The authors would like to express their gratitude to the laboratories, groups and individuals who have developed Galaxy and DAVID, and thus made NGS widely accessible for the scientific community. The help and advice provided by colleagues at the University of Pittsburgh during our bioinformatics training is acknowledged. This work was supported by an Ellison Medical Foundation New Scholar in Aging award (AG-NS-0879-12) and a grant from the National Institutes of Health (R01AG051659) to AG.

<ol>
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The authors have nothing to disclose.

Significance of the Galaxy Sequencing Platform in Modern Biology
The Galaxy Project has become instrumental in helping biologists without bioinformatics training to process and analyze high-throughput sequencing data in a fast and efficient manner. Once considered a herculean task, this publicly-available platform has made running complex bioinformatics algorithms to analyze NGS data a straightforward, reliable, and easy process. Apart from hosting a wide range of bioinformatics tools, the key to success for Galaxy is also the simplicity of its user interface that laces together the various aspects of complex sequencing analysis in an intuitive and seamless manner. Owing to these features, the Galaxy pipeline has acquired wide usage amongst biologists, including C. elegans researchers. In addition to familiarizing the user with the RNA-Seq Analysis pipeline, Galaxy also helps lay the foundation for basic biologists to grasp the concept of data analysis and understand the tools involved. This knowledge primes the user to perhaps further pursue more complex bioinformatics platforms such as &#39;R&#39; and &#39;Python&#39;. Besides Galaxy, other tools and packages are available commercially and as open-source solutions, which can be used for RNA-Seq analysis. The commercial options are often stand-alone software packages that are user-friendly but can be expensive for individual researchers who do not use NGS often. Alternatively, open source platforms such as BioWadrobe34 and ArrayExpressHTS35 require working knowledge of the command line and running scripts, which poses significant challenges for non-bioinformaticians. Hence, Galaxy remains a popular and indispensable resource.
Critical steps within the protocol
The effortless advantages of Galaxy and DAVID notwithstanding, a successful RNA-Seq experiment still relies fundamentally on careful design and execution of the experimental step. For instance, it is critical to ensure genetic homogeneity before comparing two strains by RNA-Seq, and to determine if there are differences in developmental rates. Isolating RNA from age-matched strains is critical as well. Similarly, to account for variability of gene expression within the same strain, it is important to run two or more &#39;biological replicates&#39; of each strain. This essentially means growing and harvesting worms from the strains being sequenced in at least twoindependent experiments, although three biological replicates is the recommended standard. Galaxy unifies the data from multiple biological replicates so that the reported gene-expression differences between strains are not simply a consequence of &#39;within-sample&#39; variability.
A critical design decision is about the use of single-end vs. paired-end sequencing. With single-end sequencing, each fragment is sequenced uni-directionally so the process is faster, cheaper and suited for transcriptional profiling. In paired-end sequencing, once the fragment is sequenced from one end to the other, a second round of sequencing is resumed in the opposite direction. It provides more in-depth data and additional positioning information of the genome, so is more suited for de novo genome assembly, new SNP identification and for identifying epigenetic modifications, deletions, insertions, and inversions. Similarly, the total number of reads and extent of genome coverage required for adequate differential expression studies is context dependent. For small genomes, such as bacteria and fungi, ~5 million reads is sufficient, whereas, in worms and flies ~10 million reads provide adequate coverage. For organisms with large genomes such as mice and humans, 15-25 million reads is the required range. In addition, to the read number and coverage, it is also important that most of the NGS reads align to the reference genome. An alignment of &#60;70% reads is indicative of poor NGS or the presence of contaminants. Overall, for C. elegans RNA-Seq studies, three biological replicates sequenced with 50 bp unidirectional sequencing resulting in ~10-15 million reads and ~5-10X genome coverage for each sample is an ideal aim.
Despite the ease of using Galaxy, there are a few points to remember in order to ensure a smooth and glitch-free data analysis experience. It is necessary for the user to have a basic understanding of the purpose and functioning of the various tools used. Each Galaxy tool requires selection of parameters and understanding the tool will help the user optimize settings based on the requirement of the experiment. The Galaxy help pages explain every parameter and it is recommended that the user peruse these details to decide on test variables.
The gene list obtained post RNA-Seq analysis is merely a list of genes until it is mined for biologically relevant data using DAVID. This is a crucial exercise that converts individual gene-based data into biological-process based results. Exploring the RNA-Seq gene list using the various analytics DAVID provides is therefore an integral and important part of the protocol.
Modifications, troubleshooting and limitations
A common glitch with NGS data analysis is tasks or tests that fail, especially at the quality-control stages. Of the tests that FastQC runs on a sample, a few could come up as failed. However, this does not inevitably mean the sample does not meet the fastq quality standards. The failure could have an alternative explanation that should be explored carefully.
For instance, if the &#39;Per base sequence content&#39; test fails (suggesting that there is a greater than 10% difference between bases in any position), check the method for the oligodt library preparation. Previous work has shown that Illumina NGS libraries may have a propensity for the 13th base being sequenced to have a bias for certain bases causing the sample to fail the test. Similarly, a failure of the &#39;Kmer content&#39; test can sometimes be attributed to the fact that libraries derived from random priming will nearly always show Kmer bias at the start due to an incomplete sampling of the random primers. Therefore, it is important to consider these and other impediments in the analysis pipeline before determining the fate of the experiment.
Another important feature that may impact RNA-Seq data analysis is the rapid and exponential advancements that are occurring in NGS methods and analytic software. Ideally, one expects an identical gene list to result from analyzing a sample NGS data on two pipelines or two versions of the same pipeline. However, while constantly improving algorithms are lowering aberrations in RNA-Seq analysis and producing gene lists of greater accuracy, this often leads to disparities. For instance, analyzing a sample NGS data using an older vs. newer version of the same toolset may produce significantly different gene lists. A modest variation is expected but users need to be aware that large discrepancies may be reflective of weaknesses in the design or performance of the experiment.
Collectively, the Galaxy Project and DAVID analytical tools have transformed the way NGS data can be harnessed to extract biologically relevant information. This has opened entirely new levels of independence and investigation to the scientific community, including C. elegans researchers. For instance, the constantly reducing cost of sequencing coupled with better and faster sequencing technology are ushering in an era of transcriptomics at the level of single worms, individual worm tissues and even few select worm cells. These endeavors involve dramatic increases in NGS data being generated. Keeping up with the analytical end of this workflow will be a challenge, but due to its versatility, Galaxy is likely to be instrumental in empowering the transition from whole organism transcriptomics to RNA-Seq at single cell level in C. elegans. The resulting advances in knowledge are likely to provide extraordinary insights into fundamental biology.

