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The dysregulation of vascular endothelial cells by thrombin has been implicated in the development of a number of pathologic disorders such as inflammatory conditions, cancer, diabetes, coronary heart disease. However, transcriptional regulation of vascular endothelial cells by thrombin is not completely understood.

Researchers at the University of Missouri, used Illumina RNA-Seq to profile the transcriptome in human pulmonary microvascular endothelial cells (HMVEC-L) treated with thrombin for 6 h to gain insight into thrombin’s direct effects on the endothelial function. Out of 100 million total reads from a paired end sequencing assay, 91–94% of the reads were aligned to over 16,000 genes in the reference human genome.

Thrombin upregulated 150 known genes and 480 known isoforms, and downregulated 2,190 known genes and 3,574 known isoforms by at least 2 fold. Of note, thrombin upregulated 1,775 previously unknown isoforms and downregulated 12,202 previously unknown isoforms by at least 2 fold. Many genes displayed isoform specific differential expression levels and different usage of transcriptional start sites after the thrombin treatment.

• Zhang LQ, Cheranova D, Gibson M, Ding S, Heruth DP, Fang D, Ye SQ. (2012) RNA-seq Reveals Novel Transcriptome of Genes and Their Isoforms in Human Pulmonary Microvascular Endothelial Cells Treated with Thrombin. PLoS One 7(2), e31229. [article]

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This RNA-seq study describes the first deep sequencing analysis of the T. thermophila transcriptome during the three major stages of the life cycle: growth, starvation and conjugation. Uniquely mapped reads covered more than 96% of the 24,725 predicted gene models in the somatic genome. More than 1,000 new transcribed regions were identified. The great dynamic range of RNA-seq allowed detection of a nearly six order-of-magnitude range of measurable gene expression orchestrated by this cell. RNA-seq also allowed the first prediction of transcript untranslated regions (UTRs) and an updated (larger) size estimate of the T. thermophila transcriptome: 57 Mb, or about 55% of the somatic genome. This study identified nearly 1,500 alternative splicing (AS) events distributed over 5.2% of T. thermophila genes.

• Xiong J, Lu X, Zhou Z, Chang Y, Yuan D, Tian M, Zhou Z, Wang L, Fu C, Orias E, Miao W. (2012) Transcriptome Analysis of the Model Protozoan, Tetrahymena thermophila, Using Deep RNA Sequencing. PLoS One 7(2), e30630. [article]

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Methods for the estimation of the transcript’s abundance using RNA-Seq data have been intensively studied, many of which are based on the assumption that the short-reads of RNA-Seq are uniformly distributed along the transcripts. However, the short-reads are found to be nonuniformly distributed along the transcripts, which can greatly reduce the accuracies of these methods based on the uniform assumption. Several methods are developed to adjust the biases induced by this nonuniformity, utilizing the short-read’s empirical distribution in transcript. Read more

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By a GenomeWeb staff reporter

(GenomeWeb News) – Shareholders have filed a class action lawsuit against Illumina alleging a conflict of interest and a breach of fiduciary responsibilities in connection to Roche’s $5.7 billion hostile bid for the San Diego firm.

In a lawsuit filed in the Delaware Court of Chancery, plaintiffs alleged that by refusing to engage in negotiations with Roche before and after Roche made its hostile bid, Illumina had failed to act in the best interest of its shareholders. (read more… )

 

By Leigh Jones

* Class action over poison pill
* Alleges Goldman gave bad advice
* Alleges Goldman had conflicting financial interest

(Reuters) – A group of Illumina shareholders has filed a putative class action against the life sciences company alleging that its directors adopted a poison pill plan to thwart a takeover by Swiss drugmaker Roche Holding Ltd., based on bad advice from Goldman Sachs.

The lawsuit, brought on behalf of all Illumina stockholders, was filed Tuesday in Delaware Chancery Court. It alleges that Goldman Sachs had a conflicting financial interest in advising Illumina to reject Roche’s offer to buy the company and in advising the company to adopt a poison pill plan. The lawsuit asserts that Illumina had “inexplicably chosen to receive” Goldman’s advice even though it was conflicted. (read more… )

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Within the proteomics community there is a substantial interest in development of novel label-free quantitative proteomic strategies.

