This work was mainly supported by the ANR PSYCHE (ANR-16-CE20-0009). The authors thank F. Desprez for the construction of the container of DiCoExpress. KB work is supported by the Investment for the Future ANR-10-BTBR-01-01 Amaizing program. The GQE and IPS2 laboratories benefit from the support of Saclay Plant Sciences-SPS (ANR-17-EUR-0007).

1. DiCoExpress
<ol>
	<li>Open a R studio session and set directory to Template_scripts.</li>
	<li>Open the DiCoExpress_Tutorial.R script in R studio.</li>
	<li>Load DiCoExpress functions in the R session with the following commands: 
	&#62; source(&#34;../Sources/Load_Functions.R&#34;) 
	&#62; Load_Functions() 
	&#62; Data_Directory = &#34;../Data&#34; 
	&#62; Results_Directory = &#34;../Results/&#34;</li>
	<li>Load data files in the R session with the following commands: 
	&#62; Project_Name = &#34;Tutorial&#34; 
	&#62; Filter = NULL 
	&#62; Sep=&#34;\t&#34; 
	&#62;&#160;Data_Files&#160;=&#160;Load_Data_Files(Data_Directory, Project_Name, Filter, Sep)</li>
	<li>Split the object Data_Files in several objects to manipulate them easily: 
	&#62; Project_Name = Data_Files$Project_Name 
	&#62; Target = Data_Files$Target 
	&#62; Raw_Counts = Data_Files$Raw_Counts 
	&#62; Annotation = Data_Files$Annotation 
	&#62;&#160;Reference_Enrichment&#160;=&#160;Data_Files$Reference_Enrichment</li>
	<li>Choose a strategy among &#34;NbConditions&#34;, &#34;NbReplicates&#34; or &#34;filterByExpr&#34; and a threshold to filter low expressed genes. Here we choose 
	&#62; Filter_Strategy = &#34;NbReplicates&#34; 
	&#62; CPM_Cutoff = 1</li>
	<li>Specify group colors with the command 
	&#62; Color_Group = NULL 
	NOTE: When it is set to NULL, R automatically attributes colors to the biological conditions. Otherwise enter a vector indicating a color per biological group.</li>
	<li>Choose a normalization method among those accepted by the function calcNormFactors of edgeR. As for example 
	&#62; Normalization_Method = &#34;TMM&#34;</li>
	<li>Perform the quality control by executing the following function 
	&#62; Quality_Control(Data_Directory, Results_Directory, Project_Name, Target, Raw_Counts, Filter_Strategy, Color_Group, CPM_Cutoff, Normalization_Method)</li>
	<li>State Replicate = TRUE if data are paired according to the replicate factor, FALSE otherwise.</li>
	<li>Assign Interaction = TRUE to consider an interaction between the two biological factors, FALSE otherwise.</li>
	<li>Specify the statistical model with the following commands 
	&#62;&#160;Model&#160;=&#160;GLM_Contrasts(Results_Directory, Project_Name, Target, Replicate, Interaction) 
	&#62;&#160;GLM_Model = Model$GLM_Model 
	&#62;&#160;&#160;Contrasts = Model$Contrasts</li>
	<li>Define the threshold of the False Discovery Rate, here 0.05 
	&#62; Alpha_DiffAnalysis =0.05</li>
	<li>Perform the differential analysis with the following commands 
	&#62; Index_Contrast=1:nrow(Contrasts) 
	&#62;&#160;NbGenes_Profiles = 20 
	&#62; NbGenes_Clustering = 50 
	&#62;&#160;DiffAnalysis.edgeR&#160;(Data_Directory, Results_Directory, Project_Name, Target, Raw_Counts, GLM_Model, Contrasts, Index_Contrast, Filter_Strategy, Alpha_DiffAnalysis,&#160;NbGenes_Profiles, NbGenes_Clustering,&#160;CPM_Cutoff, Normalization_Method)</li>
	<li>Fix a threshold for the enrichment analysis, here 0.01 
	&#62; Alpha_Enrichment = 0.01</li>
	<li>Perform the enrichment analysis of differentially expressed genes (DEG) lists 
	&#62; Title = NULL 
	&#62; Enrichment(Results_Directory, Project_Name, Title, Reference_Enrichment, Alpha_Enrichment)</li>
	<li>Choose DEG lists to be compared. As for example, 
	&#62; Groups = Contrasts$Contrasts[24:28]</li>
	<li>Provide a name for the list comparison. This name is used for the directory where the output files will be saved 
	&#62; Title = &#34;Interaction_with_Genotypes_1_and_2&#34;</li>
	<li>Specify the action to be done on the DEG lists by setting the parameter Operation to union or intersection. We choose 
	&#62; Operation = &#34;Union&#34;</li>
	<li>Compare the DEGs lists 
	&#62;&#160;Venn_IntersectUnion(Data_Directory, Results_Directory, Project_Name, Title, Groups, Operation)</li>
	<li>Perform a co-expression analysis with the function 
	&#62;&#160;Coexpression_coseq(Data_Directory, Results_Directory, Project_Name, Title, Target, Raw_Counts, Color_Group)</li>
	<li>Perform enrichment analysis of the co-expression clusters 
	&#62; Enrichment(Results_Directory, Project_Name, Title, Reference_Enrichment, Alpha_Enrichment)</li>
	<li>Generate two log files containing all the necessary information to reproduce the analysis 
	&#62; Save_Parameters( ) 
	NOTE: Command lines used in this protocol are shown in Figure 2. Lines that have to be modified to analyze another dataset are highlighted.</li>
</ol>

