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from The Scientist by Ed Yong

Transcriptome studies reveal new insights about unusual animals whose genomes have not been sequenced.

The red spotted newt (Notophthalmus viridescens) could sit in the palm of your hand, but its genome is ten times the size of yours—up to 10 billion base pairs. This daunting amount of DNA has kept this species off the radar of any genome sequencing projects, despite plummeting costs. It has also prevented newts and salamanders from becoming regular model organisms, despite their remarkable and medically-relevant ability to regenerate severed limbs and damaged organs.

Recently, a team of German scientists circumvented the difficulties posed by the newt’s huge genome by sequencing its transcriptome instead—the set of RNA produced from its genes. Since some of an animal’s genome is never transcribed, transcriptomes can often be decoded at a fraction of the cost and effort of a full genome, and the newt results, published last month (February 20) in Genome Biology, are part of a growing trend of using transcriptomes to understand lesser-known species. Read more

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Lexogen is supporting scientists who are interested in strand-specific RNA-Seq. They are inviting researchers to submit a research proposal in which they describe how they would take advantage of the extreme strand-specificity that SENSE provides.

The winning proposals will be completely supported by Lexogen (this includes sample preparation, sequencing, and any required bioinformatical support).

• Apply by sending your project proposal and a short description of your research interests to award@lexogen.com.
• The application deadline is March 31st, 2013.
• Proposals will be reviewed by an in-house committee at Lexogen
• Winners will be announced on April 8th.

(Find out more at: http://www.lexogen.com/sense/section/sense-study-award.html)

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A team led by researchers at Weill-Cornell Medical Center profiled microRNAs (miRNAs) in cell-free serum and plasma samples from human volunteers using deep sequencing of barcoded small RNA cDNA libraries. By introducing calibrator synthetic oligonucleotides during library preparation, they were able to calculate the total as well as specific concentrations of circulating miRNA. Studying trios of samples from newborn babies and their parents they detected placental-specific miRNA in both maternal and newborn circulations and quantitated the relative contribution of placental miRNAs to the circulating pool of miRNAs. Furthermore, sequence variation in the placental miRNA profiles could be traced to the specific placenta of origin. These deep sequencing profiles, which may serve as a model for tumor or disease detection, allow the team to define the repertoire of miRNA abundance in the circulation and potential uses as biomarkers.

Williams Z, Ben-Dov IZ, Elias R, Mihailovic A, Brown M, Rosenwaks Z, Tuschl T. (2013) Comprehensive profiling of circulating microRNA via small RNA sequencing of cDNA libraries reveals biomarker potential and limitations. Proc Natl Acad Sci U S A [Epub ahead of print]. [article]

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Degradome sequencing for identification of miRNA targets in plants

MicroRNAs (miRNAs) are endogenous regulators of a broad range of physiological processes and act by either degrading mRNA or blocking its translation. Mature miRNAs function within large complexes to negatively regulate specific target mRNAs. Plant miRNAs generally interact with their targets through perfect or near-perfect complementarity and direct mRNA target degradation.

In plants, miRNAs not only post-transcriptionally regulate their own targets but also interact with each other in regulatory networks to affect many aspects of development, such as growth, development and responses to biotic and abiotic stresses. Hundreds of miRNAs have been identified in higher plants by direct cloning or more recently by next-gen sequencing. To determine the function of these miRNAs we must first identify their targets.

Originally, plant miRNA targets have been studied via computational prediction, which is based on either perfect or near-perfect sequence complementarity between miRNA and the target mRNA or sequence conservation among different species. However, target prediction is very challenging, especially when a high level of mismatches exists in miRNA:target pairing.

Recently, a new method called degradome sequencing, which combines high-throughput RNA sequencing with bioinformatic tools, has-been successfully established to screen for miRNA targets in plants. Using degradome sequencing, many of the previously validated and predicted targets of miRNAs have been verified indicating that it is an efficient strategy to identify smRNA targets on a large scale in plants.

