BEERS2: RNA-Seq simulation through high fidelity in silico modeling

Author:

Brooks Thomas G1ORCID,Lahens Nicholas F1,Mrčela Antonijo1,Sarantopoulou Dimitra12,Nayak Soumyashant13,Naik Amruta14,Sengupta Shaon145,Choi Peter S67,Grant Gregory R18

Affiliation:

1. Institute for Translational Medicine and Therapeutics, University of Pennsylvania , PA , USA

2. Current address: National Institute on Aging, National Institutes of Health , Baltimore, MD , USA

3. Current address: Statistics and Mathematics Unit, Indian Statistical Institute , Bengaluru, Karnataka , India

4. Children’s Hospital of Philadelphia , Philadelphia, PA , USA

5. Department of Pediatrics, University of Pennsylvania Perelman School of Medicine , Philadelphia, Pennsylvania , USA

6. Division of Cancer Pathobiology, Children’s Hospital of Philadelphia , Philadelphia, PA , USA

7. Department of Pathology & Laboratory Medicine, University of Pennsylvania Perelman School of Medicine , Philadelphia, PA , USA

8. Department of Genetics, University of Pennsylvania , Philadelphia, PA , USA

Abstract

Abstract Simulation of RNA-seq reads is critical in the assessment, comparison, benchmarking and development of bioinformatics tools. Yet the field of RNA-seq simulators has progressed little in the last decade. To address this need we have developed BEERS2, which combines a flexible and highly configurable design with detailed simulation of the entire library preparation and sequencing pipeline. BEERS2 takes input transcripts (typically fully length messenger RNA transcripts with polyA tails) from either customizable input or from CAMPAREE simulated RNA samples. It produces realistic reads of these transcripts as FASTQ, SAM or BAM formats with the SAM or BAM formats containing the true alignment to the reference genome. It also produces true transcript-level quantification values. BEERS2 combines a flexible and highly configurable design with detailed simulation of the entire library preparation and sequencing pipeline and is designed to include the effects of polyA selection and RiboZero for ribosomal depletion, hexamer priming sequence biases, GC-content biases in polymerase chain reaction (PCR) amplification, barcode read errors and errors during PCR amplification. These characteristics combine to make BEERS2 the most complete simulation of RNA-seq to date. Finally, we demonstrate the use of BEERS2 by measuring the effect of several settings on the popular Salmon pseudoalignment algorithm.

Funder

Next Generation of RNA-Seq Simulators for Benchmarking Analyses

National Center for Advancing Translational Sciences

Publisher

Oxford University Press (OUP)

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