<ol>
	<li>Wang, Z., Gerstein, M., Snyder, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=RNA-Seq:+a+revolutionary+tool+for+transcriptomics.">RNA-Seq: a revolutionary tool for transcriptomics.</a> Nature reviews. Genetics. 10 (1), 57-63 (2009).</li><li>Yang, I. S., Kim, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Analysis+of+Whole+Transcriptome+Sequencing+Data:+Workflow+and+Software.">Analysis of Whole Transcriptome Sequencing Data: Workflow and Software.</a> Genomics &amp; Informatics. 13 (4), 119-125 (2015).</li><li>R Core Team. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=R:+A+language+and+environment+for+statistical+computing.">R: A language and environment for statistical computing.</a> R Foundation for Statistical Computing. , (2020).</li><li>Huber, W., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Orchestrating+high-throughput+genomic+analysis+with+Bioconductor.">Orchestrating high-throughput genomic analysis with Bioconductor.</a> Nature Methods. 12 (2), 115-121 (2015).</li><li>Smith, D. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+battle+for+user-friendly+bioinformatics.">The battle for user-friendly bioinformatics.</a> Frontiers in Genetics. 4, 187 (2013).</li><li>Pavelin, K., Cham, J. A., de Matos, P., Brooksbank, C., Cameron, G., Steinbeck, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Bioinformatics+Meets+User-Centred+Design:+A+Perspective.">Bioinformatics Meets User-Centred Design: A Perspective.</a> PLoS Computational Biology. 8 (7), 1002554 (2012).</li><li>. Shiny: web application framework Available from: <a target="_blank" href="https://rdrr.io/cran/shiny/">https://rdrr.io/cran/shiny/</a> (2021)</li><li>Lambert, I., Roux, C. P. -. L., Colella, S., Martin-Magniette, M. -. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=DiCoExpress:+a+tool+to+process+multifactorial+RNAseq+experiments+from+quality+controls+to+co-expression+analysis+through+differential+analysis+based+on+contrasts+inside+GLM+models.">DiCoExpress: a tool to process multifactorial RNAseq experiments from quality controls to co-expression analysis through differential analysis based on contrasts inside GLM models.</a> Plant methods. 16 (1), 68 (2020).</li><li>Dillies, M. -. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+comprehensive+evaluation+of+normalization+methods+for+Illumina+high-throughput+RNA+sequencing+data+analysis.">A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis.</a> Briefings in bioinformatics. 14 (6), 671-683 (2012).</li><li>Rigaill, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Synthetic+data+sets+for+the+identification+of+key+ingredients+for+RNA-seq+differential+analysis.">Synthetic data sets for the identification of key ingredients for RNA-seq differential analysis.</a> Briefings in Bioinformatics. 19 (1), (2016).</li><li>Rau, A., Maugis-Rabusseau, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Transformation+and+model+choice+for+RNA-seq+co-expression+analysis.">Transformation and model choice for RNA-seq co-expression analysis.</a> Briefings in Bioinformatics. 19 (3), (2017).</li><li>Robinson, M. D., McCarthy, D. J., Smyth, G. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=edgeR:+a+Bioconductor+package+for+differential+expression+analysis+of+digital+gene+expression+data.">edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.</a> Bioinformatics. 26 (1), 139-140 (2009).</li><li>Wilkinson, M. D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+FAIR+Guiding+Principles+for+scientific+data+management+and+stewardship.">The FAIR Guiding Principles for scientific data management and stewardship.</a> Scientific Data. 3 (1), 160018 (2016).</li><li>Stark, R., Grzelak, M., Hadfield, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=RNA+sequencing:+the+teenage+years.">RNA sequencing: the teenage years.</a> Nature Reviews Genetics. 20 (11), 631-656 (2019).</li></ol>

