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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

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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

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• Biostar – RNA-Seq

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    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"?
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