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MicroRNAs (miRNAs) are a class of non-coding RNAs approximately 21 nt in length which play important roles in regulating gene expression in plants. Although many miRNA studies have focused on a few model plants, miRNAs and their target genes remain largely unknown in hot pepper (Capsicum annuum), one of the most important crops cultivated worldwide.

Here, researchers at the Seoul National University, Korea employed high-throughput sequencing technology to identify miRNAs in pepper extensively from 10 different libraries, including leaf, stem, root, flower, and six developmental stage fruits. Based on a bioinformatics pipeline, they successfully identified 29 and 35 families of conserved and novel miRNAs, respectively. Northern blot analysis was used to validate further the expression of representative miRNAs and to analyze their tissue-specific or developmental stage-specific expression patterns. Moreover, they computationally predicted miRNA targets, many of which were experimentally confirmed using 5′ rapid amplification of cDNA ends analysis. One of the validated novel targets of miR-396 was a domain rearranged methyltransferase, the major de novo methylation enzyme, involved in RNA-directed DNA methylation in plants. This work provides the first reliable draft of the pepper miRNA transcriptome. It offers an expanded picture of pepper miRNAs in relation to other plants, providing a basis for understanding the functional roles of miRNAs in pepper.

• Hwang DG, Park JH, Lim JY, Kim D, Choi Y, Kim S, Reeves G, Yeom SI, Lee JS, Park M, Kim S, Choi IY, Choi D, Shin C. (2013) The Hot Pepper (Capsicum annuum) MicroRNA Transcriptome Reveals Novel and Conserved Targets: A Foundation for Understanding MicroRNA Functional Roles in Hot Pepper. PLoS One 8(5)e64238. [article]

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by Jeffrey M. Perkel at Biocompare

For all that we can learn from the genome, it’s the other ‘omic disciplines that spell out an organism’s biology. Want to know what proteins a cell makes? Try proteomics. Metabolites? Use metabolomics. But if your goal is RNA, you’ll need to dive into the transcriptome.

Transcriptomics studies attempt to catalog and quantify the RNA content of a cell, tissue or organism. In some cases, the goal is to target all transcripts, regardless of their structure or function. Other studies, though, home in on one specific subset of the transcriptome, such as mRNAs, microRNAs (miRNA) or long noncoding RNAs.

There essentially are three techniques for tackling the transcriptome: real-time quantitative PCR (qPCR), microarrays and “next-gen” DNA sequencing (an application called “RNA-Seq”).

The qPCR technique is highly quantitative and sensitive but generally best for interrogating a relatively small number of transcripts in a large set of samples. Microarrays and RNA-Seq offer genome-wide surveys of the transcriptome, but microarrays can only detect sequences homologous to what’s on the array. In contrast, RNA-Seq is “unbiased,” making it ideal for discovery. On the other hand, microarrays are relatively inexpensive, widely available and easily analyzed on the back end, whereas DNA sequencing remains costly and decidedly difficult, computationally speaking. Read more

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Genome-wide analyses and high-throughput screening was long reserved for biomedical applications and genetic model organisms. With the rapid development of massively parallel sequencing nanotechnology (or next-generation sequencing) and simultaneous maturation of bioinformatic tools, this situation has dramatically changed. Many in the eco-evolutionary sciences will be working with large-scale genomic data sets, and a basic understanding of the concepts and underlying methods is necessary to judge the work of others.

Here, the author briefly introduces next-generation sequencing and then focuses on transcriptome shotgun sequencing (RNA-Seq). This article gives a broad overview and provides practical guidance for the many steps involved in a typical RNA-Seq work flow from sampling, to RNA extraction, library preparation and data analysis. He focuses on principles, presents useful tools where appropriate and point out where caution is needed or progress to be expected. This tutorial is mostly targeted at beginners, but also contains potentially useful reflections for the more experienced.