The authors have nothing to disclose

Because RNA-Seq has become a ubiquitous method in biological studies, there is a constant need to develop versatile and user-friendly analytical tools. A critical step within most of the analytical workflows is often to identify with confidence the genes differentially expressed between biological conditions and/or treatments15. The production of reliable results requires proper statistical modeling, which has been the motivation for the development of DiCoExpress.
DiCo...

Next-generation RNA sequencing (RNA-Seq) technology is now the gold standard of transcriptome analysis1. Since the early days of the technology, the combined efforts of bioinformaticians and biostatisticians have resulted in the development of numerous methods tackling all the essential steps of transcriptomic analyses, from mapping to transcript quantification2. Most of the tools available today to the biologist are developed within the R software environment for statistical computing and graphs3, and many packages for biological data analysis are available in the Bioconductor repository4. These packages offer total control and customization of the analysis, but they come at the cost of extensive use of a command-line interface. Because many biologists are more comfortable with a &#34;point and click&#34; approach5, the democratization of RNA-Seq analyses requires the development of more user-friendly interfaces or protocols6. For example, it is possible to build web interfaces of R packages using Shiny7, and command-line data analysis is made more intuitive with the R-studio8 interface. The development of dedicated, step-by-step tutorials can also help the novel user. In particular, a video tutorial supplements a classic text one, leading to a deeper understanding of all the procedure steps.
We recently developed DiCoExpress9, a tool for analyzing multifactorial RNA-Seq experiments in R using methods considered to be the best ones based on neutral comparison studies10,11,12. Starting from a count table, DiCoExpress proposes a data quality control step followed by a differential gene expression analysis (edgeR package13) using a generalized linear model (GLM) and the generation of co-expression clusters using Gaussian mixture models (coseq package12). DiCoExpress handles complete and unbalanced design up to 2 biological factors (i.e., genotype and treatment) and one technical factor (i.e., replicate). The originality of DiCoExpress lies in its directory architecture storing and organizing data, scripts, and results and in the automation of the writing of the contrasts allowing the user to investigate numerous questions within the same statistical model. An effort was also made to provide graphical outputs illustrating the statistical results.
The DiCoExpress workspace is available at https://forgemia.inra.fr/GNet/dicoexpress.&#160;It contains four directories, two pdf, and two text files. The Data/ directory contains the input datasets; for this protocol, we will use the &#34;tutorial&#34; dataset. The Sources/ directory contains seven R functions necessary to perform the analysis, and must not be modified by the user. The analysis is run using scripts stored in the Template_scripts/ directory. The one used in this protocol is called DiCoExpress_Tutorial_JoVE.R and can be easily adapted to any transcriptomic project. All the results are written in the Results/ directory and stored in a subdirectory named according to the project. The README.md file contains useful installation information, and any specific details concerning the method and its use can be found in the DiCoExpress_Reference_Manual.pdf file.
This video tutorial guides the user through the different features of DiCoExpress with the aim to overcome the reluctance felt by biologists using command-line-based tools. We present here the analysis of an artificial RNA-Seq dataset describing gene expression in three biological replicates of four genotypes, with or without treatment. We will now go through the different steps of the DiCoExpress workflow illustrated in Figure 1. The script described in the Protocol section and input files are available on the site: https://forgemia.inra.fr/GNet/dicoexpress
Prepare data files 
The four csv files stored in the Data/ directory should be named according to the project name. In our example, all the names, therefore, begin with &#34;Tutorial&#34;, and we will set Project_Name = &#34;Tutorial&#34; in Step 4 of the protocol. The separator used in the csv files must be indicated in the Sep variable in Step 4. In our &#34;tutorial&#34; dataset, the separator is a tabulation. For advanced users the full dataset can be reduced to a subset by providing a list of instructions and a new Project_Name through the Filter variable. This option avoids redundant copies of the input files and verifies FAIR principles14.
Among the four csv files, only the COUNTS and TARGET files are mandatory. They contain the raw counts for every gene (here Tutorial_COUNTS.csv) and the experimental design description (here Tutorial_TARGET.csv). The TARGET.csv file describes every sample (one sample per row) with a modality for each biological or technical factor (in the columns). We strongly recommend that the names chosen for the modalities start with a letter, not a number. The name of the last column (&#34;Replicate&#34;) cannot be changed. Finally, the sample names (first column) must match the names in the headings of the COUNTS.csv file (Genotype1_control_rep1 in our example). The Enrichment.csv file in which every line contains one Gene_ID and one annotation term is only required if the user plans to run the enrichment analysis. If one gene has several annotations, they will have to be written on different lines. The Annotation.csv file is optional and is used to add a short description of every gene in the output files. The best way to get an annotation file is to retrieve the information from dedicated databases (e.g., Thalemine: https://bar.utoronto.ca/thalemine/begin.do for Arabidopsis).
Installation of DiCoExpress 
DiCoExpress requires specific R packages. Use the command line source(&#34;../Sources/Install_Packages.R&#34;) in the R console to check the required package installation status. For users on Linux, another solution is to install the container dedicated to DiCoExpress and available at https://forgemia.inra.fr/GNet/dicoexpress/container_registry. By definition, this container contains DiCoExpress with all of the parts needed, such as libraries and other dependencies.

