scholarly journals The impact of amplification on differential expression analyses by RNA-seq

2015 ◽  
Author(s):  
Swati Parekh ◽  
Christoph Ziegenhain ◽  
Beate Vieth ◽  
Wolfgang Enard ◽  
Ines Hellmann
Keyword(s):  
Differential Expression ◽  
Large Fraction ◽  
Differential Expression Analysis ◽  
Pcr Amplification ◽  
Rna Seq ◽  
False Discovery Rates ◽  
False Discovery ◽  
Appreciable Increase ◽  
Pcr Duplicates ◽  
The Impact

Background Currently quantitative RNA-Seq methods are pushed to work with increasingly small starting amounts of RNA that require PCR amplification to generate libraries. However, it is unclear how much noise or bias amplification introduces and how this effects precision and accuracy of RNA quantification. To assess the effects of amplification, reads that originated from the same RNA molecule (PCR-duplicates) need to be identified. Computationally, read duplicates are defined via their mapping position, which does not distinguish PCR- from natural duplicates that are bound to occur for highly transcribed RNAs. Hence, it is unclear how to treat duplicate reads and how important it is to reduce PCR amplification experimentally. Here, we generate and analyse RNA-Seq datasets that were prepared with three different protocols (Smart-Seq, TruSeq and UMI-seq). We find that a large fraction of computationally identified read duplicates can be explained by sampling and fragmentation bias. Consequently, the computational removal of duplicates does not improve accuracy, power or false discovery rates, but can actually worsen them. Even when duplicates are experimentally identified by unique molecular identifiers (UMIs), power and false discovery rate are only mildly improved. However, we do find that power does improve with fewer PCR amplification cycles across datasets and that early barcoding of samples and hence PCR amplification in one reaction can restore this loss of power. Conclusions Computational removal of read duplicates is not recommended for differential expression analysis. However, the pooling of samples as made possible by the early barcoding of the UMI-protocol leads to an appreciable increase in the power to detect differentially expressed genes.

scholarly journals Impact of human gene annotations on RNA-seq differential expression analysis

2021 ◽  
Author(s):  
Yu Hamaguchi ◽  
Chao Zeng ◽  
Michiaki Hamada
Keyword(s):  
Differential Expression ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Human Gene ◽  
Gene Annotation ◽  
Differential Expression Analysis ◽  
Rna Seq ◽  
Gene Annotations ◽  
Sequencing Technologies ◽  
The Impact

Abstract Background: Differential expression (DE) analysis of RNA-seq data typically depends on gene annotations. Different sets of gene annotations are available for the human genome and are continually updated–a process complicated with the development and application of high-throughput sequencing technologies. However, the impact of the complexity of gene annotations on DE analysis remains unclear.Results: Using “mappability”, a metric of the complexity of gene annotation, we compared three distinct human gene annotations, GENCODE, RefSeq, and NONCODE, and evaluated how mappability affected DE analysis. We found that mappability was significantly different among the human gene annotations. We also found that increasing mappability improved the performance of DE analysis, and the impact of mappability mainly evident in the quantification step and propagated downstream of DE analysis systematically.Conclusions: We assessed how the complexity of gene annotations affects DE analysis using mappability. Our findings indicate that the growth and complexity of gene annotations negatively impact the performance of DE analysis, suggesting that an approach that excludes unnecessary gene models from gene annotations improves the performance of DE analysis.

scholarly journals Accurate quantification of circular RNAs identifies extensive circular isoform switching events

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Jinyang Zhang ◽  
Shuai Chen ◽  
Jingwen Yang ◽  
Fangqing Zhao
Keyword(s):  
False Discovery Rate ◽  
Differential Expression ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
Circular Rnas ◽  
Rna Seq ◽  
Rnase R ◽  
False Discovery ◽  
Rrna Depletion ◽  
Accurate Expression

AbstractDetection and quantification of circular RNAs (circRNAs) face several significant challenges, including high false discovery rate, uneven rRNA depletion and RNase R treatment efficiency, and underestimation of back-spliced junction reads. Here, we propose a novel algorithm, CIRIquant, for accurate circRNA quantification and differential expression analysis. By constructing pseudo-circular reference for re-alignment of RNA-seq reads and employing sophisticated statistical models to correct RNase R treatment biases, CIRIquant can provide more accurate expression values for circRNAs with significantly reduced false discovery rate. We further develop a one-stop differential expression analysis pipeline implementing two independent measures, which helps unveil the regulation of competitive splicing between circRNAs and their linear counterparts. We apply CIRIquant to RNA-seq datasets of hepatocellular carcinoma, and characterize two important groups of linear-circular switching and circular transcript usage switching events, which demonstrate the promising ability to explore extensive transcriptomic changes in liver tumorigenesis.