The first sequencing of the human genome, performed using Fred Sanger&#39;s dideoxynucleotide-sequencing method, took 10 years, and cost an estimated US $3 billion1,2. However, in a little over a decade since its inception, Next-Generation Sequencing (NGS) technology has made it possible to sequence the entire human genome within two weeks and for US $1,000. New NGS instruments that allow ever-increasing speeds of sequencing-data collection with incredible efficiency, along with sharp reductions in cost, are revolutionizing modern biology in unimaginable ways as genome sequencing projects are rapidly becoming commonplace. In addition, these developments have galvanized progress in many other areas such as gene-expression analysis through RNA-Sequencing (RNA-Seq), study of genome-wide epigenetic modifications, DNA-protein interactions, and screening for microbial diversity in human hosts. NGS-based RNA-Seq in particular has made it possible to identify and map transcriptomes comprehensively with accuracy and sensitivity, and has replaced microarray technology as the method of choice for expression profiling. While microarray technology has been used extensively, it is limited by its reliance on pre-existing arrays with known genomic information, and other drawbacks such as cross hybridization and restricted range of expression changes that can be measured reliably. RNA-seq, on the other hand, can be used to detect both known and unknown transcripts while producing low background noise due to its unambiguous DNA mapping nature. RNA-Seq, together with the numerous genetic tools offered by model organisms such as yeast, flies, worms, fish and mice, has served as the foundation for many important recent biomedical discoveries. However, significant challenges remain that make NGS inaccessible to the wider scientific community, including limitations of storage, processing, and most of all, meaningful bioinformatics analysis of large volumes of sequencing data.
The rapid advances in sequencing technologies and exponential data accumulation have created a great need for computational platforms that will allow researchers to access, analyze and understand this information. Early systems were heavily dependent upon computer programming knowledge, whereas, genome browsers such as NCBI that allowed non-programmers to access and visualize data did not permit sophisticated analyses. The web-based, open-access platform, Galaxy (<a href="https://galaxyproject.org/" target="_blank">https://galaxyproject.org/</a>), has filled this void and proven to be a valuable pipeline that enables researchers to process NGS data and perform a spectrum of simple-to-complex bioinformatics analyses. Galaxy was initially established, and is maintained, by the laboratories of Anton Nekrutenko (Penn State University) and James Taylor (Johns Hopkins University)3. Galaxy offers a wide range of computational tasks making it a &#39;one-stop shop&#39; for innumerable bioinformatics needs, including all the steps involved in an RNA-Seq study. Itallows users to perform data processing either on its servers or locally on their own machines. Data and workflows can be reproduced and shared. Online tutorials, help section, and a wiki-page (<a href="https://wiki.galaxyproject.org/Support" target="_blank">https://wiki.galaxyproject.org/Support</a>) dedicated to the Galaxy Project provide consistent support. However, for first-time users, especially those with no bioinformatics training, the pipeline can appear daunting and the process of self-learning and familiarization can be time consuming. In addition, the biological system studied, and specifics of the experiment and methods used, impact the analytical decisions at several steps, and these can be difficult to navigate without instruction.
The Overall RNA-Seq Galaxy Workflow consists of data upload and quality check followed by analysis using the Tuxedo Suite4,5,6,7,8,9, which is a collective of various tools required for different stages of RNA-Seq data analysis10,11,12,13,14. A typical RNA-Seq experiment consists of the experimental part (sample preparation, mRNA isolation and cDNA library preparation), the NGS and the bioinformatics data analysis. An overview of these sections, and the steps involved in the Galaxy pipeline, are shown in Figure 1.
<img alt="Figure 1" src="/files/ftp_upload/55473/55473fig1.jpg" /> 
Figure 1: Overview of an RNA-Seq Workflow. Illustration of the experimental and computational steps involved in an RNA-Seq experiment to compare the gene-expression profiles of two worm strains (A and B, orange and green lines and arrows, respectively). The different modules of Galaxy utilized are shown in boxes with the corresponding step in our protocol indicated in red. The outputs of various operations are written in grey with the file formats shown in blue. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55473/55473fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
The first tool in the Tuxedo Suite is an alignment program called &#39;Tophat&#39;. It breaks down the NGS input reads into smaller fragments and then maps them to a reference genome. This two-step process ensures that reads spanning intronic regions whose alignment can otherwise be disrupted or missed are accounted for and mapped. This increases coverage and facilitates the identification of novel splice junctions. Tophat output is reported as two files, a BED file (with information about splice junctions that include genomic location) and a BAM file (with mapping details of each read). Next, the BAM file is aligned against a reference genome to estimate the abundance of individual transcripts within each sample using the subsequent tool in the Tuxedo Suite called &#39;Cufflinks&#39;. Cufflinks functions by scanning the alignment to report full-length transcript fragments or &#39;transfrags&#39; that span all the possible splice variants in the input data for every gene. Based