analyzing multifactorial rna-seq experiments with dicoexpress

The proper use of statistical modeling in NGS data analysis requires an advanced level of expertise. There has recently been a growing consensus on using generalized linear models for differential analysis of RNA-Seq data and the advantage of mixture models to perform co-expression analysis. To offer a managed setting to use these modeling approaches, we developed DiCoExpress that provides a standardized R pipeline to perform an RNA-Seq analysis. Without any particular knowledge in statistics or R programming, beginners can perform a complete RNA-Seq analysis from quality controls to co-expression through differential analysis based on contrasts inside a generalized linear model. An enrichment analysis is proposed both on the lists of differentially expressed genes, and the co-expressed gene clusters. This video tutorial is conceived as a step-by-step protocol to help users take full advantage of DiCoExpress and its potential in empowering the biological interpretation of an RNA-Seq experiment.

All the DiCoExpress outputs are saved in the Tutorial/ directory, itself placed within the Results/ directory. We provide here some guidance for assessing the overall quality of the analysis.
Quality Control 
The quality control output, located in the Quality_Control/ directory, is essential to verify that the RNA-Seq analysis results are reliable. The Data_Quality_Control.pdf file contains several plots obtained with raw and normalized data that can be used to identify a...

Watch this Scientific Journal Video about Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress at JoVE.com

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

DiCoExpress is a script-based tool implemented in R to perform an RNA-Seq analysis from quality control to co-expression. DiCoExpress handles complete and unbalanced design up to 2 biological factors. This video tutorial guides the user through the different features of DiCoExpress.

The proper use of statistical modeling in NGS data analysis requires an advanced level of expertise. There has recently been a growing consensus on using ...

analyzing-multifactorial-rna-seq-experiments-with-dicoexpress

Research

JoVE Journal

Engineering

3.2K Views.  Université Paris-Saclay, CNRS, INRAE, Univ Evry, Institute of Plant Sciences Paris-Saclay (IPS2), Orsay, France. DiCoExpress biedt een complete aoristische analyse van kwaliteitscontrole tot co-expressie. Het voert differentiële analyse uit op basis van contrasten binnen het gegeneraliseerde lineaire model. Bovendien kan het ook een verrijkingsanalyse uitvoeren op de lijst van differentieel tot expressie gebrachte genen en de co-expressiegenclusters.Het grote voordeel van DiCoExpress is dat het kan worden gebruikt door mensen zoals ik, zonder enige specifieke kennis van statistieken of luchtprog...