One strategy is to take advantage of an increasing number of studies involving integrative analysis of gene and protein expression data that are based on new technologies such as next-generation transcriptome sequencing (RNA-Seq) and highly sensitive mass spectrometry (MS) instrumentation. Thus, it becomes interesting to revisit the correlative analysis of gene and protein expression data using more recently generated datasets to determine if gene expression data can be used as an indirect benchmark for such protein-level comparisons. Read more

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RNA-Seq Atlas is a web-based repository of RNA-Seq gene expression profiles and query tools. The website offers open and easy access to RNA-Seq gene expression profiles and tools to both compare tissues and find genes with specific expression patterns. To enlarge the scope of the RNA-Seq Atlas, the data were linked to common functional and genetic databases, in particular offering information on the respective gene, signaling pathway analysis and evaluation of biological functions by means of gene ontologies. Additionally, data were linked to several microarray gene profiles, including BioGPS normal tissue profiles and NCI60 cancer cellline expression data. The data search interface allows an integrative detailed comparison between the RNA-Seq data and the microarray information.

This database’s large scale RNA-Seq applications are versatile, and will be beneficial in identifying tissue specific genes and expression profiles, comparison of gene expression profiles among diverse tissues, but also systems biology approaches linking tissue function to gene expression changes.

Availability: http://medicalgenomics.org/rna_seq_atlas

Contact: kruppm@uni-mainz.de, teufel@uni-mainz.de

Krupp M, Marquardt JU, Sahin U, Galle PR, Castle J, Teufel A. (2012) RNA-Seq Atlas – A reference database for gene expression profiling in normal tissue by next generation sequencing. Bioinformatics [Epub ahead of print]. [abstract]

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DURHAM, N.C.–(BUSINESS WIRE)–EA announced it is working with Golden Helix and Illumina to offer three (3) fully-funded grants for RNA-Seq studies and data analysis to support cutting-edge projects that show promise in identifying genetic elements important to enhance the understanding of disease and improve human health. For the studies selected, Expression Analysis will perform the sequencing and provide primary and secondary data analysis; Golden Helix will provide cloud-based secondary analysis tools and storage, plus end-user tertiary analysis tools; and, Illumina will provide the products required for the study. In all cases, there is no cost to the grant recipients. Read more

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By Fabrizio Ghiselli

Despite their biological, ecological and economical importance, very little is known about bivalve genetics/genomics and until recently (see for example Boutet et al. 2008¹; Craft et al. 2010²; Milan et al. 2011³), the structure and gene content of bivalve genomes have been poorly understood even in the most important aquacultured organisms. The Manila clam (Ruditapes philippinarum) represents, after oysters, the most important species for Global Aquaculture production. Read more

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A team led by researchers at the George Washington University has published a study that is the first of its kind to use mRNA sequencing to look at the expression of genome, at a unprecedented resolution at the current time, in three types of breast cancer. The study titled, “Transcriptomic landscape of breast cancer through mRNA sequencing,” is published in the Feb. 14 edition of the journal, Scientific Reports, a new open access Nature journal for large volume data.

Breast cancer is a heterogeneous disease with a poorly defined genetic landscape, which poses a major challenge in diagnosis and treatment. By massively parallel mRNA sequencing, the team obtained 1.2 billion reads from 17 individual human tissues belonging to TNBC, Non-TNBC, and HER2-positive breast cancers and defined their comprehensive digital transcriptome for the first time. Surprisingly, they identified a high number of novel and unannotated transcripts, revealing the global breast cancer transcriptomic adaptations. Comparative transcriptomic analyses elucidated differentially expressed transcripts between the three breast cancer groups, identifying several new modulators of breast cancer. The study also identified common transcriptional regulatory elements, such as highly abundant primary transcripts, including osteonectin, RACK1, calnexin, calreticulin, FTL, and B2M, and “genomic hotspots” enriched in primary transcripts between the three groups. Thus, this study opens previously unexplored niches that could enable a better understanding of the disease and the development of potential intervention strategies.

