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

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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SPsimSeq: semi-parametric simulation of bulk and single cell RNA sequencing data

10.1101/677740 ◽

2019 ◽

Cited By ~ 1

Author(s):

Alemu Takele Assefa ◽

Jo Vandesompele ◽

Olivier Thas

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rna Sequencing ◽

Empirical Distribution ◽

Supplementary Information ◽

Rna Seq ◽

Sequencing Data ◽

Actual Distribution ◽

Wide Range ◽

Single Cell Rna Sequencing

SummarySPsimSeq is a semi-parametric simulation method for bulk and single cell RNA sequencing data. It simulates data from a good estimate of the actual distribution of a given real RNA-seq dataset. In contrast to existing approaches that assume a particular data distribution, our method constructs an empirical distribution of gene expression data from a given source RNA-seq experiment to faithfully capture the data characteristics of real data. Importantly, our method can be used to simulate a wide range of scenarios, such as single or multiple biological groups, systematic variations (e.g. confounding batch effects), and different sample sizes. It can also be used to simulate different gene expression units resulting from different library preparation protocols, such as read counts or UMI counts.Availability and implementationThe R package and associated documentation is available from https://github.com/CenterForStatistics-UGent/SPsimSeq.Supplementary informationSupplementary data are available at bioRχiv 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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319 Application and Practices of RNA Sequencing for Understanding Transcriptional Regulation of Gene Expression by Dietary Nutrients or Feed Additives in Swine

Journal of Animal Science ◽

10.1093/jas/skaa278.099 ◽

2020 ◽

Vol 98 (Supplement_4) ◽

pp. 55-55

Author(s):

Shengfa F Liao ◽

M Shamimul Hasan

Keyword(s):

Gene Expression ◽

Best Practices ◽

Rna Sequencing ◽

Regulation Of Gene Expression ◽

Feed Additives ◽

Dietary Fatty Acids ◽

Research Approach ◽

Rna Seq ◽

Dietary Nutrients ◽

The Impact

Abstract In life science, RNA sequencing (RNA-seq) technique is a state-of-the-art research approach for tissue or cell transcriptome analyses. In recent years, RNA-seq has been applied to profile the gene expression in response to dietary nutrients or feed additives to gain thorough understanding of the complex nutrient-gene interactions in agricultural animals. In this presentation, we will selectively review the application of RNA-seq technique in nutrigenomics studies in swine. Such studies have investigated the impact of various sources and quantities of dietary fatty acids, protein (including alternative protein), energy, probiotics, and plant-derived bioactive compounds on the gene expression in major metabolic tissues, such as liver, muscle, and adipose. Although the RNA-seq methodology is a powerful quantitative tool for transcriptomics analysis, it still has various technical challenges and pitfalls throughout its practice steps that include experiment design, sample collection, sample laboratory analysis, data statistical and bioinformatic analyses, and data interpretation. Currently, many options are available for use in some steps, but a thorough understanding of each option is critical for making right decisions and avoiding getting into inconclusive results. Therefore, this presentation will also provide an overview on the “best practices” for applying RNA-seq technique in swine nutrigenomics studies, which include the aspects of appropriately designing experiments, collecting samples, and analyzing the data in order to have confidence in the results obtained from this approach. In short, the aims of this presentation are to provide some basic guidelines for researchers new in the field and to promote a discussion of standardization or “best practices” of RNA-seq methodology for animal nutrigenomics studies.

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Comprehensive analysis of RNA-seq kits for standard, low and ultra-low quantity samples

10.1101/524439 ◽

2019 ◽

Author(s):

Marie-Ange Palomares ◽

Cyril Dalmasso ◽

Eric Bonnet ◽

Céline Derbois ◽

Solène Brohard-Julien ◽

...