on this, it generates a &#39;transcriptome&#39; (assembly of all the transcripts generated per gene for every gene) for each sample being sequenced. These Cufflinks assemblies are then collapsed or merged together along with the reference genome to produce a single annotation file for downstream differential analysis using the next tool, &#39;Cuffmerge&#39;. Finally, the &#39;Cuffdiff&#39; tool measures differential gene expression between samples by comparing the TopHat outputs of each of the samples to the final Cuffmerge output file (Figure 1). Cufflinks uses FPKM/RPKM (Fragments/Reads Per Kilobase of transcript per Million mapped reads) values to report transcript abundances. These values reflect the normalization of the raw NGS data for depth (average number of reads from a sample that align to the reference genome) and gene length (genes have different lengths, so counts have to be normalized for length of a gene to compare levels between genes). FPKM and RPKM are essentially the same with RPKM being used for single-end RNA-Seq where every read corresponds to a single fragment, whereas, FPKM is used for paired-end RNA-Seq, as it accounts for the fact that two reads can correspond to the same fragment. Ultimately, the outcome of these analyses is a list of genes differentially expressed between the conditions and/or strains tested.
Once a successful Galaxy run is completed and a &#39;gene list&#39; is generated, the next logical step requires more bioinformatics analyses to deduce meaningful knowledge from the datasets. Many software packages have emerged to cater to this need, including publicly-available web-based computational packages such as DAVID (the Database for Annotation, Visualization and Integrated discovery)15. DAVID facilitates assigning biological meaning to large gene lists from high-throughput studies by comparing the uploaded gene list to its integrated biological knowledgebase and revealing the biological annotations associated with the gene list. This is followed by Enrichment Analysis, i.e., tests to identify if any biological process or gene class is overrepresented in the gene list(s) in a statistically significant manner. It has become a popular choice because of a combination of a wide, integrated knowledge-base and powerful analytical algorithms that enable researchers to detect biological themes enriched within genomics-derived &#39;gene lists&#39;10,16. Additional advantages include its ability to process gene lists created on any sequencing platform and a highly user-friendly interface.
The nematode Caenorhabditis elegans is a genetic model system, well known for its many advantages such as small size, transparent body, simple body plan, ease of culture and great amenability to genetic and molecular dissection. Worms have a small, simple and well-annotated genome that includes up to 40% conserved genes with known human homologs17. Indeed, C. elegans was the first metazoan whose genome was completely sequenced18, and one of the first species where RNA-Seq was used to map an organism&#39;s transcriptome19,20. Early worm studies involved experimentation with different methods for high-throughput RNA capture, library preparation and sequencing as well as bioinformatics pipelines that contributed to the advancement of the technology21,22. In recent years, RNA-Seq-based experimentation in worms has become commonplace. But, for traditional worm biologists the challenges posed by computational analysis of RNA-Seq data remain a major impediment for greater and better utilization of the technique.
In this article, we describe a protocol for using the Galaxy platform to analyze high-throughput RNA-Seq data generated from C. elegans. For many first-time and small-scale users, the most cost-efficient and straightforward way to undertake an RNA-Seq experiment is to isolate RNA in the lab and utilize a commercial (or in-house) NGS facility for preparation of sequencing cDNA libraries and the NGS itself. Hence, we have first detailed the steps involved in isolation, quantification and quality assessment of C. elegans RNA samples for RNA-Seq. Next, we provide step-by-step instructions for using the Galaxy interface for analyses of the NGS data, beginning with tests for post-sequencing quality-control checks followed by alignment, assembly, and differential quantification of gene expression. In addition, we have included directions to scrutinize the gene lists resulting from Galaxy for biological enrichment studies using DAVID. As a final step in the workflow, we provide instructions for uploading RNA-Seq data on to public servers such as the Sequence Read Archive (SRA) on NCBI (<a href="http://www.ncbi.nlm.nih.gov/sra" target="_blank">http://www.ncbi.nlm.nih.gov/sra</a>) to make it freely accessible to the scientific community. Overall, we anticipate that this article will provide comprehensive and sufficient information to worm biologists undertaking RNA-Seq experiments for the first time as well as frequent users running a small number of samples.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>RNase spray&#160;</td>
			<td>Fisher Scientific</td>
			<td>21-402-178</td>
		</tr>
		<tr>
			<td>Trizol&#160;</td>
			<td>Ambion</td>
			<td>15596026</td>
		</tr>
		<tr>
			<td>Sonicator</td>
			<td>Sonics Vibra Cell&#160;</td>
			<td>VCX130</td>
		</tr>
		<tr>
			<td>Centrifuge&#160;</td>
			<td>Eppendorf</td>
			<td>5415C</td>
		</tr>
		<tr>
			<td>chloroform&#160;</td>
			<td>Sigma Aldrich</td>
			<td>288306</td>
		</tr>
		<tr>
			<td>2-propanol&#160;</td>
			<td>Fisher Scientific</td>
			<td>A416P-4</td>
		</tr>
		<tr>
			<td>Ethanol</td>
			<td>Decon Labs</td>
			<td>2705HC</td>
		</tr>
		<tr>
			<td>RNase-free water&#160;</td>
			<td>Fisher Scientific</td>
			<td>BP561-1</td>
		</tr>
		<tr>
			<td>Bioanalyzer&#160;</td>
			<td>Agilent</td>
			<td>G2940CA</td>
		</tr>
		<tr>
			<td>Mac/PC</td>
			<td></td>
			<td></td>
		</tr>
	</tbody>
</table>

