Name: transcriptomic analysis of c. elegans rna sequencing data through the tuxedo suite on the galaxy project
Uploaded: 2017-04-08T13:00:00
Description: 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

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.

1. RNA Isolation
<ol>
	<li>Precautionary measures
	<ol>
		<li>Wipe down the entire working surface, instruments and pipettes using a commercially-available RNase spray to eliminate any RNases present.</li>
		<li>Wear gloves at all times, regularly changing them with fresh ones during the different steps of the protocol.</li>
		<li>Use only filter tips and keep all samples on ice as much as possible to avoid RNA degradation. 
		NOTE: In order to obtain the best data from NGS platforms, it is critic...

<ol>
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S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genome+sequence+of+the+nematode+C.+elegans:+a+platform+for+investigating+biology.">Genome sequence of the nematode C. elegans: a platform for investigating biology.</a> Science. 282 (5396), 2012-2018 (1998).</li><li>Agarwal, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comparison+and+calibration+of+transcriptome+data+from+RNA-Seq+and+tiling+arrays.">Comparison and calibration of transcriptome data from RNA-Seq and tiling arrays.</a> BMC Genomics. 11, 383 (2010).</li><li>Mortazavi, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Scaffolding+a+Caenorhabditis+nematode+genome+with+RNA-seq.">Scaffolding a Caenorhabditis nematode genome with RNA-seq.</a> Genome Res. 20 (12), 1740-1747 (2010).</li><li>Bohnert, R., Ratsch, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=rQuant.web:+a+tool+for+RNA-Seq-based+transcript+quantitation.">rQuant.web: a tool for RNA-Seq-based transcript quantitation.</a> Nucleic Acids Res. 38, W348-W351 (2010).</li><li>Lamm, A. T., Stadler, M. R., Zhang, H., Gent, J. I., Fire, A. Z. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multimodal+RNA-seq+using+single-strand,+double-strand,+and+CircLigase-based+capture+yields+a+refined+and+extended+description+of+the+C.+elegans+transcriptome.">Multimodal RNA-seq using single-strand, double-strand, and CircLigase-based capture yields a refined and extended description of the C. elegans transcriptome.</a> Genome Res. 21 (2), 265-275 (2011).</li><li>Amrit, F. R., Ratnappan, R., Keith, S. A., Ghazi, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+C.+elegans+lifespan+assay+toolkit.">The C. elegans lifespan assay toolkit.</a> Methods. 68 (3), 465-475 (2014).</li><li>Hsin, H., Kenyon, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Signals+from+the+reproductive+system+regulate+the+lifespan+of+C.+elegans.">Signals from the reproductive system regulate the lifespan of C. elegans.</a> Nature. 399 (6734), 362-366 (1999).</li><li>Alper, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Caenorhabditis+elegans+germ+line+regulates+distinct+signaling+pathways+to+control+lifespan+and+innate+immunity.">The Caenorhabditis elegans germ line regulates distinct signaling pathways to control lifespan and innate immunity.</a> J Biol Chem. 285 (3), 1822-1828 (2010).</li><li>Steinbaugh, M. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Lipid-mediated+regulation+of+SKN-1/Nrf+in+response+to+germ+cell+absence.">Lipid-mediated regulation of SKN-1/Nrf in response to germ cell absence.</a> Elife. 4, (2015).</li><li>Lapierre, L. R., Gelino, S., Melendez, A., Hansen, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Autophagy+and+lipid+metabolism+coordinately+modulate+life+span+in+germline-less.">Autophagy and lipid metabolism coordinately modulate life span in germline-less.</a> C. elegans. Curr Biol. 21 (18), 1507-1514 (2011).</li><li>Rourke, E. J., Soukas, A. A., Carr, C. 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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...

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="http://cloudflare.jove.com/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.

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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...

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 ...