• Wolf JB. (2013) Principles of transcriptome analysis and gene expression quantification: an RNA-seq tutorial. Mol Ecol Resour [Epub ahead of print]. [article]

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The whole-genome sequences of many non-model organisms have recently been determined. Using these genome sequences, next-generation sequencing based experiments such as RNA-Seq and ChIP-seq have been performed and comparisons of the experiments between related species have provided new knowledge about evolution and biological processes. Although these comparisons require transformation of the genome coordinates of the reads between the species, current software tools are not suitable to convert the massive numbers of reads to the corresponding coordinates of other species’ genomes.

Now, researchers at Ochanomizu University and the Tokyo Institute of Technology, Japan have developed a set of programs, called REad COordinate Transformer (RECOT), which is useful to compare RNA-seq, ChIP-seq and CLIP-seq sequences between closely-related species. RECOT can be used to transform the coordinates of short reads obtained from the genome of a query species being studied to that of a comparison target species after aligning the query and target gene/genome sequences. RECOT generates output in SAM format that can be viewed using recent genome browsers capable of displaying next-generation sequencing data. RECOT

They demonstrate the usefulness of RECOT in comparing ChIP-seq results between two closely-related fruit flies. The results indicate position changes of a transcription factor binding site caused sequence polymorphisms at the binding site.

Availability – RECOT is available at: http://sesejun.github.com/recot/

Izawa A, Sese J. (2013) A tool for the coordinate transformation of next-generation sequencing reads for comparative genomics and transcriptomics. Source Code Biol Med 8(1), 6. [Epub ahead of print]. [abstract]

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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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Transcriptome Analysis by High-Throughput Sequencing (RNA-Seq) – Mark Reimers – Virginia Institute for Psychiatric and Behavioral Genetics.

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The transcriptome of the human pathogen Trypanosoma brucei at single-nucleotide resolution.
Kolev NG, Franklin JB, Carmi S, Shi H, Michaeli S, Tschudi C. (2010)
PLoS Pathog. 2010 Sep 9;6(9). pii: e1001090.

Comprehensive annotation of the transcriptome of the human fungal pathogen Candida albicans using RNA-seq.
Bruno VM, Wang Z, Marjani SL, Euskirchen GM, Martin J, Sherlock G, Snyder M. (2010)
Genome Res. 2010 Sep 1. [Epub ahead of print].

RNA-Seq Atlas of Glycine max: a guide to the soybean transcriptome.
Severin AJ, Woody JL, Bolon YT, Joseph B, Diers BW, Farmer AD, Muehlbauer GJ, Nelson RT, Grant D, Specht JE, Graham MA, Cannon SB, May GD, Vance CP, Shoemaker RC. (2010)
BMC Plant Biol. 2010 Aug 5;10:160.

Function annotation of the rice transcriptome at single-nucleotide resolution by RNA-seq.
Lu T, Lu G, Fan D, Zhu C, Li W, Zhao Q, Feng Q, Zhao Y, Guo Y, Li W, Huang X, Han B. (2010)
Genome Res. 2010 Sep;20(9):1238-49.

 Using deep RNA sequencing for the structural annotation of the Laccaria bicolor mycorrhizal transcriptome.
Larsen PE, Trivedi G, Sreedasyam A, Lu V, Podila GK, Collart FR. (2010)
PLoS One. 2010 Jul 6;5(7):e9780.

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From - Genomics Technologies: The Power of Genome-Scale Quantitative Data Resolution Profiling Transcriptomes, Plant Physiology 2010

…More recently, direct sequencing of transcripts by high-throughput sequencing technologies (RNA-Seq) has become an additional alternative to microarrays and is superseding SAGE and MPSS (Busch and Lohmann, 2007). Like SAGE and MPPS, RNA-Seq does not depend on genome annotation for prior probe selection and avoids biases introduced during hybridization of microarrays. On the other hand, RNA-Seq poses novel algorithmic and logistic challenges, and current wet-lab RNA-Seq strategies require lengthy library preparation procedures. Therefore, RNA-Seq is the method of choice in projects using nonmodel organisms and for transcript discovery and genome annotation. Because of their robust sample processing and analysis pipelines, often microarrays are still a preferable choice for projects that involve large numbers of samples for profiling transcripts in model organisms with well-annotated genomes. Read more

