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>
	<li>Venter, J. C., 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+sequence+of+the+human+genome.">The sequence of the human genome.</a> Science. 291 (5507), 1304-1351 (2001).</li><li>Lander, E. 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=Initial+sequencing+and+analysis+of+the+human+genome.">Initial sequencing and analysis of the human genome.</a> Nature. 409 (6822), 860-921 (2001).</li><li>Afgan, E., 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+Galaxy+platform+for+accessible,+reproducible+and+collaborative+biomedical+analyses:+2016+update.">The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2016 update.</a> Nucleic Acids Res. 44 (W1), W3-W10 (2016).</li><li>Trapnell, C., Pachter, L., Salzberg, S. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=TopHat:+discovering+splice+junctions+with+RNA-Seq.">TopHat: discovering splice junctions with RNA-Seq.</a> Bioinformatics. 25 (9), 1105-1111 (2009).</li><li>Trapnell, C., 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=Transcript+assembly+and+quantification+by+RNA-Seq+reveals+unannotated+transcripts+and+isoform+switching+during+cell+differentiation.">Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation.</a> Nat Biotechnol. 28 (5), 511-515 (2010).</li><li>Roberts, A., Trapnell, C., Donaghey, J., Rinn, J. L., Pachter, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Improving+RNA-Seq+expression+estimates+by+correcting+for+fragment+bias.">Improving RNA-Seq expression estimates by correcting for fragment bias.</a> Genome Biol. 12 (3), R22 (2011).</li><li>Roberts, A., Pimentel, H., Trapnell, C., Pachter, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Identification+of+novel+transcripts+in+annotated+genomes+using+RNA-Seq.">Identification of novel transcripts in annotated genomes using RNA-Seq.</a> Bioinformatics. 27 (17), 2325-2329 (2011).</li><li>Trapnell, C., 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=Differential+gene+and+transcript+expression+analysis+of+RNA-seq+experiments+with+TopHat+and+Cufflinks.">Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks.</a> Nat Protoc. 7 (3), 562-578 (2012).</li><li>Trapnell, C., 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=Differential+analysis+of+gene+regulation+at+transcript+resolution+with+RNA-seq.">Differential analysis of gene regulation at transcript resolution with RNA-seq.</a> Nat Biotechnol. 31 (1), 46-53 (2013).</li><li>Huang da, W., Sherman, B. T., Lempicki, R. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Systematic+and+integrative+analysis+of+large+gene+lists+using+DAVID+bioinformatics+resources.">Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.</a> Nat Protoc. 4 (1), 44-57 (2009).</li><li>Giardine, B., 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=Galaxy:+a+platform+for+interactive+large-scale+genome+analysis.">Galaxy: a platform for interactive large-scale genome analysis.</a> Genome Res. 15 (10), 1451-1455 (2005).</li><li>Han, Y., Gao, S., Muegge, K., Zhang, W., Zhou, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Advanced+Applications+of+RNA+Sequencing+and+Challenges.">Advanced Applications of RNA Sequencing and Challenges.</a> Bioinform Biol Insights. 9 (1), 29-46 (2015).</li><li>Mardis, E. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Next-generation+sequencing+platforms.">Next-generation sequencing platforms.</a> Annu Rev Anal Chem (Palo Alto Calif). 6, 287-303 (2013).</li><li>Yang, I. S., Kim, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Analysis+of+Whole+Transcriptome+Sequencing+Data:+Workflow+and+Software.">Analysis of Whole Transcriptome Sequencing Data: Workflow and Software.</a> Genomics Inform. 13 (4), 119-125 (2015).</li><li>Khatri, P., Draghici, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Ontological+analysis+of+gene+expression+data:+current+tools,+limitations,+and+open+problems.">Ontological analysis of gene expression data: current tools, limitations, and open problems.</a> Bioinformatics. 21 (18), 3587-3595 (2005).</li><li>Huang da, W., Sherman, B. T., Lempicki, R. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Bioinformatics+enrichment+tools:+paths+toward+the+comprehensive+functional+analysis+of+large+gene+lists.">Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists.</a> Nucleic Acids Res. 37 (1), 1-13 (2009).</li><li>Shaye, D. D., Greenwald, I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=OrthoList:+a+compendium+of+C.+elegans+genes+with+human+orthologs.">OrthoList: a compendium of C. elegans genes with human orthologs.</a> PLoS One. 6 (5), e20085 (2011).</li><li>Consortium, C. e. 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. E., Ruvkun, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=C.+elegans+major+fats+are+stored+in+vesicles+distinct+from+lysosome-related+organelles.">C. elegans major fats are stored in vesicles distinct from lysosome-related organelles.</a> Cell Metab. 10 (5), 430-435 (2009).</li><li>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=Transcriptional+networks+that+mediate+signals+from+reproductive+tissues+to+influence+lifespan.">Transcriptional networks that mediate signals from reproductive tissues to influence lifespan.</a> Genesis. 51 (1), 1-15 (2013).</li><li>Ghazi, A., Henis-Korenblit, S., 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=A+transcription+elongation+factor+that+links+signals+from+the+reproductive+system+to+lifespan+extension+in+Caenorhabditis+elegans.">A transcription elongation factor that links signals from the reproductive system to lifespan extension in Caenorhabditis elegans.</a> PLoS Genet. 5 (9), e1000639 (2009).</li><li>Amrit, F. R., 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=DAF-16+and+TCER-1+Facilitate+Adaptation+to+Germline+Loss+by+Restoring+Lipid+Homeostasis+and+Repressing+Reproductive+Physiology+in+C.+elegans.">DAF-16 and TCER-1 Facilitate Adaptation to Germline Loss by Restoring Lipid Homeostasis and Repressing Reproductive Physiology in C. elegans.</a> PLoS Genet. 12 (2), e1005788 (2016).</li><li>Wang, M. C., O'Rourke, E. J., Ruvkun, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fat+metabolism+links+germline+stem+cells+and+longevity+in+C.+elegans.">Fat metabolism links germline stem cells and longevity in C. elegans.</a> Science. 322 (5903), 957-960 (2008).</li><li>McCormick, M., Chen, K., Ramaswamy, P., 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=New+genes+that+extend+Caenorhabditis+elegans'+lifespan+in+response+to+reproductive+signals.">New genes that extend Caenorhabditis elegans' lifespan in response to reproductive signals.</a> Aging Cell. 11 (2), 192-202 (2012).</li><li>Kartashov, A. V., Barski, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=BioWardrobe:+an+integrated+platform+for+analysis+of+epigenomics+and+transcriptomics+data.">BioWardrobe: an integrated platform for analysis of epigenomics and transcriptomics data.</a> Genome Biol. 16, 158 (2015).</li><li>Goncalves, A., Tikhonov, A., Brazma, A., Kapushesky, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+pipeline+for+RNA-seq+data+processing+and+quality+assessment.">A pipeline for RNA-seq data processing and quality assessment.</a> Bioinformatics. 27 (6), 867-869 (2011).</li></ol>

