• Subscribe to the RNA-Seq Blog

  
  

  
  Or subscribe by RSS Reader
  
• 6,130 feed subscribers
• 
  

• Featured Events

  

• 

• 

• Media Partners

  
  

• 

• Polls

  How long have you been involved with RNA Sequencing?

  • Just now learning about RNA-Seq
  • less 1 year
  • more than 1 year
  • more than 2 years
  • more than 3 years
  • more than 4 years
  • more than 5 years

  View Results

   Loading ...
• Search for:

• Data Analysis Tools

  Transcriptome Assembly Tools

  Expression & Quantification

  Unpliced Mapping Tools

  Splicing and Junction Mapping

  Analysis Pipelines

  Clouding Platforms

  Databases

  Other Tools

• Tag Cloud

  allele-specific expression alternative splicing bioconductor bioinformatics breast cancer chip-seq cloud computing cufflinks Data Analysis data analysis pipeline deep sequencing de novo assembly differential expression encode ensembl expression analysis galaxy gene expression high-throughput sequencing illumina ion torrent junction mapping library preparation life technologies lncrna microarray microarrays microrna mirna next-generation sequencing next-gen sequencing pacific biosciences poll results RNA-Seq rnaseq rna sequencing roche small rna tophat transcriptome transcriptome analysis transcriptome assembly transcriptomes transcriptome sequencing transcriptomics

  WP Cumulus Flash tag cloud by Roy Tanck requires Flash Player 9 or better.

• Categories

  Select Category Advertisers Analysis Pipelines Annotation Clouding Platforms Controls Data Analysis Data Analysis Data Sets Databases Events Expression and Quantification Grants / Funding Industry News Information Jobs Library Preparation Methods News Other Tools Patents Pathway Analysis Poll Results Presentations Press Release Publications Reader Conributions Request for RNA-Seq Services Review Sequencing Protocols SNP Detection Splicing and Junction Mapping Statistical Analysis Transcriptome Assembly Tools Transcriptome Sequenced Uncategorized Unspliced Mapping Tools Web Tools Workflow
• 
  
  

• Recent Comments

  • Brian Haas on Interview – Brian Haas Manager of Genome Annotation, Outreach, Bioinformatics and Analysis Broad Institute
  • Bret on The Magic of RNA-Seq
  • Bob Copeman on RobiNA – a user-friendly, integrated software solution for RNA-Seq-based transcriptomics
  • Oz Solomon on Expression Analysis Announces 2013 Grant Program
  • Dr. Jayakishan Meher on Bioinformatics postdoctoral research fellowship available

• Popular Search Terms

  • grape
  • tirthadipa pradhan
  • rna seq analysis
  • rna-seq analysis
  • next generation sequencing ppt
  • rna seq data analysis
  • rna seq vs microarray
  • wine grapes
  • RSEM
  • mangrove

• Recent Search Terms

  • zea mays pdf
  • dnanexus rna seq analysis
  • top hat reference transcriptome
  • GC content abnormal illumina RNA-seq
  • RNA-Seq challenge
  • web based rna seq analysis
  • rna-seq application
  • error in library(goseq) : there is no package called goseq
  • rna seq freeware
  • www rna-seqblog com

• Archives

  • May 2013
  • April 2013
  • March 2013
  • February 2013
  • January 2013
  • December 2012
  • November 2012
  • October 2012
  • September 2012
  • August 2012
  • July 2012
  • June 2012
  • May 2012
  • April 2012
  • March 2012
  • February 2012
  • January 2012
  • December 2011
  • November 2011
  • October 2011
  • September 2011
  • August 2011
  • July 2011
  • June 2011
  • May 2011
  • April 2011
  • March 2011
  • February 2011
  • January 2011
  • December 2010
  • November 2010
  • October 2010
  • September 2010
  • August 2010
  • July 2010
  • June 2010
  • May 2010
  • April 2010
  • February 2010
  • January 2010
  • January 2009

MediSapiens Ltd, has released the most comprehensive map of human gene expression yet for public use. The data is available through a graphical tool, Transcriptome Viewer, allowing exploration of the expression activity of genes across chromosomes in tens of healthy human tissues.

