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Next generation sequencing is transforming our understanding of transcriptomes. It can determine the expression level of transcripts with a dynamic range of over six orders of magnitude from multiple tissues, developmental stages or conditions. Patterns of gene expression provide insight into functions of genes with unknown annotation.

This new RNA Seq-Atlas provides a record of high-resolution gene expression in a set of fourteen diverse tissues. The authors found dramatic tissue-specific gene expression of both the most highly-expressed genes and the genes specific to legumes in seed development and nodule tissues. Analysis of the gene expression profiles of over 2,000 genes with preferential gene expression in seed suggests there are more than 177 genes with functional roles that are involved in the economically important seed filling process.

The RNA-Seq atlas of Glycine max can be found here –  http://www.soybase.org/soyseq

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) RNA-Seq Atlas of Glycine max: A guide to the soybean transcriptome. BMC Plant Biology 10, 160. [abstract]

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The winner and new heavyweight champion is?… It’s a draw.

by Scott Peterson, Scientific Director, PFGRC at JCVI

In the past year or so there have been several articles stating that the death of microarray technology is growing near. These proclamations are due to the more recently introduced methodology referred to as RNAseq. At first glance I wrote these claims off as being silly and premature. Over time though I am starting to appreciate that while the claim is still clearly wrong, the issue isn’t about technology displacement at all. My group works on a wide variety of gene expression problems ranging from the simple in vitro microbial gene expression studies to problems involving metagenomic samples of enormous complexity (http://pfgrc.jcvi.org). In my experience, the decision of whether to use DNA microarrays or RNAseq seems straight-forward and unambiguous. In reality the two technologies couldn’t be more complementary. Given the simple in vitro gene expression study as an example, the low cost, short turn-around time, exceptional quantitative accuracy and ease of data generation all make the glass slide microarray the clear choice. (Read more… )

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The development of next generation technologies is enabling the complete mapping and further understanding of microRNAs (miRNAs).  Deep sequencing (NGS) provides complete coverage of the small transcriptome and new bioinformatics tools such as ACGT101-miR enable an exhaustive analysis of the sequencing data. The application of these new technologies together with the availability of a nearly complete pig genome has provided the basis for further defining the molecular and cellular function of these small regulatory molecules in the pig.

The domestic pig (Sus scrofa) is an important species from various standpoints.  First, it is a major protein source in the human diet world-wide.  Additionally, its anatomy, physiology, and genome size are very similar to the human species, and there has been increasing molecular genetic evidence showing the comparability of human and pig, making it a suitable model system for human biology.  Pigs are now model animals for biomedical research of cardiovascular, immunological, cancer, diabetes, and a range of other diseases.  Finally, the pig has become an important source of organs and tissue for transplantation into humans.

Recently, a world-wide collaboration of groups, from Houston, Texas to Sichuan, China, set out to establish a porcine miRNA atlas (microRNAome). The findings they report lay the groundwork for a greater understanding of the species through further mapping of tissue- and stage-specific miRNAs¹.

Prior to this study, miRbase², the primary public repository for miRNA sequence data, listed only 77 pig pre-miRs and 73 unique mature pig miRNAs; this out of a total of 10,883 database entries encompassing over 100 species.  The number of pre-miRs for pig was significantly lower than for other species with similar size genomes (such as Human with 721 entries) suggesting the existence of far more pig miRNAs. Read more

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Strand-specific, massively parallel cDNA sequencing (RNA-seq) is a powerful tool for transcript discovery, genome annotation and expression profiling. There are multiple published methods for strand-specific RNA-seq, but no consensus exists as to how to choose between them. Researchers at MIT and the Hebrew University, Jerusalem compared seven library-construction protocols and found marked differences in strand specificity, library complexity, evenness and continuity of coverage, agreement with known annotations and accuracy for expression profiling.

Included in the supplemental materials are some nice tables summarizing the details of each method as well as the advantages and disadvantages of each method.

