生物
转录组
计算生物学
RNA序列
丰度(生态学)
推论
基因
可解释性
基因表达
遗传学
计算机科学
人工智能
生态学
作者
Charlotte Soneson,Michael I. Love,Mark D. Robinson
出处
期刊:F1000Research
[F1000 Research Ltd]
日期:2015-12-30
卷期号:4: 1521-1521
被引量:3120
标识
DOI:10.12688/f1000research.7563.1
摘要
High-throughput sequencing of cDNA (RNA-seq) is used extensively to characterize the transcriptome of cells. Many transcriptomic studies aim at comparing either abundance levels or the transcriptome composition between given conditions, and as a first step, the sequencing reads must be used as the basis for abundance quantification of transcriptomic features of interest, such as genes or transcripts. Several different quantification approaches have been proposed, ranging from simple counting of reads that overlap given genomic regions to more complex estimation of underlying transcript abundances. In this paper, we show that gene-level abundance estimates and statistical inference offer advantages over transcript-level analyses, in terms of performance and interpretability. We also illustrate that while the presence of differential isoform usage can lead to inflated false discovery rates in differential expression analyses on simple count matrices and transcript-level abundance estimates improve the performance in simulated data, the difference is relatively minor in several real data sets. Finally, we provide an R package (tximport) to help users integrate transcript-level abundance estimates from common quantification pipelines into count-based statistical inference engines.
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