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The prevalence of Alzheimer’s disease (AD) is increasing rapidly in the Western world and is poised to have a significant economic and societal impact. Current treatments do not alter the underlying disease processes meaning new treatments are required if this imminent epidemic is to be averted. The clinical manifestations of AD are secondary to a substantial loss of cortical neurons.

Effective neuroprotective strategies will require the discovery of both preclinical markers to identify susceptible patients and the early pathogenic mechanisms to serve as therapeutic targets. While the biomarkers and pathogenic mechanisms may overlap, it is likely that new approaches are required to identify novel elements of the disease.

Transcriptomic analyses, that assume no a priori etiological hypotheses, promise much in elucidating the pathogenesis of complex diseases like AD. RNA-Seq is not only highly suited to investigations of the genomically complex human brain tissue but it can potentially overcome technical issues inherent to case-control comparisons of postmortem brain tissue in neurodegenerative diseases. Moreover, RNA-Seq goes beyond the detection of transcripts that correspond to an existing genomic sequence. With this technique, the exact location of transcription boundaries can be identified with single base resolution. These features make RNA-Seq particularly useful for studying complex transcriptomes, such as those found in the human brain.

Sutherland GT, Janitz M, Kril JJ. (2010) RNA-Seq and the pathogenesis of Alzheimer’s disease.  Journal of Neurochemistry [Epub ahead of print]. [article]

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Drosophila melanogaster is an important non-mammalian model system that has had a critical role in basic biological discoveries, such as identifying chromosomes as the carriers of genetic information and uncovering the role of genes in development. However, despite the fact that Drosophila melanogaster is one of the most well studied genetic model organisms, its genome still contains unannotated coding and non-coding genes, transcripts, exons and RNA editing sites.

Full discovery and annotation are pre-requisites for understanding how the regulation of transcription, splicing and RNA editing directs the development of this complex organism. So the authors set out to provide a high-resolution view of transcriptome dynamics throughout development and used RNA-Seq, tiling microarrays and cDNA sequencing to explore the transcriptome in 30 distinct developmental stages

They identified 111,195 new elements, including thousands of genes, coding and non-coding transcripts, exons, splicing and editing events, and inferred protein isoforms that previously eluded discovery using established experimental, prediction and conservation-based approaches. These data substantially expand the number of known transcribed elements in the Drosophila genome.

Graveley BR, Brooks AN, Carlson JW, Duff MO, Landolin JM, Yang L, Artieri CG, van Baren MJ, Boley N, Booth BW, Brown JB, Cherbas L, Davis CA, Dobin A, Li R, Lin W, Malone JH, Mattiuzzo NR, Miller D, Sturgill D, Tuch BB, Zaleski C, Zhang D, Blanchette M, Dudoit S, Eads B, Green RE, Hammonds A, Jiang L, Kapranov P, Langton L, Perrimon N, Sandler JE, Wan KH, Willingham A, Zhang Y, Zou Y, Andrews J, Bickel PJ, Brenner SE, Brent MR, Cherbas P, Gingeras TR, Hoskins RA, Kaufman TC, Oliver B, Celniker SE. (2010) The developmental transcriptome of Drosophila melanogaster. Nature [Epub ahead of print]. [abstract]

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The RNAseq genome annotation assessment project was launched in 2005 to assess the current progress of automatic gene building using RNAseq as its primary dataset.

While the two previous RGASP challenges evaluated computational methods used to estimate gene and transcript activity, RGASP 3 will evaluate methods used to map RNA-seq reads to reference genomes and annotated transcripts.

The principal aim of the RGASP3 project is to allow a fair evaluation of different analysis methods within the community generating high-quality RNASeq read alignments that can be used for efficient transcriptome characterization (transcript discovery and quantitation).

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from poster presented at SABCS 2010
Background: Our efforts to prevent and treat breast cancer are significantly impeded by a lack of knowledge of the biology and developmental genetics of the normal mammary gland. The Susan G. Komen for the Cure Tissue Bank at the IU Simon Cancer Center (KTB) was established expressly to address and remedy this deficiency. The KTB acquires and banks normal breast tissue, that is, breast tissue from volunteer donors with no clinical evidence of breast malignancy. This tissue is NOT from reduction mammoplasties or from histologically normal tissue adjacent to a malignancy.
The breast is one of the most complex genetic organs within the body. This is because the expression of its genes is under the control and influence of the hormonal milieu present in the circulating plasma, which changes as a function of age; for premenopausal women as a function of the menstrual cycle; and as a consequence of pregnancy. Therefore, there is unlikely to be a singular “normal” breast. We propose to produce a molecular encyclopedia of the normal breast which covers the entire spectrum of normal: puberty to menopause, low risk to high risk, nulliparous and parous. Read more