Video: Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved (Non-model) Organisms

This workflow allows novice researchers to leverage advanced computational resources such as cloud computing to carry out pairwise comparative transcriptomics. It also serves as a primer for biologists to develop data scientist computational skills, e.g. executing bash commands, visualization and management of large data sets. All command line code and further explanations of each command or step can be found on the wiki (<a href="https://wiki.cyverse.org/wiki/x/dgGtAQ">https://wiki.cyverse.org/wiki/x/dgGtAQ</a>). The Discovery Environment and Atmosphere platforms are connected together through the CyVerse Data Store. As such, once the initial raw sequencing data has been uploaded there is no more need to transfer large data files over an Internet connection, minimizing the amount of time needed to conduct analyses. This protocol is designed to analyze only two experimental treatments or conditions. Differential gene expression analysis is conducted through pairwise comparisons, and will not be suitable to test multiple factors. This workflow is also designed to be manual rather than automated. Each step must be executed and investigated by the user, yielding a better understanding of data and analytical outputs, and therefore better results for the user. Once complete, this protocol will yield de novo assembled transcriptome(s) for underserved (non-model) organisms without the need to map to previously assembled reference genomes (which are usually not available in underserved organism). These de novo transcriptomes are further used in pairwise differential gene expression analysis to investigate genes differing between two experimental conditions. Differentially expressed genes are then functionally annotated to understand the genetic response organisms have to experimental conditions. In total, the data derived from this protocol is used to test hypotheses about biological responses of underserved organisms.

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

This protocol outlines a comparative de novo transcriptome assembly and annotation workflow for novice bioinformaticians. The workflow is available for free entirely through CyVerse and connected by the Data Store. Command line and graphical user interfaces are used, but all code needed is available to copy and paste.

This workflow allows novice researchers to leverage advanced computational resources such as cloud computing to carry out pairwise comparative ...

Genetics

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Upon activation, cells rapidly change their functional programs and, thereby, their gene expression profile. Massive changes in gene expression occur, for example, during cellular differentiation, morphogenesis, and functional stimulation (such as activation of immune cells), or after exposure to drugs and other factors from the local environment. Depending on the stimulus and cell type, these changes occur rapidly and at any possible level of gene regulation. Displaying all molecular processes of a responding cell to a certain type of stimulus/drug is one of the hardest tasks in molecular biology. Here, we describe a protocol that enables the simultaneous analysis of multiple layers of gene regulation. We compare, in particular, transcription factor binding (Chromatin-immunoprecipitation-sequencing (ChIP-seq)), de novo transcription (4-thiouridine-sequencing (4sU-seq)), mRNA processing, and turnover as well as translation (ribosome profiling). By combining these methods, it is possible to display a detailed and genome-wide course of action.
Sequencing newly transcribed RNA is especially recommended when analyzing rapidly adapting or changing systems, since this depicts the transcriptional activity of all genes during the time of 4sU exposure (irrespective of whether they are up- or downregulated). The combinatorial use of total RNA-seq and ribosome profiling additionally allows the calculation of RNA turnover and translation rates. Bioinformatic analysis of high-throughput sequencing results allows for many means for analysis and interpretation of the data. The generated data also enables tracking co-transcriptional and alternative splicing, just to mention a few possible outcomes.
The combined approach described here can be applied for different model organisms or cell types, including primary cells. Furthermore, we provide detailed protocols for each method used, including quality controls, and discuss potential problems and pitfalls.

This protocol describes the combinatorial use of ChIP-seq, 4sU-seq, total RNA-seq, and ribosome profiling for cell lines and primary cells. It enables tracking changes in transcription-factor binding, de novo transcription, RNA processing, turnover and translation over time, and displaying the overall course of events in activated and/or rapidly changing cells.

Upon activation, cells rapidly change their functional programs and, thereby, their gene expression profile. Massive changes in gene expression occur, for ...

Characterization of In Vitro Differentiation of Human Primary Keratinocytes by RNA-Seq Analysis

Human primary keratinocytes are often used as in vitro models for studies on epidermal differentiation and related diseases. Methods have been reported for in vitro differentiation of keratinocytes cultured in two-dimensional (2D) submerged manners using various induction conditions. Described here is a procedure for 2D in vitro keratinocyte differentiation method by contact inhibition and subsequent molecular characterization by RNA-seq. In brief, keratinocytes are grown in defined keratinocyte medium supplemented with growth factors until they are fully confluent. Differentiation is induced by close contacts between the keratinocytes and further stimulated by excluding growth factors in the medium. Using RNA-seq analyses, it is shown that both 1) differentiated keratinocytes exhibit distinct molecular signatures during differentiation and 2) the dynamic gene expression pattern largely resembles cells during epidermal stratification. As for comparison to normal keratinocyte differentiation, keratinocytes carrying mutations of the transcription factor p63 exhibit altered morphology and molecular signatures, consistent with their differentiation defects. In conclusion, this protocol details the steps for 2D in vitro keratinocyte differentiation and its molecular characterization, with an emphasis on bioinformatic analysis of RNA-seq data. Because RNA extraction and RNA-seq procedures have been well-documented, it is not the focus of this protocol. The experimental procedure of in vitro keratinocyte differentiation and bioinformatic analysis pipeline can be used to study molecular events during epidermal differentiation in healthy and diseased keratinocytes.

