RNAontheBENCH: computational and empirical resources for benchmarking RNAseq quantification and differential expression methods

Germain, Pierre-Luc; Vitriolo, Alessandro; Adamo, Antonio; Laise, Pasquale; Das, Vivek; Testa, Giuseppe

doi:10.1093/nar/gkw448

Cited by 50 publications

(46 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The NanoString nCounter technology is considered to be a highly reproducible and robust method for detecting gene and isoform expression (Kulkarni, 2011). As a consequence, the NanoString measurements are widely used as a benchmark for isoform expression (Germain et al, 2016; Steijger et al, 2013). We compare our MSIQ method with three other estimation methods, Cufflinks, iReckon, and MISO, based on their performances on six samples of the human HepG2 (liver hepatocellular carcinoma) immortalized cell line (see Supplementary Table S3 for detailed description).…”

Section: Resultsmentioning

confidence: 99%

MSIQ: Joint modeling of multiple RNA-seq samples for accurate isoform quantification

Zhao

Zhang

2018

Ann. Appl. Stat.

View full text Add to dashboard Cite

Next-generation RNA sequencing (RNA-seq) technology has been widely used to assess full-length RNA isoform abundance in a high-throughput manner. RNA-seq data offer insight into gene expression levels and transcriptome structures, enabling us to better understand the regulation of gene expression and fundamental biological processes. Accurate isoform quantification from RNA-seq data is challenging due to the information loss in sequencing experiments. A recent accumulation of multiple RNA-seq data sets from the same tissue or cell type provides new opportunities to improve the accuracy of isoform quantification. However, existing statistical or computational methods for multiple RNA-seq samples either pool the samples into one sample or assign equal weights to the samples when estimating isoform abundance. These methods ignore the possible heterogeneity in the quality of different samples and could result in biased and unrobust estimates. In this article, we develop a method, which we call “joint modeling of multiple RNA-seq samples for accurate isoform quantification” (MSIQ), for more accurate and robust isoform quantification by integrating multiple RNA-seq samples under a Bayesian framework. Our method aims to (1) identify a consistent group of samples with homogeneous quality and (2) improve isoform quantification accuracy by jointly modeling multiple RNA-seq samples by allowing for higher weights on the consistent group. We show that MSIQ provides a consistent estimator of isoform abundance, and we demonstrate the accuracy and effectiveness of MSIQ compared with alternative methods through simulation studies on D. melanogaster genes. We justify MSIQ’s advantages over existing approaches via application studies on real RNA-seq data from human embryonic stem cells, brain tissues, and the HepG2 immortalized cell line. We also perform a comprehensive analysis of how the isoform quantification accuracy would be affected by RNA-seq sample heterogeneity and different experimental protocols.

show abstract

Section: Resultsmentioning

confidence: 99%

MSIQ: Joint modeling of multiple RNA-seq samples for accurate isoform quantification

Zhao

Zhang

2018

Ann. Appl. Stat.

View full text Add to dashboard Cite

show abstract

“…Since the structures and abundance of expressed isoforms are unobservable in real RNA-seq data, we seek to evaluate the performance of different methods by comparing their estimated isoform expression levels in the FPKM unit with the NanoString counts, which could serve as benchmark data for isoform abundance when PCR validation is not available (Steijger et al 2013;Geiss et al 2008;Germain et al 2016;). The NanoString nCounter technology is considered as one of the most reproducible and robust medium-throughput assays for quantifying gene and isoform expression levels (Kulkarni 2011;Prokopec et al 2013;Veldman-Jones et al 2015).…”

Section: Aide Improves Isoform Abundance Estimation On Real Datamentioning

confidence: 99%

AIDE: annotation-assisted isoform discovery with high precision

Tong

et al. 2018

Preprint

View full text Add to dashboard Cite

Genome-wide accurate identification and quantification of full-length mRNA isoforms is crucial for investigating transcriptional and post-transcriptional regulatory mechanisms of biological phenomena. Despite continuing efforts in developing effective computational tools to identify or assemble full-length mRNA isoforms from second-generation RNA-seq data, it remains a challenge to accurately identify mRNA isoforms from short sequence reads due to the substantial information loss in RNA-seq experiments. Here we introduce a novel statistical method, AIDE (Annotation-assisted Isoform DiscovEry), the first approach that directly controls false isoform discoveries by implementing the testing-based model selection principle. Solving the isoform discovery problem in a stepwise and conservative manner, AIDE prioritizes the annotated isoforms and precisely identifies novel isoforms whose addition significantly improves the explanation of observed RNA-seq reads. We evaluate the performance of AIDE based on multiple simulated and real RNA-seq datasets followed by a PCR-Sanger sequencing validation. Our results show that AIDE effectively leverages the annotation information to compensate the information loss due to short read lengths. AIDE achieves the highest precision in isoform discovery and the lowest error rates in isoform abundance estimation, compared with three state-of-the-art methods Cufflinks, SLIDE, and StringTie. As a robust bioinformatics tool for transcriptome analysis, AIDE will enable researchers to discover novel transcripts with high confidence.

show abstract

“…In various assessments on simulated data [8][9][10] , these lightweight methods have compared favorably to well-tested but much slower methods for abundance estimation, like RSEM 11 , coupled with alignment methods such as Bowtie2 12 . However, assessments based primarily (or entirely) upon simulated data often fail to capture important aspects of real experiments, and similar performance among methods on such simulated datasets does not necessarily generalize to experimental data.…”

Section: Introductionmentioning

confidence: 99%

Alignment and mapping methodology influence transcript abundance estimation

Srivastava

Malik

Sarkar

et al. 2019

Preprint

View full text Add to dashboard Cite

Background: The accuracy of transcript quantification using RNA-seq data depends on many factors, such as the choice of alignment or mapping method and the quantification model being adopted. While the choice of quantification model has been shown to be important, considerably less attention has been given to comparing the effect of various read alignment approaches on quantification accuracy.Results: We investigate the influence of mapping and alignment on the accuracy of transcript quantification in both simulated and experimental data, as well as the effect on subsequent differential expression analysis. We observe that, even when the quantification model itself is held fixed, the effect of choosing a different alignment methodology, or aligning reads using different parameters, on quantification estimates can sometimes be large, and can affect downstream differential expression analyses as well. These effects can go unnoticed when assessment is focused too heavily on simulated data, where the alignment task is often simpler than in experimentally-acquired samples. We also introduce a new alignment methodology, called selective alignment, to overcome the shortcomings of lightweight approaches without incurring the computational cost of traditional alignment.Conclusion: We observe that, on experimental datasets, the performance of lightweight mapping and alignment-based approaches varies significantly and highlight some of the underlying factors. We show this variation both in terms of quantification and downstream differential expression analysis. In all comparisons, we also show the improved performance of our proposed selective alignment method and suggest best practices for performing RNA-seq quantification. * Contributed equally.

show abstract

RNAontheBENCH: computational and empirical resources for benchmarking RNAseq quantification and differential expression methods

Cited by 50 publications

References 40 publications

MSIQ: Joint modeling of multiple RNA-seq samples for accurate isoform quantification

MSIQ: Joint modeling of multiple RNA-seq samples for accurate isoform quantification

AIDE: annotation-assisted isoform discovery with high precision

Alignment and mapping methodology influence transcript abundance estimation

Contact Info

Product

Resources

About