Data upon which this tool is built (over 300 million manually curated data points) were collected from public science and in itself it is the largest fully integrated gene expression collection in the world.

The fully integrated gene expression information is available online at: http://www.medisapiens.com/transcriptome-viewer-overview/

(Read the press release… )

Incoming search terms:

• rna seq animation
• transcriptome viewer
• rna-seq animation
• high throughput rna sequencing animation
• image of cell microtubules
• rna sequencing at harvard
• rna sequencing harvard
• how to make a 3D model of mitochondria
• rna-seq animnation
• rnaseq animation

Transcriptome assembly based on RNA-Seq data, aims at reconstructing all full-length mRNA transcripts simultaneously from millions of short reads.

IsoLasso is a new RNA-Seq based transcriptome assembly tool. IsoLasso is based on the well-known LASSO algorithm, a multivariate regression method designated to seek a balance between the maximization of prediction accuracy and the minimization of interpretation. By including some additional constraints in the quadratic program involved in LASSO, IsoLasso is able to make the set of assembled transcripts as complete as possible. Experiments on simulated and real RNA-Seq datasets show that IsoLasso achieves, simultaneously, higher sensitivity and precision than the state-of-art transcript assembly tools. Read more

Incoming search terms:

• IsoLasso
• lasso prediction method methods for the novice
• lasso rnaseq

Using IPA® to analyze Illumina RNA-Seq data reveals abundance-specific biological signatures in Alzheimer’s Disease

Date: Monday, October 10, 2011     Time: 9:00 a.m. (PST)
Speaker: Darryl Gietzen, Ph.D., Field Application Scientist, Ingenuity Systems

IPA was used to interpret Alzheimer’s disease biology by comparing Illumina RNA-Seq data from Alzheimer’s disease (AD) and normal brain samples. This analysis revealed very specific biological changes in certain classes of transcript expression, demonstrating how the unique benefits of RNA-Seq can help characterize disease changes.

In this webinar, we will discuss how:

• IPA maximizes RNA-Seq data analysis
• Grouping and analyzing all significantly changed genes in RNA-Seq data can provide pathway level information
• The sensitivity and accuracy of RNA-Seq enables pathway analysis on a subset of genes to elucidate more subtle biological changes

Incoming search terms:

• rna seq webinar
• alzheimer\s disease rnaseq
• RNA-seq disease
• alzheimer deep generation sequencing
• strand specific rnaseq human frontal lobe
• rnaseq in disease
• rna seq human brain
• rna seq alzheimer
• rna in alzheimers
• ngs sequence analysis on alzheimers disease

Researchers at Harvard Medical School and Massachusetts General Hospital used RNA-seq to study the microtubule-interacting trancriptome of the frog Xenopus tropicalis. They identified ∼450 mRNAs that showed significant enrichment on microtubules (MT-RNAs). In addition, they demonstrated that the MT-RNAs incenp, xrhamm, and tpx2 associate with spindle microtubules in vivo. MT-RNAs are enriched with transcripts associated with cell division, spindle formation, and chromosome function, demonstrating an over-representation of genes involved in mitotic regulation. Their data suggest MT-RNAs are likely to contribute to spindle-localized mitotic translation.

• Sharp JA, Plant JJ, Ohsumi TK, Borowsky M, Blower MD. (2011) Functional analysis of the microtubule-interacting transcriptome. Mol Biol Cell [Epub ahead of print]. [article]

Incoming search terms:

• mitosis gif
• microtubule
• microtubule capture
• mt rna data set
• rna microtubules
• RNA-seq interaction

Reptiles are largely under-represented in comparative genomics despite the fact that they are substantially more diverse in many respects than mammals. Given the high divergence of reptiles from classical model species, next-generation sequencing of their transcriptomes is an approach of choice for gene identification and annotation.