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Planarians (flatworms) are widely used as a model system and their genome is fairly well defined. Currently planarian researchers are supported by the existence of a whole genome shotgun assembly (43,294 contigs with no chromosomal structure ) and 74,388 ESTs for Schmidtea mediterranea. (See SmedGD) However, the establishment of massively parallel sequencing technologies has provided the opportunity to define genetic content, and in particular transcriptomes, in unprecedented detail.

Researchers at the University of Nottingham, UK have used a dual platform approach to transcript discovery for the planarian Schmidtea mediterranea to establish RNAseq for stem cell and regeneration biology

First, they used 454 long read transcriptome sequencing technology for gene discovery. The sequencing of this library generated 743,464 reads. These reads had a mean length of 278 bp, and an overall length distribution characteristic of 454 transcriptome sequencing with Titanium chemistry. Assembly detected 16,967 putative “isogroups” (genes) and a total of 21,030 putative “isotigs” (isoforms). They added 74,388 publicly available ESTs for Schmidtea mediterranea. The combined data utilized 581,365 of the 454 reads in the assembly and resulted in 17,628 putative genes and 22,698 isoforms.

Next, the authors performed short read sequencing on the SOLiD platform for iterative mapping to increase the definition of splice junctions between exons and define alternative transcript sequences. Massively parallel sequencing generated 903,642,430 50 bp reads using two flow cells on the SOLiD 3+ sequencing platform. 507,719,814 high quality reads were mapped,  representing a 1,060 fold coverage of the planarian transcriptome. Cufflinks was used to interpret the high quality mapped reads to produce a new annotated GTF. This initial transcriptome GTF file comprised 19,429 putative multiple exon transcripts defined entirely by those genes that were previously annotated, and over 153,038 putative single exon transcripts.

This work has defined an extensive planarian transcriptome and the authors anticipate that other ‘omic approaches will build on this comprehensive data set including RNAseq across many planarian regenerative stages, scenarios, tissues and phenotypes generated by RNAi.

Blythe MJ, Kao D, Malla S, Rowsell J, Wilson R, Evans D, Jowett J, Hall A, Lemay V, Lam S, Aboobaker A. (2010) A Dual Platform Approach to Transcript Discovery for the Planarian Schmidtea Mediterranea to Establish RNAseq for Stem Cell and Regeneration Biology. PLoS ONE 5(12), e15617. [article]

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Researchers compare two strategies for estimating gene expression levels from RNA-seq data.

The estimation of genes’ transcript abundance levels or gene expression levels is an important question in research on the transcriptional regulation and gene functions.

There are two commonly used strategies, however they produce different results.

1.       UI-based – Reads Per Kilo-base per Million reads (RPKM), taking the union-intersection genes

2.       Isoform-based – summing up inferred isoform abundance

Their results showed that the isoform-based method gives not only more accurate estimation but also has less uncertainty than the UI-based strategy. If taking into account the non-uniformity of read distribution, the isoform-based method can further reduce estimation errors. They applied both strategies to real RNA-seq datasets of technical replicates, and found that the isoform-based strategy also displays a better performance. For a more accurate estimation of gene expression levels from RNA-seq data, even if the abundance levels of isoforms are not of interest, it is still better to first infer the isoform abundance and sum them up to get the expression level of a gene as a whole.

Wang X, Wu Z, Zhang X. (2010) Isoform Abundance Inference Provides a More Accurate Estimation of Gene Expression Levels in RNA-Seq. J Bioinform Comput Biol 8(supp01), 177-92. [abstract]

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The FLUX SIMULATOR

The FluxSimulator is the part of the FLUX project that aims at providing an in silico reproduction of the experimental pipelines for RNA-Seq, adopting a minimal set of parameters. Corresponding models were established after analyzing RNA-Seq experiments from different cell types, sample preparation protocols and sequencing platforms. The first step of the FLUX project is-in fact-a transcriptome simulator. Subsequently, common sources of systematic bias in the abundance and distribution of produced reads are mimicked-whether they incur during library construction, or, in the sequencing process. The FluxSimulator provides a flexible base to design benchmark experiments based on the new sequencing technologies, as for instance abundance predictions of the FluxCapacitor. Read more

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Check out the RNA-Seq blog on your mobile device. We have installed a plugin which formats the blog with a mobile theme for visitors on Apple iPhone / iPod touch, Google Android, Blackberry Storm and Torch, Palm Pre and other touch-based smartphones. Note – There is a toggle switch at the bottom of the main page to switch between mobile and full version.