Degradome sequencing reveals miRNA targets by globally identifying the remnants of small RNA-directed target cleavage by sequencing the 5′ ends of uncapped RNAs. Sequencing reads are mapped to mRNAs and the 5′ terminal nucleotide of miRNA-cleaved mRNA fragments corresponds to the nucleotide that is complementary to the 10th nucleotide of the miRNA. Therefore, the cleaved RNA targets have distinct peaks in the degradome sequence reads at the predicted cleavage site relative to other regions of the transcript. Confirmed miRNA targets are presented in the form of target plots (t-plots).

Read more

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from genengnews.com – by Shawn C. Baker, Ph.D, CSO BlueSEQ (www.blueseq.com)- Reprinted with permission from Genetic Engineering & Biotechnology News (GEN)

Is it time to switch?

With recent advancements and a radical decline in sequencing costs, the popularity of next generation sequencing (NGS) has skyrocketed. As costs become less prohibitive and methods become simpler and more widespread, researchers are choosing NGS over microarrays for more of their genomic applications.

Rising maturity in NGS systems and ancillary protocols such as library preparation and data analysis tools have certainly contributed to the increasing popularity among the research community. Whether it’s a need for more accurate data, better resolution, pressure from granting agencies, or just plain fear of being left behind the technology forefront, it’s clear that the demand for revolutionary sequencing technologies that deliver fast, inexpensive, and accurate genomic information has never been greater. Read more

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by Josh P. Roberts at  Biocompare

When next-gen sequencing exploded onto the scene, it brought in its wake a host of innovations. Among these is the deep-sequencing of RNA (RNA-Seq), which is giving unprecedented breadth and depth to our understanding of the way cells develop, regulate themselves and each other, and respond to their environment. Although the study of cellular RNA is not new, the scale on which researchers are now undertaking transcriptomic investigations and many of the questions they are now able to ask, would not have been possible with earlier technologies. Read more

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seqnews.net: discover and share news on next-generation sequencing, genomics and biological data analysis

Next-generation sequencing rapidly changes the face of biology and medicine, and it’s hard to keep track of all recent developments. This site shall help to discover new findings, techniques and tools.

Main topics:

__________________

This guide is meant to offer an easy to follow guide to the analysis of RNA-seq data, aimed at those without any prior experience analyzing next-gen data. However, a basic level of familiarity with R, the next-gen sequencing procedures and using the unix shell are assumed. It was primarily written by Matthew Young (myoung@wehi.edu.au) and is a work in progress. Most of the steps described here are outlined in our review article which can be cited if people are using this guide in their work… The pathogen example was provided by B. Usadel and makes use of a different set of tools.

http://seqanswers.com/wiki/How-to/RNASeq_analysis

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Well, we let this poll go on a little bit longer than normal hoping that one of the choices would pull away from the other.  But alas, the split has been nearly equal since the poll has been up.  There have been many predictions on the Death of Microarrays but it seems they may be here to stay.

The future of microarrays?

Here to stay (i.e. Affy and Agilent affirm continued commitment) (50%, 88 Votes)

Its curtains (i.e. Roche shuts down Nimblegen, Combimatrix trading at $0.85) (50%, 87 Votes)

Total Voters: 175

Check out our latest poll in the left-hand sidebar and cast your vote.

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Up to date research in biology, biotechnology, and medicine requires fast genome and transcriptome analysis technologies for the investigation of cellular state, physiology, and activity. Here, microarray technology and next generation sequencing of transcripts (RNA-Seq) are state of the art. This chapter presents a detailed description of next-generation sequencing (NGS), describes the impact of this technology on transcriptome analysis and explains its possibilities to explore the modern RNA world.

• Mutz KO, Heilkenbrinker A, Lönne M, Walter JG, Stahl F. (2012) Transcriptome analysis using next-generation sequencing. Curr Opin Biotechnol [Epub ahead of print]. [abstract]

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Good day to all our RNA-Seq Blog subscribers!

We have some important information regarding our news blog feed.  Google has decided to shut down Feedburner.  This is the service we were using to deliver our news posts and manage our email subscriptions.  They will discontinue this service as of Oct 20th, 2012 so we have changed over to WordPress RSS feeds and Mailchimp email subscriptions.