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The current revolution in sequencing technologies allows us to obtain a much more detailed picture of transcriptomes via RNA-Sequencing. We have developed the first integrative online platform, Oqtans, for quantitatively analyzing RNA-Seq experiments. It is based on the Galaxy-framework and provides tools for read mapping, transcript reconstruction and quantitation. Read more

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In recent years, the introduction of massively parallel sequencing platforms for Next Generation Sequencing (NGS) protocols, able to simultaneously sequence hundred thousand DNA fragments, dramatically changed the landscape of the genetics studies. RNA-Seq for transcriptome studies, Chip-Seq for DNA-proteins interaction, CNV-Seq for large genome nucleotide variations are only some of the intriguing new applications supported by these innovative platforms. Among them RNA-Seq is perhaps the most complex NGS application. Expression levels of specific genes, differential splicing, allele-specific expression of transcripts can be accurately determined by RNA-Seq experiments to address many biological-related issues. All these attributes are not readily achievable from previously widespread hybridization-based or tag sequence-based approaches. However, the unprecedented level of sensitivity and the large amount of available data produced by NGS platforms provide clear advantages as well as new challenges and issues. This technology brings the great power to make several new biological observations and discoveries, it also requires a considerable effort in the development of new bioinformatics tools to deal with these massive data files. The paper aims to give a survey of the RNA-Seq methodology, particularly focusing on the challenges that this application presents both from a biological and a bioinformatics point of view. (read more… )

Costa V, Angelini C, De Feis I, Ciccodicola A. (2010) Uncovering the complexity of transcriptomes with RNA-Seq. J Biomed Biotechnol 2010, 853916. [article]

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• SEQanswers – RNA Sequencing

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• Biostar – RNA-Seq

  • edgeR: very low p-value and very high variance within the group of replicates. What's my problem??
    I'm using edgeR in order to perform differential expression analysis from RNA-seq experiment. I have 6 samples of tumor cell, same tumor and same treatment: 3 patient with good prognosis and 3 patient with bad prognosis. I want to compare the gene expression among the two groups. I ran the edgeR pakage like follow: x […]
  • Normalising tag count to RPKM
    Hi! I was wondering if their is a way to normalise the number of reads in a region and the RPKM of the nearest gene to that region, so that a correlation could be computed. Like the following data shows number of tags in first column and RPKM in second column Tags RPKM 15 0.14619 11 0 203 0.2259 129 10.701 300 7.0772 122 2.3234 346 10.666 77 3.117 201 16.749 […]
  • a simple question on RNA-Seq terminology
    This question may be very simple and basic, but I just need to confirm that I understand the differences among those terminologies in the RNA-Seq context. Suppose I have a sample called SLR, and it is sequenced on 5 lanes, so I have (among other output files) BAM files like L1_SLR, L2_SLR, L3_SLR, L5_SLR and L7_SLR.bam. Here, the letter "L" denotes […]
  • FInding regions of interest with minimum coverage
    Hi, I have a bam file of all my accepted hits (tophat output) and an gtf file with my genes of interest for which I am trying to find potential antisense transcripts. I would like to create a list - preferably one that can be visualized in a genome browser - that shows all genes that have antisense reads in the accepted hits.bam file provided that there are […]
  • How to remove the intronic reads before counting
    I got RNASeq data in several samples. I checked the FastQC, seems the read quality are good (Hiseq 2000). But the problem is many reads are mapped to intronic region, and the regions have no any reference exons there (Refseq, ensembl, gencode). We don't know what they are. We guess the problem happend in library preparation, the concentration was low. N […]
  • Which strand of the mRNA molecule does the sequencer output as a "read"?
    In Illumina Stranded RNA-Seq (using the dUTP method), do the final reads in the fastq files correspond to the initial molecule (that was transcribed), or to the reverse complement of the molecule? C […]