We focused the differential expression analysis on subpopulations with high transcriptome coverage and highly correlated biological replicates (e.g., Pearson's r > 0.85, and > 6,000 and 2,000 transcripts detected in macrophages and Candida, respectively; Supplementary Figure 1B).
W Xue, YP Zhu, GY Hou and XF Kong performed comparative genome assessment, transcriptome coverage and homolog completeness.
The transcriptome coverage and completeness achieved by these sub-assemblies were then evaluated by matching with annotated known genes.
To identify differentially expressed transcripts, we first filtered the sequences, estimated the transcriptome coverage, and identified expressed genes.
To more directly investigate the relationship between sequencing depth and transcriptome coverage, we performed a simulation approach where mRNA-Seq was the most cost effective strategy to equal a microarray in terms of total genes detected with a minimum of ~13.5 million reads needed.
An estimation of transcriptome coverage of lily and tulip genotypes was made (Table 2).
To more directly address the question whether sequencing from multiple tissue types improves the breadth of transcriptome coverage, orthologous genes between G. maderense and A. thaliana and between P. x hortorum and A. thaliana were identified.
The sequencing of meiocyte, anther and seedling transcriptomes generated average genome and transcriptome coverage over 10×.
However to further improve transcriptome coverage, sequencing different tissues and developmental stages is needed.
Based on the transcript and transcriptome coverage, the number of sequence reads was sufficient to tag the majority of transcripts but not to assemble the transcriptome to completeness.
