scholarly journals SPEAQeasy: a Scalable Pipeline for Expression Analysis and Quantification for R/Bioconductor-powered RNA-seq analyses

2020 ◽  
Author(s):  
Nicholas J. Eagles ◽  
Emily E. Burke ◽  
Jabob Leonard ◽  
Brianna K. Barry ◽  
Joshua M. Stolz ◽  
...  
Keyword(s):  
Expression Analysis ◽  
Computational Domain ◽  
Rna Seq ◽  
Entry Barrier ◽  
One Step ◽  
Differential Gene ◽  
Main Input ◽  
Set Up ◽  
Downstream Analysis ◽  
User Friendly

RNA sequencing (RNA-seq) is a common and widespread biological assay, and an increasing amount of data is generated with it. In practice, there are a large number of individual steps a researcher must perform before raw RNA-seq reads yield directly valuable information, such as differential gene expression data. Existing software tools are typically specialized, only performing one step-- such as alignment of reads to a reference genome-- of a larger workflow. The demand for a more comprehensive and reproducible workflow has led to the production of a number of publicly available RNA-seq pipelines. However, we have found that most require computational expertise to set up or share among several users, are not actively maintained, or lack features we have found to be important in our own analyses. In response to these concerns, we have developed a Scalable Pipeline for Expression Analysis and Quantification (SPEAQeasy), which is easy to install and share, and provides a bridge towards R/Bioconductor downstream analysis solutions. SPEAQeasy is user-friendly and lowers the computational-domain entry barrier for biologists and clinicians to RNA-seq data processing as the main input file is a table with sample names and their corresponding FASTQ files. SPEAQeasy is portable across computational frameworks (SGE, SLURM, local, docker integration) and different configuration files are provided.

scholarly journals SPEAQeasy: a scalable pipeline for expression analysis and quantification for R/bioconductor-powered RNA-seq analyses

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Nicholas J. Eagles ◽  
Emily E. Burke ◽  
Jacob Leonard ◽  
Brianna K. Barry ◽  
Joshua M. Stolz ◽  
...  
Keyword(s):  
Expression Analysis ◽  
Computational Domain ◽  
Rna Seq ◽  
Entry Barrier ◽  
One Step ◽  
Differential Gene ◽  
Main Input ◽  
Set Up ◽  
Downstream Analysis ◽  
User Friendly

Abstract Background RNA sequencing (RNA-seq) is a common and widespread biological assay, and an increasing amount of data is generated with it. In practice, there are a large number of individual steps a researcher must perform before raw RNA-seq reads yield directly valuable information, such as differential gene expression data. Existing software tools are typically specialized, only performing one step–such as alignment of reads to a reference genome–of a larger workflow. The demand for a more comprehensive and reproducible workflow has led to the production of a number of publicly available RNA-seq pipelines. However, we have found that most require computational expertise to set up or share among several users, are not actively maintained, or lack features we have found to be important in our own analyses. Results In response to these concerns, we have developed a Scalable Pipeline for Expression Analysis and Quantification (SPEAQeasy), which is easy to install and share, and provides a bridge towards R/Bioconductor downstream analysis solutions. SPEAQeasy is portable across computational frameworks (SGE, SLURM, local, docker integration) and different configuration files are provided (http://research.libd.org/SPEAQeasy/). Conclusions SPEAQeasy is user-friendly and lowers the computational-domain entry barrier for biologists and clinicians to RNA-seq data processing as the main input file is a table with sample names and their corresponding FASTQ files. The goal is to provide a flexible pipeline that is immediately usable by researchers, regardless of their technical background or computing environment.

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 Dashboard-style interactive plots for RNA-seq analysis are R Markdown ready with Glimma 2.0

2021 ◽  
Author(s):  
Has Kariyawasam ◽  
Shian Su ◽  
Oliver Voogd ◽  
Matthew E Ritchie ◽  
Charity W Law
Keyword(s):  
Gene Expression ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Interactive Graphics ◽  
Rna Seq ◽  
Experimental Conditions ◽  
Differential Gene Expression Analysis ◽  
Differential Gene ◽  
High Level ◽  
User Friendly

Glimma 1.0 introduced intuitive, point-and-click interactive graphics for differential gene expression analysis. Here, we present a major update to Glimma which brings improved interactivity and reproducibility using high-level visualisation frameworks for R and JavaScript. Glimma 2.0 plots are now readily embeddable in R Markdown, thus allowing users to create reproducible reports containing interactive graphics. The revamped multidimensional scaling plot features dashboard-style controls allowing the user to dynamically change the colour, shape and size of sample points according to different experimental conditions. Interactivity was enhanced in the MA-style plot for comparing differences to average expression, which now supports selecting multiple genes, export options to PNG, SVG or CSV formats and includes a new volcano plot function. Feature-rich and user-friendly, Glimma makes exploring data for gene expression analysis more accessible and intuitive and is available on Bioconductor and GitHub.

scholarly journals Dashboard-style interactive plots for RNA-seq analysis are R Markdown ready with Glimma 2.0

