Modelling and simulating generic RNA-Seq experiments with the flux simulator

By Thasso Griebel, Benedikt Zacher, Paolo Ribeca, E. Raineri, Vincent Lacroix, Roderic Guigó, M. Sammeth. Nucleic Acids Research, 2012

301 citations recorded by the source corpus.

High-throughput sequencing of cDNA libraries constructed from cellular RNA complements (RNA-Seq) naturally provides a digital quantitative measurement for every expressed RNA molecule. Nature, impact and mutual interference of biases in different experimental setups are, however, still poorly understood—mostly due to the lack of data from intermediate protocol steps. We analysed multiple RNA-Seq experiments, involving different sample preparation protocols and sequencing platforms: we broke them down into their common—and currently indispensable—technical components (reverse transcription, fragmentation, adapter ligation, PCR amplification, gel segregation and sequencing), investigating how such different steps influence abundance and distribution of the sequenced reads. For each of those steps, we developed universally applicable models, which can be parameterised by empirical attributes of any experimental protocol. Our models are implemented in a computer simulation pipeline called the Flux Simulator, and we show that read distributions generated by different combinations of these models reproduce well corresponding evidence obtained from the corresponding experimental setups. We further demonstrate that our in silico RNA-Seq provides insights about hidden precursors that determine the final configuration of reads along gene bodies; enhancing or compensatory effects that explain apparently controversial observations can be observed. Moreover, our simulations identify hitherto unreported sources of systematic bias from RNA hydrolysis, a fragmentation technique currently employed by most RNA-Seq protocols.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/867eae3e270a8100229d96624ed93e9dd0dc15d9

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Citations in the corpus

Tools for mapping high-throughput sequencing data (2012, background): “Time column presents the pre-processing time (includes indexing), and the R.Aligned column presents the number of reported aligned reads.”
Improved transcriptome assembly using a hybrid of long and short reads with StringTie (2021, methodology): “We simulated 13,361,612 reads (the same number of reads in the NA12878-dRNA sample used build the model) by running the simulator.py module of NanoSim in transcriptome mode with the following command: To match the expression levels of the long reads to the short reads, we used the.pro file generated”
StringTie enables improved reconstruction of a transcriptome from RNA-seq reads (2015, methodology): “Flux Simulator tries to overcome these limitations by the use of parameterized models that reproduce the common sources of systematic bias in each of the main steps in sample preparation.”
TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions (2013, methodology): “Instead of trying to precisely mimic real RNA-seq experiments, which may not be possible in any practical sense, we generated data with relatively simple settings and expression levels, calculated using a model from the Flux Simulator system [18], as follows.”
Rcorrector: efficient and accurate error correction for Illumina RNA-seq reads (2015, methodology): “Reads were generated with FluxSimulator [15] starting from the human GENCODE v.17 gene annotations.”
Sailfish enables alignment-free isoform quantification from RNA-seq reads using lightweight algorithms (2013, methodology): “Unlike synthetic test data used in previous work [5, 10], the procedure used by the Flux Simulator is not based specifically on the generative model underlying our estimation procedure.”
RNA Sequencing and Analysis (2015, unknown): “Computational tools such as Cuf ﬂ inks (Trapnell et al. 2010), FluxCapacitor (Mont-gomery et al. 2010; Griebel et al. 2012), and MISO (Katz et al. 2010), quantify expression by counting the number of reads that map to full-length transcripts (Table 2).”
DeepBound: accurate identification of transcript boundaries via deep convolutional neural fields (2017, methodology): “Flux-Simulator utilizes the known transcriptome annotation database (in our experiments, we use ENSEMBL release-87 annotation) and reference genome to simulate the expressed transcripts and the sequenced reads, following certain empirical distributions.”