(read more… )

• Eswaran J, Cyanam D, Mudvari P, Reddy SDNR, Pakala SB, Nair SS, Florea L, Fuqua SAW, Godbole S, Kumar R. (2012) Transcriptomic landscape of breast cancers through mRNA sequencing. Scientific Reports [Epub ahead of print]. [abstract]

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SPEAKER:
Professor Ping Ma
Department of Statistics
Univ. of Illinois at Urbana-Champaign

TIME & PLACE:
Monday, February 20, 2012
12:00-1:00p

The Forum, WID/MIR Building
Dept of Statistics
Univ of Wisconsin, Madison

ABSTRACT: With the rapid development of second-generation sequencing technologies, RNA-Seq has become a popular tool for transcriptome analysis. It offers the chance to detect novel transcripts by obtaining tens of millions of short reads. After mapped to the genome and/or to the reference transcripts, RNA-Seq data can be summarized by a tremendous number of short-read counts. The huge number of short-read counts enables researchers to make transcript quantification in ultra-high resolution. Recent work found that short-read counts have significant sequence bias, which makes simple transcript quantification methods questionable. Thus, more elaborate statistical models that can effectively remove the sequence bias of the short-read counts are highly desirable to make transcript quantification more accurate. In this talk, I will present some statistical analysis for bias correction in RNA-Seq short-read counts. Since the sample size is over tens of millions, routine statistical computing is infeasible. Our statistical computing is conducted using a subsampling method called leveraging. I will present some statistical properties of the leveraging algorithm. Real RNA-Seq examples will also be presented to demonstrate the empirical performance of our method.

(read more…)

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Last week an article appeared in BioArray News as part of the coverage of the ongoing Roche/Illumina story – Illumina Array Customers ‘Apprehensive’ of Possible Roche Takeover, Fear Platform ‘Stagnation’

Yesterday, I received an email from Life Technologies (I guess I’m on their marketing list) that seemed unusual to me.  No message, just an image with a link to the article.

This email was clearly intended to draw attention to the article and highlight the fears of current Illumina customers about the possible deal.  Were they trying to build opposition to the deal?  Do they feel the combined financial strength of Roche and the technical strength of Illumina would be a formidable competitor?  You decide…

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A Team of researchers from Taiwan & Denmark have performed a comprehensive profiling of the RNA editome of a male Han Chinese individual based on analysis of ~767 million sequencing reads from poly(A)⁺, poly(A)⁻ and small RNA samples.

• Developed a computational pipeline that carefully controls for false positives while calling RNA editing events from genome and whole-transcriptome data of the same individual.

• Identified 22,688 RNA editing events in noncoding genes and introns, untranslated regions and coding sequences of protein-coding genes. Most changes (~93%) converted A to I(G), consistent with known editing mechanisms based on adenosine deaminase acting on RNA (ADAR).

• Found evidence of other types of nucleotide changes; however, these were validated at lower rates.

• Found 44 editing sites in microRNAs (miRNAs), suggesting a potential link between RNA editing and miRNA-mediated regulation.

This approach facilitates large-scale studies to profile and compare editomes across a wide range of samples.

• Peng Z, Cheng Y, Tan BC, Kang L, Tian Z, Zhu Y, Zhang W, Liang Y, Hu X, Tan X, Guo J, Dong Z, Liang Y, Bao L,Wang J. (2012) Comprehensive analysis of RNA-Seq data reveals extensive RNA editing in a human transcriptome. Nature Biotech [Epub ahead of print]. [abstract]

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In this study, researchers evaluated how gene expression differs in mature Pseudomonas aeruginosa biofilms as opposed to planktonic cells by the use of RNA sequencing technology that gives rise to both quantitative and qualitative information on the transcriptome.

This study reports the first transcriptome study on P. aeruginosa that employs RNA sequencing technology and provides insights into the quantitative and qualitative transcriptome including the expression of small RNAs in P. aeruginosa biofilms

• Dötsch A, Eckweiler D, Schniederjans M, Zimmermann A, Jensen V, Scharfe M, Geffers R, Häussler S. (2012) The Pseudomonas aeruginosa Transcriptome in Planktonic Cultures and Static Biofilms Using RNA Sequencing. PLoS One 7(2), e31092. [article]

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• SEQanswers – RNA Sequencing

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• 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. […]