Presented here is a stepwise procedure for in vitro differentiation of human primary keratinocytes by contact inhibition followed by characterization at the molecular level by RNA-seq analysis.

Human primary keratinocytes are often used as in vitro models for studies on epidermal differentiation and related diseases. Methods have been reported ...

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Gene expression analysis by RNA sequencing (RNA-seq) enables unique insights into clinical samples that can potentially lead to mechanistic understanding of the basis of various diseases as well as resistance and/or susceptibility mechanisms. However, FFPE tissues, which represent the most common method for preserving tissue morphology in clinical specimens, are not the best sources for gene expression profiling analysis. The RNA obtained from such samples is often degraded, fragmented, and chemically modified, which leads to suboptimal sequencing libraries. In turn, these generate poor quality sequence data that may not be reliable for gene expression analysis and mutation discovery. In order to make the most of FFPE samples and obtain the best possible data from low quality samples, it is important to take certain precautions while planning experimental design, preparing sequencing libraries, and during data analysis. This includes the use of appropriate metrics for precise sample quality control (QC), identifying the best methods for various steps during the sequencing library generation, and careful library QC. In addition, applying correct software tools and parameters for sequence data analysis is critical in order to identify artifacts in RNA-seq data, filter out contamination and low quality reads, assess uniformity of gene coverage, and measure the reproducibility of gene expression profiles among biological replicates. These steps can ensure high accuracy and reproducibility for profiling of very heterogeneous RNA samples. Here we describe the various steps for sample QC, library preparation and QC, sequencing, and data analysis that can help to increase the amount of useful data obtained from low quality RNA, such as that obtained from FFPE-RNA tissues.

This method describes the steps to improve the quality and quantity of sequence data that can be obtained from formalin-fixed paraffin-embedded (FFPE) RNA samples. We describe the methodology to more accurately assess the quality of FFPE-RNA samples, prepare sequencing libraries, and analyze the data from FFPE-RNA samples.

Gene expression analysis by RNA sequencing (RNA-seq) enables unique insights into clinical samples that can potentially lead to mechanistic understanding ...

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis

Many cancers are characterized by chromosomal translocations which result in the expression of oncogenic fusion transcription factors. Typically, these proteins contain an intrinsically disordered domain (IDD) fused with the DNA-binding domain (DBD) of another protein and orchestrate widespread transcriptional changes to promote malignancy. These fusions are often the sole recurring genomic aberration in the cancers they cause, making them attractive therapeutic targets. However, targeting oncogenic transcription factors requires a better understanding of the mechanistic role that low-complexity, IDDs play in their function. The N-terminal domain of EWSR1 is an IDD involved in a variety of oncogenic fusion transcription factors, including EWS/FLI, EWS/ATF, and EWS/WT1. Here, we use RNA-sequencing to investigate the structural features of the EWS domain important for transcriptional function of EWS/FLI in Ewing sarcoma. First shRNA-mediated depletion of the endogenous fusion from Ewing sarcoma cells paired with ectopic expression of a variety of EWS-mutant constructs is performed. Then RNA-sequencing is used to analyze the transcriptomes of cells expressing these constructs to characterize the functional deficits associated with mutations in the EWS domain. By integrating the transcriptomic analyses with previously published information about EWS/FLI DNA binding motifs, and genomic localization, as well as functional assays for transforming ability, we were able to identify structural features of EWS/FLI important for oncogenesis and define a novel set of EWS/FLI target genes critical for Ewing sarcoma. This paper demonstrates the use of RNA-sequencing as a method to map the structure-function relationship of the intrinsically disordered domain of oncogenic transcription factors.

Intrinsically disordered domains are important for oncogenic fusion transcription factor function. To therapeutically target these proteins, a more detailed understanding of the regulatory mechanisms employed by these domains is required. Here, we use transcriptomics to map important structural features of the intrinsically disordered EWS domain in Ewing sarcoma.

Many cancers are characterized by chromosomal translocations which result in the expression of oncogenic fusion transcription factors. Typically, these ...

