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

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.

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RNA-seq analyses: Benchmarking differential expression analyses tools reveals the effect of higher number of replicates on performance

10.1101/2020.06.10.144063 ◽

2020 ◽

Author(s):

Samson Pandam Salifu ◽

Hannah Nyarkoah Nyarko ◽

Albert Doughan ◽

Haward Keteyo Msatsi ◽

Isabel Mensah ◽

...

Keyword(s):

Differential Expression ◽

Expression Analysis ◽

Differential Expression Analysis ◽

Pairwise Comparison ◽

Analysis Tool ◽

Rna Seq ◽

Analysis Tools ◽

Daunting Task ◽

Study Population ◽

Downstream Analysis

AbstractThe introduction of several differential gene expression analysis tools has made it difficult for researchers to settle on a particular tool for RNA-seq analysis. This coupled with the appropriate determination of biological replicates to give an optimum representation of the study population and make biological sense. To address these challenges, we performed a survey of 8 tools used for differential expression in RNA-seq analysis. We simulated 39 different datasets (from 10 to 200 replicates, at an interval of 5) using compcodeR with a maximum of 100 replicates. Our goal was to determine the effect of varying the number of replicates on the performance (F1-score, recall and precision) of the tools. EBSeq and edgeR-glmRT recorded the highest (0.9385) and lowest (0.6505) average F1-score across all replicates, respectively. We also performed a pairwise comparison of all the tools to determine their concordance with each other in identifying differentially expressed genes. We found the greatest concordance to be between limma voom treat and limma voom ebayes. Finally, we recommend employing edgeR-glmRT for RNA-seq experiments involving 10-50 replicates and edgeR-glmQLF for studies with 55 to 200 replicates.Author summaryDownstream analysis of RNA-seq data in R often poses several challenges to researchers as it is a daunting task to choose a specific differential expression analysis tool over another. Researchers also find it challenging to determine the number (replicates) of samples to use in order to give comparable and accurate results. In this paper, we surveyed eight differential expression analysis tools using different number of replicates of simulated RNA-seq count data. We measured the performance of each tool and based on the recorded F1-scores, recall and precision, we made the following recommendations; consider edgeR-glmRT and edgeR-glmQLF for replicates of 10-50 and 55-200 respectively.

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

10.21203/rs.3.rs-27674/v1 ◽

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.

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

NAR Genomics and Bioinformatics ◽

10.1093/nargab/lqab028 ◽

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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Survey of Methods Used for Differential Expression Analysis on RNA Seq Data

Learning and Analytics in Intelligent Systems - Biologically Inspired Techniques in Many-Criteria Decision Making ◽

10.1007/978-3-030-39033-4_21 ◽

2020 ◽

pp. 226-239

Author(s):

Reema Joshi ◽

Rosy Sarmah

Keyword(s):

Differential Expression ◽

Expression Analysis ◽

Differential Expression Analysis ◽

Rna Seq

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Alignment and mapping methodology influence transcript abundance estimation

10.1101/657874 ◽

2019 ◽

Cited By ~ 6

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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