Researchers have used 454 technology to sequence the brain transcriptome of four divergent reptilian and one reference avian species:

• Nile Crocodile
• Corn Snake
• Bearded Dragon
• Red-eared Turtle
• Chicken

Read more

Incoming search terms:

• ribo-zero reptile
• RNA-seq data reptile
• reptilian heart RNA-Seq
• rna and reptilians
• RNA-seq data heart reptile
• rna-seq ecology and evolution
• rna-seq phylogeny
• transcriptome profiling reptile

Mosquito-borne infectious diseases pose a severe threat to public health in many areas of the world. Efficient approaches are required for screening wild mosquito populations to detect known and unknown pathogens.

Recent advances in high throughput sequencing technology have made its application easier, cheaper, more convenient and more efficient allowing it to evolve into a powerful tool for identification of novel human pathogens. Due to the short length of the small RNA molecules, sequencing is even faster and cheaper than standard high throughput sequencing using longer DNA or RNA fragments. This makes high throughput sequencing of small RNA an attractive method for pathogen detection in plants and insects. Read more

Incoming search terms:

• virus RNA-seq
• RNA-seq virus
• next generation sequencing rna virus
• rna virus in insects
• RNAseq and virus
• rna virus sequencing ion torrent pgm
• rna virus sequencing illumina
• RNA virus and RNAseq
• rna seq virus
• rna seq viral genome

BACKGROUND: Cultivated watermelon [Citrullus lanatus (Thunb.) Matsum. & Nakai var. lanatus] is an important agriculture crop world-wide. The fruit of watermelon undergoes distinct stages of development with dramatic changes in its size, color, sweetness, texture and aroma. In order to better understand the genetic and molecular basis of these changes and significantly expand the watermelon transcript catalog, we have selected four critical stages of watermelon fruit development and used Roche/454 next-generation sequencing technology to generate a large expressed sequence tag (EST) dataset and a comprehensive transcriptome profile for watermelon fruit flesh tissues. Read more

“The most significant finding of the “Cell” paper is that the Perth-based scientists and their collaborators used cutting-edge gene sequencing technology to gain a complete understanding of mitochondrial genes and how they work.” – WAIMR Press Release

The human mitochondrial genome comprises a distinct genetic system transcribed as precursor polycistronic transcripts that are subsequently cleaved to generate individual mRNAs, tRNAs, and rRNAs.

Researchers from WAIMR and The University of Queensland have provided a comprehensive analysis of the human mitochondrial transcriptome across multiple cell lines and tissues. Using directional deep sequencing and parallel analysis of RNA ends, they demonstrate wide variation in mitochondrial transcript abundance and precisely resolve transcript processing and maturation events. They identify previously undescribed transcripts, including small RNAs, and observe the enrichment of several nuclear RNAs in mitochondria. Using high-throughput in vivo DNaseI footprinting, they establish the global profile of DNA-binding protein occupancy across the mitochondrial genome at single-nucleotide resolution, revealing regulatory features at mitochondrial transcription initiation sites and functional insights into disease-associated variants.

This integrated analysis of the mitochondrial transcriptome reveals unexpected complexity in the regulation, expression, and processing of mitochondrial RNA and provides a resource for future studies of mitochondrial function.

The data can be accessed at http://mitochondria.matticklab.com

(Read the WAIMR Press Release… ) 

• Mercer TR et. al (2011) The Human Mitochondrial Transcriptome. Cell 146(4), 645-58.[abstract]

Incoming search terms:

• mitochondrial rna
• human mtdna rna-seq
• mitochondrial transcripts database
• mitochondria rna ppt
• chip seq mitochondria
• rnaseq analysis of mitochondria
• rnaseq and mitochondria
• rnaseq tophat mitochondrial
• mitochondrial rnaseq
• mitochondrial transcript database

Speaker:  Lior Pachter, PhD

Location:  Bren Hall 6011 UCI

Date:  Friday, September 30, 2011 – 11:00 – 13:00

RNA-Seq is an encompassing term for methods that probe a transcriptome by high-throughput sequencing.  We will provide an overview of RNA-Seq protocols, and will then focus on methods for analysis of RNA-Seq data.  A recurring them will be the subtle, yet crucial connections between experimental design and analysis choices.  We will focus on two tools we have developed:  the Cufflinks suite for RNA-Seq reference assembly, quantification and differential expression, and eXpress which allows for rapid analysis of massive datasets.  Results will be presented on a number of different datasets from human, mouse and Drosophila RNA-Seq experiments.

