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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 劉力瑜(Li-Yu Daisy Liu) | |
dc.contributor.author | Chia-Chien Yeh | en |
dc.contributor.author | 葉佳蒨 | zh_TW |
dc.date.accessioned | 2021-06-16T22:57:01Z | - |
dc.date.available | 2017-08-15 | |
dc.date.copyright | 2012-08-15 | |
dc.date.issued | 2012 | |
dc.date.submitted | 2012-08-09 | |
dc.identifier.citation | 1.Oshlack A, Robinson MD, Young MD: From RNA-seq reads to differential expression results. Genome Biology 2010, 11(12):220.
2.Wang Z, Gerstein M, Snyder M: RNA-seq: a revolutionary tool for transcriptomics. Nature Reviews Genetics 2009, 10(1):57-63. 3.Li B, Dewey CN: RSEM: accurate transcript quantification from RNA-seq data with or without a reference genome. BMC Bioinformatics 2011, 12(1):323. 4.Oshlack A, Wakefield MJ: Transcript length bias in RNA-seq data confounds systems biology. Biology Direct 2009, 4(1):14. 5.Hansen KD, Brenner SE, Dudoit S: Biases in Illumina transcriptome sequencing caused by random hexamer priming. Nucleic Acids Research 2010, 38(12):e131. 6.Bohnert R, Ratsch G: rQuant. web: a tool for RNA-seq-based transcript quantitation. Nucleic Acids Research 2010, 38(suppl 2):W348-W351. 7. Nicolae M, Mangul S, Măndoiu I, Zelikovsky A: Estimation of alternative splicing isoform frequencies from RNA-seq data. Algorithms in Bioinformatics 2010:202-214. 8. Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, Van Baren MJ, Salzberg SL, Wold BJ, Pachter L: Transcript assembly and quantification by RNA-seq reveals unannotated transcripts and isoform switching during cell differentiation. Nature Biotechnology 2010, 28(5):511-515. 9.Roberts A, Trapnell C, Donaghey J, Rinn JL, Pachter L: Improving RNA-seq expression estimates by correcting for fragment bias. Genome Biology 2011, 12(3):R22. 10.Zheng W, Chung LM, Zhao H: Bias detection and correction in RNA-sequencing data. BMC Bioinformatics 2011, 12(1):290. 11.Langmead B, Trapnell C, Pop M, Salzberg SL: Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biology 2009, 10(3):R25. 12.Trapnell C, Pachter L, Salzberg SL: TopHat: discovering splice junctions with RNA-seq. Bioinformatics 2009, 25(9):1105-1111. 13.Shi L, Reid LH, Jones WD, Shippy R, Warrington JA, Baker SC, Collins PJ, De Longueville F, Kawasaki ES, Lee KY: The MicroArray Quality Control (MAQC) project shows inter-and intraplatform reproducibility of gene expression measurements. Nature Biotechnology 2006, 24(9):1151-1161. 14. Bullard JH, Purdom E, Hansen KD, Dudoit S: Evaluation of statistical methods for normalization and differential expression in mRNA-seq experiments. BMC Bioinformatics 2010, 11(1):94. 15.Wang ET, Sandberg R, Luo S, Khrebtukova I, Zhang L, Mayr C, Kingsmore SF, Schroth GP, Burge CB: Alternative isoform regulation in human tissue transcriptomes. Nature 2008, 456(7221):470-476. 16.Au KF, Jiang H, Lin L, Xing Y, Wong WH: Detection of splice junctions from paired-end RNA-seq data by SpliceMap. Nucleic Acids Research 2010, 38(14):4570-4578. 17.Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B: Mapping and quantifying mammalian transcriptomes by RNA-seq. Nature Methods 2008, 5(7):621-628. 18.Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R: The sequence alignment/map format and SAMtools. Bioinformatics 2009, 25(16):2078-2079. 19.Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J: Bioconductor: open software development for computational biology and bioinformatics. Genome Biology 2004, 5(10):R80. 20.Durinck S, Moreau Y, Kasprzyk A, Davis S, De Moor B, Brazma A, Huber W: BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis. Bioinformatics 2005, 21(16):3439-3440. 21.Li J, Jiang H, Wong WH: Method Modeling non-uniformity in short-read rates in RNA-seq data. Genome Biology 2010, 11(5):R25. 22.Lee S, Seo CH, Lim B, Yang JO, Oh J, Kim M, Lee B, Kang C: Accurate quantification of transcriptome from RNA-seq data by effective length normalization. Nucleic Acids Research 2011, 39(2):e9. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/64659 | - |
