Produced by bone marrow megakaryocytes, platelets are small anucleate elements of the blood that play a pivotal role in hemostasis. They are involved in fibrinolysis and Ginsenoside-F2 repair of the vessel wall, while circulating in the blood as sentinels of vascular integrity. Platelets lack genomic DNA but retain the ability for protein synthesis from Ginsenoside-Rg3.gif)
cytoplasmic mRNA. Platelet mRNA was first isolated and converted to a cDNA library more than two decades ago. In recent years, several studies utilizing genomewide techniques for gene expression profiling, such as Acetylcorynoline microarrays and Serial Analysis of Gene Expression in concert with computer-assisted bioinformatics, have reported that thousands of gene transcripts are present in human platelets. While microarrays and SAGE have made significant contributions to the characterization of the platelet transcriptome, they also have serious limitations. Hybridization-based approaches rely on probetarget binding of selected sequences and do not detect novel transcripts or unknown genes. In contrast, SAGE uses sequence tags from individual mRNAs and has an advantage over microarrays by detecting unknown genes but does not provide information on splice isoforms and is biased toward short tags, which cannot be uniquely mapped to the human genome. Recently, mass sequencing of transcripts by next generation sequencing technologies has emerged as a powerful approach for quantitative transcript discovery. RNA-Seq has clear advantages over other approaches and shows higher levels of reproducibility for both technical and biological replicates. Two recently published studies used NGS technology to characterize the platelet transcriptome. One of these used cDNA from poly isolated mRNA and the other cDNA from ribosomal RNA-depleted total RNA. Both studies used relatively short reads for alignment to the human genome. In this context, we now report results from both polyA+ mRNA and rRNA-depleted total RNA approaches utilizing 100 bp long sequencing reads for investigating the transcriptional profile of unstimulated human platelets. We have also for the first time applied a de novo assembly of platelet transcripts to confirm the reference-guided alignments. We believe that our data may provide important clues for understanding the elusive platelet transcriptome and its role in the coagulation system and hemostasis. In a typical RNA-Seq experiment, reads are sampled from RNA extracts and either mapped back to a reference genome or used for de novo assembly. Alignment and assembly of short or inaccurate reads poses a problem, which we have avoided by using 100 bp high quality Illumina reads. How closely the cDNA sequencing reflects the original RNA population is supposedly mainly determined in the library preparation step. As expected, our mapping of polyA+ reads showed a substantial bias for the 39end of gene transcripts due to the selection of mRNA using oligodT during the RNA extraction procedure and the following cDNA preparation step. This 39-UTR bias follows an exponential decay function. After length correction of coverage figures using that function for mRNA and FPKM-values for total RNA, we obtained a reasonably good agreement between quantitative estimates from mapping of polyA+ mRNA and rRNA-depleted total RNA reads to the human genome GRCh37/hg19. It is a notoriously difficult problem to assign reads to a particular isoform if there are many transcript variants with overlaps between them. Very high coverage figures are needed for satisfactory results. This is one of the reasons why RNA-Seq with low coverage has many of the same limitations as other RNA expression analysis pipelines. Obviously, mapping of reads against the human genome and also mapping against the human exome both rely on the accuracy of gene and transcript annotations.