We thank Adriana Briscoe, Gil Smith, Rabi Murad and Aline G. Rangel for advice and guidance in incorporating some of these steps into our workflow. We are also grateful to Katherine Williams, Elisabeth Rebboah, and Natasha Picciani for comments on the manuscript. This work was supported in part by a George E. Hewitt Foundation for Medical research fellowship to A.M.M.

This protocol follows UC Irvine animal care guidelines.
1. RNA-seq library preparation
<ol>
	<li>Isolate RNA using the following methods.
	<ol>
		<li>Collect samples. If RNA is to be extracted at a later time, flash freeze the sample or place in RNA storage solution15 (Table of Materials).</li>
		<li>Euthanize and dissect the organism to separate tissues of interest.</li>
		<li>Extract total RNA using an extraction kit and purify the RNA using an RNA purification kit (Table of Materials) 
		NOTE: There are protocols and kits that may work better for different species and tissue types16,17. We have extracted RNA from different body tissues of a butterfly18 and a gelatinous Hydra19 (see discussion).</li>
		<li>Measure the concentration and quality of the RNA of each sample (Table of Materials). Use samples with RNA integrity numbers (RIN) higher than 8, ideally closer to 920 to construct cDNA libraries.</li>
	</ol>
	</li>
	<li>Construct cDNA library and sequence as follows.
	<ol>
		<li>Build cDNA libraries according to the library prep instruction manual (see discussion).</li>
		<li>Determine cDNA concentration and quality (Table of Materials).</li>
		<li>Multiplex the libraries and sequence them.</li>
	</ol>
	</li>
</ol>
2. Access a computer cluster
NOTE: RNA-seq analysis requires manipulation of large files and is best done on a computer cluster (Table of Materials).
<ol>
	<li>Login to the computer cluster account using the command ssh username@clusterlocation on a terminal (Mac) or PuTTY (Windows) application window.</li>
</ol>
3. Obtain RNA-seq reads
<ol>
	<li>Obtain RNA-seq reads from the sequencing facility or, for data generated in a publication, from the data repository where it was deposited (3.2 or 3.3).</li>
	<li>To download data from repositories such as ArrayExpress do the following:
	<ol>
		<li>Search the site using the accession number.</li>
		<li>Find the link to download the data, then left-click and select Copy Link.</li>
		<li>On the terminal window, type wget and select Paste link to copy the data to the directory for analysis.</li>
	</ol>
	</li>
	<li>To download NCBI Short Read Archive (SRA) data follow these alternative steps:
	<ol>
		<li>On the terminal download SRA Toolkit v. 2.8.1 using wget. 
		NOTE: Downloading and installing programs to the computer cluster may require root access, contact your computer cluster administrator if installation fails.</li>
		<li>Finish installing the program by typing tar -xvf $TARGZFILE.</li>
		<li>Search NCBI for the SRA accession number for the samples you want to download, it should have the format SRRXXXXXX.</li>
		<li>Obtain the RNA-seq data by typing [sratoolkit location]/bin/prefetch SRRXXXXXX in the terminal window.</li>
		<li>For paired-end files type [sratoolkit location]/bin/fastq-dump --split-files SRRXXXXXX to get two fastq files (SRRXXXXXX_1.FASTQ and SRRXXXXXX_2.FASTQ). 
		NOTE: To do a Trinity de novo assembly use the command [sratoolkit location]/bin/fastq-dump --defline-seq &#39;@$sn[_$rn]/$ri&#39; --split-files SRRXXXXXX</li>
	</ol>
	</li>
</ol>
4. Trim adapters and low-quality reads (optional)
<ol>
	<li>Install or load Trimmomatic21 v. 0.35 on the computing cluster.</li>
	<li>In the directory where the RNA-seq data files are located, type a command that includes the location of the trimmomatic jar file, the input FASTQ files, output FASTQ files, and optional parameters such as read length and quality. 
	NOTE: The command will vary by the raw and desired quality and length of the reads. For Illumina 43 bp reads with Nextera primers, we used: java -jar /data/apps/trimmomatic/0.35/trimmomatic-0.35.jar PE $READ1.FASTQ $READ2.FASTQ paired_READ1.FASTQ unpaired_READ1.FASTQ paired_READ2.FASTQ unpaired_READ2.FASTQ ILLUMINACLIP:adapters.fa:2:30:10 LEADING:20 TRAILING:20 SLIDINGWINDOW:4:17 MINLEN:30.</li>
</ol>
5. Obtain reference assembly
<ol>
	<li>Search google, EnsemblGenomes, and NCBI Genomes and Nucleotide TSA (Transcriptome Shotgun Assembly) for a reference genome or assembled transcriptome for the species of interest (Figure 1). 
	NOTE: If a reference genome or transcriptome are not available or low-quality, proceed to STEP 6 to generate a de novo assembly.</li>
	<li>If a reference genome or assembled transcriptome exists, download it as a fasta file to where the analysis will be performed following the steps below.
	<ol>
