Determination of tag density required for digital transcriptome analysis: Application to an androgen-sensitive prostate cancer model

Abstract

High-throughput sequencing has rapidly gained popularity for transcriptome analysis in mammalian cells because of its ability to generate digital and quantitative information on annotated genes and to detect transcripts and mRNA isoforms. Here, we described a double-random priming method for deep sequencing to profile double poly(A)-selected RNA from LNCaP cells before and after androgen stimulation. From Ϸ20 million sequence tags, we uncovered 71% of annotated genes and identified hormone-regulated gene expression events that are highly correlated with quantitative real time PCR measurement. A fraction of the sequence tags were mapped to constitutive and alternative splicing events to detect known and new mRNA isoforms expressed in the cell. Finally, curve fitting was used to estimate the number of tags necessary to reach a ''saturating'' discovery rate among individual applications. This study provides a general guide for analysis of gene expression and alternative splicing by deep sequencing.alternative splicing ͉ androgen-regulated gene expression in prostate cancer cells ͉ curve regression ͉ high-throughput sequencing M icroarray-based approaches, especially unbiased tiling arrays, suggest that up to 80% of the genome may be transcribed to produce a huge number of uncharacterized transcripts relative to current gene annotation (1-4). In contrast, recent transcriptome analysis by deep sequencing indicates that the vast majority of expressed transcripts in mammalian tissues and cell lines are confined to annotated genes and exons (5, 6). Although microarraybased approaches suffer from a great degree of uncertainty in relating detected hybridization signals to defined transcripts, sequencing-based approaches tend to be overwhelmed by abundant transcripts in the cell. Construction of ''normalized'' libraries for deep sequencing might facilitate the discovery of low abundance transcripts, many of which may act as noncoding, regulatory RNA in mammalian cells.An advantage of transcriptome analysis by deep sequencing is the ability to detect structural variation of individual transcripts. It is well known that different transcripts from the same genes may be generated by differential promoter usage, heterogeneous transcriptional start sites and alternative 3Ј end formation (2). A recent Pol II ChIP-chip study indicates that protein-coding genes may have an average of 3 to 5 promoters in both the mouse and human genomes (7). Further adding to the diversity in the transcriptome is alternative RNA processing of most protein-coding genes as a consequence of alternative 5Ј and 3Ј splice site choices, exon inclusion/ skipping, intron retention, and combinatorial use of alternative exons (8). It is estimated that up to 74% of human genes undergo alternative splicing, which is believed to contribute to the complexity of the proteome in mammalian cells (9).It is striking to note that individual laboratories now have the capacity to generate sequenced tags that are on the same order of sequenced mRNA/ESTs in publicly availab...

A review of connectivity map and computational approaches in pharmacogenomics

“…Specifically, their study used a DNA microarray and RNA sequencing platform, and they identified the same gene signature for which the resulting drug (cotinine) suppressed androgen-driven cell proliferation [61]. Furthermore, they experimentally validated cotinine, which inhibits proliferation in LNCaP cells [60].…”

Current-generation high-throughput sequencing: deepening insights into mammalian transcriptomes

“…In contrast, RNA-Seq provides a relatively unbiased and direct digital readout of cDNA sequence generated from an RNA sample. Several studies have demonstrated that RNA-Seq provides an extremely reproducible and quantitative readout of transcript abundance (e.g., Li et al 2008;Marioni et al 2008;Pan et al 2008;Wang et al 2008). Because RNA-Seq is performed using tagged libraries of short cDNAs, prepared from fragmented or unfragmented RNA, it does not require prior knowledge of the sequences to be profiled.…”

Detection and Quantification of Alternative Splicing Variants Using RNA-seq

“…Finally, the fragments are reverse transcribed using primers complementary to the adapters. Several elaborations of the RNA fragmentation approach have been developed (6)(7)(8) to facilitate strand-specifi c sequencing.…”