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This article is part of the supplement: Proceedings of the Eighth Annual MCBIOS Conference. Computational Biology and Bioinformatics for a New Decade

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

Ying Wang1, Noushin Ghaffari23, Charles D Johnson2, Ulisses M Braga-Neto3, Hui Wang4, Rui Chen4 and Huaijun Zhou1*

Author Affiliations

1 Department of Poultry Science, Texas A &M University College Station, TX 77843-2472, USA

2 AgriLife Genomics and Bioinformatics Services, Texas A&M University, College Station, TX 77843-2312, USA

3 Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA

4 Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA

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BMC Bioinformatics 2011, 12(Suppl 10):S5  doi:10.1186/1471-2105-12-S10-S5

Published: 18 October 2011

Abstract

Background

RNA-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.

Results

Two 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.

Conclusion

The 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.