Biology subjects

Rafael A Irizarry

Publications and source records attributed to Rafael A Irizarry.

5 recordsLinked to original sources

bioRxiv2015

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

RNA-seq technology is widely used in biomedical and basic science research. These studies rely on complex computational methods that quantify expression levels for observed transcripts. We find that current computational methods can lead to hundreds of false positive results related to alternative isoform usage. This flaw in the current methodology stems from a lack of modeling sample-specific bias that leads to drops in coverage and is related to sequence features like fragment GC content and GC stretches. By incorporating features that explain this bias into transcript expression models, we greatly increase the specificity of transcript expression estimates, with more than a four-fold reduction in the number of false positives for reported changes in expression. We introduce alpine, a method for estimation of bias-corrected transcript abundance. The method is available as a Bioconductor package that includes data visualization tools useful for bias discovery.

Bioinformatics↗

bioRxiv2015

On the widespread and critical impact of systematic bias and batch effects in single-cell RNA-Seq data

Until recently, high-throughput gene expression technology, such as RNA-Sequencing (RNA-seq) required hundreds of thousands of cells to produce reliable measurements. Recent technical advances permit genome-wide gene expression measurement at the single-cell level. Single-cell RNA-Seq (scRNA-seq) is the most widely used and numerous publications are based on data produced with this technology. However, RNA-Seq and scRNA-seq data are markedly different. In particular, unlike RNA-Seq, the majority of reported expression levels in scRNA-seq are zeros, which could be either biologically-driven, genes not expressing RNA at the time of measurement, or technically-driven, gene expressing RNA, but not at a sufficient level to detected by sequencing technology. Another difference is that the proportion of genes reporting the expression level to be zero varies substantially across single cells compared to RNA-seq samples. However, it remains unclear to what extent this cell-to-cell variation is being driven by technical rather than biological variation. Furthermore, while systematic errors, including batch effects, have been widely reported as a major challenge in high-throughput technologies, these issues have received minimal attention in published studies based on scRNA-seq technology. Here, we use an assessment experiment to examine data from published studies and demonstrate that systematic errors can explain a substantial percentage of observed cell-to-cell expression variability. Specifically, we present evidence that some of these reported zeros are driven by technical variation by demonstrating that scRNA-seq produces more zeros than expected and that this bias is greater for lower expressed genes. In addition, this missing data problem is exacerbated by the fact that this technical variation varies cell-to-cell. Then, we show how this technical cell-to-cell variability can be confused with novel biological results. Finally, we demonstrate and discuss how batch-effects and confounded experiments can intensify the problem.

Genomics↗

bioRxiv2015

Salmon provides accurate, fast, and bias-aware transcript expression estimates using dual-phase inference

We introduce Salmon, a new method for quantifying transcript abundance from RNA-seq reads that is highly-accurate and very fast. Salmon is the first transcriptome-wide quantifier to model and correct for fragment GC content bias, which we demonstrate substantially improves the accuracy of abundance estimates and the reliability of subsequent differential expression analysis compared to existing methods that do not account for these biases. Salmon achieves its speed and accuracy by combining a new dual-phase parallel inference algorithm and feature-rich bias models with an ultra-fast read mapping procedure. These innovations yield both exceptional accuracy and order-of-magnitude speed benefits over alignment-based methods.

Bioinformatics↗

bioRxiv2015

Flexible expressed region analysis for RNA-seq with derfinder

BackgroundDifferential expression analysis of RNA sequencing (RNA-seq) data typically relies on reconstructing transcripts or counting reads that overlap known gene structures. We previously introduced an intermediate statistical approach called differentially expressed region (DER) finder that seeks to identify contiguous regions of the genome showing differential expression signal at single base resolution without relying on existing annotation or potentially inaccurate transcript assembly.\n\nResultsWe present the derfinder software that improves our annotation-agnostic approach to RNA-seq analysis by: (1) implementing a computationally efficient bump-hunting approach to identify DERs which permits genome-scale analyses in a large number of samples, (2) introducing a flexible statistical modeling framework, including multi-group and time-course analyses and (3) introducing a new set of data visualizations for expressed region analysis. We apply this approach to public RNA-seq data from the Genotype-Tissue Expression (GTEx) project and BrainSpan project to show that derfinder permits the analysis of hundreds of samples at base resolution in R, identifies expression outside of known gene boundaries and can be used to visualize expressed regions at base-resolution. In simulations our base resolution approaches enable discovery in the presence of incomplete annotation and is nearly as powerful as feature-level methods when the annotation is complete.\n\nConclusionsderfinder analysis using expressed region-level and single base-level approaches provides a compromise between full transcript reconstruction and feature-level analysis.\n\nThe package is available from Bioconductor at www.bioconductor.org/packages/derfinder.

Bioinformatics↗

bioRxiv2014

IVT-seq reveals extreme bias in RNA-sequencing

BackgroundRNA sequencing (RNA-seq) is a powerful technique for identifying and quantifying transcription and splicing events, both known and novel. However, given its recent development and the proliferation of library construction methods, understanding the bias it introduces is incomplete but critical to realizing its value.\n\nResultsHere we present a method, in vitro transcription sequencing (IVT-seq), for identifying and assessing the technical biases in RNA-seq library generation and sequencing at scale. We created a pool of > 1000 in vitro transcribed (IVT) RNAs from a full-length human cDNA library and sequenced them with poly-A and total RNA-seq, the most common protocols. Because each cDNA is full length and we show IVT is incredibly processive, each base in each transcript should be equivalently represented. However, with common RNA-seq applications and platforms, we find [~]50% of transcripts have > 2-fold and [~]10% have > 10-fold differences in within-transcript sequence coverage. Strikingly, we also find > 6% of transcripts have regions of high, unpredictable sequencing coverage, where the same transcript varies dramatically in coverage between samples, confounding accurate determination of their expression. To get at causal factors, we used a combination of experimental and computational approaches to show that rRNA depletion is responsible for the most significant variability in coverage and that several sequence determinants also strongly influence representation.\n\nConclusionsIn sum, these results show the utility of IVT-seq in promoting better understanding of bias introduced by RNA-seq and suggest caution in its interpretation. Furthermore, we find that rRNA-depletion is responsible for substantial, unappreciated biases in coverage. Perhaps most importantly, these coverage biases introduced during library preparation suggest exon level expression analysis may be inadvisable.

Genomics↗