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.

<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...

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.

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

Research

JoVE Journal

Genetics

Optogenetic Random Mutagenesis Using Histone-miniSOG in C. elegans

Forward genetic screening in model organisms is the workhorse to discover functionally important genes and pathways in many biological processes. In most mutagenesis-based screens, researchers have relied on the use of toxic chemicals, carcinogens, or irradiation, which requires designated equipment, safety setup, and/or disposal of hazardous materials. We have developed a simple approach to induce heritable mutations in C. elegans using germline-expressed histone-miniSOG, a light-inducible potent generator of reactive oxygen species. This mutagenesis method is free of toxic chemicals and requires minimal laboratory safety and waste management. The induced DNA modifications include single-nucleotide changes and small deletions, and complement those caused by classical chemical mutagenesis. This methodology can also be used to induce integration of extrachromosomal transgenes. Here, we provide the details of the LED setup and protocols for standard mutagenesis and transgene integration.

Optogenetic Random Mutagenesis Using Histone-miniSOG in C. elegans

Genetically-encoded histone-miniSOG induces genome-wide heritable mutations in a blue&#160;light-dependent manner. This mutagenesis method is simple, fast, free of toxic chemicals, and well-suited for forward genetic screening and transgene integration.

Forward genetic screening in model organisms is the workhorse to discover functionally important genes and pathways in many biological processes. In most ...

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.