Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation

Love, Michael I.; Hogenesch, John B.; Irizarry, Rafael A.

doi:10.1038/nbt.3682

Cited by 155 publications

(141 citation statements)

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“…The bias has also been observed in RNA-seq data (Love et al 2016). Solutions to this bias have been published for genomic DNA (Benjamini and Speed 2012;Jiang et al 2015) and RNA-seq data (Hansen et al 2012;Love et al 2016). However, below we explain why these approaches are not directly applicable to ChIP-seq data.…”

mentioning

confidence: 69%

“…Published work on GC-content bias correction has found that modeling GC-content effects at the fragment level is, currently, the optimal approach (Benjamini and Speed 2012;Love et al 2016). However, this approach is not directly applicable to ChIPseq data.…”

Section: Mixture Model Estimates Gc-content Effect For Background Andmentioning

confidence: 99%

“…For genomic DNA data, PCR amplification of DNA fragments during library preparation is one factor that introduces this bias (Aird et al 2011;Ross et al 2013). The bias has also been observed in RNA-seq data (Love et al 2016). Solutions to this bias have been published for genomic DNA (Benjamini and Speed 2012;Jiang et al 2015) and RNA-seq data (Hansen et al 2012;Love et al 2016).…”

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confidence: 99%

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Accounting for GC-content bias reduces systematic errors and batch effects in ChIP-seq data

Teng

Irizarry

2017

Genome Res.

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The main application of ChIP-seq technology is the detection of genomic regions that bind to a protein of interest. A large part of functional genomics' public catalogs is based on ChIP-seq data. These catalogs rely on peak calling algorithms that infer protein-binding sites by detecting genomic regions associated with more mapped reads (coverage) than expected by chance, as a result of the experimental protocol's lack of perfect specificity. We find that GC-content bias accounts for substantial variability in the observed coverage for ChIP-seq experiments and that this variability leads to false-positive peak calls. More concerning is that the GC effect varies across experiments, with the effect strong enough to result in a substantial number of peaks called differently when different laboratories perform experiments on the same cell line. However, accounting for GC content bias in ChIP-seq is challenging because the binding sites of interest tend to be more common in high GC-content regions, which confounds real biological signals with unwanted variability. To account for this challenge, we introduce a statistical approach that accounts for GC effects on both nonspecific noise and signal induced by the binding site. The method can be used to account for this bias in binding quantification as well to improve existing peak calling algorithms. We use this approach to show a reduction in false-positive peaks as well as improved consistency across laboratories.

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confidence: 69%

Section: Mixture Model Estimates Gc-content Effect For Background Andmentioning

confidence: 99%

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confidence: 99%

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Accounting for GC-content bias reduces systematic errors and batch effects in ChIP-seq data

Teng

Irizarry

2017

Genome Res.

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“…For example, Dohm et al (2008) found that GC-rich regions tend to have more reads than AT-rich regions. More recently, Risso, Schwartz, Sherlock, and Dudoit (2011) proposed different types of GC-content normalisation for the RNA-Seq data and Love, Hogenesch, and Irizarry (2016) found that modelling RNA-Seq GC-content bias was able to reduce the systematic errors in the transcript abundance estimation. Anther type of bias identified in RNA-Seq data is the gene length bias.…”

Section: Introductionmentioning

confidence: 99%

Modelling RNA‐Seq data with a zero‐inflated mixture Poisson linear model

Liu

Jiang

2019

Genetic Epidemiology

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RNA sequencing (RNA‐Seq) has been frequently used in genomic studies and has generated a vast amount of data. The RNA‐Seq data are composed of two parts: (a) a sequence of nucleotides of the genome; and (b) a corresponding sequence of counts, standing for the number of short reads whose mapped positions start at each position of the genome. One common feature of these count data is that they are typically nonuniform; recent studies have revealed that the nonuniformity is partially owing to a systematic bias resulted from the sequencing preference. Existing works in the literature model the nonuniformity with a single component Poisson linear model that incorporates the effects of the sequencing preference. However, we observe consistently that the short reads mapped to a gene may have a mixture structure and can be zero‐inflated. A single component model may not suffice to model the complexity of such data. In this paper, we propose a zero‐inflated mixture Poisson linear model for the RNA‐Seq count data and derive a fast expectation–maximisation‐based algorithm for estimating the unknown parameters. Numerical studies are conducted to illustrate the effectiveness of our method.

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“…Failing to correct these biases will likely lead to high false discovery rates in isoform discovery and unreliable statistical results in downstream analyses, such as differential isoform expression analysis (Patro et al 2017). Current computational methods account for the non-uniformity of reads using three main approaches: to adjust read counts summarized in defined genomic regions to offset the non-uniformity bias (Li et al 2010;Zheng et al 2011;Roberts et al 2011b), to assign a weight to each single read to adjust for bias (Hansen et al 2010), and to incorporate the bias as a model parameter in likelihood-based methods (Bohnert and Rätsch 2010;Roberts et al 2011b;Jiang and Salzman 2015;Love et al 2016).…”

Section: Introductionmentioning

confidence: 99%

AIDE: annotation-assisted isoform discovery with high precision

Tong

et al. 2018

Preprint

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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.

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Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation

Cited by 155 publications

References 35 publications

Accounting for GC-content bias reduces systematic errors and batch effects in ChIP-seq data

Accounting for GC-content bias reduces systematic errors and batch effects in ChIP-seq data

Modelling RNA‐Seq data with a zero‐inflated mixture Poisson linear model

AIDE: annotation-assisted isoform discovery with high precision

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