E-GEOD-46323 - Evaluating the Impact of Sequencing Depth on Transcriptome Profiling in Human Adipose

Released on 12 July 2013, last updated on 2 May 2014
Homo sapiens
Samples (2)
Protocols (2)
Recent advances in RNA sequencing (RNA-Seq) have enabled the discovery of novel transcriptomic variations that are not possible with traditional microarray-based methods. Tissue and cell specific transcriptome changes during pathophysiological stress, in disease cases versus controls and in response to therapies are of particular interest to investigators studying cardiometabolic diseases. Thus, knowledge on the relationships between sequencing depth and detection of transcriptomic variation is needed for designing RNA-Seq experiments and for interpreting results of analyses. Using deeply sequenced RNA-Seq data derived from adipose of a healthy individual before and after systemic administration of endotoxin (LPS), we investigated the sequencing depths needed for studies of gene expression and alternative splicing (AS). We found that to detect expressed genes and AS events, ~100 million (M) filtered reads were needed. However, the requirement on sequencing depth for the detection of LPS modulated differential expression (DE) and differential alternative splicing (DAS) was much higher. To detect 80% of events, ~300M filtered reads were needed for DE analysis whereas at least 400M filtered reads were necessary for detecting DAS. Although the majority of expressed genes and AS events can be detected with modest sequencing depths (~100M filtered reads), the estimated gene expression levels and exon/intron inclusion levels were less accurate. We report the first study that evaluates the relationship between RNA-Seq depth and the ability to detect DE and DAS in human adipose. Our results suggest that a much higher sequencing depth is needed to reliably identify DAS events than for DE genes. Random sampling the RNA-seq data in different depth for gene and alternative-splicing analysis
Experiment type
RNA-seq of coding RNA 
Yichuan Liu <geo@ncbi.nlm.nih.gov>, Brian Gregory, Chenyi Xue, Ian M Silverman, Jane Ferguson, Mingyao Li, Muredach Reilly
Evaluating the impact of sequencing depth on transcriptome profiling in human adipose. Liu Y, Ferguson JF, Xue C, Silverman IM, Gregory B, Reilly MP, Li M. , Europe PMC 23826166
Exp. designProtocolsFactorsProcessedSeq. reads
Investigation descriptionE-GEOD-46323.idf.txt
Sample and data relationshipE-GEOD-46323.sdrf.txt