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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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htSeqTools is a Bioconductor package with quality assessment, processing and visualization tools for high-throughput sequencing data, with emphasis in ChIP-seq and RNA-seq studies. It includes detection of outliers and biases, inefficient immuno-precipitation and overamplification artifacts, de novo identification of read-rich genomic regions and visualization of the location and coverage of genomic region lists.

Availability: http://watson.nci.nih.gov/bioc_mirror/packages/2.9/bioc/html/htSeqTools.html

Contact: david.rossell@irbbarcelona.org

• Planet E, Stephan-Otto Attolini C, Reina O, Flores O, Rossell D. (2011) htSeqTools: High-Throughput Sequencing Quality Control, Processing and Visualization in R. Bioinformatics [Epub ahead of print]. [abstract]

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GenomicTools is a flexible computational platform for the analysis and manipulation of high-throughput sequencing data such as RNA-seq and ChIP-seq. GenomicTools implements a variety of mathematical operations between sets of genomic regions thereby enabling the prototyping of computational pipelines that can address a wide spectrum of tasks from preprocessing and quality control to meta-analyses. More specifically, the user can easily create average read profiles across transcriptional start sites or enhancer sites, quickly prototype customized peak discovery methods for ChIP-seq experiments, perform genome-wide statistical tests such as enrichment analyses, design controls via appropriate randomization schemes, among other applications. Read more

RNA-Seq or high-throughput sequencing of transcriptomes is sweeping through clinical microbiology, transforming the discipline in its wake. Early studies have uncovered a wealth of novel coding sequences and non-coding RNA, and are revealing a transcriptional landscape that increasingly mirrors that of eukaryotes.

Croucher NJ, Thomson NR. (2010) Studying bacterial transcriptomes using RNA-seq. Curr Opin Microbiol 13(5), 619-24. [abstract]

van Vliet AH. (2010) Next generation sequencing of microbial transcriptomes: challenges and opportunities. FEMS Microbiol Lett 302(1), 1-7. [abstract]

Pallen MJ, Loman NJ, Penn CW. (2010) High-throughput sequencing and clinical microbiology: progress, opportunities and challenges. Curr Opin Microbiol 13(5), 625-31. [abstract]

Skvortsov TA, Azhikina TL. (2010) Transcriptome analysis of bacterial pathogens in vivo: problems and solutions. Bioorg Khim 36(5), 596-606. [abstract]

Passalacqua KD, Varadarajan A, Ondov BD, Okou DT, Zwick ME, Bergman NH. (2009) Structure and complexity of a bacterial transcriptome. J Bacteriol 191(10), 3203-11. [abstract]

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Two recent studies (published as early access papers from the Journal of Experimental Botany) describe the combined use of RNA-Seq and custom microarrays to uncover more transcriptomics information about agriculturally important species.

MicroRNAs (miRNAs) are small, non-coding RNAs that play essential roles in plant growth, development, and stress response.

¹In the first study, researchers characterize the miRNA profile of the shoot apical meristem (SAM) of an important legume crop, soybean, by integrating high-throughput sequencing data with miRNA microarray analysis.

A total of 8423 non-redundant sRNAs were obtained from two libraries derived from micro-dissected SAM or mature leaf tissue. Sequence analysis allowed the identification of 32 conserved miRNA families as well as 8 putative novel miRNAs.

A custom soybean miRNA microarray was designed containing miRNA and several miRNA* sequences derived from this RNA-Seq data as well as other soybean miRNAs available in the public miRNA database (miRBase). This microarray was subsequently utilized to compare the repertoire of miRNAs in the SAM and mature leaf as well as to verify the expression of novel miRNA candidates identified.

²The second study presents an efficient method for genome-wide discovery of new drought stress responsive miRNAs in Populus euphratica, a typical abiotic stress-resistant woody species, through the combined use of RNA-Seq and miRNA microarray profiling data.

High-throughput sequencing of P. euphratica leaves found 197 conserved miRNAs between P. euphratica and Populus trichocarpa. Additionally, 58 new miRNAs belonging to 38 families were identified, an increase in the number of P. euphratica miRNAs.

Comparison of high-throughput sequencing with miRNA microarray profiling data indicated that 104 miRNA sequences were up-regulated, whereas 27 were down-regulated under drought stress. The method of combining high-throughput sequencing and microarray technologies allowed the successful discovery of new and stress responsive miRNAs and will serve as a basis for future comparative functional genomic analyses using syntenic orthologues.

1. Chui E. Wong, Ying-Tao Zhao, Xiu-Jie Wang, Larry Croft, Zhong-Hua Wang, Farzad Haerizadeh, John S. Mattick, Mohan B. Singh, Bernard J. Carroll, and Prem L. Bhalla. (2011) MicroRNAs in the shoot apical meristem of soybean. J Exp Bot [Epub ahead of print]. [article]
2. Bosheng Li, Yurong Qin, Hui Duan, Weilun Yin, and Xinli Xia (2011) Genome-wide characterization of new and drought stress responsive microRNAs in Populus euphratica. J Exp Bot [Epub ahead of print]. [article]

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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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• 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. […]