		<li>Find the link to download the genome, left-click and Copy Link.</li>
		<li>On the terminal window type wget and paste the link address. If available, also copy the GTF file and protein FASTA file for the reference genome.</li>
	</ol>
	</li>
</ol>
6. Generate a de novo assembly (Alternative to Step 5)
<ol>
	<li>Combine the RNA-seq READ1 and READ2 fastq files for all samples by typing cat *READ1.FASTQ &#62; $all_READ1.FASTQ and cat *READ2.FASTQ &#62; all_READ2.FASTQ on the terminal window.</li>
	<li>Install or load Trinity22 v.2.8.5 on the computing cluster.</li>
	<li>Generate and assembly by typing on the terminal: Trinity --seqType fq --max_memory 20G --left $all_READ1.FASTQ --right $all_READ2.FASTQ.</li>
</ol>
7. Map reads to the genome (7.1) or de novo transcriptome (7.2)
<ol>
	<li>Map reads to the reference genome using STAR23 v. 2.6.0c and RSEM24 v. 1.3.0.
	<ol>
		<li>Install or load STAR v. 2.6.0c. and RSEM v. 1.3.0 to the computing cluster.</li>
		<li>Index the genome by typing rsem-prepare-reference --gtf $GENOME.GTF --star -p 16 $GENOME.FASTA $OUTPUT.</li>
		<li>Map reads and calculate expression for each sample by typing rsem-calculate-expression -p 16 --star --paired-end $READ1.FASTQ $READ2.FASTQ $INDEX $OUTPUT.</li>
		<li>Rename the results file to something descriptive using mv RSEM.genes.results $sample.genes.results.</li>
		<li>Generate a matrix of all counts by typing rsem-generate-data-matrix *[genes/isoforms.results] &#62; $OUTPUT.</li>
	</ol>
	</li>
	<li>Map RNA-seq to the Trinity de novo assembly using RSEM and bowtie.
	<ol>
		<li>Install or load Trinity22 v.2.8.5, Bowtie25 v. 1.0.0, and RSEM v. 1.3.0.</li>
		<li>Map reads and calculate expression for each sample by typing [trinity_location]/align_and_estimate_abundance.pl --prep-reference --transcripts $TRINITY.FASTA --seqType fq --left $READ1.FASTQ --right $READ2.FASTQ --est_method RSEM --aln_method bowtie --trinity_mode --output_dir $OUTPUT.</li>
		<li>Rename the results file to something descriptive using mv RSEM.genes.results $sample.genes.results.</li>
		<li>Generate a matrix of all counts by typing [trinity_location]/abundance_estimates_to_matrix.pl --est_method RSEM *[genes/isoforms].results</li>
	</ol>
	</li>
</ol>
8. Identify genes of interest
NOTE: The following steps can be done with nucleotide or protein FASTA files but work best and are more straightforward with protein sequences. BLAST searches using protein to protein is more likely to give results when searching between different species.
<ol>
	<li>For a reference genome, use the protein FASTA file from STEP 5.2.2 or see Supplemental Materials to generate a custom gene feature GTF.</li>
	<li>For a de novo transcriptome, generate a protein FASTA using TransDecoder.
	<ol>
		<li>Install or load TransDecoder v. 5.5.0 on the computer cluser.</li>
		<li>Find the longest open reading frame and predicted peptide sequence by typing [Transdecoder location]/TransDecoder.LongOrfs -t $TRINITY.FASTA.</li>
	</ol>
	</li>
	<li>Search NCBI Genbank for homologs in closely related species.
	<ol>
		<li>Open an internet browser window and go to https://www.ncbi.nlm.nih.gov/genbank/.</li>
		<li>On the search bar type the name of the gene of interest and the name of closely related species which have been sequenced or genus or phylum. On the left of the search bar select protein then click search.</li>
		<li>Extract sequences by clicking Send to and then select File. Under Format, select FASTA then click Create File.</li>
		<li>Move FASTA file of homologs to the computer cluster by typing scp $FASTA username@clusterlocation:/$DIR on a local terminal window or use FileZilla to transfer files to and from computer and cluster.</li>
	</ol>
	</li>
	<li>Search for candidate genes using BLAST+26.
	<ol>
		<li>Install or load BLAST+ v. 2.8.1 on the computer cluster.</li>
		<li>On the computer cluster, make a BLAST database from the genome or transcriptome translated protein FASTA by typing [BLAST+ location]/makeblastdb -in $PEP.FASTA -dbtype prot -out $OUTPUT</li>
		<li>BLAST the homologous gene sequences from NCBI to the database of the species of interest by typing [BLAST+ location]/blastp -db $DATABASE -query $FASTA -evalue 1e-10 -outfmt 6 -max_target_seqs 1 -out $OUTPUT.</li>
		<li>View the output file using the command more. Copy unique gene IDs from the species of interest to a new text file.</li>
		<li>Extract the sequences of candidate genes by typing perl -ne &#39;if(/^&#62;(\S+)/){$c=$i{$1}}$c?print:chomp;$i{$_}=1 if @ARGV&#39; $gene_id.txt $PEP.FASTA &#62; $OUTPUT.</li>
	</ol>
	</li>
	<li>Confirm gene annotation using reciprocal BLAST.
	<ol>
		<li>On the internet browser go to https://blast.ncbi.nlm.nih.gov/Blast.cgi.</li>