transcriptomic analysis of c. elegans rna sequencing data through the tuxedo suite on the galaxy project

Next generation sequencing (NGS) technologies have revolutionized the nature of biological investigation. Of these, RNA Sequencing (RNA-Seq) has emerged as a powerful tool for gene-expression analysis and transcriptome mapping. However, handling RNA-Seq datasets requires sophisticated computational expertise and poses inherent challenges for biology researchers. This bottleneck has been mitigated by the open access Galaxy project that allows users without bioinformatics skills to analyze RNA-Seq data, and the Database for Annotation, Visualization, and Integrated Discovery (DAVID), a Gene Ontology (GO) term analysis suite that helps derive biological meaning from large data sets. However, for first-time users and bioinformatics' amateurs, self-learning and familiarization with these platforms can be time-consuming and daunting. We describe a straightforward workflow that will help C. elegans researchers to isolate worm RNA, conduct an RNA-Seq experiment and analyze the data using Galaxy and DAVID platforms. This protocol provides stepwise instructions for using the various Galaxy modules for accessing raw NGS data, quality-control checks, alignment, and differential gene expression analysis, guiding the user with parameters at every step to generate a gene list that can be screened for enrichment of gene classes or biological processes using DAVID. Overall, we anticipate that this article will provide information to C. elegans researchers undertaking RNA-Seq experiments for the first time as well as frequent users running a small number of samples.

