범람으로 데이터 저장, 데이터 분석 및 관리 등에 있어서 해결해야 할 많은 문제들이 부각되고 있다. NGS 데이터 분석은 단편서열과 참조서열간의 서열정렬, 염기식별, 다형성 발견, 쌍단편 서열이나 비쌍단편 서열 등을 이용한 어셈블리 작업, 구조변이 발견, 유전체 브라우징 등을 본질적으로 포함한다. 본 논문은 주요 차세대 염기서열 결정기술과 NGS 데이터 분석을 위한 생물정보학적 분석도구들에 대해 개관적으로 소개하고자 한다.
매우 빠른 속도로 발전하고 있는 차세대 염기서열 분석 플랫폼과 최신 생물정보학적 분석도구들로 말미암아, 1,000달러 이하의 가격으로 인간 유전체 염기서열을 해독하고자 하는 궁극적인 목표가 조만간 곧 실현될 수 있을 것 같다. 차세대 염기서열 분석 분야의 급속한 기술적 진전은 NGS 데이터의 분석과 관리를 위한 통계적 방법과 생물정보학적 분석도구들에 대한 수요를 꾸준히 증대시키고 있다. NGS 플랫폼이 상용화되어 쓰이기 시작한 초창기부터, NGS 데이터를 분석하고 해석하거나, 가시화 해주는 다수의 응용프로그램이나 도구들이 개발되어 활용되어 왔다. 그러나, NGS 데이터의 엄청난 범람으로 데이터 저장, 데이터 분석 및 관리 등에 있어서 해결해야 할 많은 문제들이 부각되고 있다. NGS 데이터 분석은 단편서열과 참조서열간의 서열정렬, 염기식별, 다형성 발견, 쌍단편 서열이나 비쌍단편 서열 등을 이용한 어셈블리 작업, 구조변이 발견, 유전체 브라우징 등을 본질적으로 포함한다. 본 논문은 주요 차세대 염기서열 결정기술과 NGS 데이터 분석을 위한 생물정보학적 분석도구들에 대해 개관적으로 소개하고자 한다.
With the ongoing development of next-generation sequencing (NGS) platforms and advancements in the latest bioinformatics tools at an unprecedented pace, the ultimate goal of sequencing the human genome for less than $1,000 can be feasible in the near future. The rapid technological advances in NGS h...
With the ongoing development of next-generation sequencing (NGS) platforms and advancements in the latest bioinformatics tools at an unprecedented pace, the ultimate goal of sequencing the human genome for less than $1,000 can be feasible in the near future. The rapid technological advances in NGS have brought about increasing demands for statistical methods and bioinformatics tools for the analysis and management of NGS data. Even in the early stages of the commercial availability of NGS platforms, a large number of applications or tools already existed for analyzing, interpreting, and visualizing NGS data. However, the availability of this plethora of NGS data presents a significant challenge for storage, analyses, and data management. Intrinsically, the analysis of NGS data includes the alignment of sequence reads to a reference, base-calling, and/or polymorphism detection, de novo assembly from paired or unpaired reads, structural variant detection, and genome browsing. While the NGS technologies have allowed a massive increase in available raw sequence data, a number of new informatics challenges and difficulties must be addressed to improve the current state and fulfill the promise of genome research. This review aims to provide an overview of major NGS technologies and bioinformatics tools for NGS data analyses.
With the ongoing development of next-generation sequencing (NGS) platforms and advancements in the latest bioinformatics tools at an unprecedented pace, the ultimate goal of sequencing the human genome for less than $1,000 can be feasible in the near future. The rapid technological advances in NGS have brought about increasing demands for statistical methods and bioinformatics tools for the analysis and management of NGS data. Even in the early stages of the commercial availability of NGS platforms, a large number of applications or tools already existed for analyzing, interpreting, and visualizing NGS data. However, the availability of this plethora of NGS data presents a significant challenge for storage, analyses, and data management. Intrinsically, the analysis of NGS data includes the alignment of sequence reads to a reference, base-calling, and/or polymorphism detection, de novo assembly from paired or unpaired reads, structural variant detection, and genome browsing. While the NGS technologies have allowed a massive increase in available raw sequence data, a number of new informatics challenges and difficulties must be addressed to improve the current state and fulfill the promise of genome research. This review aims to provide an overview of major NGS technologies and bioinformatics tools for NGS data analyses.
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제안 방법
Indeed new technologies are producing data at a rate that outpaces our ability to analyze its biological meaning. Researchers are ad- dressing this challenge by adopting mathematical and stat- istical software, computer modeling, and other computa- tional and engineering methods. As a result, bioinformatics has become the latest engineering discipline.
However, only an handful of tools have been implemented [23, 28, 29, 35, 40] for SNP and small (1-5 bp) indel discovery. The goal of these programs consist in judging the likelihood that a locus is a heterozygous or homozygous variant given the error rates of the platform, the probability of bad mappings, and the amount of coverage. For these reasons, all the avail- able tools for SNP and indel discovery follow two main steps: the first is for data preparation and in the second each nucleotide is called under a Bayesian framework.
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