		<li>Select tblastn, then paste the candidate sequences, select the Non-redundant protein sequence database and click BLAST.</li>
	</ol>
	</li>
	<li>Identify additional genes by annotating all genes in the genome or transcriptome with gene ontology (GO) terms (see discussion).
	<ol>
		<li>Transfer the protein FASTA to the local computer.</li>
		<li>Download and install Blast2GO27,28,29 v. 5.2 to the local computer.</li>
		<li>Open Blast2GO, click File, go to Load, go to Load Sequences, click Load Fasta File (fasta). Select the FASTA file and click Load.</li>
		<li>Click on Blast, choose NCBI Blast, and click Next. Edit parameters or click Next, edit parameters and click Run to find the most similar gene description.</li>
		<li>Click mapping then click Run to search Gene Ontology annotations for similar proteins.</li>
		<li>Next click interpro, select EMBL-EBI InterPro, and click Next. Edit parameters or click Next, and click Run to search for signatures of known gene families and domains.</li>
		<li>Export the annotations by clicking File, select Export, click Export Table. Click Browse, name the file, click Save, click Export.</li>
		<li>Search the annotation table for GO terms of interest to identify additional candidate genes. Extract the sequences from the FASTA file (STEP 8.4.5)</li>
	</ol>
	</li>
</ol>
9. Phylogenetic trees
<ol>
	<li>Download and install MEGA30 v. 7.0.26 to your local computer.</li>
	<li>Open MEGA, click on Align, click Edit/Build Alignment, select Create a new alignment click OK, select Protein.</li>
	<li>When the alignment window opens, click on Edit, click Insert sequences from file and select the FASTA with protein sequences of candidate genes and probable homologs.</li>
	<li>Select all sequences. Find the arm symbol and hover over it. It should say Align sequences using MUSCLE31 algorithm. Click on the arm symbol and then click Align Protein to align the sequences. Edit parameters or click OK to align using default parameters.</li>
	<li>Visually inspect and make any manual changes then Save and close the alignment window.</li>
	<li>In the main MEGA window, click on Models, click Find Best DNA/Protein models (ML), select the alignment file and select corresponding parameters such as: Analysis: Model Selection (ML), Tree to use: Automatic (neighbor-joining tree), Statistical Method: Maximum Likelihood, Substitution Type: Amino Acid, Gap/missing data treatment: Use all sites, Branch site filter: None.</li>
	<li>Once the best model for the data is determined, go to the main MEGA window. Click Phylogeny and click Contruct/Test Maximum Likelihood Tree and then select the alignment, if necessary. Select the appropriate parameters for the tree: Statistical method: Maximum Likelihood, Test of Phylogeny: Bootstrap method with 100 replicates, substitution type: amino acid, model: LG with Freqs. (+F), rates among sites: gamma distributed (G) with 5 discrete gamma categories, gap/missing data treatment: use all sites, ML heuristic method: Nearest-Neighbor-Interchange (NNI).</li>
</ol>
10. Visualize gene expression using TPM
<ol>
	<li>For Trinity, on the computer cluster go to the directory where abundance_estimates_to_matrix.pl was run and one of the outputs should be matrix.TPM.not_cross_norm. Transfer this file to your local computer. 
	NOTE: See Supplemental Materials for cross sample normalization.</li>
	<li>For TPMs from a genome analysis follow the steps below.
	<ol>
		<li>On the computer cluster, go to the RSEM installation location. Copy rsem-generate-data-matrix by typing scp rsem-generate-data-matrix rsem-generate-TPM-matrix. Use nano to edit the new file and change &#8220;my $offsite = 4&#8221; from 4 to 5 for TPM, it should now read &#8220;my $offsite = 5&#8221;.</li>
	</ol>
	</li>
	<li>Go to the directory where the RSEM output files .genes.results are and now use rsem-generate-TPM-matrix *[genes/isoforms.results] &#62; $OUTPUT to generate a TPM matrix. Transfer results to a local computer.</li>
	<li>Visualize the results in ggplot2.
	<ol>
		<li>Download R v. 4.0.0 and RStudio v. 1.2.1335 to a local computer.</li>
		<li>Open RStudio on the right of the screen go to the Packages tab and click Install. Type ggplot2 and click install.</li>
		<li>On the R script window read in the TPM table by typing data&#60;-read.table(&#34;$tpm.txt&#34;,header = T)</li>
		<li>For bar graphs similar to Figure 4&#160;type something similar to: p&#60;- ggplot() + geom_bar(aes(y=TPM, x=Symbol, fill=Tissue), data=data, stat=&#34;identity&#34;) 
		fill&#60;-c(&#34;#d7191c&#34;,&#34;#fdae61&#34;, &#34;#ffffbf&#34;, &#34;#abd9e9&#34;, &#34;#2c7bb6&#34;) 
		p&#60;-p+scale_fill_manual(values=fill) 
		p + theme(axis.text.x = element_text(angle = 90))</li>
	</ol>
	</li>
</ol>