Cancer Research

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

RNA sequencing (RNA-seq) is one of the most widely used technologies in transcriptomics as it can reveal the relationship between the genetic alteration and complex biological processes and has great value in diagnostics, prognostics, and therapeutics of tumors. Differential analysis of RNA-seq data is crucial to identify aberrant transcriptions, and limma, EdgeR and DESeq2 are efficient tools for differential analysis. However, RNA-seq differential analysis requires certain skills with R language and the ability to choose an appropriate method, which is lacking in the curriculum of medical education.
Herein, we provide the detailed protocol to identify differentially expressed genes (DEGs) between cholangiocarcinoma (CHOL) and normal tissues through limma, DESeq2 and EdgeR, respectively, and the results are shown in volcano plots and Venn diagrams. The three protocols of limma, DESeq2 and EdgeR are similar but have different steps among the processes of the analysis. For example, a linear model is used for statistics in limma, while the negative binomial distribution is used in edgeR and DESeq2. Additionally, the normalized RNA-seq count data is necessary for EdgeR and limma but is not necessary for DESeq2.
Here, we provide a detailed protocol for three differential analysis methods: limma, EdgeR and DESeq2. The results of the three methods are partly overlapping. All three methods have their own advantages, and the choice of method only depends on the data.

A detailed protocol of differential expression analysis methods for RNA sequencing was provided: limma, EdgeR, DESeq2.

RNA sequencing (RNA-seq) is one of the most widely used technologies in transcriptomics as it can reveal the relationship between the genetic alteration ...

RNA-seq Analysis of Transcriptomes in Thrombin-treated and Control Human Pulmonary Microvascular Endothelial Cells

The characterization of gene expression in cells via measurement of mRNA levels is a useful tool in determining how the transcriptional machinery of the cell is affected by external signals (e.g. drug treatment), or how cells differ between a healthy state and a diseased state. With the advent and continuous refinement of next-generation DNA sequencing technology, RNA-sequencing (RNA-seq) has become an increasingly popular method of transcriptome analysis to catalog all species of transcripts, to determine the transcriptional structure of all expressed genes and to quantify the changing expression levels of the total set of transcripts in a given cell, tissue or organism1,2 . RNA-seq is gradually replacing DNA microarrays as a preferred method for transcriptome analysis because it has the advantages of profiling a complete transcriptome, providing a digital type datum (copy number of any transcript) and not relying on any known genomic sequence3.
Here, we present a complete and detailed protocol to apply RNA-seq to profile transcriptomes in human pulmonary microvascular endothelial cells with or without thrombin treatment. This protocol is based on our recent published study entitled "RNA-seq Reveals Novel Transcriptome of Genes and Their Isoforms in Human Pulmonary Microvascular Endothelial Cells Treated with Thrombin,"4 in which we successfully performed the first complete transcriptome analysis of human pulmonary microvascular endothelial cells treated with thrombin using RNA-seq. It yielded unprecedented resources for further experimentation to gain insights into molecular mechanisms underlying thrombin-mediated endothelial dysfunction in the pathogenesis of inflammatory conditions, cancer, diabetes, and coronary heart disease, and provides potential new leads for therapeutic targets to those diseases.
The descriptive text of this protocol is divided into four parts. The first part describes the treatment of human pulmonary microvascular endothelial cells with thrombin and RNA isolation, quality analysis and quantification. The second part describes library construction and sequencing. The third part describes the data analysis. The fourth part describes an RT-PCR validation assay. Representative results of several key steps are displayed. Useful tips or precautions to boost success in key steps are provided in the Discussion section. Although this protocol uses human pulmonary microvascular endothelial cells treated with thrombin, it can be generalized to profile transcriptomes in both mammalian and non-mammalian cells and in tissues treated with different stimuli or inhibitors, or to compare transcriptomes in cells or tissues between a healthy state and a disease state.

This protocol presents a complete and detailed procedure to apply RNA-seq, a powerful next-generation DNA sequencing technology, to profile transcriptomes in human pulmonary microvascular endothelial cells with or without thrombin treatment. This protocol is generalizable to various cells or tissues affected by different reagents or disease states.

The characterization of gene expression in cells via measurement of mRNA levels is a useful tool in determining how the transcriptional machinery of the ...

