次世代定序技術的發展提供了生物學家龐大的基因體資料。比起以往陣列技術只能量測出每個基因的單一表現量，次世代定序技術可以更進一步求出基因內每個鹼基位置的片段計數，目前在很多研究領域上都佔有一席之地。計算出這種鹼基層級的片段計數需要將大量的片段比對到參考基因體上，而這些比對結果難以閱讀，因此許多檢視器被開發出來，將這些資料轉換成易於閱讀的圖表，如Savant、Tablet、Integrative Genomics Viewer (IGV)等均是。但目前就我們所知，並沒有能夠動態即時繪製全基因體範圍的鹼基層級片段計數。因此，本研究提出一個高效率繪製流程，且將此流程實作在一個輕量級的資料檢視器上。檢視器提供友善的使用者介面，亦能快速響應使用者的操作。

Next generation sequencing (NGS) technologies have brought an unprecedented scale of genomic data to biologists. NGS technologies, unlike array-based profiling technologies, can provides the read count variation at the base level in a transcript rather than a single expression value. Such base-level read coverage has been used in many research field. Calculating the base-level read coverage requires alignment of numerous reads on reference genome, but the result is difficult to read and analysis by human. Many viewers, such as Savant, Tablet and Integrative Genomics Viewer, has been developed to solve this problem, converting those read alignment into friendly graphic profile. However, to our best knowledge, none of them can timely visualize a genome-wide base-level read coverage in an interactive environment.
This study proposes an efficient visualization pipeline for NGS data and implements a lightweight read coverage viewer with the proposed pipeline. The proposed viewer provides friendly user interface and can rapidly response to user’s input.

1. Stein, L.D., The case for cloud computing in genome informatics. Genome Biol, 2010. 11(5): p. 207.
2. Shih, A.C.-C. and T. LiuJr, Predicting MicroRNAs, in Systems Biology: Applications in Cancer-related Research. 2012. p. 189.
3. Schena, M., et al., Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science, 1995. 270(5235): p. 467.
4. McKenna, A., et al., The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome research, 2010. 20(9): p. 1297-1303.
5. Trapnell, C., et al., Differential analysis of gene regulation at transcript resolution with RNA-seq. Nature biotechnology, 2012. 31(1): p. 46-53.
6. Carver, T., et al., Artemis: an integrated platform for visualization and analysis of high-throughput sequence-based experimental data. Bioinformatics, 2012. 28(4): p. 464-469.
7. Fiume, M., et al., Savant: genome browser for high-throughput sequencing data. Bioinformatics, 2010. 26(16): p. 1938-1944.
8. Milne, I., et al., Using Tablet for visual exploration of second-generation sequencing data. Briefings in bioinformatics, 2013. 14(2): p. 193-202.
9. Thorvaldsdóttir, H., J.T. Robinson, and J.P. Mesirov, Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration. Briefings in bioinformatics, 2013. 14(2): p. 178-192.
10. Seow, S.C., Designing and engineering time: the psychology of time perception in software. 2008: Addison-Wesley Professional.
11. Trapnell, C., et al., Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nature protocols, 2012. 7(3): p. 562-578.
12. Trapnell, C., L. Pachter, and S.L. Salzberg, TopHat: discovering splice junctions with RNA-Seq. Bioinformatics, 2009. 25(9): p. 1105-1111.
13. Hubbard, T., et al., The Ensembl genome database project. Nucleic acids research, 2002. 30(1): p. 38-41.