In C. elegans, elimination of the germline stem cells (GSCs) extends lifespan, enhances stress resilience, and elevates body fat24,28. Loss of GSCs, either brought about by laser-ablation or by mutations such as glp-1, causes lifespan extension through activation of a network of transcription factors29. One such factor, TCER-1, encodes the worm homolog of the human transcription elongation and splicing factor, TCERG130. The following representative results illustrate how RNA-Seq was used to identify genes whose expression is modulated by TCER-1/TCERG1 following germline loss in our recently published study31. The transcriptomes of age-matched, day 2 adults of glp-1 and tcer-1;glp-1 mutants were compared. For each strain, mRNA was isolated from two biological replicates (four samples totally) using the protocol described in section 1. RNA samples were shipped to a commercial service provider that prepared cDNA libraries from the four samples and performed 50 bp single end sequencing. The raw NGS data was downloaded as described in section 2.1.
Post sequencing data evaluation
Table 1 is a compilation of test results to assess the quality of raw sequencing reads. &#39;FASTQ&#39; quality check analysis highlights the number of sequences read with no &#39;poor quality&#39; reads along with 48-49% GC content and a constant sequence read length of 51 bp. This step also checks the sequencing data for many other features such as Kmer content and is collectively made up of 11 tests in total. The C. elegans genome is ~100 Mbp. Based on the number of sequencing reads from each sample that mapped to the genome, the genome coverage (last column) was estimated using the Lander/Waterman equation &#39;C = LN/G&#39;, wherein, C stands for coverage, G is the haploid genome length, L is the read length and N is the number of reads. We used default parameters for all the steps and obtained 48 - 49% GC content in all the samples. As can be seen, genome coverage was between 9x to 11x in the samples.
Identification of TCER-1/TCERG-1-regulated Genes by Differential Gene Expression Analysis on Galaxy
Through the steps detailed in sections 2.2 to 2.4, the Galaxy pipeline3 was used to obtain a list of genes differentially expressed between glp-1 and tcer-1;glp-1 mutants. Galaxy enabled us to combine the NGS data from the two replicates for each strain and performed differential analysis to generate tabular files highlighting the genome wide expression profile. Using a threshold of at least one-fold change in magnitude and P value of at least 0.05, a list of 835 genes that were differentially expressed between the two strains was generated31. The list was divided based on whether expression of the genes was down-regulated in tcer-1;glp-1 mutants (359 UP genes whose transcription is likely enhanced by TCER-1/TCERG1) or up-regulated (476 DOWN genes whose transcription is likely repressed by TCER-1/TCERG1) as compared to glp-1 (Figure 4).
<img alt="Figure 4" src="/files/ftp_upload/55473/55473fig4.jpg" /> 
Figure 4: Identification of TCER-1/TCERG1-regulated Genes in Germline-less C. elegans Mutants using RNA-Seq: Results of Galaxy (A) and DAVID (B) Analyses. (A) Differential gene expression analysis of RNA-Seq data comparing the transcriptomes of glp-1 and tcer-1;glp-1 yielded a total of 835 genes, of which 359 were identified as being up-regulated by TCER-1/TCERG1 (UP) and 476 as down-regulated by TCER-1/TCERG1 (DOWN). (B) Results of &#39;Functional Annotation Clustering&#39; analysis of genes identified as TCER-1/TCERG1 targets using DAVID. Percentage enrichment of Biological Processes for both the Up-regulated (UP) and Down-regulated (DOWN) Classes of TCER-1/TCERG1 targets. The graphic shown here is obtained by plotting the enriched gene groups (X-axis) and their respective percent enrichment (Y-axis) obtained as the output of DAVID analysis. Figure modified from Amrit et al.31 and reproduced with permission. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55473/55473fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Gene Ontology Enrichment Analysis
To obtain an overview of the gene classes enriched in TCER-1/TCERG1 targets, we carried out gene ontology (GO) term analysis using DAVID. The TCER-1/TCERG1-regulated UP and DOWN gene lists were uploaded independently onto DAVID and analyzed as described in section 3. Little was known about the genes and cellular processes targeted by TCER-1/TCERG1 previously30, so we found the DAVID analysis to be especially revealing and helpful. Functional Annotation analysis of the UP genes revealed five Annotation Clusters with an Enrichment Score of &#62;1.3, the highest including Cytochrome P450 enzyme-encoding genes and xenobiotic response genes, followed by genes implicated in lipid modifications. This was reinforced by the results of the Gene Functional Classification analysis that identified groups attributed with similar molecular activities with significant enrichment scores. Using spreadsheet, the identified groups were plotted against their respective enrichment scores (Figure 4). Our previous data suggested that TCER-1/TCERG1 functioned with the conserved longevity transcription factor, DAF-16/FOXO3A, to promote the longevity of GSC-less adults30. DAF-16/FOXO3A, in turn, has been implicated in modulating lipid metabolism in recent studies27,32,33. Based on this evidence, and the identification of lipid-metabolic genes and pathways as potential TCER-1/TCERG1 targets in the DAVID analyses, we focused on the fat metabolism genes identified in the RNA-Seq study for detailed mechanistic studies. Following this lead, and through subsequent molecular genetic, biochemical, and functional experimentation, we demonstrated that TCER-1/TCERG1 along with DAF-16/FOXO3A coordinately enhanced both lipid catabolic and anabolic processes in response to germline loss31. Similarly, Functional Annotation Clustering of the DOWN TCER-1/TCERG1 targets identified Annotation Clusters enriched for cytoskeletal functions, positive regulation of growth, reproduction and aging (Figure 4). These observations, and our supporting experimental evidences, suggest that upon germline loss, TCER-1/TCERG1 also represses growth and reproductive physiology in somatic cells as well as the expression of anti-longevity genes31.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Sample</td>
			<td>Total Sequences</td>
			<td>Length</td>
			<td>%GC</td>
			<td>Total Reads (Galaxy)</td>
			<td>Mapped Reads (Galaxy)</td>
			<td>Genome Coverage</td>
		</tr>
		<tr>
			<td>glp-1</td>
			<td>4000000</td>
			<td>51</td>
			<td>49</td>
			<td>20700539</td>
			<td>~16,000,000</td>
			<td>11x</td>
		</tr>
		<tr>
			<td>glp-1; tcer-1</td>
			<td>4000000</td>
			<td>51</td>
			<td>49</td>
			<td>18055444</td>
			<td>~13,000,000</td>
			<td>9x</td>
		</tr>
		<tr>
			<td>glp-1</td>
			<td>4000000</td>
			<td>51</td>
			<td>48</td>
			<td>18947463</td>
			<td>~14,000,000</td>
			<td>10x</td>
		</tr>
		<tr>
			<td>glp-1; tcer-1</td>
			<td>4000000</td>
			<td>51</td>
			<td>48</td>
			<td>13829643</td>
			<td>~10,000,000</td>
			<td>7x</td>
		</tr>
	</tbody>
</table>
Table 1: RNA-Seq Sample Details. Compilation of raw data attributes evaluated post-sequencing to confirm the success of the sequencing run. Sequencing data from the representative experiment consists of two biological conditions, a control strain (glp-1) and a mutant strain (tcer-1;glp-1) with two biological replicates sequenced for each. &#39;FastQC&#39; quality check analysis highlights the number of sequences read with no &#34;poor quality&#34; reads, 48 - 49% GC content and a constant sequence read length of 51bp. Modified and reproduced with permission from Amrit et al.31.
Supplemental File: Command chain in brief for the tools run on the Galaxy pipeline for RNA-Seq data analysis. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55473/Amrit_Ghazi_JoVE_Suppl.docx">Please click here to download this file.</a>

