DEAR-O: Differential Expression Analysis based on RNA-seq data - Online

AbstractSummaryDifferential expression analysis using high-throughput RNA sequencing (RNA-seq) data is widely applied in transcriptomic studies and many software tools have been developed for this purpose. Active development of existing popular tools, together with emergence of new tools means that studies comparing the performance of differential expression analysis methods become rapidly out-of-date. In order to enable researchers to evaluate new and updated software in a timely manner, we developed DEAR-O, a user-friendly platform for performance evaluation of differential expression analysis based on RNA-seq data. The platform currently includes four of the most popular tools: DESeq, DESeq2, edgeR and Cuffdiff2. Based on the DEAR-O platform, researchers can evaluate the performance of different tools, or the same tool with different versions, with a customised number of biological replicates using already curated RNA-seq datasets. We also initiated an online forum for discussion of RNA-seq differential expression analysis. Through this forum, new useful tools and benchmarking datasets can be introduced. Our platform will be actively maintained to ensure new major versions of existing tools and new popular tools are included. DEAR-O will serve the community by providing timely evaluations of tools, versions and number of replicates for RNA-seq differential expression analysis.Availability and implementationThe DEAR-O platform is available at http://cnsgenomics.com/software/dear-o; the online discussion forum is https://groups.google.com/d/forum/[email protected] and [email protected]

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Valid post-clustering differential analysis for single-cell RNA-Seq

10.1101/463265 ◽

2018 ◽

Cited By ~ 1

Author(s):

Jesse M. Zhang ◽

Govinda M. Kamath ◽

David N. Tse

Keyword(s):

Single Cell ◽

Differential Expression ◽

Expression Analysis ◽

State Of The Art ◽

Differential Expression Analysis ◽

Differential Analysis ◽

Rna Seq ◽

Analysis Framework ◽

Link Type ◽

False Discoveries

SummarySingle-cell computational pipelines involve two critical steps: organizing cells (clustering) and identifying the markers driving this organization (differential expression analysis). State-of-the-art pipelines perform differential analysis after clustering on the same dataset. We observe that because clustering forces separation, reusing the same dataset generates artificially low p-values and hence false discoveries. We introduce a valid post-clustering differential analysis framework which corrects for this problem. We provide software at https://github.com/jessemzhang/tn_test.

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Docker4Circ: A Framework for the Reproducible Characterization of circRNAs from RNA-Seq Data

International Journal of Molecular Sciences ◽

10.3390/ijms21010293 ◽

2019 ◽

Vol 21 (1) ◽

pp. 293 ◽

Cited By ~ 3

Author(s):

Giulio Ferrero ◽

Nicola Licheri ◽

Lucia Coscujuela Tarrero ◽

Carlo De Intinis ◽

Valentina Miano ◽

...

Keyword(s):

Differential Expression ◽

Expression Analysis ◽

Gene Expression Regulation ◽

Differential Expression Analysis ◽

Model Organisms ◽

Complete Analysis ◽

Rna Seq ◽

Sequence Reconstruction ◽

Reproducible Analysis ◽

User Friendly

Recent improvements in cost-effectiveness of high-throughput technologies has allowed RNA sequencing of total transcriptomes suitable for evaluating the expression and regulation of circRNAs, a relatively novel class of transcript isoforms with suggested roles in transcriptional and post-transcriptional gene expression regulation, as well as their possible use as biomarkers, due to their deregulation in various human diseases. A limited number of integrated workflows exists for prediction, characterization, and differential expression analysis of circRNAs, none of them complying with computational reproducibility requirements. We developed Docker4Circ for the complete analysis of circRNAs from RNA-Seq data. Docker4Circ runs a comprehensive analysis of circRNAs in human and model organisms, including: circRNAs prediction; classification and annotation using six public databases; back-splice sequence reconstruction; internal alternative splicing of circularizing exons; alignment-free circRNAs quantification from RNA-Seq reads; and differential expression analysis. Docker4Circ makes circRNAs analysis easier and more accessible thanks to: (i) its R interface; (ii) encapsulation of computational tasks into docker images; (iii) user-friendly Java GUI Interface availability; and (iv) no need of advanced bash scripting skills for correct use. Furthermore, Docker4Circ ensures a reproducible analysis since all its tasks are embedded into a docker image following the guidelines provided by Reproducible Bioinformatics Project.

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

Genome Biology ◽

10.1186/s13059-021-02337-8 ◽

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.

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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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