ProkSeq for complete analysis of RNA-Seq data from prokaryotes

Abstract Summary Since its introduction, RNA-Seq technology has been used extensively in studies of pathogenic bacteria to identify and quantify differences in gene expression across multiple samples from bacteria exposed to different conditions. With some exceptions, the current tools for studying gene expression, determination of differential gene expression, downstream pathway analysis, and normalization of data collected in extreme biological conditions is still lacking. Here we describe ProkSeq, a user-friendly, fully automated RNA-Seq data analysis pipeline designed for prokaryotes. ProkSeq provides a wide variety of options for analysing differential expression, normalizing expression data, and visualizing data and results. Availability and implementation ProkSeq is implemented in Python and is published under the MIT source license. The pipeline is available as a Docker container https://hub.docker.com/repository/docker/snandids/prokseq-v2.0, or can be used through Anaconda: https://anaconda.org/snandiDS/prokseq. The code is available on Github: https://github.com/snandiDS/prokseq and a detailed user documentation, including a manual and tutorial can be found at https://prokseqV20.readthedocs.io Supplementary information Supplementary data are available at Bioinformatics online

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ProkSeq for complete analysis of RNA-seq data from prokaryotes

10.1101/2020.06.09.135822 ◽

2020 ◽

Cited By ~ 2

Author(s):

A K M Firoj Mahmud ◽

Soumyadeep Nandi ◽

Maria Fällman

Keyword(s):

Gene Expression ◽

Pathogenic Bacteria ◽

Complete Analysis ◽

Rna Seq ◽

Link Type ◽

Eukaryotic Genes ◽

Differential Gene ◽

User Friendly ◽

Multiple Samples

AbstractSummarySince its introduction, RNA-seq technology has been used extensively in studies of pathogenic bacteria to identify and quantify differences in gene expression across multiple samples from bacteria exposed to different conditions. With some exceptions, the current tools for assessing gene expression have been designed around the structures of eukaryotic genes. There are a few stand-alone tools designed for prokaryotes, and they require improvement. A well-defined pipeline for prokaryotes that includes all the necessary tools for quality control, determination of differential gene expression, downstream pathway analysis, and normalization of data collected in extreme biological conditions is still lacking. Here we describe ProkSeq, a user-friendly, fully automated RNA-seq data analysis pipeline designed for prokaryotes. ProkSeq provides a wide variety of options for analysing differential expression, normalizing expression data, and visualizing data and results, and it produces publication-quality figures.Availability and implementationProkSeq is implemented in Python and is published under the ISC open source license. The tool and a detailed user manual are hosted at Docker: https://hub.docker.com/repository/docker/snandids/prokseq-v2.1, Anaconda: https://anaconda.org/snandiDS/prokseq; Github: https://github.com/snandiDS/prokseq.

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IRIS-DGE: An integrated RNA-seq data analysis and interpretation system for differential gene expression

10.1101/283341 ◽

2018 ◽

Cited By ~ 4

Author(s):

Brandon Monier ◽

Adam McDermaid ◽

Jing Zhao ◽

Anne Fennell ◽

Qin Ma

Keyword(s):

Gene Expression ◽

Differential Gene Expression ◽

Large Scale ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Supplementary Information ◽

Analysis Tool ◽

Rna Seq ◽

Differential Gene ◽

User Friendly

AbstractMotivationNext-Generation Sequencing has made available much more large-scale genomic and transcriptomic data. Studies with RNA-sequencing (RNA-seq) data typically involve generation of gene expression profiles that can be further analyzed, many times involving differential gene expression (DGE). This process enables comparison across samples of two or more factor levels. A recurring issue with DGE analyses is the complicated nature of the comparisons to be made, in which a variety of factor combinations, pairwise comparisons, and main or blocked main effects need to be tested.ResultsHere we present a tool called IRIS-DGE, which is a server-based DGE analysis tool developed using Shiny. It provides a straightforward, user-friendly platform for performing comprehensive DGE analysis, and crucial analyses that help design hypotheses and to determine key genomic features. IRIS-DGE integrates the three most commonly used R-based DGE tools to determine differentially expressed genes (DEGs) and includes numerous methods for performing preliminary analysis on user-provided gene expression information. Additionally, this tool integrates a variety of visualizations, in a highly interactive manner, for improved interpretation of preliminary and DGE analyses.AvailabilityIRIS-DGE is freely available at http://bmbl.sdstate.edu/IRIS/[email protected] informationSupplementary data are available at Bioinformatics online.

