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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2017.1
- 수록면
- 51 - 53 (3page)
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High-throughput transcriptome sequencing, also known as RNA sequencing (RNA-Seq), is a standard technology for measuring gene expression with unprecedented accuracy. Numerous bioconductor packages have been developed for the statistical analysis of RNA-Seq data. However, these tools focus on specific aspects of the data analysis pipeline, and are difficult to appropriately integrate with one another due to their disparate data structures and processing methods. They also lack visualization methods to confirm the integrity of the data and the process. In this paper, we propose an R-based RNA-Seq analysis pipeline called TRAPR, an integrated tool that facilitates the statistical analysis and visualization of RNA-Seq expression data. TRAPR provides various functions for data management, the filtering of low-quality data, normalization, transformation, statistical analysis, data visualization, and result visualization that allow researchers to build customized analysis pipelines.
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참고문헌 (12)
참고문헌 신청
Wang Z / 2009 / RNA-Seq: a revolutionary tool for transcriptomics / Nat Rev Genet 10(1):57~63
Garber M / 2011 / Computational methods for transcriptome annotation and quantification using RNA-seq / Nat Methods 8:469~477
Auer PL / 2012 / Differential expression: the next generation and beyond / Brief Funct Genomics 11:57~62
Gentleman RC / 2004 / Bioconductor: open software development for computational biology and bioinformatics / Genome Biol 5:R80
Robinson MD / 2010 / edgeR: a Bioconductor package for differential expression analysis of digital gene expression data / Bioinformatics 26:139~140
Garber M / 2011 / Computational methods for transcriptome annotation and quantification using RNA-seq / Nat Methods 8:469~477
Auer PL / 2012 / Differential expression: the next generation and beyond / Brief Funct Genomics 11:57~62
Gentleman RC / 2004 / Bioconductor: open software development for computational biology and bioinformatics / Genome Biol 5:R80
Robinson MD / 2010 / edgeR: a Bioconductor package for differential expression analysis of digital gene expression data / Bioinformatics 26:139~140
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