Biology

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Pathogens can cause a wide variety of infectious diseases. The biological processes induced by the host in response to infection determine the severity of the disease.&#160;To study such processes, researchers can use high-throughput sequencing techniques (RNA-seq) that measure the dynamic changes of the host transcriptome at different stages of infection, clinical outcomes, or disease severity.This investigation can lead to a better understanding of the diseases, as well as uncovering potential drug targets and treatments. The protocol presented here describes a complete pipeline to analyze RNA-sequencing data from raw reads to functional analysis. The pipeline is divided into five steps: (1) quality control of the data; (2) mapping and annotation of genes; (3) statistical analysis to identify differentially expressed genes and co-expressed genes; (4) determination of the molecular degree of the perturbation of samples; and (5) functional analysis. Step 1 removes technical artifacts that may impact the quality of downstream analyses. In step 2, genes are mapped and annotated according to standard library protocols.&#160;The statistical analysis in step 3 identifies genes that are differentially expressed or co-expressed in infected samples, in comparison with non-infected ones. Sample variability and the presence of potential biological outliers are verified using the molecular degree of perturbation approach in step 4. Finally, the functional analysis in step 5 reveals the pathways associated with the disease phenotype. The presented pipeline aims to support researchers through the RNA-seq data analysis from host-pathogen interaction studies and drive future&#160;in vitro or in vivo experiments, that are essential to understand the molecular mechanism of infections.

The protocol presented here describes a complete pipeline to analyze RNA-sequencing transcriptome data from raw reads to functional analysis, including quality control and preprocessing steps to advanced statistical analytical approaches.

Pathogens can cause a wide variety of infectious diseases. The biological processes induced by the host in response to infection determine the severity of ...

Immunology and Infection

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

Hematopoietic stem cells (HSCs) are used clinically for transplantation treatment to rebuild a patient&#39;s hematopoietic system in many diseases such as leukemia and lymphoma. Elucidating the mechanisms controlling HSCs self-renewal and differentiation is important for application of HSCs for research and clinical uses. However, it is not possible to obtain large quantity of HSCs due to their inability to proliferate in vitro. To overcome this hurdle, we used a mouse bone marrow derived cell line, the EML (Erythroid, Myeloid, and Lymphocytic) cell line, as a model system for this study.
RNA-sequencing (RNA-Seq) has been increasingly used to replace microarray for gene expression studies. We report here a detailed method of using RNA-Seq technology to investigate the potential key factors in regulation of EML cell self-renewal and differentiation. The protocol provided in this paper is divided into three parts. The first part explains how to culture EML cells and separate Lin-CD34+ and Lin-CD34- cells. The second part of the protocol offers detailed procedures for total RNA preparation and the subsequent library construction for high-throughput sequencing. The last part describes the method for RNA-Seq data analysis and explains how to use the data to identify differentially expressed transcription factors between Lin-CD34+ and Lin-CD34- cells. The most significantly differentially expressed transcription factors were identified to be the potential key regulators controlling EML cell self-renewal and differentiation. In the discussion section of this paper, we highlight the key steps for successful performance of this experiment.
In summary, this paper offers a method of using RNA-Seq technology to identify potential regulators of self-renewal and differentiation in EML cells. The key factors identified are subjected to downstream functional analysis in vitro and in vivo.

RNA-sequencing and bioinformatics analyses were used to identify significantly and differentially expressed transcription factors in Lin-CD34+ and Lin-CD34- subpopulations of mouse EMLcells. These transcription factors might play important roles in determining the switch between self-renewing Lin-CD34+ and partially differentiated Lin-CD34- cells.

Hematopoietic stem cells (HSCs) are used clinically for transplantation treatment to rebuild a patient&#39;s hematopoietic system in many diseases such as ...

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

As well as the typical analysis of RNA-Seq to measure differential gene expression (DGE) across experimental/biological conditions, RNA-seq data can also be utilized to explore other complex regulatory mechanisms at the exon level. Alternative splicing and polyadenylation play a crucial role in the functional diversity of a gene by generating different isoforms to regulate gene expression at the post-transcriptional level, and limiting analyses to the whole gene level can miss this important regulatory layer. Here, we demonstrate detailed step by step analyses for identification and visualization of differential exon and polyadenylation site usage across conditions, using Bioconductor and other packages and functions, including DEXSeq, diffSplice from the Limma package, and rMATS.

Alternative splicing (AS) and alternative polyadenylation (APA) expand the diversity of transcript isoforms and their products. Here, we describe bioinformatic protocols to analyze bulk RNA-seq and 3' end sequencing assays to detect and visualize AS and APA varying across experimental conditions.

As well as the typical analysis of RNA-Seq to measure differential gene expression (DGE) across experimental/biological conditions, RNA-seq data can also ...

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

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