The authors have nothing to disclose.

The purpose of this protocol is to provide an outline of the steps for characterizing a gene family using RNA-seq data. These methods have been proven to work for a variety of species and datasets4,34,35. The pipeline established here has been simplified and should be easy enough to be followed by a novice in bioinformatics. The significance of the protocol is that it outlines all the steps and necessary programs to complete a p...

Advances in sequencing technologies have facilitated the sequencing of genomes and transcriptomes of non-model organisms. In addition to the increased feasibility of sequencing DNA and RNA from many organisms, an abundance of data is publicly available to study genes of interest. The purpose of this protocol is to provide bioinformatic steps to investigate the molecular evolution and expression of genes that may play an important role in the organism of interest.
Investigating the evolution of a gene or gene family can provide insight into the evolution of biological systems. Members of a gene family are typically determined by identifying conserved motifs or homologous gene sequences. Gene family evolution was previously investigated using genomes from distantly related model organisms1. A limitation to this approach is that it is not clear how these gene families evolve in closely related species and the role of different environmental selective pressures. In this protocol, we include a search for homologs in closely related species. By generating a phylogeny at a phylum level, we can note trends in gene family evolution such as that of conserved genes or lineage-specific duplications. At this level, we can also investigate whether genes are orthologs or paralogs. While many homologs likely function similarly to each other, that is not necessarily the case2. Incorporating phylogenetic trees in these studies is important to resolve whether these homologous genes are orthologs or not. In eukaryotes, many orthologs retain similar functions within the cell as evidenced by the ability of mammalian proteins to restore the function of yeast orthologs3. However, there are instances where a non-orthologous gene carries out a characterized function4.
Phylogenetic trees begin to delineate relationships between genes and species, yet function cannot be assigned solely based on genetic relationships. Gene expression studies combined with functional annotations and enrichment analysis&#160;provide strong support for gene function. Cases where gene expression can be quantified and compared across individuals or tissue types can be more telling of potential function. The following protocol follows methods used in investigating opsin genes in Hydra vulgaris7, but they can be applied to any species and any gene family. The results of such studies provide a foundation for further investigation into gene function and gene networks in non-model organisms. As an example, the investigation of the phylogeny of opsins, which are proteins that initiate the phototransduction cascade, gives context to the evolution of eyes and light detection8,9,10,11. In this case, non-model organisms especially basal animal species such as cnidarians or ctenophores can elucidate conservation or changes in the phototransduction cascade and vision across clades12,13,14. Similarly, determining the phylogeny, expression, and networks of other gene families will inform us about the molecular mechanisms underlying adaptations.