scholarly journals EBSeq: improving mixing computations for multi-group differential expression analysis

2020 ◽  
Author(s):  
Xiuyu Ma ◽  
Christina Kendziorski ◽  
Michael A. Newton
Keyword(s):  
Differential Expression ◽  
Clustering Algorithms ◽  
Differential Expression Analysis ◽  
Synthetic Data ◽  
Data Sets ◽  
Empirical Bayesian ◽  
False Discovery Rates ◽  
Data Analyst ◽  
False Discovery ◽  
Multiple Sample

ABSTRACTEBSeq is a Bioconductor package designed to calculate empirical-Bayesian inference summaries from sequence-based gene-expression (RNA-Seq) data. It produces gene or isoform-specific scores that measure various patterns of differential expression among a set of sample groups, and is most commonly deployed to measure differential expression between two groups. Its use of local posterior probabilities from a fitted mixture model provides the data analyst a direct way to score the false discovery rate of any reported list of genes, and it is one of the only tools that can address local false discovery rates when analyzing multiple sample groups. Contemporary applications have increasing numbers of sample groups, and the algorithms deployed in EBSeq are neither space nor time efficient in this important case. We describe a version update utilizing code improvements and novel pruning and clustering algorithms in order to reduce the complexity of mixture computations. The algorithms are supported by a theoretical analysis and tested empirically on a variety of benchmark and synthetic data sets.

scholarly journals Nonparametric expression analysis using inferential replicate counts

2019 ◽  
Author(s):  
Anqi Zhu ◽  
Avi Srivastava ◽  
Joseph G. Ibrahim ◽  
Rob Patro ◽  
Michael I. Love
Keyword(s):  
False Discovery Rate ◽  
Differential Expression ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
Transcript Level ◽  
Parametric Model ◽  
Statistical Testing ◽  
Rna Seq ◽  
Nonparametric Models ◽  
False Discovery

AbstractA primary challenge in the analysis of RNA-seq data is to identify differentially expressed genes or transcripts while controlling for technical biases present in the observations. Ideally, a statistical testing procedure should incorporate information about the inherent uncertainty of the abundance estimates, whether at the gene or transcript level, that arise from quantification of abundance. Most popular methods for RNA-seq differential expression analysis fit a parametric model to the counts or scaled counts for each gene or transcript, and a subset of methods can incorporate information about the uncertainty of the counts. Previous work has shown that nonparametric models for RNA-seq differential expression may in some cases have better control of the false discovery rate, and adapt well to new data types without requiring reformulation of a parametric model. Existing nonparametric models do not take into account the inferential uncertainty of the observations, leading to an inflated false discovery rate, in particular at the transcript level. Here we propose a nonparametric model for differential expression analysis using inferential replicate counts, extending the existing SAMseq method to account for inferential uncertainty, batch effects, and sample pairing. We compare our method, “SAMseq With Inferential Samples Helps”, or Swish, with popular differential expression analysis methods. Swish has improved control of the false discovery rate, in particular for transcripts with high inferential uncertainty. We apply Swish to a singlecell RNA-seq dataset, assessing sensitivity to recover DE genes between sub-populations of cells, and compare its performance to the Wilcoxon rank sum test.

scholarly journals Nonparametric expression analysis using inferential replicate counts

2019 ◽  
Vol 47 (18) ◽  
pp. e105-e105 ◽  
Author(s):  
Anqi Zhu ◽  
Avi Srivastava ◽  
Joseph G Ibrahim ◽  
Rob Patro ◽  
Michael I Love
Keyword(s):  
False Discovery Rate ◽  
Differential Expression ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
Parametric Model ◽  
Statistical Testing ◽  
Wilcoxon Test ◽  
Rna Seq ◽  
Nonparametric Models ◽  
False Discovery