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The advent of next-generation sequencing for functional genomics has given rise to quantities of sequence information that are often so large that they are difficult to handle. Moreover, sequence reads from a specific individual can contain sufficient information to potentially identify and genetically characterize that person, raising privacy concerns. Read more

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iAssembler is a standalone package to assemble ESTs generated using Sanger and/or Roche-454 pyrosequencing technologies into contigs. The pipeline gives much higher accuracy in EST assembly than other existing assemblers by employing an iterative assembly strategy and automated error corrections of mis-assemblies. iAssembler first performs iterative assemblies using MIRA and CAP3 (default: four cycles of MIRA assemblies followed by one CAP3 assembly) to correct assembly errors (mostly sequences derived from the same transcript fail to be assembled together) which occur frequently in just one round of assembly. Read more

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Bio-IT World Expo

Location: World Trade Center, Boston, MA
April 12th-14th, 2011

WORKSHOP 6: Tools and Methods for RNA-seq Analysis
Tuesday, April 12, 2011 8:00-11:30 AM

(W6) Tools and Methods for RNA-seq Analysis
Michael Reich, Director of Cancer Informatics Development, Broad Institute of MIT and Harvard

This workshop will explore RNA-seq analysis methods in GenePattern (Alignment, Isoform detection, Transcript quantitation, Transcriptome reconstruction) and visualizing RNA-seq data in the Integrative Genomics Viewer (IGV).Michael Reich is Director of Cancer Informatics at the Broad Institute, and his group has had direct responsibility for all software development, including GenePattern and Portal development, with particular emphasis on community outreach. His expertise in interacting with the NCI community will be particularly invaluable in the development of community-enabling computational tools.

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RNA-Seq and ChiP-Seq Data Analysis

Location: EMBL Heidelberg, Germany
Monday 20 June – Wednesday 22 June 2011

Scientific Organisers:
Vladimir Benes, EMBL, Germany
Wolfgang Huber, EMBL, Germany
Martin Morgan, Fred Hutchinson Cancer Research Center, USA

Conference Organiser: Gwen Sanderson, EMBL, Germany

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Methods for Quantitative RNA-seq-based Genome Analyses

Monday December 6, 2010 (Today)
11:00 AM to 12:00 PM

Type: Seminar

Sponsor: Computational Biology Program

Speaker(s):

Gunnar Rätsch, PhD
Group Leader
Friedrich Miescher Laboratory of the Max Planck Society
Tubingen, Germany

Audience: This program is for the research community.

Location:

Memorial Sloan-Kettering Cancer Center
Zuckerman Research Center
417 East 68th Street
Room ZRC 105
New York, NY 10065

Host: Chris Sander
Tea will be served at 10:45 AM.

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The MeV team has been working furiously to build a version of MeV that can load and analyze next generation sequencing data. Today we are proud to announce the first public beta version of an RNA-Seq capable MeV.

This project has shown that it is, indeed, feasible to adjust MEV’s data model and processing functions to handle this new data; that the memory footprint is not untenable, and that the existing features so important to microarray data analysis can easily be applied to the richer datasets now available. Read more

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DNA variations in expression quantitative trait loci (eQTLs) alter the expression levels and patterns of many human genes.   The resulting variation of gene expression across individuals has been postulated to be a determinant of phenotypic variation and susceptibility to complex disease.

Massively parallel RNA sequencing (RNA-seq) provides unprecedented resolution, allowing us to accurately monitor not only the expression output of each genomic locus but also reconstruct and quantify alternatively spliced transcripts. RNA-seq also provides new insights into the regulatory mechanisms underlying eQTLs.

Here is a discussion of the major advances introduced by RNA-seq and a summary of the current progress towards understanding the role of eQTLs in determining human phenotypic diversity.

Majewski  J, Pastinen T. (2010) The study of eQTL variations by RNA-seq: from SNPs to phenotypes. Trends in Genetics [Epub ahead of print]. [abstract]

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• SEQanswers – RNA Sequencing

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• Biostar – RNA-Seq

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    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
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  • 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 […]
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    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 […]