Watch this Scientific Journal Video about Transcriptomic Analysis of C. elegans RNA Sequencing Data Through the Tuxedo Suite on the Galaxy Project at JoVE.com

Transcriptomic Analysis of C. elegans RNA Sequencing Data Through the Tuxedo Suite on the Galaxy Project

Galaxy and DAVID have emerged as popular tools that allow investigators without bioinformatics training to analyze and interpret RNA-Seq data. We describe a protocol for C. elegans researchers to perform RNA-Seq experiments, access and process the dataset using Galaxy and obtain meaningful biological information from the gene lists using DAVID.

Transcriptomic Analysis of C. elegans RNA Sequencing Data Through the Tuxedo Suite on the Galaxy Project

Next generation sequencing (NGS) technologies have revolutionized the nature of biological investigation. Of these, RNA Sequencing (RNA-Seq) has emerged ...

transcriptomic-analysis-c-elegans-rna-sequencing-data-through-tuxedo

Nanyang Technological University

Research

JoVE Journal

Genetics

17.2K Views.  University of Pittsburgh School of Medicine, Children's Hospital of Pittsburgh. Galaxy and DAVID have emerged as popular tools that allow investigators without bioinformatics training to analyze and interpret RNA-Seq data. We describe a protocol for C. elegans researchers to perform RNA-Seq experiments, access and process the dataset using Galaxy and obtain meaningful biological information from the gene lists using DAVID.

Video: Transcriptomic Analysis of C. elegans RNA Sequencing Data Through the Tuxedo Suite on the Galaxy Project

Conjugative Mating Assays for Sequence-specific Analysis of Transfer Proteins Involved in Bacterial Conjugation

The transfer of genetic material by bacterial conjugation is a process that takes place via complexes formed by specific transfer proteins. In Escherichia coli, these transfer proteins make up a DNA transfer machinery known as the mating pair formation, or DNA transfer complex, which facilitates conjugative plasmid transfer. The objective of this paper is to provide a method that can be used to determine the role of a specific transfer protein that is involved in conjugation using a series of deletions and/or point mutations in combination with mating assays. The target gene is knocked out on the conjugative plasmid and is then provided in trans through the use of a small recovery plasmid harboring the target gene. Mutations affecting the target gene on the recovery plasmid can reveal information about functional aspects of the target protein that result in the alteration of mating efficiency of donor cells harboring the mutated gene. Alterations in mating efficiency provide insight into the role and importance of the particular transfer protein, or a region therein, in facilitating conjugative DNA transfer. Coupling this mating assay with detailed three-dimensional structural studies will provide a comprehensive understanding of the function of the conjugative transfer protein as well as provide a means for identifying and characterizing regions of protein-protein interaction.

Here, we present a protocol to knockout a gene of interest involved in plasmid conjugation and subsequently analyze the impact of its absence using mating assays. The function of the gene is further explored to a specific region of its sequence using deletion or point mutations.

The transfer of genetic material by bacterial conjugation is a process that takes place via complexes formed by specific transfer proteins. In Escherichia ...

Detection of Copy Number Alterations Using Single Cell Sequencing

Detection of genomic changes at single cell resolution is important for characterizing genetic heterogeneity and evolution in normal tissues, cancers, and microbial populations. Traditional methods for assessing genetic heterogeneity have been limited by low resolution, low sensitivity, and/or low specificity. Single cell sequencing has emerged as a powerful tool for detecting genetic heterogeneity with high resolution, high sensitivity and, when appropriately analyzed, high specificity. Here we provide a protocol for the isolation, whole genome amplification, sequencing, and analysis of single cells. Our approach allows for the reliable identification of megabase-scale copy number variants in single cells. However, aspects of this protocol can also be applied to investigate other types of genetic alterations in single cells.

Single cell sequencing is an increasingly popular and accessible tool for addressing genomic changes at high resolution. We provide a protocol that uses single cell sequencing to identify copy number alterations in single cells.

Detection of genomic changes at single cell resolution is important for characterizing genetic heterogeneity and evolution in normal tissues, cancers, and ...

Controllable Ion Channel Expression through Inducible Transient Transfection

Transfection, the delivery of foreign nucleic acids into a cell, is a powerful tool in protein research. Through this method, ion channels can be investigated through electrophysiological analysis, biochemical characterization, mutational studies, and their effects on cellular processes. Transient transfections offer a simple protocol in which the protein becomes available for analysis within a few hours to days. Although this method presents a relatively straightforward and time efficient protocol, one of the critical components is calibrating the expression of the gene of interest to physiological relevant levels or levels that are suitable for analysis. To this end, many different approaches that offer the ability to control the expression of the gene of interest have emerged. Several stable cell transfection protocols provide a way to permanently introduce a gene of interest into the cellular genome under the regulation of a tetracycline-controlled transcriptional activation. While this technique produces reliable expression levels, each gene of interest requires a few weeks of skilled work including calibration of a killing curve, selection of cell colonies, and overall more resources. Here we present a protocol that uses transient transfection of the Transient Receptor Potential cation channel subfamily V member 1 (TRPV1) gene in an inducible system as an efficient way to express a protein in a controlled manner which is essential in ion channel analysis. We demonstrate that using this technique, we are able to perform calcium imaging, whole cell, and single channel analysis with controlled channel levels required for each type of data collection with a single transfection. Overall, this provides a replicable technique that can be used to study ion channels structure and function.