Abstract A primary challenge in the analysis of RNA-seq data is to identify differentially expressed genes or transcripts while controlling for technical biases. Ideally, a statistical testing procedure should incorporate the inherent uncertainty of the abundance estimates arising from the quantification step. Most popular methods for RNA-seq differential expression analysis fit a parametric model to the counts for each gene or transcript, and a subset of methods can incorporate uncertainty. Previous work has shown that nonparametric models for RNA-seq differential expression may have better control of the false discovery rate, and adapt well to new data types without requiring reformulation of a parametric model. Existing nonparametric models do not take into account inferential uncertainty, leading to an inflated false discovery rate, in particular at the transcript level. We propose a nonparametric model for differential expression analysis using inferential replicate counts, extending the existing SAMseq method to account for inferential uncertainty. We compare our method, Swish, with popular differential expression analysis methods. Swish has improved control of the false discovery rate, in particular for transcripts with high inferential uncertainty. We apply Swish to a single-cell RNA-seq dataset, assessing differential expression between sub-populations of cells, and compare its performance to the Wilcoxon test.

scholarly journals Effects of duplicated mapped read PCR artifacts on RNA-seq differential expression analysis based on qRNA-seq

2018 ◽  
Author(s):  
Anna C. Salzberg ◽  
Jiafen Hu ◽  
Elizabeth J. Conroy ◽  
Nancy M. Cladel ◽  
Robert M. Brucklacher ◽  
...  
Keyword(s):  
Differentially Expressed Genes ◽  
Differential Expression Analysis ◽  
Pcr Amplification ◽  
False Positives ◽  
Differentially Expressed ◽  
Rna Seq ◽  
Gold Standard Method ◽  
Pcr Duplicates ◽  
Cdna Molecule

AbstractBest practices to handling duplicated mapped reads in RNA-seq analyses has long been discussed but a gold standard method has yet to be established, as such duplicates could originate from valid biological transcripts or they could be PCR-related artifacts. Here we used the NEXTflex™qRNA-SeqTM(aka Molecular Indexing™) technology to identify PCR duplicates via the random attachment of unique molecular labels to each cDNA molecule prior to PCR amplification. We found that up to 64.3% of the single end and 19.3% of the mouse paired end duplicates originated from valid biological transcripts rather than PCR artifacts. For single end reads, either removing or retaining all duplicates resulted in a substantial number of false positives (up to 47.0%) and false negatives (up to 12.1%) in the sets of significantly differentially expressed genes. For paired end reads, only the alignment retaining all duplicates resulted in a substantial number of false positives. This is the first effort to evaluate the performance of qRNA-seq using ‘real-world’ biomedical samples, and we found that PCR duplicate identification provided minor benefits for paired end reads but greatly improved the sensitivity and specificity in the determination of the significantly differentially expressed genes for single end reads.

scholarly journals Joint between-sample normalization and differential expression detection through ℓ0-regularized regression

2019 ◽  
Vol 20 (S16) ◽  
Author(s):  
Kefei Liu ◽  
Li Shen ◽  
Hui Jiang
Keyword(s):  
Differential Expression ◽  
Fundamental Problem ◽  
Large Fraction ◽  
Differential Expression Analysis ◽  
Specific Antigen ◽  
Differentially Expressed ◽  
Detection Accuracy ◽  
Continuous Variables ◽  
Rna Seq ◽  
Experimental Conditions

Abstract Background A fundamental problem in RNA-seq data analysis is to identify genes or exons that are differentially expressed with varying experimental conditions based on the read counts. The relativeness of RNA-seq measurements makes the between-sample normalization of read counts an essential step in differential expression (DE) analysis. In most existing methods, the normalization step is performed prior to the DE analysis. Recently, Jiang and Zhan proposed a statistical method which introduces sample-specific normalization parameters into a joint model, which allows for simultaneous normalization and differential expression analysis from log-transformed RNA-seq data. Furthermore, an ℓ0 penalty is used to yield a sparse solution which selects a subset of DE genes. The experimental conditions are restricted to be categorical in their work. Results In this paper, we generalize Jiang and Zhan’s method to handle experimental conditions that are measured in continuous variables. As a result, genes with expression levels associated with a single or multiple covariates can be detected. As the problem being high-dimensional, non-differentiable and non-convex, we develop an efficient algorithm for model fitting. Conclusions Experiments on synthetic data demonstrate that the proposed method outperforms existing methods in terms of detection accuracy when a large fraction of genes are differentially expressed in an asymmetric manner, and the performance gain becomes more substantial for larger sample sizes. We also apply our method to a real prostate cancer RNA-seq dataset to identify genes associated with pre-operative prostate-specific antigen (PSA) levels in patients.