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How well do RNA-Seq differential gene expression tools perform in a complex eukaryote? A case study in Arabidopsis thaliana

Bioinformatics ◽

10.1093/bioinformatics/btz089 ◽

2019 ◽

Vol 35 (18) ◽

pp. 3372-3377 ◽

Cited By ~ 2

Author(s):

Kimon Froussios ◽

Nick J Schurch ◽

Katarzyna Mackinnon ◽

Marek Gierliński ◽

Céline Duc ◽

...

Keyword(s):

Gene Expression ◽

Arabidopsis Thaliana ◽

Normal Distribution ◽

Differential Gene Expression ◽

Negative Binomial Distribution ◽

Binomial Distribution ◽

Negative Binomial ◽

Supplementary Information ◽

Rna Seq ◽

Differential Gene

Abstract Motivation RNA-seq experiments are usually carried out in three or fewer replicates. In order to work well with so few samples, differential gene expression (DGE) tools typically assume the form of the underlying gene expression distribution. In this paper, the statistical properties of gene expression from RNA-seq are investigated in the complex eukaryote, Arabidopsis thaliana, extending and generalizing the results of previous work in the simple eukaryote Saccharomyces cerevisiae. Results We show that, consistent with the results in S.cerevisiae, more gene expression measurements in A.thaliana are consistent with being drawn from an underlying negative binomial distribution than either a log-normal distribution or a normal distribution, and that the size and complexity of the A.thaliana transcriptome does not influence the false positive rate performance of nine widely used DGE tools tested here. We therefore recommend the use of DGE tools that are based on the negative binomial distribution. Availability and implementation The raw data for the 17 WT Arabidopsis thaliana datasets is available from the European Nucleotide Archive (E-MTAB-5446). The processed and aligned data can be visualized in context using IGB (Freese et al., 2016), or downloaded directly, using our publicly available IGB quickload server at https://compbio.lifesci.dundee.ac.uk/arabidopsisQuickload/public_quickload/ under ‘RNAseq>Froussios2019’. All scripts and commands are available from github at https://github.com/bartongroup/KF_arabidopsis-GRNA. Supplementary information Supplementary data are available at Bioinformatics online.

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Evaluation of Seven Different RNA-Seq Alignment Tools Based on Experimental Data from the Model Plant Arabidopsis thaliana

International Journal of Molecular Sciences ◽

10.3390/ijms21051720 ◽

2020 ◽

Vol 21 (5) ◽

pp. 1720 ◽

Cited By ~ 5

Author(s):

Stephanie Schaarschmidt ◽

Axel Fischer ◽

Ellen Zuther ◽

Dirk K. Hincha

Keyword(s):

Gene Expression ◽

Arabidopsis Thaliana ◽

Reference Genome ◽

Physiological Data ◽

Rna Seq ◽

Genetic Perturbations ◽

Model Plant Arabidopsis Thaliana ◽

Differential Gene ◽

Highly Correlated

Quantification of gene expression is crucial to connect genome sequences with phenotypic and physiological data. RNA-Sequencing (RNA-Seq) has taken a prominent role in the study of transcriptomic reactions of plants to various environmental and genetic perturbations. However, comparative tests of different tools for RNA-Seq read mapping and quantification have been mainly performed on data from animals or humans, which necessarily neglect, for example, the large genetic variability among natural accessions within plant species. Here, we compared seven computational tools for their ability to map and quantify Illumina single-end reads from the Arabidopsis thaliana accessions Columbia-0 (Col-0) and N14. Between 92.4% and 99.5% of all reads were mapped to the reference genome or transcriptome and the raw count distributions obtained from the different mappers were highly correlated. Using the software DESeq2 to determine differential gene expression (DGE) between plants exposed to 20 °C or 4 °C from these read counts showed a large pairwise overlap between the mappers. Interestingly, when the commercial CLC software was used with its own DGE module instead of DESeq2, strongly diverging results were obtained. All tested mappers provided highly similar results for mapping Illumina reads of two polymorphic Arabidopsis accessions to the reference genome or transcriptome and for the determination of DGE when the same software was used for processing.