Studying ion channels through a heterologously expressing system has become a core technique in biomedical research. In this manuscript, we present a time efficient method to achieve tightly controlled ion channel expression by performing transient transfection under the control of an inducible promoter.

Transfection, the delivery of foreign nucleic acids into a cell, is a powerful tool in protein research. Through this method, ion channels can be ...

Analysis of the c-KIT Ligand Promoter Using Chromatin Immunoprecipitation

Multiple cellular processes, including DNA replication and repair, DNA recombination, and gene expression, require interactions between proteins and DNA. Therefore, DNA-protein interactions regulate multiple physiological, pathophysiological, and biological functions, such as cell differentiation, cell proliferation, cell cycle control, chromosome stability, epigenetic gene regulation, and cell transformation. In eukaryotic cells, the DNA interacts with histone and nonhistone proteins and is condensed into chromatin. Several technical tools can be used to analyze DNA-protein interactions, such as the Electrophoresis (gel) Mobility Shift Assay (EMSA) and DNase I footprinting. However, these techniques analyze the protein-DNA interaction in vitro, not within the cellular context. Chromatin immunoprecipitation (ChIP) is a technique that captures proteins at their specific DNA binding sites, thereby allowing for the identification of DNA-protein interactions within their chromatin context. It is done by fixation&#160;of the DNA-protein interaction, followed by&#160;immunoprecipitation of the protein of interest. Subsequently, the genomic site that the protein was bound to is characterized. Here, we describe and discuss ChIP and demonstrate its analytical value for the identification of the Transforming Growth Factor-&#946; (TGF-&#946;)-induced binding of the transcription factor SMAD2 to SMAD Binding Elements (SBE) within the promoter region of the tyrosine-protein kinase Kit (c-KIT) receptor ligand Stem Cell Factor (SCF).

DNA-protein interactions are essential for multiple biological processes. During the evaluation of cellular functions, the analysis of DNA-protein interactions is indispensable for understanding gene regulation. Chromatin immunoprecipitation (ChIP) is a powerful tool to analyze such interactions in vivo.

Multiple cellular processes, including DNA replication and repair, DNA recombination, and gene expression, require interactions between proteins and DNA. ...

Manipulation of Ploidy in Caenorhabditis elegans

Mechanisms that involve whole genome polyploidy play important roles in development and evolution; also, an abnormal generation of tetraploid cells has been associated with both the progression of cancer and the development of drug resistance. Until now, it has not been feasible to easily manipulate the ploidy of a multicellular animal without generating mostly sterile progeny. Presented here is a simple and rapid protocol for generating tetraploid Caenorhabditis elegans animals from any diploid strain. This method allows the user to create a bias in chromosome segregation during meiosis, ultimately increasing ploidy in C. elegans. This strategy relies on the transient reduction of expression of the rec-8 gene to generate diploid gametes. A rec-8 mutant produces diploid gametes that can potentially produce tetraploids upon fertilization. This tractable scheme has been used to generate tetraploid strains carrying mutations and chromosome rearrangements to gain insight into chromosomal dynamics and interactions during pairing and synapsis in meiosis. This method is efficient for generating stable tetraploid strains without genetic markers, can be applied to any diploid strain, and can be used to derive triploid C. elegans. This straightforward method is useful for investigating other fundamental biological questions relevant to genome instability, gene dosage, biological scaling, extracellular signaling, adaptation to stress, development of resistance to drugs, and mechanisms of speciation.

Manipulation of Ploidy in Caenorhabditis elegans

This method allows for the generation of tetraploid and triploid Caenorhabditis nematodes from any diploid strain. Polyploid strains generated by this method have been used to study chromosome interactions in meiotic prophase, and this method is useful for examining important basic questions in cell, developmental, evolutionary, and cancer biology.

Mechanisms that involve whole genome polyploidy play important roles in development and evolution; also, an abnormal generation of tetraploid cells has ...

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

We have adapted the yeast 2-hybrid assay to simultaneously uncover dozens of transient and static protein interactions within a single screen utilizing high-throughput short-read DNA sequencing. The resulting sequence datasets can not only track what genes in a population that are enriched during selection for positive yeast 2-hybrid interactions, but also give detailed information about the relevant subdomains of proteins sufficient for interaction. Here, we describe a full suite of stand-alone software programs that allow non-experts to perform all the bioinformatics and statistical steps to process and analyze DNA sequence fastq files from a batch yeast 2-hybrid assay. The processing steps covered by these software include: 1) mapping and counting sequence reads corresponding to each candidate protein encoded within a yeast 2-hybrid prey library; 2) a statistical analysis program that evaluates the enrichment profiles; and 3) tools to examine the translational frame and position within the coding region of each enriched plasmid that encodes the interacting proteins of interest.

