Abstract:The External RNA Control Consortium (ERCC) is an ad-hoc group with approximately 70 members from private, public, and academic organizations. The group is developing a set of external RNA control transcripts that can be used to assess technical performance in gene expression assays. The ERCC is now initiating the Testing Phase of the project, during which candidate external RNA controls will be evaluated in both microarray and QRT-PCR gene expression platforms. This document describes the proposed experiments … Show more
“…Traditional RNA-seq experiments cannot provide accurate differences in expression per cell (Loven et al, 2012; Percharde et al, 2017), since libraries are created from equal amounts of RNA and data are normalized to read depth. For CNN analysis, we isolated RNA from equal numbers of male and female PGCs or soma from Oct4/GFP mice and added synthetic External RNA Controls Consortium (ERCC) RNA spike-ins according to cell number prior to library generation (Figure 3A) (Reid. and ERCC, 2005).…”
Summary
Primordial germ cells (PGCs) are vital for inheritance and evolution. Their transcriptional program has been extensively studied and is assumed to be well-known. We report here a remarkable global upregulation of the transcriptome of mouse PGCs compared to somatic cells. Using cell-number normalized genome-wide analyses, we uncover significant transcriptional amplification in PGCs, including mRNAs, rRNA and transposable elements. Hypertranscription preserves tissue-specific gene expression patterns, correlates with cell size, and can still be detected in E15.5 male germ cells, when proliferation has ceased. PGC hypertranscription occurs at the level of nascent transcription, is accompanied by increased translation rates, and is driven by Myc factors n-Myc and l-Myc (but not c-Myc) and by P-TEFb. This study provides a paradigm for transcriptional analyses during development and reveals a major global hyperactivity of the germline transcriptome.
“…Traditional RNA-seq experiments cannot provide accurate differences in expression per cell (Loven et al, 2012; Percharde et al, 2017), since libraries are created from equal amounts of RNA and data are normalized to read depth. For CNN analysis, we isolated RNA from equal numbers of male and female PGCs or soma from Oct4/GFP mice and added synthetic External RNA Controls Consortium (ERCC) RNA spike-ins according to cell number prior to library generation (Figure 3A) (Reid. and ERCC, 2005).…”
Summary
Primordial germ cells (PGCs) are vital for inheritance and evolution. Their transcriptional program has been extensively studied and is assumed to be well-known. We report here a remarkable global upregulation of the transcriptome of mouse PGCs compared to somatic cells. Using cell-number normalized genome-wide analyses, we uncover significant transcriptional amplification in PGCs, including mRNAs, rRNA and transposable elements. Hypertranscription preserves tissue-specific gene expression patterns, correlates with cell size, and can still be detected in E15.5 male germ cells, when proliferation has ceased. PGC hypertranscription occurs at the level of nascent transcription, is accompanied by increased translation rates, and is driven by Myc factors n-Myc and l-Myc (but not c-Myc) and by P-TEFb. This study provides a paradigm for transcriptional analyses during development and reveals a major global hyperactivity of the germline transcriptome.
“…Before absolute quantification of inferred transcripts can be
performed, the dynamic range and transcript detection limits must be evaluated
using spike-ins. For this we employed the ERCC RNA spike-in set as recommended
by the National Institute of Standards and Technology (NIST) [35-37]. First, we computed the properly mapped read alignments from quality
controlled sequence read pairs to the ERCC spike-in reference sequences for each
embryological timepoint.…”
BackgroundThe de novo assembly of transcriptomes from short shotgun sequences
raises challenges due to random and non-random sequencing biases and
inherent transcript complexity. We sought to define a pipeline for de
novo transcriptome assembly to aid researchers working with
emerging model systems where well annotated genome assemblies are not
available as a reference. To detail this experimental and computational
method, we used early embryos of the sea anemone, Nematostella
vectensis, an emerging model system for studies of animal body plan
evolution. We performed RNA-seq on embryos up to 24 h of development
using Illumina HiSeq technology and evaluated independent de novo
assembly methods. The resulting reads were assembled using either the
Trinity assembler on all quality controlled reads or both the Velvet and
Oases assemblers on reads passing a stringent digital normalization filter.
A control set of mRNA standards from the National Institute of Standards and
Technology (NIST) was included in our experimental pipeline to invest our
transcriptome with quantitative information on absolute transcript levels
and to provide additional quality control.ResultsWe generated >200 million paired-end reads from directional cDNA libraries
representing well over 20 Gb of sequence. The Trinity assembler pipeline,
including preliminary quality control steps, resulted in more than 86% of
reads aligning with the reference transcriptome thus generated.
Nevertheless, digital normalization combined with assembly by Velvet and
Oases required far less computing power and decreased processing time while
still mapping 82% of reads. We have made the raw sequencing reads and
assembled transcriptome publically available.ConclusionsNematostella vectensis was chosen for its strategic position in the
tree of life for studies into the origins of the animal body plan, however,
the challenge of reference-free transcriptome assembly is relevant to all
systems for which well annotated gene models and independently verified
genome assembly may not be available. To navigate this new territory, we
have constructed a pipeline for library preparation and computational
analysis for de novo transcriptome assembly. The gene models
defined by this reference transcriptome define the set of genes transcribed
in early Nematostella development and will provide a valuable
dataset for further gene regulatory network investigations.
“…The spike-in can be done at low levels that would nonetheless generate a large enough number of reads to enable identification of minor contaminants. The idea of spiked-in fragments is not new; ERCC RNA spike-ins
[14] are routinely used to normalise RNA expression levels between different experiments, combinations of primer pairs specifying control fragments of defined length have been advocated for genotyping studies
[15] and Illumina include optional spike-in fragments, to diagnose the efficiency of library preparation steps, within their TruSeq kits.Figure 1
Diagrammatic representation of the SASI-Seq process. Amplicons of a reference sequence (here we use PhiX174) are generated with unique barcodes at their 5’ end.…”
BackgroundA minor but significant fraction of samples subjected to next-generation sequencing methods are either mixed-up or cross-contaminated. These events can lead to false or inconclusive results. We have therefore developed SASI-Seq; a process whereby a set of uniquely barcoded DNA fragments are added to samples destined for sequencing. From the final sequencing data, one can verify that all the reads derive from the original sample(s) and not from contaminants or other samples.ResultsBy adding a mixture of three uniquely barcoded amplicons, of different sizes spanning the range of insert sizes one would normally use for Illumina sequencing, at a spike-in level of approximately 0.1%, we demonstrate that these fragments remain intimately associated with the sample. They can be detected following even the tightest size selection regimes or exome enrichment and can report the occurrence of sample mix-ups and cross-contamination.As a consequence of this work, we have designed a set of 384 eleven-base Illumina barcode sequences that are at least 5 changes apart from each other, allowing for single-error correction and very low levels of barcode misallocation due to sequencing error.ConclusionSASI-Seq is a simple, inexpensive and flexible tool that enables sample assurance, allows deconvolution of sample mix-ups and reports levels of cross-contamination between samples throughout NGS workflows.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-110) contains supplementary material, which is available to authorized users.
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