Keyword(s):

Gene Expression ◽

Rna Sequencing ◽

Alternative Transcript ◽

Library Preparation ◽

Rna Seq ◽

Reference Tissue ◽

Gold Standard Method ◽

Differential Gene ◽

Commercial Kits ◽

Whole Transcriptome

ABSTRACTHigh-throughput RNA-sequencing has become the gold standard method for whole-transcriptome gene expression analysis, and is widely used in numerous applications to study cell and tissue transcriptomes. It is also being increasingly used in a number of clinical applications, including expression profiling for diagnostics and alternative transcript detection. However, despite its many advantages, RNA sequencing can be challenging in some situations, for instance in cases of low input amounts or degraded RNA samples. Several protocols have been proposed to overcome these challenges, and many are available as commercial kits. In this study, we comprehensively test three recent commercial technologies for RNA-seq library preparation (TruSeq, SMARTer and SMARTer Ultra-Low) on human reference tissue preparations, using standard (1μg), low (100 and 10 ng) and ultra-low (< 1 ng) input amounts, and for mRNA and total RNA, stranded or unstranded. The results are analyzed using read quality and alignment metrics, gene detection and differential gene expression metrics. Overall, we show that the TruSeq kit performs well with an input amount of 100 ng, while the SMARTer kit shows degraded performance for inputs of 100 and 10 ng, and the SMARTer Ultra-Low kit performs relatively well for input amounts < 1 ng. All the results are discussed in detail, and we provide guidelines for biologists for the selection of a RNA-seq library preparation kit.

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ReQTL: identifying correlations between expressed SNVs and gene expression using RNA-sequencing data

Bioinformatics ◽

10.1093/bioinformatics/btz750 ◽

2019 ◽

Vol 36 (5) ◽

pp. 1351-1359 ◽

Cited By ~ 6

Author(s):

Liam F Spurr ◽

Nawaf Alomran ◽

Pavlos Bousounis ◽

Dacian Reece-Stremtan ◽

N M Prashant ◽

...

Keyword(s):

Gene Expression ◽

Genetic Variation ◽

Rna Sequencing ◽

Variant Allele ◽

Supplementary Information ◽

Phenotypic Trait ◽

Continuous Measure ◽

Rna Seq ◽

Variant Allele Fraction ◽

Allele Fraction

Abstract Motivation By testing for associations between DNA genotypes and gene expression levels, expression quantitative trait locus (eQTL) analyses have been instrumental in understanding how thousands of single nucleotide variants (SNVs) may affect gene expression. As compared to DNA genotypes, RNA genetic variation represents a phenotypic trait that reflects the actual allele content of the studied system. RNA genetic variation at expressed SNV loci can be estimated using the proportion of alleles bearing the variant nucleotide (variant allele fraction, VAFRNA). VAFRNA is a continuous measure which allows for precise allele quantitation in loci where the RNA alleles do not scale with the genotype count. We describe a method to correlate VAFRNA with gene expression and assess its ability to identify genetically regulated expression solely from RNA-sequencing (RNA-seq) datasets. Results We introduce ReQTL, an eQTL modification which substitutes the DNA allele count for the variant allele fraction at expressed SNV loci in the transcriptome (VAFRNA). We exemplify the method on sets of RNA-seq data from human tissues obtained though the Genotype-Tissue Expression (GTEx) project and demonstrate that ReQTL analyses are computationally feasible and can identify a subset of expressed eQTL loci. Availability and implementation A toolkit to perform ReQTL analyses is available at https://github.com/HorvathLab/ReQTL. Supplementary information Supplementary data are available at Bioinformatics online.

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ReQTL – an allele-level measure of variation-expression genomic relationships

10.1101/464206 ◽

2018 ◽

Author(s):

Liam Spurr ◽

Nawaf Alomran ◽

Piotr Słowiński ◽

Muzi Li ◽

Pavlos Bousounis ◽

...

Keyword(s):