<table><tbody><tr><td>Bioanalyzer-DNA kit</td><td>Agilent</td><td>5067-4626</td><td>wet lab materials</td></tr><tr><td>Bioanalyzer-RNA kit</td><td>Agilent</td><td>5067-1513</td><td>wet lab materials</td></tr><tr><td>BLAST+ v. 2.8.1</td><td></td><td></td><td>On computer cluster*&lt;br/&gt; https://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/LATEST/</td></tr><tr><td>Blast2GO (on your PC)</td><td></td><td></td><td>On local computer&lt;br/&gt; https://www.blast2go.com/b2g-register-basic</td></tr><tr><td>boost v. 1.57.0</td><td></td><td></td><td>On computer cluster</td></tr><tr><td>Bowtie v. 1.0.0</td><td></td><td></td><td>On computer cluster&lt;br/&gt; https://sourceforge.net/projects/bowtie-bio/files/bowtie/1.3.0/</td></tr><tr><td>Computing cluster (highly recommended)</td><td></td><td></td><td>NOTE: Analyses of genomic data are best done on a high-performance computing cluster because files are very large.</td></tr><tr><td>Cufflinks v. 2.2.1</td><td></td><td></td><td>On computer cluster</td></tr><tr><td>edgeR v. 3.26.8 (in R)</td><td></td><td></td><td>In Rstudio&lt;br/&gt; https://bioconductor.org/packages/release/bioc/html/edgeR.html</td></tr><tr><td>gcc v. 6.4.0</td><td></td><td></td><td>On computer cluster</td></tr><tr><td>Java v. 11.0.2</td><td></td><td></td><td>On computer cluster</td></tr><tr><td>MEGA7 (on your PC)</td><td></td><td></td><td>On local computer&lt;br/&gt; https://www.megasoftware.net</td></tr><tr><td>MEGAX v. 0.1</td><td></td><td></td><td>On local computer&lt;br/&gt; https://www.megasoftware.net</td></tr><tr><td>NucleoSpin RNA II kit</td><td>Macherey-Nagel</td><td>740955.5</td><td>wet lab materials</td></tr><tr><td>perl 5.30.3</td><td></td><td></td><td>On computer cluster</td></tr><tr><td>python</td><td></td><td></td><td>On computer cluster</td></tr><tr><td>Qubit 2.0 Fluorometer</td><td>ThermoFisher</td><td>Q32866</td><td>wet lab materials</td></tr><tr><td>R v.4.0.0</td><td></td><td></td><td>On computer cluster&lt;br/&gt; https://cran.r-project.org/src/base/R-4/</td></tr><tr><td>RNAlater</td><td>ThermoFisher</td><td>AM7021</td><td>wet lab materials</td></tr><tr><td>RNeasy kit</td><td>Qiagen</td><td>74104</td><td>wet lab materials</td></tr><tr><td>RSEM v. 1.3.0</td><td></td><td></td><td>Computer software&lt;br/&gt; https://deweylab.github.io/RSEM/</td></tr><tr><td>RStudio v. 1.2.1335</td><td></td><td></td><td>On local computer&lt;br/&gt; https://rstudio.com/products/rstudio/download/#download</td></tr><tr><td>Samtools v. 1.3</td><td></td><td></td><td>Computer software</td></tr><tr><td>SRA Toolkit v. 2.8.1</td><td></td><td></td><td>On computer cluster&lt;br/&gt; https://github.com/ncbi/sra-tools/wiki/01.-Downloading-SRA-Toolkit</td></tr><tr><td>STAR v. 2.6.0c</td><td></td><td></td><td>On computer cluster&lt;br/&gt; https://github.com/alexdobin/STAR</td></tr><tr><td>StringTie v. 1.3.4d</td><td></td><td></td><td>On computer cluster&lt;br/&gt; https://ccb.jhu.edu/software/stringtie/</td></tr><tr><td>Transdecoder v. 5.5.0</td><td></td><td></td><td>On computer cluster&lt;br/&gt; https://github.com/TransDecoder/TransDecoder/releases</td></tr><tr><td>Trimmomatic v. 0.35</td><td></td><td></td><td>On computer cluster&lt;br/&gt; http://www.usadellab.org/cms/?page=trimmomatic</td></tr><tr><td>Trinity v.2.8.5</td><td></td><td></td><td>On computer cluster&lt;br/&gt; https://github.com/trinityrnaseq/trinityrnaseq/releases</td></tr><tr><td>TRIzol</td><td>ThermoFisher</td><td>15596018</td><td>wet lab materials</td></tr><tr><td>TruSeq RNA Library Prep Kit v2</td><td>Illumina</td><td>RS-122-2001</td><td>wet lab materials</td></tr><tr><td>TURBO DNA-free Kit</td><td>ThermoFisher</td><td>AM1907</td><td>wet lab materials</td></tr><tr><td></td><td></td><td></td><td></td></tr><tr><td>*Downloads and installation on the computer cluster may require root access. Contact your network administrator.</td><td></td><td></td><td></td></tr></tbody></table>