scholarly journals Impact of human gene annotations on RNA-seq differential expression analysis

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yu Hamaguchi ◽  
Chao Zeng ◽  
Michiaki Hamada
Keyword(s):  
Differential Expression ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Human Gene ◽  
Gene Annotation ◽  
Differential Expression Analysis ◽  
Rna Seq ◽  
Gene Annotations ◽  
Sequencing Technologies ◽  
The Impact

Abstract Background Differential expression (DE) analysis of RNA-seq data typically depends on gene annotations. Different sets of gene annotations are available for the human genome and are continually updated–a process complicated with the development and application of high-throughput sequencing technologies. However, the impact of the complexity of gene annotations on DE analysis remains unclear. Results Using “mappability”, a metric of the complexity of gene annotation, we compared three distinct human gene annotations, GENCODE, RefSeq, and NONCODE, and evaluated how mappability affected DE analysis. We found that mappability was significantly different among the human gene annotations. We also found that increasing mappability improved the performance of DE analysis, and the impact of mappability mainly evident in the quantification step and propagated downstream of DE analysis systematically. Conclusions We assessed how the complexity of gene annotations affects DE analysis using mappability. Our findings indicate that the growth and complexity of gene annotations negatively impact the performance of DE analysis, suggesting that an approach that excludes unnecessary gene models from gene annotations improves the performance of DE analysis.

scholarly journals Best practices on the differential expression analysis of multi-species RNA-seq

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Matthew Chung ◽  
Vincent M. Bruno ◽  
David A. Rasko ◽  
Christina A. Cuomo ◽  
José F. Muñoz ◽  
...  
Keyword(s):  
Best Practices ◽  
Differential Expression ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
Single Species ◽  
Rna Seq ◽  
Species Analysis ◽  
Differential Gene ◽  
Multiple Species ◽  
Downstream Analysis

AbstractAdvances in transcriptome sequencing allow for simultaneous interrogation of differentially expressed genes from multiple species originating from a single RNA sample, termed dual or multi-species transcriptomics. Compared to single-species differential expression analysis, the design of multi-species differential expression experiments must account for the relative abundances of each organism of interest within the sample, often requiring enrichment methods and yielding differences in total read counts across samples. The analysis of multi-species transcriptomics datasets requires modifications to the alignment, quantification, and downstream analysis steps compared to the single-species analysis pipelines. We describe best practices for multi-species transcriptomics and differential gene expression.

scholarly journals The long and the short of it: unlocking nanopore long-read RNA sequencing data with short-read differential expression analysis tools

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Xueyi Dong ◽  
Luyi Tian ◽  
Quentin Gouil ◽  
Hasaru Kariyawasam ◽  
Shian Su ◽  
...  
Keyword(s):  
Differential Expression ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
Transcriptomic Analysis ◽  
Statistical Testing ◽  
Rna Seq ◽  
Sequencing Data ◽  
Short Read ◽  
Sequencing Platform ◽  
Long Read

Abstract Application of Oxford Nanopore Technologies’ long-read sequencing platform to transcriptomic analysis is increasing in popularity. However, such analysis can be challenging due to the high sequence error and small library sizes, which decreases quantification accuracy and reduces power for statistical testing. Here, we report the analysis of two nanopore RNA-seq datasets with the goal of obtaining gene- and isoform-level differential expression information. A dataset of synthetic, spliced, spike-in RNAs (‘sequins’) as well as a mouse neural stem cell dataset from samples with a null mutation of the epigenetic regulator Smchd1 was analysed using a mix of long-read specific tools for preprocessing together with established short-read RNA-seq methods for downstream analysis. We used limma-voom to perform differential gene expression analysis, and the novel FLAMES pipeline to perform isoform identification and quantification, followed by DRIMSeq and limma-diffSplice (with stageR) to perform differential transcript usage analysis. We compared results from the sequins dataset to the ground truth, and results of the mouse dataset to a previous short-read study on equivalent samples. Overall, our work shows that transcriptomic analysis of long-read nanopore data using long-read specific preprocessing methods together with short-read differential expression methods and software that are already in wide use can yield meaningful results.

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