Levin JZ, Yassour M, Adiconis X, Nusbaum C, Thompson DA, Friedman N, Gnirke A, Regev A. (2010) Comprehensive comparative analysis of strand-specific RNA sequencing methods. Nat Methods [Epub ahead of print]. [abstract]

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 National Institutes of Health Funding Enables Use of SOLiD 4 Technology for Cancer Biomarker Research

CARLSBAD & LA JOLLA, Calif.–(BUSINESS WIRE)–Life Technologies Corporation (NASDAQ: LIFE) and the University of California at San Diego Moores Cancer Center today announced a partnership to use SOLiD™ 4 genomic analysis technology in a research program to study chronic lymphocytic leukemia (CLL), a cancer of the white blood cells. The CLL research is made possible by funding from the National Institutes of Health and will enable scientists to survey the whole transcriptomes of 96 CLL tumor samples for potential biomarkers.

CLL is one of four main types of leukemia, which primarily affects adults averaging 70 years old, and causes a slow increase in the number of white blood cells called B cells in the bone marrow. The cancerous cells spread from the marrow to the blood, and can also affect the lymph nodes and other organs, such as the liver and spleen. CLL eventually causes the bone marrow to fail and weakens the immune system. Read more

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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 functional complexity of the rice transcriptome is not yet fully elucidated despite many studies having reported the use of DNA microarrays. Next-generation DNA sequencing technologies provide a powerful approach for mapping and quantifying the transcriptome, termed RNA sequencing (RNA-seq). In this study, we applied RNA-seq to globally sample transcripts of the cultivated rice Oryza sativa indica and japonica subspecies for resolving the whole-genome transcription profiles. We identified 15,708 novel transcriptional active regions (nTARs), of which 51.7% have no homolog to public protein data and more than 63% are putative single exon transcripts, which are highly different from protein-coding genes (<20%). We found that ~48% rice genes show alternative splicing patterns, a percentage considerably higher than previous estimations. On the basis of the available rice gene models, 83.1% (46,472 genes) of the current rice gene models were validated by RNA-seq, and 6,228 genes were identified to be extended at the 5′ and/or 3′ ends by at least 50 bp. Comparative transcriptome analysis demonstrated that 3,464 genes exhibited differential expression patterns. The ratio of SNPs with nonsynonymous/synonymous mutations was nearly 1:1.06. In total, we interrogated and compared transcriptomes of the two rice subspecies to reveal the overall transcriptional landscape at maximal resolution.

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) Function annotation of rice transcriptome at single nucleotide resolution by RNA-seq. Genome Res [Epub ahead of print]. [article]

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• SEQanswers – RNA Sequencing

  • RNAseq (SOLiD) from 18 - 200 nt June 18, 2013
    We are interested in small non-coding RNAs. Whomever you ask about the size range of small RNAs, you get a different answer. ;) Lets assume, small... […]
    GenomicIBK
  • Unmapped ratio very high on mouse genome June 17, 2013
    Hi, My problem regards RNA-Seq data. I've downloaded public data (SAGE libs w/ 6 different samples from mouse liver ) to analyse using ArrayStudio.... […]
    le.nono
  • RNASeq: Read length different from expected June 17, 2013
    Hello all, I have received paired-end reads for 40 samples. The reads are supposed to be 100bp per end. Instead, 20 of my samples are 101bp per... […]
    gogodidi
  • How to install xgawk June 16, 2013
    Hi, This is Shrujan, i have a problem while running RNA Sequencing QC. It shows an error that xgawk is not found. So please help me installing... […]
    shrujan
  • RNA Sequencing QC Error while using with Sequence_QC.sh file June 15, 2013
    Hi, This is Shrujan kumar Madadha, I had an error while running QC for Drosophila Yukuba fastq RNA file using Sequence_QC.sh file of FASTX... […]
    shrujan
  • Cuffmerge related query June 12, 2013
    I have a query regarding what samples should be merged using cuffmerge, when you have multiple phenotypes (each with replicates). Lets say my mouse... […]
    ParthavJailwala

• 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 […]