a bioinformatics pipeline for investigating molecular evolution and gene expression using rna-seq

Distilling and reporting large datasets, such as whole genome or transcriptome data, is often a daunting task. One way to break down results is to focus on one or more gene families that are significant to the organism and study. In this protocol, we outline bioinformatic steps to generate a phylogeny and to quantify the expression of genes of interest. Phylogenetic trees can give insight into how genes are evolving within and between species as well as reveal orthology. These results can be enhanced using RNA-seq data to compare the expression of these genes in different individuals or tissues. Studies of molecular evolution and expression can reveal modes of evolution and conservation of gene function between species. The characterization of a gene family can serve as a springboard for future studies and can highlight an important gene family in a new genome or transcriptome paper.

The methods above are summarized in Figure 1 and were applied to a data set of Hydra vulgaris tissues. H. vulgaris is a fresh-water invertebrate that belongs to the phylum Cnidaria which also includes corals, jellyfish, and sea anemones. H. vulgaris can reproduce asexually by budding and they can regenerate their head and foot when bisected. In this study, we aimed to investigate the evolution and expression of opsin genes in Hydra<sup class="xref...

Watch this Scientific Journal Video about A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq at JoVE.com

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

The purpose of this protocol is to investigate the evolution and expression of candidate genes using RNA sequencing data.

Distilling and reporting large datasets, such as whole genome or transcriptome data, is often a daunting task. One way to break down results is to focus ...

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9.4K Views.  University of California, Irvine. Dit protocol schetst bio-informatische stappen voor het onderzoeken van de moleculaire evolutie en expressie van kandidaatgenen. Hier geven we grondige instructies zodat iedereen met minimale bio-informatische ervaring dit protocol kan doorlopen. Deze pijpleiding kan worden toegepast op elk organisme en elke genfamilie.Een veelvoorkomend probleem bij het uitvoeren van bioinformatica is dat shellscripts mislukken. Wanneer u dit protocol probeert, moet u ervoor zorgen dat u de meest rece...

Video: A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models

Drug resistance remains a major problem in the treatment of cancer for both hematological malignancies and solid tumors. Intrinsic or acquired resistance can be caused by a range of mechanisms, including increased drug elimination, decreased drug uptake, drug inactivation and alterations of drug targets. Recent data showed that other than by well-known genetic (mutation, amplification) and epigenetic (DNA hypermethylation, histone post-translational modification) modifications, drug resistance mechanisms might also be regulated by splicing aberrations. This is a rapidly growing field of investigation that deserves future attention in order to plan more effective therapeutic approaches. The protocol described in this paper is aimed at investigating the impact of aberrant splicing on drug resistance in solid tumors and hematological malignancies. To this goal, we analyzed the transcriptomic profiles of several in vitro models through RNA-seq and established a qRT-PCR based method to validate candidate genes. In particular, we evaluated the differential splicing of DDX5 and PKM transcripts. The aberrant splicing detected by the computational tool MATS was validated in leukemic cells, showing that different DDX5 splice variants are expressed in the parental vs. resistant cells. In these cells, we also observed a higher PKM2/PKM1 ratio, which was not detected in the Panc-1 gemcitabine-resistant counterpart compared to parental Panc-1 cells, suggesting a different mechanism of drug-resistance induced by gemcitabine exposure.

