Taming of the wild: a new method for cross study RNA-seq analysis

2020 ◽  
Author(s):  
Diana Lobo ◽  
Raquel Godinho ◽  
John Archer
Keyword(s):  
Differential Expression ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Differential Expression Analysis ◽  
Differentially Expressed ◽  
Rna Seq ◽  
Differential Gene ◽  
Selection For ◽  
Insight Into

Abstract Background In the last decades, the evolution of RNA-Seq has yielded archived datasets that possess the potential for providing unprecedented inter-study insight into transcriptome evolution, once background noise has been reduced. Here we present a method to quantify intra-condition variation and to remove reference-based transcripts associated with highly variable read counts, prior to differential expression analysis. The method utilizes variation within pairwise distances between normalized read counts for each transcript across all included samples of a given condition. As a case study, we demonstrate our approach at an inter and intra-study level using RNA-seq data from brain samples of dogs, wolves, and two strains of fox (aggressive and tame) prior to performing differential expression analysis to identify common genes associated with tame behaviour. Results By applying our method, the distribution of the gene-wise dispersion estimates improved and the number of outliers detected in differential expression analysis decreased. Several genes that initially were differentially expressed in the non-filtered datasets were removed due to high intra-condition variation. Additionally, by optimizing the detection of differentially expressed transcripts, the overall number increased between dogs vs wolves and tame vs aggressive foxes when compared to the non-filtered datasets. Using these filtered sets, we found common over expressed genes in dogs and tame foxes, including those involved in brain development, neurotransmission and immunity, factors known to be involved in domestication. Conclusions We presented a method to quantify and remove intra-condition variation from RNA-seq count data and demonstrate its usage in improving the distribution of gene-wise dispersion estimates and ultimately, reduce the number of false positives in differential gene expression analysis. We provide the method as a freely available tool, to aid studies using RNA-seq to calculate and characterize the variation present within data prior to perform differential expression analysis. Additionally, we identify candidate genes involved with selection for tameness, which seems to have played a crucial role in the canine domestication.

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 A comparative study of techniques for differential expression analysis on RNA-Seq data

2014 ◽  
Author(s):  
Zong Hong Zhang ◽  
Dhanisha J. Jhaveri ◽  
Vikki M. Marshall ◽  
Denis C. Bauer ◽  
Janette Edson ◽  
...  
Keyword(s):  
Comparative Study ◽  
Differential Expression ◽  
Differentially Expressed Genes ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
False Positives ◽  
Sequencing Depth ◽  
Differentially Expressed ◽  
Rna Seq ◽  
Next Generation Sequencing Technology

Recent advances in next-generation sequencing technology allow high-throughput cDNA sequencing (RNA-Seq) to be widely applied in transcriptomic studies, in particular for detecting differentially expressed genes between groups. Many software packages have been developed for the identification of differentially expressed genes (DEGs) between treatment groups based on RNA-Seq data. However, there is a lack of consensus on how to approach an optimal study design and choice of suitable software for the analysis. In this comparative study we evaluate the performance of three of the most frequently used software tools: Cufflinks-Cuffdiff2, DESeq and edgeR. A number of important parameters of RNA-Seq technology were taken into consideration, including the number of replicates, sequencing depth, and balanced vs. unbalanced sequencing depth within and between groups. We benchmarked results relative to sets of DEGs identified through either quantitative RT-PCR or microarray. We observed that edgeR performs slightly better than DESeq and Cuffdiff2 in terms of the ability to uncover true positives. Overall, DESeq or taking the intersection of DEGs from two or more tools is recommended if the number of false positives is a major concern in the study. In other circumstances, edgeR is slightly preferable for differential expression analysis at the expense of potentially introducing more false positives.

scholarly journals A novel feature selection for RNA-seq analysis

2017 ◽  
Author(s):  
Henry Han
Keyword(s):  
Feature Selection ◽  
Differential Expression ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
Feature Selection Method ◽  
Selection Method ◽  
Singular Value ◽  
Data Driven ◽  
Rna Seq ◽  
Selection For

AbstractRNA-seq data are challenging existing omics data analytics for its volume and complexity. Although quite a few computational models were proposed from different standing points to conduct differential expression (D.E.) analysis, almost all these methods do not provide a rigorous feature selection for high-dimensional RNA-seq count data. Instead, most or even all genes are invited into differential calls no matter they have real contributions to data variations or not. Thus, it would inevitably affect the robustness of D.E. analysis and lead to the increase of false positive ratios.In this study, we presented a novel feature selection method: nonnegative singular value approximation (NSVA) to enhance RNA-seq differential expression analysis by taking advantage of RNA-seq count data’s non-negativity. As a variance-based feature selection method, it selects genes according to its contribution to the first singular value direction of input data in a data-driven approach. It demonstrates robustness to depth bias and gene length bias in feature selection in comparison with its five peer methods. Combining with state-of-the-art RNA-seq differential expression analysis, it contributes to enhancing differential expression analysis by lowering false discovery rates caused by the biases. Furthermore, we demonstrated the effectiveness of the proposed feature selection by proposing a data-driven differential expression analysis: NSVA-seq, besides conducting network marker discovery.

scholarly journals Do count-based differential expression methods perform poorly when genes are expressed in only one condition?

2015 ◽  
Author(s):  
Xiaobei Zhou ◽  
Mark D Robinson
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Differential Gene Expression ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Comprehensive Evaluation ◽  
Rna Seq ◽  
D Genome ◽  
Differential Gene Expression Analysis ◽  
Differential Gene

A correspondence with respect to: Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data Rapaport F, Khanin R, Liang Y, Pirun M, Krek A, Zumbo P, Mason CE, Socci ND and Betel D, Genome Biol 2013, 14:R95

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.

scholarly journals Alignment and mapping methodology influence transcript abundance estimation

2019 ◽  
Author(s):  
Avi Srivastava ◽  
Laraib Malik ◽  
Hirak Sarkar ◽  
Mohsen Zakeri ◽  
Fatemeh Almodaresi ◽  
...  
Keyword(s):  
Differential Expression ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
Computational Cost ◽  
Simulated Data ◽  
Transcript Abundance ◽  
Mapping Method ◽  
Rna Seq ◽  
Transcript Quantification ◽  
Quantification Model

AbstractBackgroundThe 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.ResultsWe 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.ConclusionWe 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.

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