Unfortunately, if you had subscribed to the RNA-Seq Blog in an RSS reader, you will need to update your subscription from:

http://feeds.feedburner.com/rna-seqblog to:

http://www.rna-seqblog.com/feed/

We apologize for this most annoying inconvenience.

If you had subscribed to the RNA-Seq Blog by email, there is no action required.  You will continue to receive emails each time we make a post to the blog.  The email will have a slightly different look, as you can see.  We think it looks much nicer.  What do you think?

Sincerely,

The RNA-Seq Blog Team

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• SEQanswers – RNA Sequencing

  • How to increase rowsize in heatmap? May 16, 2013
    Hi, I am a complete newbie to all things cummeRbund and am currently fighting with generating readable heatmaps. When I use ... […]
    Mags
  • novoalign mapping May 15, 2013
    Hi, I want to use novoalign to map reads - allowing up to 15 mismatches for 100 bp paired-end reads I am new to novoalign(went through the... […]
    abh
  • Design of expt across multiple lanes May 15, 2013
    Hi, I am performing an RNA-seq experiment to look at differential expression. The design is as follows: 2 populations x 3 biological... […]
    jbono
  • RNA kinds expected in RNA-seq results May 15, 2013
    Hi, We use RNA isolation and library preparation protocols which capture polyadenylated RNA. My question is what kinds of RNA can we expect to... […]
    Kocur
  • Discrepancy between genotype and expressed alleles May 15, 2013
    Hi all, I am working on the analysis of allele-specific expression using both genotype information and RNA-seq data from the same individuals. I... […]
    RedMary
  • Does Cufflinks Give Me Trascriptomes? May 14, 2013
    Hi Everyone, I'm a beginner in this area, please forget any silly question. My situation is that I have a raw scaffold whole genome... […]
    hchang10

• Biostar – RNA-Seq

  • How do TopHat options -g , --supress-hits, and Bowtie options interplay?
    Hi, I am currently using TopHat2 to map RNA-seq runs. I think there have been some changes pertaining the -g option. Does anyone know how it works now? I used to think that setting -g would look for n alignments for a given read, report them [if top-scoring] and discard those reads that had more than g [top scoring] alignments. Now, the description sounds mo […]
  • What happened to -k in TopHat for multiple-mapping reads?
    Selecting -g n in tophat does not discard reads mapping more than n, but instead only reports n alignments for those out all all their TOP scoring alignments. I think there used to be an option -k that would allow one to discard reads that topped x alignments -- whatever happened to that? I only see -g in the tophat 2 manual, no reporting options like before […]
  • Does tophat use the library-type information for mapping, or just for the XS flag?
    When I specify library-type to TopHat, i.e., first-strand, second-strand, unstranded, TopHat appends a value + or - to the XS:A tag, which is useful for subsequent analyses, such as annotation. However, does this information influence the "mappability" of reads, or is this unaffected? My guess is that the information will be considered for mapping […]
  • Purpose of Y-shaped adapters in Illumina Sequencing?
    Hi all, Y adapters different sequences to be annealed to the 5' and 3' ends of each molecule in a library. The arms of the Y are unique, and the middle part, connected to the DNA fragment, is complementary. What are the advantages of this? My take of this over having fully-complementary adapters (ADAPTER1 - - - - - ADAPTER1) is that: -Upon primer a […]
  • Cell Type composition in a tissue based on gene marker expression
    I am not sure if the following would even make sense.... Tissues are composed of composite cell types, and often there are studies such as microarray/NGS where we perform a collective sampling of cells from these tissues. Information about the composition (say percentage of cell type) is not taken into consideration. In some case (such as brain/cancer), ther […]
  • Which SNP caller / method to use after aligning RNA-seq with TopHat
    Which SNP caller / method can / should I use after aligning RNA-seq data with TopHat? For genomic data I use GATK, but supposedly it is not just as easy as running GATK on the TopHat RNA-seq data. The team from Broad has no information / documentation on how to use GATK for RNA-seq data. I don't have any variants yet from DNA re-sequencing. […]