A comparison of transcriptome analysis methods with reference genome.
A comparison of transcriptome analysis methods with reference genome.
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转录组分析方法与参考基因组的比较
DOI：
10.1186/s12864-022-08465-0
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发表时间：
2022-03-25
期刊：
影响因子：
4.4
通讯作者：
Ye H
中科院分区：
文献类型：
--
作者：
Liu X;Zhao J;Xue L;Zhao T;Ding W;Han Y;Ye H
背景在过去的几年中，RNA-seq 技术的应用变得更加广泛，可用的分析程序的数量也有所增加。选择合适的工作流程已成为该领域研究人员的一个重要问题。方法在我们的研究中，分别使用来自小鼠、人类、大鼠和猕猴的四个 RNA-seq 数据集对六种流行的分析程序/流程进行了比较。评估基因表达值、基因表达倍数变化和统计显着性，以比较六种程序之间的异同。进行 qRT-PCR 来验证所有六个程序中的差异表达基因 (DEG)。结果 Cufflinks-Cuffdiff 需要最高的计算资源，而 Kallisto-Sleuth 需要最少。基因表达值、倍数变化、差异表达 (DE) 分析的 pandq 值在使用 HTseq 进行定量的程序之间高度相关。对于中等表达丰度的基因，使用不同程序确定的表达值相似。表达值的主要差异来自于表达水平特别高或低的基因。HISAT2-StringTie-Ballgown 对低表达水平的基因更敏感，而 Kallisto-Sleuth 可能仅适用于评估中到高丰度的基因。当在DE分析中选择相同的倍数变化阈值和p值时，StringTie-Ballgown产生最少数量的DEG，而HTseq-DESeq2、-edgeR或-limma通常产生更多DEG。 Cufflinks-Cuffdiff 和 Kallisto-Sleuth 在不同数据集中的性能有所不同。对于中等表达水平的DEG，所有程序的生物学验证率相似。结论使用HTseq进行定量的RNA-seq分析程序之间的结果高度相关。基因表达值的差异主要来自于表达水平特别高或低的基因。此外，对于具有中等表达水平的基因，所有六种程序的 DEG 的生物学验证率相似。研究人员可以根据可用的计算机资源，或者是否感兴趣高表达水平或低表达水平的基因来选择分析程序。如果计算机资源丰富，可以利用多个程序获得结果的交集以获得最可靠的DEG，或者获得结果的组合以获得更全面的转录组DE谱。
BackgroundThe application of RNA-seq technology has become more extensive and the number of analysis procedures available has increased over the past years. Selecting an appropriate workflow has become an important issue for researchers in the field.MethodsIn our study, six popular analytical procedures/pipeline were compared using four RNA-seq datasets from mouse, human, rat, and macaque, respectively. The gene expression value, fold change of gene expression, and statistical significance were evaluated to compare the similarities and differences among the six procedures. qRT-PCR was performed to validate the differentially expressed genes (DEGs) from all six procedures.ResultsCufflinks-Cuffdiffdemands the highest computing resources andKallisto-Sleuthdemands the least. Gene expression values, fold change,pandqvalues of differential expression (DE) analysis are highly correlated among procedures usingHTseqfor quantification. For genes with medium expression abundance, the expression values determined using the different procedures were similar. Major differences in expression values come from genes with particularly high or low expression levels.HISAT2-StringTie-Ballgownis more sensitive to genes with low expression levels, whileKallisto-Sleuthmay only be useful to evaluate genes with medium to high abundance. When the same thresholds for fold change andpvalue are chosen in DE analysis,StringTie-Ballgownproduce the least number of DEGs, whileHTseq-DESeq2, -edgeRor -limmagenerally produces more DEGs. The performance ofCufflinks-CuffdiffandKallisto-Sleuthvaries in different datasets. For DEGs with medium expression levels, the biological verification rates were similar among all procedures.ConclusionResults are highly correlated among RNA-seq analysis procedures usingHTseqfor quantification. Difference in gene expression values mainly come from genes with particularly high or low expression levels. Moreover, biological validation rates of DEGs from all six procedures were similar for genes with medium expression levels. Investigators can choose analytical procedures according to their available computer resources, or whether genes of high or low expression levels are of interest. If computer resources are abundant, one can utilize multiple procedures to obtain the intersection of results to get the most reliable DEGs, or to obtain a combination of results to get a more comprehensive DE profile for transcriptomes.
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DOI：
10.1093/bioinformatics/btu638
发表时间：
2015-01-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Anders S;Pyl PT;Huber W
通讯作者：
Huber W
影响因子：
64.8
作者：
通讯作者：
--
影响因子：
12.3
作者：
Robinson MD;Oshlack A
通讯作者：
Oshlack A
影响因子：
14.9
作者：
Nookaew I;Papini M;Pornputtapong N;Scalcinati G;Fagerberg L;Uhlén M;Nielsen J
通讯作者：
Nielsen J
影响因子：
9.5
作者：
Seyednasrollah F;Laiho A;Elo LL
通讯作者：
Elo LL