Gene Expression ◽

Genetic Variation ◽

Rna Sequencing ◽

High Performance ◽

Supplementary Information ◽

Phenotypic Trait ◽

Sequencing Data ◽

Single Nucleotide Variants ◽

Genomic Relationships ◽

Supplementary Material

MotivationBy testing for association of DNA genotypes with gene expression levels, expression quantitative trait locus (eQTL) analyses have been instrumental in understanding how thousands of single nucleotide variants (SNVs) may affect gene expression. As compared to DNA genotypes, RNA genetic variation represents a phenotypic trait that reflects the actual allele content of the studied system. RNA genetic variation can be measured at expressed genome regions, and differs from the DNA genotype in sites subjected to regulatory forces. Therefore, assessment of correlation between RNA genetic variation and gene expression can reveal regulatory genomic relationships in addition to eQTLs.ResultsWe introduce ReQTL, an eQTL modification which substitutes the DNA allele count for the variant allele frequency (VAF) at expressed SNV loci in the transcriptome. We exemplify the method on sets of RNA-sequencing data from human tissues obtained though the Genotype-Tissue Expression Project (GTEx) and demonstrate that ReQTL analyses show consistently high performance and sufficient power to identify both previously known and novel molecular associations. The majority of the SNVs implicated in significant cis-ReQTLs identified by our analysis were previously reported as significant cis-eQTL loci. Notably, trans ReQTL loci in our data were substantially enriched in RNA-editing sites. In summary, ReQTL analyses are computationally feasible and do not require matched DNA data, hence they have a high potential to facilitate the discovery of novel molecular interactions through exploration of the increasingly accessible RNA-sequencing datasets.Availability and implementationSample scripts used in our ReQTL analyses are available with the Supplementary Material (ReQTL_sample_code)[email protected] or [email protected] InformationRe_QTL_Supplementary_Data.zip

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ViDGER: An R package for integrative interpretation of differential gene expression results of RNA-seq data

10.1101/268896 ◽

2018 ◽

Cited By ~ 4

Author(s):

Adam McDermaid ◽

Brandon Monier ◽

Jing Zhao ◽

Qin Ma

Keyword(s):

Gene Expression ◽

Rna Sequencing ◽

Differential Gene Expression ◽

Differentially Expressed Genes ◽

R Package ◽

Differentially Expressed ◽

Rna Seq ◽

Differential Gene

AbstractDifferential gene expression (DGE) is one of the most common applications of RNA-sequencing (RNA-seq) data. This process allows for the elucidation of differentially expressed genes (DEGs) across two or more conditions. Interpretation of the DGE results can be non-intuitive and time consuming due to the variety of formats based on the tool of choice and the numerous pieces of information provided in these results files. Here we present an R package, ViDGER (Visualization of Differential Gene Expression Results using R), which contains nine functions that generate information-rich visualizations for the interpretation of DGE results from three widely-used tools, Cuffdiff, DESeq2, and edgeR.

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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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ProkSeq for complete analysis of RNA-Seq data from prokaryotes

Bioinformatics ◽

10.1093/bioinformatics/btaa1063 ◽

2020 ◽

Author(s):

A K M Firoj Mahmud ◽

Nicolas Delhomme ◽

Soumyadeep Nandi ◽

Maria Fällman

Keyword(s):

Gene Expression ◽

Pathogenic Bacteria ◽

Supplementary Information ◽

Complete Analysis ◽

Rna Seq ◽

Differential Gene ◽

User Friendly ◽

Multiple Samples ◽

Data Analysis Pipeline

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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Assessment of a highly multiplexed RNA sequencing platform and comparison to existing high-throughput gene expression profiling techniques

10.1101/419838 ◽

2018 ◽

Author(s):

Eric Reed ◽

Elizabeth Moses ◽

Xiaohui Xiao ◽

Gang Liu ◽

Joshua Campbell ◽

...

Keyword(s):

Gene Expression ◽

Rna Sequencing ◽

Differential Gene Expression ◽

Low Cost ◽

Digital Gene Expression ◽

Data Generation ◽

Rna Seq ◽

Sequencing Platform ◽

Differential Gene

AbstractThe need to reduce per sample cost of RNA-seq profiling for scalable data generation has led to the emergence of highly multiplexed RNA-seq. These technologies utilize barcoding of cDNA sequences in order to combine samples into single sequencing lane to be separated during data processing. In this study, we report the performance of one such technique denoted as sparse full length sequencing (SFL), a ribosomal RNA depletion-based RNA sequencing approach that allows for the simultaneous sequencing of 96 samples and higher. We offer comparisons to well established single-sample techniques, including: full coverage Poly-A capture RNA-seq and microarray, as well as another low-cost highly multiplexed technique known as 3’ digital gene expression (3’DGE). Data was generated for a set of exposure experiments on immortalized human lung epithelial (AALE) cells in a two-by-two study design, in which samples received both genetic and chemical perturbations of known oncogenes/tumor suppressors and lung carcinogens. SFL demonstrated improved performance over 3’DGE in terms of coverage, power to detect differential gene expression, and biological recapitulation of patterns of differential gene expression from in vivo lung cancer mutation signatures.

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