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GeneQC: A quality control tool for gene expression estimation based on RNA-sequencing reads mapping

10.1101/266445 ◽

2018 ◽

Cited By ~ 3

Author(s):

Adam McDermaid ◽

Xin Chen ◽

Yiran Zhang ◽

Juan Xie ◽

Cankun Wang ◽

...

Keyword(s):

Gene Expression ◽

Rna Sequencing ◽

Model Fitting ◽

Functional Enrichment ◽

Supplementary Information ◽

Rna Seq ◽

Quality Control Tool ◽

Differential Gene ◽

Supplementary Material ◽

The Impact

AbstractMotivationOne of the main benefits of using modern RNA-sequencing (RNA-Seq) technology is the more accurate gene expression estimations compared with previous generations of expression data, such as the microarray. However, numerous issues can result in the possibility that an RNA-Seq read can be mapped to multiple locations on the reference genome with the same alignment scores, which occurs in plant, animal, and metagenome samples. Such a read is so-called a multiple-mapping read (MMR). The impact of these MMRs is reflected in gene expression estimation and all downstream analyses, including differential gene expression, functional enrichment, etc. Current analysis pipelines lack the tools to effectively test the reliability of gene expression estimations, thus are incapable of ensuring the validity of all downstream analyses.ResultsOur investigation into 95 RNA-Seq datasets from seven species (totaling 1,951GB) indicates an average of roughly 22% of all reads are MMRs for plant and animal species. Here we present a tool called GeneQC (Gene expression Quality Control), which can accurately estimate the reliability of each gene’s expression level. The underlying algorithm is designed based on extracted genomic and transcriptomic features, which are then combined using elastic-net regularization and mixture model fitting to provide a clearer picture of mapping uncertainty for each gene. GeneQC allows researchers to determine reliable expression estimations and conduct further analysis on the gene expression that is of sufficient quality. This tool also enables researchers to investigate continued re-alignment methods to determine more accurate gene expression estimates for those with low reliability.AvailabilityGeneQC is freely available at http://bmbl.sdstate.edu/GeneQC/[email protected] informationSupplementary data are available at Bioinformatics online.

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Galaxy and MEAN Stack to Create a User-Friendly Workflow for the Rational Optimization of Cancer Chemotherapy

Frontiers in Genetics ◽

10.3389/fgene.2021.624259 ◽

2021 ◽

Vol 12 ◽

Author(s):

Jorge Guerra Pires ◽

Gilberto Ferreira da Silva ◽

Thomas Weyssow ◽

Alessandra Jordano Conforte ◽

Dante Pagnoncelli ◽

...

Keyword(s):

Gene Expression ◽

Healthcare Professionals ◽

Rna Seq ◽

Protein Targets ◽

Internet Portal ◽

Inhibition Of Tumor Growth ◽

Differential Gene ◽

Tumor Growth And Metastasis ◽

User Friendly

One aspect of personalized medicine is aiming at identifying specific targets for therapy considering the gene expression profile of each patient individually. The real-world implementation of this approach is better achieved by user-friendly bioinformatics systems for healthcare professionals. In this report, we present an online platform that endows users with an interface designed using MEAN stack supported by a Galaxy pipeline. This pipeline targets connection hubs in the subnetworks formed by the interactions between the proteins of genes that are up-regulated in tumors. This strategy has been proved to be suitable for the inhibition of tumor growth and metastasis in vitro. Therefore, Perl and Python scripts were enclosed in Galaxy for translating RNA-seq data into protein targets suitable for the chemotherapy of solid tumors. Consequently, we validated the process of target diagnosis by (i) reference to subnetwork entropy, (ii) the critical value of density probability of differential gene expression, and (iii) the inhibition of the most relevant targets according to TCGA and GDC data. Finally, the most relevant targets identified by the pipeline are stored in MongoDB and can be accessed through the aforementioned internet portal designed to be compatible with mobile or small devices through Angular libraries.

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

10.1101/2021.07.30.454464 ◽

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.

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

NAR Genomics and Bioinformatics ◽

10.1093/nargab/lqab116 ◽

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.

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