Evaluation of the coverage and depth of transcriptome by RNA-Seq in chickens

Abstract

BackgroundRNA-Seq is the recently developed high-throughput sequencing technology for profiling the entire transcriptome in any organism. It has several major advantages over current hybridization-based approach such as microarrays. However, the cost per sample by RNA-Seq is still prohibitive for most laboratories. With continued improvement in sequence output, it would be cost-effective if multiple samples are multiplexed and sequenced in a single lane with sufficient transcriptome coverage. The objective of this analysis is to evaluate what sequencing depth might be sufficient to interrogate gene expression profiling in the chicken by RNA-Seq.ResultsTwo cDNA libraries from chicken lungs were sequenced initially, and 4.9 million (M) and 1.6 M (60 bp) reads were generated, respectively. With significant improvements in sequencing technology, two technical replicate cDNA libraries were re-sequenced. Totals of 29.6 M and 28.7 M (75 bp) reads were obtained with the two samples. More than 90% of annotated genes were detected in the data sets with 28.7-29.6 M reads, while only 68% of genes were detected in the data set with 1.6 M reads. The correlation coefficients of gene expression between technical replicates within the same sample were 0.9458 and 0.8442. To evaluate the appropriate depth needed for mRNA profiling, a random sampling method was used to generate different number of reads from each sample. There was a significant increase in correlation coefficients from a sequencing depth of 1.6 M to 10 M for all genes except highly abundant genes. No significant improvement was observed from the depth of 10 M to 20 M (75 bp) reads.ConclusionThe analysis from the current study demonstrated that 30 M (75 bp) reads is sufficient to detect all annotated genes in chicken lungs. Ten million (75 bp) reads could detect about 80% of annotated chicken genes, and RNA-Seq at this depth can serve as a replacement of microarray technology. Furthermore, the depth of sequencing had a significant impact on measuring gene expression of low abundant genes. Finally, the combination of experimental and simulation approaches is a powerful approach to address the relationship between the depth of sequencing and transcriptome coverage.

Calculating Sample Size Estimates for RNA Sequencing Data

“…Our results are in general agreement with Wang et al (2011) that 30-40 million reads is sufficient to be technically precise in measuring gene expression for most genes, which is not surprising given the methods used. We and Wang et al (2011) used raw count data, rather than the ''fragments per kilobase of exon model per million mapped reads'' (FPKM) normalized data as implemented in the Cufflinks software others, 2010, 2012).…”

“…We and Wang et al (2011) used raw count data, rather than the ''fragments per kilobase of exon model per million mapped reads'' (FPKM) normalized data as implemented in the Cufflinks software others, 2010, 2012). The mathematical derivations necessary for sample size computation depend on using a valid model for the count data, a task that is tractable for the raw count data but would be much more difficult after the per-isoform scaling of FPKM.…”

“…They concluded that only 6% of genes are within 10% of their true expression level when 100 million reads are sequenced, but the percentage of genes jumped to 72% when five-fold more reads are sequenced. In contrast, Wang et al (2011) suggested that only 30 million reads are necessary to quantify gene expression in chicken lungs, and that 10 million reads could reliably estimate the level of expression of 80% of genes. This broad range of estimates, and the consequences for planning experiments, provides an attractive research opportunity to clarify the influence of variability.…”

The power and promise of RNA‐seq in ecology and evolution

“…(ii) Sequencing efforts in the range of 5-20 M mapped reads per sample provide sufficient depth to accurately quantify gene expression across a broad range of expression levels in diverse eukaryotic transcriptomes (Tarazona et al 2011;Wang et al 2011;Hart et al 2013;Vijay et al 2013;Ching et al 2014;Liu et al 2014;Williams et al 2014). For example, Hart et al (2013) examined expression distributions for 127 RNAseq experiments (six replicated studies; human and zebrafish), finding that 10 M mapped reads were sufficient to cover approximately 90% of transcripts with >10 reads in a range of biosamples (cell lines, tissue/organ and population comparisons).…”

“…2). Here, given the relatively low cost of sequencing, pilot work can be expanded to obtain~100 M paired-end reads (>100 bp), recommended in the current literature as sufficient to capture the majority of RNAs expressed in eukaryotic samples (Wang et al 2011;Francis et al 2013;Vijay et al 2013;Wolf 2013). …”