Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

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Analysis of Pro-Inflammatory Gene Activation and RNA Processing by RNAseq of Nascent Transcripts [supplement]

ABSTRACT: Note: These datasets are in supplement to, but separate from, Geo GSE32916. We examined pro-inflammatory gene activation in activated wildtype and IFNAR-/- murine macrophages by performing RNA-Seq with poly(A) enriched transcripts. This experimental strategy allowed a global, high-resolution analysis of the transcriptional regulation of diverse classes of co-expressed genes and allowed us to identify co-expressed gene classes that may depend on IFN-signaling for its expression. The results provide quantitative insights into the relationship between transcription and distinct promoter and chromatin properties. RNA-seq of poly(A) enriched RNA from five stimulation time-points in wildtype and IFNAR-/- macrophages

ORGANISM(S): Mus musculus

SUBMITTER: Stephen Smale

PROVIDER: E-GEOD-38892 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:Background RNA sequencing (RNA-seq) is a powerful technique for identifying and quantifying transcription and splicing events, both known and novel. However, given its recent development and the proliferation of library construction methods, understanding the bias it introduces is incomplete but critical to realizing its value. Results Here we present a method, in vitro transcription sequencing (IVT-seq), for identifying and assessing the technical biases in RNA-seq library generation and sequencing at scale. We created a pool of > 1000 in vitro transcribed (IVT) RNAs from a full-length human cDNA library and sequenced them with poly-A and total RNA-seq, the most common protocols. Because each cDNA is full length and we show IVT is incredibly processive, each base in each transcript should be equivalently represented. However, with common RNA-seq applications and platforms, we find ~50% of transcripts have > 2-fold and ~10% have > 10-fold differences in within-transcript sequence coverage. Strikingly, we also find > 6% of transcripts have regions of high, unpredictable sequencing coverage, where the same transcript varies dramatically in coverage between samples, confounding accurate determination of their expression. To get at causal factors, we used a combination of experimental and computational approaches to show that rRNA depletion is responsible for the most significant variability in coverage and that several sequence determinants also strongly influence representation. Conclusions In sum, these results show the utility of IVT-seq in promoting better understanding of bias introduced by RNA-seq and suggest caution in its interpretation. Furthermore, we find that rRNA-depletion is responsible for substantial, unappreciated biases in coverage. Perhaps most importantly, these coverage biases introduced during library preparation suggest exon level expression analysis may be inadvisable. 5 rRNA-depleted samples with duplicates, 1 polyA selected, 1 total RNA, and 1 plasmid library all without replicates.

Dataset Information

Analysis of Pro-Inflammatory Gene Activation and RNA Processing by RNAseq of Nascent Transcripts [supplement]

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