End Sequence Analysis ToolKit (ESAT) expands the extractable information from single cell RNA-Seq data
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ABSTRACT:
ORGANISM(S): Mus musculus Rattus norvegicus
PROVIDER: GSE79651 | GEO | 2016/07/27
SECONDARY ACCESSION(S): PRJNA316604
REPOSITORIES: GEO
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Project description:Test data used for the evaluation of ESAT performance and results files for data from 3' and 5' end-sequencing RNA-Seq protocols and droplet-based single-cell RNA-Seq. Quantification and analysis of Tophat-aligned (v2.0.9) samples from mouse bone-marrow derived dendritic cells (mBMDC) timecourse (0, 2, 4 and 6 hours) post LPS stimulation and non-diabetic BBDR rat pancreatic islet cells. Since end-sequencing (3' or 5') is used for all samples, alignments are only required for the R2 (sequence-containing) read.
2016-07-27 | E-GEOD-79651 | biostudies-arrayexpress
Project description:End Sequence Analysis ToolKit (ESAT) expands the extractable information from single cell RNA-Seq data
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Project description:RNA-seq protocols that focus on transcript termini are well suited for applications in which template quantity is limiting. Here we show that, when applied to end-sequencing data, analytical methods designed for global RNA-seq produce computational artifacts. To remedy this, we created the End Sequence Analysis Toolkit (ESAT). As a test, we first compared end-sequencing and bulk RNA-seq using RNA from dendritic cells stimulated with lipopolysaccharide (LPS). As predicted by the telescripting model for transcriptional bursts, ESAT detected an LPS-stimulated shift to shorter 3'-isoforms that was not evident by conventional computational methods. Then, droplet-based microfluidics was used to generate 1000 cDNA libraries, each from an individual pancreatic islet cell. ESAT identified nine distinct cell types, three distinct β-cell types, and a complex interplay between hormone secretion and vascularization. ESAT, then, offers a much-needed and generally applicable computational pipeline for either bulk or single-cell RNA end-sequencing.
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Project description:We present primary results from the Sequencing Quality Control (SEQC) project, coordinated by the United States Food and Drug Administration. Examining Illumina HiSeq, Life Technologies SOLiD and Roche 454 platforms at multiple laboratory sites using reference RNA samples with built-in controls, we assess RNA sequencing (RNA-seq) performance for sequence discovery and differential expression profiling and compare it to microarray and quantitative PCR (qPCR) data using complementary metrics. At all sequencing depths, we discover unannotated exon-exon junctions, with >80% validated by qPCR. We find that measurements of relative expression are accurate and reproducible across sites and platforms if specific filters are used. In contrast, RNA-seq and microarrays do not provide accurate absolute measurements, and gene-specific biases are observed, for these and qPCR. Measurement performance depends on the platform and data analysis pipeline, and variation is large for transcriptlevel profiling. The complete SEQC data sets, comprising >100 billion reads (10Tb), provide unique resources for evaluating RNA-seq analyses for clinical and regulatory settings. The well-characterized reference RNA samples A (pooled cell lines) and B (human brain) from the MAQC consortium, adding spike-ins of synthetic RNA from the External RNA Control Consortium (ERCC). Samples C and D were then constructed by combining A and B in known mixing ratios, 3:1 and 1:3, respectively. All samples were distributed to several independent sites for RNA-Seq library construction and profiling by Illumina HiSeq 2000 and LifeTech SOLiD 5500 platforms. Also, vendors created their own cDNA libraries that were then distributed to each test site, in order to examine the degree of a M-bM-^@M-^\site effectM-bM-^@M-^] that was independent of the library preparation process. To support an assessment of gene models, samples A and B were also sequenced at independent sites by the Roche 454 platform, providing longer reads. For comparison to other technologies, these data were also compared to the MAQC-I Affymetrix U133 Plus2 microarray, several current microarray platforms, and also assessed by 20,801 PrimePCR reactions.
2014-08-08 | E-GEOD-56457 | biostudies-arrayexpress