On September 20, 2011, Pacific Biosciences of California, Inc. (the “Company”) implemented a reduction in its workforce of approximately 130 employees, or approximately 28% of its total workforce. The actions taken were in consideration of uncertainties associated with the economic environment and to position the Company for long-term success. The Company’s current infrastructure was staffed to support a faster adoption rate for its products. The reduction implemented will allow the Company to continue support of its growing customer base with improved service and continued product enhancements, while at the same time conserving cash.

The company reported the layoffs in a document filed with the US Securities and Exchange Commission.

(read the document…)

Incoming search terms:

• pacific biosciences lays off 130 employees

RNA-Seq reveals plant microRNAs regulating expression in mammals

from The Scientist

Chen-Yu Zhang, a molecular biologist at Nanjing University in China, hypothesized that exogenous microRNAs, such as those ingested through the consumption of milk, could also be found circulating in the serum of mammals. To test this idea, Zhang and his team of researchers sequenced the blood microRNAs of 31 healthy human subjects and searched for the presence of plant microRNAs. Because plant microRNAs are structurally different from those of mammals, they react differently to oxidizing agents, and the researchers were able to differentiate the two by treating them with sodium periodate, which oxidizes mammal but not plant microRNAs.

To their surprise, they found about 40 types of plant microRNAs circulating in the subjects’ blood—some of which were found in concentrations that were comparable to major endogenous human microRNAs—and that these exogenous plant microRNAs are primarily acquired orally, through food intake.

(Read more…)

L. Zhang, et. al. (2011) Exogenous plant MIR168a specifically targets mammalian LDLRAP1: evidence of cross-kingdom regulation by microRNA. Cell Research [Epub ahead of print]. [abstract]

Incoming search terms:

• Exogenous plant MIR168a specifically targets mammalian LDLRAP1: evidence of cross-kingdom regulation by microRNA
• exogenous plant mir168a specifically targets mammalian
• cross kingdom regulation
• dexseq model presentation pdf
• evidence of cross kingdom regulation by microrna 2013
• microrna dr chen yu-zhang

Sesame is an important oil crop, but limited transcriptomic and genomic data are currently available. This information is essential to clarify the fatty acid and lignan biosynthesis molecular mechanism. In addition, a shortage of sesame molecular markers limits the efficiency and accuracy of genetic breeding. High-throughput transcriptomic sequencing is essential to generate a large transcriptome sequence dataset for gene discovery and molecular marker development. Read more

Incoming search terms:

• sesamum indicum
• sesamum
• Sesamum indicum L
• sesame genomics
• gwas sesamum
• sesamum indicum r
• characterization of the sesame
• sesamum sesame
• sesamum indicum crops
• sesamum genome map

The Transcriptional Landscape of the Yeast Genome Defined by RNA Sequencing

The identification of untranslated regions, introns, and coding regions within an organism remains challenging. We developed a quantitative sequencing-based method called RNA-Seq for mapping transcribed regions, in which complementary DNA fragments are subjected to high-throughput sequencing and mapped to the genome. We applied RNA-Seq to generate a high-resolution transcriptome map of the yeast genome and demonstrated that most (74.5%) of the nonrepetitive sequence of the yeast genome is transcribed. We confirmed many known and predicted introns and demonstrated that others are not actively used. Alternative initiation codons and upstream open reading frames also were identified for many yeast genes. We also found unexpected 3′-end heterogeneity and the presence of many overlapping genes. These results indicate that the yeast transcriptome is more complex than previously appreciated.

Nagalakshmi U, Wang Z, Waern K, Shou C, Raha D, Gerstein M, Snyder M. (2008) The transcriptional landscape of the yeast genome defined by RNA sequencing. Science 320(5881), 1344-49. [abstract]

Incoming search terms:

• yeast rna seq junction library
• rna-seq yeast
• strand-specific yeast rna-seq
• yeast NGS RNA-seq
• yeast transcriptome ngs
• yeast transcriptome sequencing

Next Page →

• 

• 

• 

• 

• Social Networking Pages

  

• 

• 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 […]