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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

  • DESeq; can I omit timepoints during dispersal estimation? May 24, 2013
    I have a bacterial timecourse with 2 biological replicates per timepoint. There is a fair bit of variance between my replicates. I have spent the... […]
    amcloon
  • HT Seq Count stranded options May 24, 2013
    I am very new to bioinformatics, so I would be really grateful for some help! I have been using *HTSeq Count v0.5.3* and I am bit confused about... […]
    qwrissie
  • Tophat 2.0.8b installation error May 24, 2013
    I install tophat-2.0.8b to rerun the mapping. but when i make it, the error appears like this. make[1]: Entering directory... […]
    canhu
  • reason for low mapping rate?? May 23, 2013
    we did RNASeq using HiSeq 2000 100PE. When the data were back, I mapping them to the reference sequence, but got very low mapping rate (30-40%). I... […]
    miaom
  • cross-species data - questions about normalization May 23, 2013
    Hi, I have some data form various samples (cell types) in different species. I want to compare and analyze gene expression variability across the... […]
    trelek2
  • CuffDiff strange output May 23, 2013
    Hi, I hope that someone can be so gentle to help me. I'm analizing some data from RNA-Seq with TopHat and Cufflinks and I focus my attention on... […]
    Pruexel

• Biostar – RNA-Seq

  • Why am I getting so many unmapped reads in STAR, classified as "too short"?
    I am currently using STAR to map several Hi-SEQ mRNA runs. I'm having trouble getting a decent amount of reads to map, but I don't really understand why. I'm hoping you can shed some light :) In the final log, only about 50% (or less) of the reads map to the reference. I'm using a GTF in addition to the genome. The unmapped bin that most […]
  • What are the best practices for SNP identification in RNA seq transcriptome data
    I have 20 RICE RNA seq tranascriptome data hiseq 2000 platform paired end reads. I aligned fasta reads with BWA and remove PCR duplicates with PICARD. Later I call SNP with samtools using various parameters. I would like to clarify what parameters should I used while alinging to reference rice genome for looking SNP location 100 bp upstream and 250 bp downst […]
  • 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 flag, which is useful for subsequent analyses, such as annotation. However, does this information actually influence the "mappability" of reads, or is this unaffected? My thinking is that the information would be considere […]
  • 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 […]