2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Hasaru Kariyawasam ◽  
Shian Su ◽  
Oliver Voogd ◽  
Matthew E Ritchie ◽  
Charity W Law
Keyword(s):  
Gene Expression ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Interactive Graphics ◽  
Rna Seq ◽  
Experimental Conditions ◽  
Differential Gene Expression Analysis ◽  
Differential Gene ◽  
High Level ◽  
User Friendly

Abstract Glimma 1.0 introduced intuitive, point-and-click interactive graphics for differential gene expression analysis. Here, we present a major update to Glimma that brings improved interactivity and reproducibility using high-level visualization frameworks for R and JavaScript. Glimma 2.0 plots are now readily embeddable in R Markdown, thus allowing users to create reproducible reports containing interactive graphics. The revamped multidimensional scaling plot features dashboard-style controls allowing the user to dynamically change the colour, shape and size of sample points according to different experimental conditions. Interactivity was enhanced in the MA-style plot for comparing differences to average expression, which now supports selecting multiple genes, export options to PNG, SVG or CSV formats and includes a new volcano plot function. Feature-rich and user-friendly, Glimma makes exploring data for gene expression analysis more accessible and intuitive and is available on Bioconductor and GitHub.

scholarly journals Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks

2012 ◽  
Vol 7 (3) ◽  
pp. 562-578 ◽  
Author(s):  
Cole Trapnell ◽  
Adam Roberts ◽  
Loyal Goff ◽  
Geo Pertea ◽  
Daehwan Kim ◽  
...  
Keyword(s):  
Expression Analysis ◽  
Transcript Expression ◽  
Rna Seq ◽  
Differential Gene

scholarly journals MetaMap, an interactive webtool for the exploration of metatranscriptomic reads in human disease-related RNA-seq data

2018 ◽  
Author(s):  
LM Simon ◽  
G Tsitsiridis ◽  
P Angerer ◽  
FJ Theis
Keyword(s):  
Human Disease ◽  
Expression Patterns ◽  
Heterogeneous Data ◽  
Rna Seq ◽  
Web Tool ◽  
Differential Abundance ◽  
Downstream Analysis ◽  
User Friendly ◽  
Differential Abundance Analysis ◽  
The Impact

AbstractMotivationThe MetaMap resource contains metatranscriptomic expression data from screening >17,000 RNA-seq samples from >400 archived human disease-related studies for viral and microbial reads, so-called “metafeatures”. However, navigating this set of large and heterogeneous data is challenging, especially for researchers without bioinformatic expertise. Therefore, a user-friendly interface is needed that allows users to visualize and statistically analyse the data.ResultsWe developed an interactive frontend to facilitate the exploration of the MetaMap resource. The webtool allows users to query the resource by searching study abstracts for keywords or browsing expression patterns for specific metafeatures. Moreover, users can manually define sample groupings or use the existing annotation for downstream analysis. The web tool provides a large variety of analyses and visualizations including dimension reduction, differential abundance analysis and Krona visualizations. The MetaMap webtool represents a valuable resource for hypothesis generation regarding the impact of the microbiome in human disease.AvailabilityThe presented web tool can be accessed at https://github.com/theislab/MetaMap

scholarly journals The evaluation of RNA-Seq de novo assembly by PacBio long read sequencing

2019 ◽  
Author(s):  
Yifan Yang ◽  
Michael Gribskov
Keyword(s):  
Real Time ◽  
De Novo ◽  
Critical Issue ◽  
Evaluation Methods ◽  
Model Organisms ◽  
Rna Seq ◽  
Long Reads ◽  
Long Read ◽  
Set Up ◽  
Downstream Analysis

AbstractRNA-Seq de novo assembly is an important method to generate transcriptomes for non-model organisms before any downstream analysis. Given many great de novo assembly methods developed by now, one critical issue is that there is no consensus on the evaluation of de novo assembly methods yet. Therefore, to set up a benchmark for evaluating the quality of de novo assemblies is very critical. Addressing this challenge will help us deepen the insights on the properties of different de novo assemblers and their evaluation methods, and provide hints on choosing the best assembly sets as transcriptomes of non-model organisms for the further functional analysis. In this article, we generate a “real time” transcriptome using PacBio long reads as a benchmark for evaluating five de novo assemblers and two model-based de novo assembly evaluation methods. By comparing the de novo assmblies generated by RNA-Seq short reads with the “real time” transcriptome from the same biological sample, we find that Trinity is best at the completeness by generating more assemblies than the alternative assemblers, but less continuous and having more misassemblies; Oases is best at the continuity and specificity, but less complete; The performance of SOAPdenovo-Trans, Trans-AByss and IDBA-Tran are in between of five assemblers. For evaluation methods, DETONATE leverages multiple aspects of the assembly set and ranks the assembly set with an average performance as the best, meanwhile the contig score can serve as a good metric to select assemblies with high completeness, specificity, continuity but not sensitive to misassemblies; TransRate contig score is useful for removing misassemblies, yet often the assemblies in the optimal set is too few to be used as a transcriptome.

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