dc.description.abstract | RNA-seq是最近廣泛使用於轉錄體學的工具。藉由高通量定序科技,能達到更精確的基因表現量測定。然而,RNA-seq的定量結果可能因為生物特性或是實驗流程而有所偏差,近年來研究者們針對偏差校正開發出許多定量軟體,本篇論文主要目的即是比較RSEM、Cufflinks、IsoEM、Genominator、以及RNASeqBias等五種軟體的優劣。我們採用軟體所跑出的表現量取對數值和Taqman qRT-PCR對數表現量值的spearman相關係數來評判。根據mapping的結果顯示,我們使用的MAQC人腦樣本有長度偏差的問題。除了使用MAQC計畫中的Taqman qRT-PCR做為定量表現的基準外,我們也評估各軟體在偏差校正上的效果。分析成果指出五種軟體皆能夠達到長度效應的校正。另外,雖然Cufflinks、IsoEM、Genominator、和RNASeqBias都具有校正sequence-specific偏差的功能,但只有Cufflinks稍微較明顯能減緩此效應。整體來說,Cufflinks在RNA-seq資料上具有最好的校正效果與表現。 | zh_TW |
dc.description.abstract | RNA-seq is a more accurate technology in measuring transcripts levels by using high-throughput sequencing of cDNA. However, the quantification of mRNA abundances from RNA-seq data may be biased due to various biological or statistical effects. Several RNA-seq quantification software, including RSEM, Cufflinks, IsoEM, Genominator, and RNASeqBias, had been recently proposed to correct such biases. The objective of this study was to compare the above five software by applying RNA-seq analysis to a benchmark MAQC human brain data while the Taqman qRT-PCR dataset was treated as the golden-standard to evaluate them. Different software was compared to each other based on their associations between the log expression values that obtained from each method and the Taqman log value. In addition, we also discussed the level of biases correction for the programs. According to the mapping results, it was observed that the transcript length effects did exist in the MAQC data. The analytic results showed that all software can reduce length biases. Although Cufflinks, IsoEM, Genominator, and RNASeqBias have the functions to correct sequence-specific biases, only Cufflinks has a bit apparent to correct sequence-specific biases. In conclusion, Cufflinks has the best performance in biases correction for RNA-seq data. | en |
dc.description.provenance | Made available in DSpace on 2021-06-16T22:57:01Z (GMT). No. of bitstreams: 1 ntu-101-R99621201-1.pdf: 1553259 bytes, checksum: ed39594e9d2d3af9cd13ad123cc4ba26 (MD5) Previous issue date: 2012 | en |
dc.description.tableofcontents | 口試委員會審定書 I
致謝 II 摘要 III Abstract IV 1 INTRODUCTION 1 2 MATERIAL AND METHOD 4 2.1 Data 6 2.1.1 Benchmark Taqman qRT-PCR Data 6 2.1.2 Sample RNA-seq and Reference Data 7 2.2 Quantification Software 8 2.2.1 RSEM 9 2.2.2 Cufflinks 10 2.2.3 IsoEM 10 2.2.4 Genominator 11 2.2.5 RNASeqBias 12 2.3 Optional Parameters Setting 13 3 Results 17 3.1 Taqman qRT-PCR Validation 17 3.2 Length Effects Correction 20 3.3 Sequence-specific Effects Correction 25 3.4 Running Time 29 4 Conclusion 31 4.1 Summary 31 4.2 Discussion 31 4.3 Future Work 33 Reference 35 Appendix 38 A.1 R Code of Genominator 38 A.2 R Code of RNASeqBias 39 A.3 Abbreviation 41 | |
dc.language.iso | en | |
dc.title | RNA-seq 定量軟體之比較 | zh_TW |
dc.title | Comparison of RNA-seq Quantification Software | en |
dc.type | Thesis | |
dc.date.schoolyear | 100-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 張孟基,侯藹玲 | |
dc.subject.keyword | RNA-seq定量軟體,偏差校正,RSEM,Cufflinks,IsoEM,Genominator,RNASeqBias, | zh_TW |
dc.subject.keyword | RNA-seq Quantification Software,Biases Correction,RSEM,Cufflinks,IsoEM,Genominator,RNASeqBias, | en |
dc.relation.page | 41 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2012-08-10 | |
dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
dc.contributor.author-dept | 農藝學研究所 | zh_TW |
顯示於系所單位: | 農藝學系 |
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