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models

Here we describe a protocol aimed at investigating the impact of aberrant splicing on drug resistance in solid tumors and hematological malignancies. To this goal, we analyzed the transcriptomic profiles of parental and resistant in vitro models through RNA-seq and established a qRT-PCR based method to validate candidate genes.

Drug resistance remains a major problem in the treatment of cancer for both hematological malignancies and solid tumors. Intrinsic or acquired resistance ...

Cancer Research

RNA Next-Generation Sequencing and a Bioinformatics Pipeline to Identify Expressed LINE-1s at the Locus-Specific Level

Long INterspersed Elements-1 (LINEs/L1s) are repetitive elements that can copy and randomly insert in the genome resulting in genomic instability and mutagenesis. Understanding the expression patterns of L1 loci at the individual level will lend to the understanding of the biology of this mutagenic element. This autonomous element makes up a significant portion of the human genome with over 500,000 copies, though 99% are truncated and defective. However, their abundance and dominant number of defective copies make it challenging to identify authentically expressed L1s from L1-related sequences expressed as part of other genes. It is also challenging to identify which specific L1 locus is expressed due to the repetitive nature of the elements. Overcoming these challenges, we present an RNA-Seq bioinformatic approach to identify L1 expression at the locus specific level. In summary, we collect cytoplasmic RNA, select for polyadenylated transcripts, and utilize strand-specific RNA-Seq analyses to uniquely map reads to L1 loci in the human reference genome. We visually curate each L1 locus with uniquely mapped reads to confirm transcription from its own promoter and adjust mapped transcript reads to account for mappability of each individual L1 locus. This approach was applied to a prostate tumor cell line, DU145, to demonstrate the ability of this protocol to detect expression from a small number of the full-length L1 elements.

Here, we present a bioinformatic approach and analyses to identify LINE-1 expression at the locus specific level.

Long INterspersed Elements-1 (LINEs/L1s) are repetitive elements that can copy and randomly insert in the genome resulting in genomic instability and ...

Genetics

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 ...

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 ...

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.

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 ...

Engineering

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 ...

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

RNA sequencing (RNAseq) is a versatile method that can be utilized to detect and characterize gene expression, mutations, gene fusions, and noncoding RNAs. Standard RNAseq requires 30 - 100 million sequencing reads and can include multiple RNA products such as mRNA and noncoding RNAs. We demonstrate how targeted RNAseq (capture) permits a focused study on selected RNA products using a desktop sequencer. RNAseq capture can characterize unannotated, low, or transiently expressed transcripts that may otherwise be missed using traditional RNAseq methods. Here we describe the extraction of RNA from cell lines, ribosomal RNA depletion, cDNA synthesis, preparation of barcoded libraries, hybridization and capture of targeted transcripts and multiplex sequencing on a desktop sequencer. We also outline the computational analysis pipeline, which includes quality control assessment, alignment, fusion detection, gene expression quantification and identification of single nucleotide variants. This assay allows for targeted transcript sequencing to characterize gene expression, gene fusions, and mutations.

We describe a targeted RNA sequencing-based method that includes preparation of indexed cDNA libraries, hybridization and capture with custom probes and data analysis to interrogate selected transcripts for gene expression, mutations, and gene fusions. Targeted RNAseq permits cost-effective, rapid evaluation of selected transcripts on a desktop sequencer.

RNA sequencing (RNAseq) is a versatile method that can be utilized to detect and characterize gene expression, mutations, gene fusions, and noncoding ...

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 ...

RNA-Seq

Among different methods to evaluate gene expression, the high-throughput sequencing of RNA, or RNA-seq. is particularly attractive, as it can be performed and analyzed without relying on prior available genomic information. During RNA-seq, RNA isolated from samples of interest is used to generate a DNA library, which is then amplified and sequenced. Ultimately, RNA-seq can determine which genes are expressed, the levels of their expression, and the presence of any previously unknown transcripts.Here, JoVE presents the basic principles behind RNA-seq. We then discuss the experimental and analytical steps of a general RNA-seq protocol. Finally, we examine how researchers are currently using RNA-seq, for example, to compare gene expression between different biological samples, or to characterize protein-RNA interactions.

Next-Gen Transcriptomics Using RNA-Seq

Among different methods to evaluate gene expression, the high-throughput sequencing of RNA, or RNA-seq. is particularly attractive, as it can be performed ...

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

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