The overall goal of this protocol is to help C.elegans researchers without bioinformatics expertise to conduct an RNA sequencing experiment and analyze the data using the open access platform Galaxy. This method can help analyze complex high through put sequencing data to provide insights into the transcriptomic signatures behind phenotypes in C.elegans. The main advantage of this technique is that it enables scientists with no prior bioinformatics training to analyze raw sequencing data and produce a list of differentially expressed genes, along with their associated gene ontology terms.Though this method is specifically geared towards the nuances of C.elegans sequencing data it can also be applied to other organisms and biological context where genomes key transcription changes need to be examined. This video deals with the web based usage of the Galaxy pipeline. If feasible, run the work flow locally.Download and install Galaxy according to instructions on the wiki page. After utilizing the start here tutorial provided on the Galaxy homepage, follow this video. It is critical for the user to familiarize and orient themselves to the Galaxy user interface and the tools used in the work flow.To begin, click on analyze data in the header panel to access the analysis home view. The progress bar in the upper right monitors how much disc space has been used. Next, access the tools menu on the left panel and click on NGS RNA analysis.This provides options to use all the tools required for analysis of RNA sequence data. Now, start a new analysis history. Go to the history panel on the right.Click on the gear icon and choose the create new option from the pop up menu. Then, provide a name under history to identify the analysis. To proceed, go to the tools menu, and under get data, click the upload file function to upload raw fast queue files.After the task opens in the analysis interface, click on choose local file, or choose FTP file to navigate to and select the appropriate sequence data. By default, Galaxy will automatically detect the file type. Then select the organism from the pull down menu which in this case is C.elegans.Next, click on start to initiate the data upload. After the file is uploaded, the action is saved in the history panel from which the data can be selected and accessed. Now, convert files from the fast queue format one at a time to the fast queue sanger format by accessing the NGS QC and manipulation menu, selecting fast queue groomer, choosing the file under file to groom.Select the appropriate input fast equality scores type and run the tool using the default parameters. The data file can now be analyzed. Pay special attention to the file formats and test parameters used throughout the protocol.This knowledge is valuable for troubleshooting failed tests and other issues. Quality control tests can be used before proceeding. Details on running them are provided in the text protocol.Once a file is ready to analyze map the sequencing data by first opening the NGS RNA analysis section and then clicking on the top hat tool. From the drop down menu, fill in the answer to the question is this single or paired end data? Next, choose the appropriate fast queue file.Select use a built in genome in the next drop down menu and choose the reference C.elegans genome data. Select default for all other parameter options, and then click execute. To estimate the relative abundance of transcripts in the data set select the cuff links tool in the NGS RNA analysis section and from the first drop down menu choose the mapped accepted hits bam format file obtained from top hat analysis.From the second drop down menu, set the reference annotation to the GTF file containing the current genome data. When presented with the perform bias correction option select yes and run the task using the default settings. Next, from the NGS RNA analysis menu open the cuff merge tool to merge the assembled transcripts produced for all the RNA sequence samples.The first box in the tool loads all the GTF files produced using cuff links in the previous step. Now, select the assembled transcripts file for each of the strains or conditions tested including the biological replicates of the same strain condition. Select yes for user reference annotation, and choose the reference genome data file.Next, select yes for the use sequence data option. This will automatically detect and choose the appropriate reference genome. Leave all other parameters in their default setting and click execute.A single GTF file will be produced. To compare multiple strains or conditions go back to the NGS RNA analysis section and select the cuff div tool. Then from the transcripts menu in the cuff div tool select the merged output file from cuff merge.Then enter labels for the two conditions. For each condition go to replicates and select the individual accepted hits output files from top hat that correspond to the different biological replicates of that condition. To select multiple files simultaneously hold down the command or control key.After selecting the files, use the default parameter settings and click execute to run the task. Download differentially expressed data by clicking the save icon in the gene differential testing box generated in the history panel. To begin, access David from the website.From the header of the webpage choose start analysis. Copy the list of genes obtained from Galaxy into box A and in this example select the gene identifier as worm base gene ID.Then under list type in question three choose gene list and click on the submit list icon. Now the analysis wizard homepage will open up from which David tasks can be selected.This video segment describes a few of these options. First, choose functional annotation clustering to go to the summary page. Leave the annotation categories on their default settings and click on functional annotation clustering.This option generates clusters of similar annotation terms ranked by their enrichment score. Now, return to the analysis wizard, and select the functional annotation chart option to identify significantly overrepresented biological terms associated with the gene list. A valuable David feature is the option to make a functional annotation table.This lists all the annotations associated with the genes without showing any statistical calculations. This can be useful for gene by gene analysis and for finding specific genes of interest. Another useful David tool to review is the gene functional classification.This option segregates the genes into a list of functionally related groups ranked by their enrichment score. The described protocol was used to identify genes whose expression is modulated by tcer-1 following germ line loss. The transcriptome of long lived germ line list glp-1 mutants was compared with tcer-1 glp-1 double mutants that our germ line lists but do not exhibit lifespan extension.A quality check of the sequences found no poor quality reads, 48 to 49%GC content, and a constant sequence read length of 51 base pairs. Genome coverage of the samples was estimated to be between seven fold and 11 fold. Galaxy enabled combination of the NGS data from the two replicates of each strain and perform differential analysis to generate gene lists highlighting the genome wide expression profile.Overall, 835 genes were differentially expressed between the two strains using a P value cut off of 0.05. Functional annotation analysis of the up regulated genes revealed four annotation clusters with high enrichment scores. The highest including cytochrome P450 and xenobiotic response genes, followed by genes implicated in lipid modifications.Functional annotation clustering of the down regulated targets also identified a variety of annotation clusters. These included clusters enriched for cytoskeletal function, positive regulation of growth, reproduction, and aging. The Galaxy pipeline has paved the way for researchers in a wide range of biological disciplines, to analyze large scale gene expression changes rapidly and efficiently.After watching this video, you should be able to conduct an RNA seek experiment, and analyze the raw high through put sequencing data using the Galaxy pipeline. You should also be able to extract biologically relevant information from the Galaxy data using the David platform.

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