Deep sequencing of yeast populations selected for positive yeast 2-hybrid interactions potentially yields a wealth of information about interacting partner proteins. Here, we describe the operation of specific bioinformatics tools and customized updated software to analyze sequence data from such screens.

We have adapted the yeast 2-hybrid assay to simultaneously uncover dozens of transient and static protein interactions within a single screen utilizing ...

Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Pancreatic islets comprise of endocrine cells with distinctive hormone expression patterns. The endocrine cells show functional differences in response to normal and pathological conditions. The goal of this protocol is to generate high-quality, large-scale transcriptome data of each endocrine cell type with the use of a droplet-based microfluidic single-cell RNA sequencing technology. Such data can be utilized to build the gene expression profile of each endocrine cell type in normal or specific conditions. The process requires careful handling, accurate measurement, and rigorous quality control. In this protocol, we describe detailed steps for human pancreatic islets dissociation, sequencing, and data analysis. The representative results of about 20,000 human single islet cells demonstrate the successful application of the protocol.

Here, we present a protocol to generate high-quality, large-scale transcriptome data of single cells from isolated human pancreatic islets using a droplet-based microfluidic single-cell RNA sequencing technology.

Pancreatic islets comprise of endocrine cells with distinctive hormone expression patterns. The endocrine cells show functional differences in response to ...

Identification of Footprints of RNA:Protein Complexes via RNA Immunoprecipitation in Tandem Followed by Sequencing (RIPiT-Seq)

RNA immunoprecipitation in tandem (RIPiT) is a method for enriching RNA footprints of a pair of proteins within an RNA:protein (RNP) complex. RIPiT employs two purification steps. First, immunoprecipitation of a tagged RNP subunit is followed by mild RNase digestion and subsequent non-denaturing affinity elution. A second immunoprecipitation of another RNP subunit allows for enrichment of a defined complex. Following a denaturing elution of RNAs and proteins, the RNA footprints are converted into high-throughput DNA sequencing libraries. Unlike the more popular ultraviolet (UV) crosslinking followed by immunoprecipitation (CLIP) approach to enrich RBP binding sites, RIPiT is UV-crosslinking independent. Hence RIPiT can be applied to numerous proteins present in the RNA interactome and beyond that are essential to RNA regulation but do not directly contact the RNA or UV-crosslink poorly to RNA. The two purification steps in RIPiT provide an additional advantage of identifying binding sites where a protein of interest acts in partnership with another cofactor. The double purification strategy also serves to enhance signal by limiting background. Here, we provide a step-wise procedure to perform RIPiT and to generate high-throughput sequencing libraries from isolated RNA footprints. We also outline RIPiT&#39;s advantages and applications and discuss some of its limitations.

Identification of Footprints of RNA:Protein Complexes via RNA Immunoprecipitation in Tandem Followed by Sequencing RIPiT-Seq

Here, we present a protocol to enrich endogenous RNA binding sites or &#34;footprints&#34;&#160;of RNA:protein (RNP) complexes from mammalian cells. This approach involves two immunoprecipitations of RNP subunits and is therefore dubbed RNA immunoprecipitation in tandem (RIPiT).

RNA immunoprecipitation in tandem (RIPiT) is a method for enriching RNA footprints of a pair of proteins within an RNA:protein (RNP) complex. RIPiT ...

Identification of Circular RNAs using RNA Sequencing

Circular RNAs (circRNAs) are a class of non-coding RNAs involved in functions including micro-RNA (miRNA) regulation, mediation of protein-protein interactions, and regulation of parental gene transcription. In classical next generation RNA sequencing (RNA-seq), circRNAs are typically overlooked as a result of poly-A selection during construction of mRNA libraries, or are found at very low abundance, and are therefore difficult to isolate and detect. Here, a circRNA library construction protocol was optimized by comparing library preparation kits, pre-treatment options and various total RNA input amounts. Two commercially available whole transcriptome library preparation kits, with and without RNase R pre-treatment, and using variable amounts of total RNA input (1 to 4 &#181;g), were tested. Lastly, multiple tissue types; including liver, lung, lymph node, and pancreas; as well as multiple brain regions; including the cerebellum, inferior parietal lobe, middle temporal gyrus, occipital cortex, and superior frontal gyrus; were compared to evaluate circRNA abundance across tissue types. Analysis of the generated RNA-seq data using six different circRNA detection tools (find_circ, CIRI, Mapsplice, KNIFE, DCC, and CIRCexplorer) revealed that a stranded total RNA library preparation kit with RNase R pre-treatment and 4 &#181;g RNA input is the optimal method for identifying the highest relative number of circRNAs. Consistent with previous findings, the highest enrichment of circRNAs was observed in brain tissues compared to other tissue types.

Circular RNAs (circRNAs) are non-coding RNAs that may have roles in transcriptional regulation and mediating interactions between proteins. Following assessment of different parameters for construction of circRNA sequencing libraries, a protocol was compiled utilizing stranded total RNA library preparation with RNase R pre-treatment and is presented here.

Circular RNAs (circRNAs) are a class of non-coding RNAs involved in functions including micro-RNA (miRNA) regulation, mediation of protein-protein ...

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 ...